Technologies doi: 10.3390/technologies14110729

Authors: Alpamis Kutlimuratov Islambek Saymanov Bekmurod Khujamuratov Dilfuza Uskenbaeva Fazliddin Makhmudov Mekhridin Rakhmanov Ulugbek Bekmurodov Jakhongir Karimboev Kabul Khudaybergenov

Dynamic facial expression recognition (DFER) has advanced rapidly, but leading systems use transformer backbones with more than 80 million parameters and assume fully decoded, high-quality video. Real deployments instead deliver small, heavily compressed and partially occluded faces to hardware with a few-watt power budget. We address this gap in three ways. First, DFEW-D and FERV39k-D form a reproducible degradation protocol that jointly sweeps face resolution, codec constant rate factor and occlusion over five severity levels and three codecs. Second, CoDA-Net is a 6.8 M-parameter network that reads motion vectors and residuals from the coded bitstream, combines them with appearance features from sparse intra frames and aggregates the sequence with a bidirectional state-space core, without reconstructing inter-coded frames. Third, quality-conditioned distillation uses a small quality head to estimate per-frame degradation and scales the supervision from a frozen teacher accordingly. We evaluate on DFEW, FERV39k and MAFW. On DFEW-D, CoDA-Net retains 84.2% of its reference-encoding weighted average recall at the most severe degradation level, against 66.7% for the strongest baseline, and runs at 32.3 clips/s on a Jetson Orin Nano and 5.4 clips/s on a Raspberry Pi 5. This suffices for stride-one prediction on the Jetson and for non-overlapping windows on the Pi 5. CoDA-Net is thus a robustness–efficiency trade-off for edge hardware, not a replacement for server-scale models.

Technologies doi: 10.3390/technologies14110728

Authors: Anna Resch Lukas Wohofsky Laura Kroll Daniela Krainer Suchi Priyadarshani Andrea Mercusa Martina Di Prisco Arianna Marzi

People on the autism spectrum and other neurodivergent people may experience sensory-rich environments as distressing, while teachers and care professionals must often identify and respond to environmental discomfort with limited objective information. This study reports the human-centered requirement elicitation, prototype development, and quantitative pilot evaluation of an environmental-monitoring system that relies on rule-based thresholds. Across three case-study settings, 125 stakeholder participations informed the development and design of a smart sensor system measuring sound, light, temperature, and air quality. The subsequent pre–post pilot study was conducted at four Austrian and Italian centers from February to June 2026. In total, 42 clients or pupils and 26 teachers or care professionals participated in the evaluation measures of the pilot study. Post-study acceptance was positive: overall meCUE user experience scores ranged from 4.88 to 4.94 on a seven-point scale, and AttrakDiff overall attractiveness averaged 1.21 (SD = 1.06) on a scale from −3 to +3. The results support the usability and perceived value of the prototype to maintain supportive environmental conditions.

Technologies doi: 10.3390/technologies14110727

Authors: Natthakan Rueangnetr Satra Tor. Wattanaphol Santi Koonkarnkhai Simon John Greaves Chanon Warisarn

Dual-layer bit-patterned magnetic recording (DL-BPMR) is a promising approach to extend areal-density scaling by exploiting the vertical dimension; however, reliable retrieval is challenged by the inherently weaker lower-layer readback and additional interference mechanisms, inter-symbol interference, inter-track interference, and inter-layer interference, that distort the mixed readback signal. This study presents a co-designed dual-layer BPMR scheme with an areal density of 2.0 to 6.0 Terabits per square inch (Tbits/in2), based on two complementary approaches. First, we vary the head–media spacing (HMS) to reduce the distance between the read head and the recording layers, especially the lower layer, strengthening the lower-layer contribution and making the mixed readback clearer and more reliable. Second, conditioned-HMS readback is processed using a combined-processing receiver tailored to a staggered (down-track-offset) dual-layer layout, where oversampling enables a unified one-dimensional sequence representation for dual-layer detection without enforcing early layer separation. To further improve detection efficiency, we also introduce a two-step minimum mean square error design that first determines a short monic generalized partial response target and then computes a minimum mean square error equalizer matched to the resulting readback, producing an equalized sequence suitable for soft-output sequence detection and low-density parity-check coded decoding. The simulation results indicated that reducing the head-media spacing improves readback separability (i.e., reliability) and that the proposed two-step combined-processing partial-response maximum-likelihood method achieves a consistently lower mean-squared error than conventional dual-layer processing across signal-to-noise ratios (SNRs). More importantly, in end-to-end bit-error-rate (BER) evaluation under electronic noise, e.g., the proposed dual-layer BPMR system delivers a clear reliability advantage over a single-layer BPMR baseline, achieving SNR gains of 4.2 dB at BER = 10−4 and 3.7 dB at BER = 10−5 in the favorable head-media spacing regime, thereby demonstrating robust high-density dual-layer retrieval beyond the single-layer baseline.

Technologies doi: 10.3390/technologies14110725

Authors: Sangbin Ha Muhammad Waqas Sang Min Kim

Cyanobacterial blooms threaten the water supply in regulated reservoirs, but the weekly early-warning skill and the usefulness of basin-scale water-quality gradients are not-well quantified. This study created a weekly bloom-risk framework for Jinyang Lake, South Korea, based on the probability of cyanobacteria exceeding 1000 cells/mL in the next 1–4 weeks. Weekly data from 16 stations (2011–2025) trained Deep Long Short-Term Memory (LSTM) and hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) classifiers (training 2011–2022, tuning 2023, independent testing 2024–2025; 48 weeks). The results of principal component analysis (PCA) indicated that the first two components accounted for 46.9–88.1% of the variance by variable, so no single station represented basin-wide conditions. At the 4-week horizon, the hybrid CNN-LSTM achieved F1 = 0.849 (95% CI: 0.74–0.93) and critical success index (CSI) = 0.738 at Naedong, and F1 = 0.758 (0.62–0.88) and CSI = 0.610 at Panmun, exceeding Random Forest (F1 = 0.58–0.62) and XGBoost (F1 = 0.55–0.58). At Naedong, the area under the curve (AUC) was 0.523, while, at Panmun, it was 0.768, due to the class-weighted training that resulted in probabilities being squeezed into a narrow range. PCA-derived features numerically increased the F1 score at Panmun by 0.020–0.041 (not statistically significant) and reduced the Naedong hybrid F1 score from 0.849 to 0.786. Platt scaling reduced hybrid-model Brier scores by 27–29% at both lake stations, improving the usability of the probabilities for operational decisions. The multi-week bloom-risk framework offers reservoir operators and municipal drinking-water systems probabilistic early-warning decision support for preventive action before critical bloom development.

Technologies doi: 10.3390/technologies14110726

Authors: Larisa Taskina Ksenia Gerasimova Timofey Kazarkin Leonid Abakumov

Collecting and annotating representative aerial imagery remains a constraint on automated cattle monitoring. We evaluate simulation-generated images as a complementary training resource for cattle detection from unmanned aerial vehicles (UAVs). A pipeline developed in Unreal Engine 5.3.2 combines randomized agricultural scenes, herd movement, asynchronous animal animations, camera variation, and automatic bounding-box annotation. Quality filtering retained 2084 of 6986 rendered images. We compared real-only training (A), sequential synthetic-to-real training (B), and real:synthetic mixtures of 50:50 (C50) and 75:25 (C75) across five YOLOv8n training seeds, with 2000 optimizer updates per strategy. On the 93-image test split, mean mAP50–95 ± sample standard deviation was 0.5153 ± 0.0063 for A, 0.5083 ± 0.0092 for B, 0.4898 ± 0.0066 for C50, and 0.5027 ± 0.0041 for C75. C75 improved on C50, while real-only training retained the highest mean mAP50–95 under the evaluated protocol. Validation-selected thresholds and counting errors characterized the balance between false detections and misses. A separate three-seed comparison of static and animated synthetic scenes showed fewer misses with animation and movement, alongside more false detections on average, while the mAP50–95 differences varied across seeds. The pipeline provides an annotated-data resource and controlled comparisons of its use in training. Our findings concern fixed synthetic subsets and the existing CowDetect partition. Independent acquisitions and separate animation and movement conditions remain to be evaluated.

Technologies doi: 10.3390/technologies14110724

Authors: Wisam Makki Alwash Belal Al-Khateeb Hasan Hüseyin Balık

Three-dimensional (3D) intraoral scans (IOS) retain patient-specific morphology after removal of direct identifiers, creating privacy risks when geometric records are released. We introduce DentPriv-Bench3D, an adversarial security benchmark evaluating identity leakage under fixed and mechanism-aware attackers alongside segmentation-based utility. Using 1800 scans from 900 patients, we established patient-disjoint training, development-validation, and locked-test cohorts (600/100/200 patients). We evaluated classical registration, Dynamic Graph Convolutional Neural Network (DGCNN), PointNet++, adaptive retraining, semantic-only linkage, and source-independent standardization. On the locked test, raw-geometry Rank-1 identification reached 67.00%, 12.00%, and 64.50%, respectively, against 0.50% chance. For the learned protected release, Rank-1 increased from 14.08% under fixed DGCNN to 99.25% under mechanism-aware adaptive DGCNN, a paired difference of 85.17 percentage points (95% confidence interval, 81.50–88.58), while segmentation-label agreement was 95.92%. In supplementary development-validation analysis (100 patients), ground-truth tooth-segmentation macro-F1 was 0.8142 for clean and 0.8097 for protected geometry, a 0.45-percentage-point decrease. Coarse dental semantics yielded 32.00% Rank-1. Source-independent standardization yielded worst-case Rank-1 values of 0.75% and 1.50% across two gallery scenarios, but missed one pre-specified confidence-bound criterion; synthetic-label macro-F1 was 0.3714. These findings show that fixed-attacker assessments can substantially underestimate recoverable identity information and motivate mechanism-aware privacy auditing.

Technologies doi: 10.3390/technologies14110723

Authors: Krisztián Horváth

Sensor relocation can induce structural domain shift because gearbox vibration depends on fault excitation and the transmission path. This study evaluates transferability using 180 source recordings representing ten states, six accelerometer positions, and three coupled operating conditions. In the independently reproduced 170-feature ExtraTrees evaluation, raw nine-fault balanced accuracy was 43.8% with a held-out sensor position versus 84.6% with a held-out operating condition; local healthy referencing increased cross-position accuracy to 61.7% but requires a target-position healthy baseline. Fault-by-position interaction increased from 9.7% below 2 kHz to 25.3% at 5–10 kHz. The original non-nested bandwidth sweep found a raw gear-fault model-mean maximum of 84.4% below 1 kHz (62.6% for 0–10 kHz). New nested source-position-only cutoff selection yielded 82.2% for gear faults versus 62.6% over the full band, selecting cutoffs between 1 and 2 kHz across folds and classifiers. Raw bearing-fault accuracy did not benefit consistently from nested cutoff selection. The findings establish a within-rig baseline for frequency-localized structural portability rather than a universal frequency limit or new classifier; independent machine-level validation remains necessary.

Technologies doi: 10.3390/technologies14110722

Authors: Linghan Wang Jun Wei Shuhai Liu

The installation of grouted connections in deep-water offshore wind turbine jackets faces severe visibility constraints and limited diver operation time, necessitating an unmanned, real-time monitoring solution using remotely operated vehicles (ROVs). However, the lack of dedicated datasets and the challenge of balancing segmentation accuracy with edge-deployment efficiency remain major obstacles. This paper addresses these gaps by constructing, to the best of our knowledge, the first underwater grout overflow instance segmentation dataset reported in the published literature, comprising 2088 images and 5733 polygon instances annotated under a two-level semantic system (“overflow” and “overflow warning”). On this basis, we propose a collaborative optimization method based on YOLOv11n-seg and validate it with a strict 12-fold leave-one-group-out cross-validation (LOGOCV; 2088 images, video-level disjoint groups, unweighted macro averages). We first design a lightweight Multi-Path Feature Extractor (MPFE), MPFE-lite, retaining only the B3 asymmetric strip-convolution branch (depthwise 1 × 5 and 5 × 1 kernels). MPFE-lite improves mask mAP@0.5 over the YOLOv11n-seg baseline by 3.85 percentage points (35.9% → 39.8%; paired t-test p = 0.017, Wilcoxon p = 0.027, 10 wins out of 12 folds) while using 11.6% fewer parameters (2.54 M vs. 2.88 M) and slightly fewer GFLOPs (9.80 vs. 9.90). The full three-branch MPFE is also significantly better than baseline (mask mAP@0.5 +3.53 points, p = 0.007/0.002), but adding its dilated multi-scale and fixed Sobel branches to B3 gives no significant cross-group gain; the Dual-Branch Channel-Spatial Attention (DCSA) attention module and DySample upsampling, which appeared beneficial on a single fixed split, likewise produce no significant LOGOCV improvement and are therefore removed. For deployment-oriented compression, a Bidirectional Feature Pyramid Network (BiFPN)-based variant (1.21 M parameters, 7.38 GFLOPs) offers a 58.0% parameter reduction at a cost of 3.10 mask-mAP points, providing an optional lightweight configuration. Against peer lightweight models over an eight-fold partial comparison, MPFE-lite is significantly more accurate than YOLOv5n-seg and YOLOv6s-seg and comparable to YOLOv8n-seg while being lighter. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations provide qualitative supporting evidence consistent with the strip-convolution design rather than definitive confirmation of its mechanism. This work provides a complete engineering closed-loop from model inference to on-site visualization, filling critical data and algorithmic gaps in underwater grout monitoring.

Technologies doi: 10.3390/technologies14110721

Authors: Vladimir Malikov Sergey Voinash Farmon Mamatov Marat Kalimullin Igor Garkin Linar Sabitov Alexey Ishkov Alexander Filippov

Defects in thin metallic films may remain undetected until later manufacturing steps have already consumed additional materials and energy. This study developed and experimentally evaluated an automated non-contact eddy current platform for local quality monitoring of thin metallic films. The system combines a miniaturized high-frequency transformer-type probe with a conical ferrite core, three-axis positioning, digital acquisition, and Python (v3.14)-based processing. Cu and Ni films 100–800 nm thick were tested. Across 4–25 MHz, 25 MHz produced the highest signal-to-background ratio. Finite element modeling showed that the ferrite core increased peak eddy current density in the film by more than a factor of two. Two artificial defects measuring 6.2 × 0.11 mm and 4.5 × 0.17 mm were localized; signal gradient analysis reduced length estimation errors to 1.6–6.7%, compared with 6.4–17.8% for direct amplitude analysis. Eddy current spatial profiles agreed with independent photometric measurements (R2 = 0.89). A separate scenario-based engineering assessment examined potential resource savings from post-deposition inspection. Early detection can prevent unnecessary downstream processing of defective substrates, supporting waste prevention and resource efficiency. The sustainability values reported here are scenario estimates rather than direct industrial measurements.

Technologies doi: 10.3390/technologies14110720

Authors: Turki Saud Alotaibi Ahmed Saud Alanizi Qazi Emad Ul Haq Tariq M. Khan

Late fusion can combine the outputs of separately trained RGB and thermal detectors without retraining them. We evaluate such training-free fusion, together with a rule-based reliability-aware controller that adapts per-frame thresholds and modality weights, on M3FD, FLIR-aligned, and FLIR ADAS v2. Box-level COCO metrics on the registered M3FD and aligned FLIR-aligned pairs are the primary results, and frame-level class-set metrics are secondary. At the operating threshold of 0.35, fixed fusion of two YOLOv8n detectors raised AP50 over the thermal detector from 0.319 to 0.377 on M3FD and from 0.623 to 0.666 on FLIR-aligned (from 0.724 to 0.758 over all detections down to a confidence of 0.001). Over all detections, the FLIR-aligned gain was 0.0198 to 0.0345 over three training seeds, 0.0168 when the detectors were trained for 60 instead of 30 epochs, and 0.0222 with larger YOLOv8m detectors (AP50 from 0.797 to 0.819); the 60-epoch and YOLOv8m results are single runs and do not show whether the gain decreases for better detectors. With YOLOv8m, fusion still raised AP50 but lowered box-level F1 at the operating threshold of 0.35 used for all systems, a reduction observed at this single operating point. On the unregistered FLIR ADAS v2 pairs, fusion lowered AP50, and its frame-level gain depends on how the frame label is defined. On the same detections, our single-score ProbEn-style rule was 0.092 AP50 below fixed fusion; published results obtained with other detectors and evaluation code are not directly comparable. The controller shifts the operating point toward slightly higher precision and lower recall; a threshold change of fixed fusion reproduces this shift; and at equal recall, fixed fusion is at least as precise, so we report the controller as a negative result. Fusion takes 0.4–1.0 ms per frame on a CPU for detections with a confidence of at least 0.35 and 8.8–9.6 ms when all detections are fused.

Technologies doi: 10.3390/technologies14110719

Authors: Chen Zhong Damiano Padovani Andrea Cioncolini Angelo Alessandri

Electromagnetic levitation systems are instrumental in rail transportation, manufacturing machinery, contactless bearings, and precision scientific instrumentation. These systems are inherently open-loop-unstable, require accurate motion tracking to detect the position of the levitated object, and need effective real-time control to adjust the electromagnetic force. In this paper, we propose a machine-vision-based magnetic levitation system comprising a Raspberry Pi microcomputer programmed in Python, a machine-vision camera with software triggering, and a lifting electromagnet actuated using a model-based PID controller with compensation and feedforward augmentation. Since all components are available off-the-shelf, the present magnetic levitation system is cost-effective and easy to replicate. To the best of our knowledge, the present setup seems to be the first entirely based on a Raspberry Pi microcomputer; the first in which the electromagnetic force is predicted with an empirical correlation based on dedicated measurements, making analytical/numerical predictions unnecessary; and the first where the added mass is incorporated in the equation of motion of the levitated object (it is a non-negligible phenomenon, function of the position, for levitation in liquids). The visual tracking system has an accuracy of about ±15 micrometers, and the controller operates at 100 Hz. When actuating a sphere large enough to significantly distort the electromagnetic field, this magnetic levitation setup achieves position-tracking errors (RMS errors in the range of 74 to 194 μm during quasi-static and dynamic tests) comparable to or better than those of machine-vision-based magnetic levitation systems proposed in previous investigations, at a fraction of the cost.

Technologies doi: 10.3390/technologies14110718

Authors: Abadila Alaktif Meriyem Chergui Gmira Faiq Abdelkarim Ammoumou

Why is machine-generated text detectable, and how much does the answer depend on the model’s scale, its post-training, and the writing task? We measure detectability with a validation-tuned linear probe on frozen embeddings from RoBERTa, a pretrained transformer language encoder, under a preregistered protocol in which every model stage receives an identical plain prompt, every output is exactly 256 tokens from a single attempt, and all evaluation rules were fixed before any score existed. Across nine model configurations evaluated in thirteen model–task cells (four Tulu-3 post-training stages under both rewriting and free generation; five Pythia sizes from 70M to 6.9B parameters), detectability at full instrument strength is near-perfect in every cell: ten of thirteen cells reach an area under the curve (AUROC) of 1.0, including the unaligned base model. With the instrument deliberately weakened to a 32-token view, exact ordered tests support a small increase in detectability across post-training stages in both tasks, but the rewrite and free-generation conditions do not differ (difference-in-differences −0.002, 95% CI [−0.033,0.029]), and no association between detectability and model scale is detected within the examined 70M–6.9B range (exact Spearman ρ=−0.1, p=0.475). At the level of individual texts, rewrites that copy more of their source are reliably harder to detect (clustered ρ=−0.27 to −0.53, all ten generators negative). The picture that emerges, for the examined configurations, domain, and instrument, is pervasive detectability with small, task-independent post-training effects, and it implies that detector evaluations need ceiling checks, matched prompts, and instrument-strength ladders before their comparisons can be trusted.

Technologies doi: 10.3390/technologies14110717

Authors: Jiaqi Hao Xiaogang Yang Huajun Liu Jinlin Miao Jiangtao Liu Jianxin Chen

Autonomous underwater vehicles must plan through observations affected by color cast, low illumination, scattering, and blur. This study develops a reproducible framework that converts the expert pixel annotations of the Segmentation of Underwater IMagery (SUIM) dataset into binary free-water/obstacle traversability labels, trains SegFormer-B0 with U-Net and DeepLabv3 baselines, estimates pixel-wise softmax entropy, and fuses obstacle probability with uncertainty into a grid risk field for A* replanning. Navigation under perceptual uncertainty is posed as a shortest-path problem on a 32 × 32 image-plane grid: the per-cell obstacle probability and entropy obtained from the network outputs enter the A* cost, so ambiguous cells are penalized without using labels at run time. All navigation experiments are image-plane grid simulations in which the vehicle cell is known exactly; real inertial navigation, localization error, and vehicle dynamics are not included. Four deterministic degradation levels are applied to held-out images, and 300 paired navigation scenarios with fixed start and goal cells are built from 60 test scenes, both for static planning and for a receding-horizon loop with limited sensing and route hysteresis. SegFormer-B0 reaches 0.913 obstacle-class IoU and 0.954 F1 on Clear and retains 0.843 obstacle-class IoU and 0.915 F1 at L4, with an expected calibration error below 0.04; retraining with five seeds confirms the backbone ranking from Clear to L3 but shows a large spread at L4 (0.733 ± 0.085 obstacle-class IoU). Mean normalized entropy increases from 0.373 to 0.414, while the top-10% hit rate in boundary or error regions increases from 29.4% to 34.9%. Risk-aware A* improves safety success over probability-cost A* on all conditions and reduces unsafe crossing by 2.86–10.23% with a mean path-length cost below 1.27%; the gain is small but consistent (8 of 8 discordant scenarios, McNemar p = 0.008) and shrinks when the baseline cost scale is tuned on validation data. The segmentation backbone has a larger effect (72.0% vs. 56.0% safe routes against DeepLabv3), and route hysteresis removes 96% of route switches in receding-horizon replanning.

Technologies doi: 10.3390/technologies14110715

Authors: Vasileios P. Rekkas Nicholas Vretos

Over the past years, large language models (LLMs) have gained significant attention and have led to major breakthroughs in natural language processing (NLP). Despite their remarkable efficiency and contextual understanding, LLMs still remain limited by their tendency to generate hallucinated text, their reliance on static internal knowledge, and their limited interpretability. To address these limitations, retrieval-augmented generation (RAG) techniques have emerged as a hybrid architecture that couples the generative capabilities of LLMs with external knowledge retrieval processes. By drawing on external knowledge at inference time, RAG architectures are intended to enhance the accuracy and relevance of their generated output, and they have already been widely used in areas where extensive, detailed and up-to-date knowledge is needed. For this study, Scopus, IEEE Xplore, and the ACM Digital Library were explored and utilized, with Google Scholar used only for backward and forward citation checking, resulting in 191 peer-reviewed journal articles published between January 2024 and April 2026. This methodological review presents the research efforts regarding the use of RAG in LLMs for a variety of application fields. Additionally, this review summarizes the main RAG architectural approaches, deployment strategies, and evaluation metrics. Furthermore, the main open issues and future directions are examined, to motivate further research on the field.

Technologies doi: 10.3390/technologies14110714

Authors: Kuntharrgyal Khysru Chenlin Zhao Wenjie Tang Di Jin Jianguo Wei

Snoring is a common sleep-related phenomenon and is relevant to research on sleep-disordered breathing. Automatic snoring recognition may provide useful acoustic information for sleep breathing monitoring. In this study, sleep breathing sounds were recorded using a bone-conduction microphone attached to the skin over the throat. The recordings were manually annotated using Praat with five acoustic labels: snoring, breathing, apnea, speech, and noise. Segments labeled as speech or noise were excluded from the classification experiment, which focused on snoring, breathing, and apnea. Mel-frequency cepstral coefficients (MFCCs) were extracted from the retained recordings, and a hidden Markov model (HMM) implemented using the Hidden Markov Toolkit (HTK) was used to recognize sequences of breathing, snoring, and apnea labels. Using MFCC feature vectors with 24 dimensions and an HMM with 10 states, the exploratory analysis yielded 89.47% HTK word correctness and 28.95% HTK word accuracy on the same test data used for parameter selection. These exploratory results describe recognition of manually annotated acoustic categories in selected recordings. Further independent evaluation is required to assess generalizability and practical reliability.

Technologies doi: 10.3390/technologies14110716

Authors: Amir Farrokhi Ali Shanaghi Ali Shamsipur Ying Zhao Artem Okulov

NiTi shape memory alloys (SMAs) are increasingly considered for seismic energy-dissipating devices embedded in reinforced concrete, where passive film stability under simultaneous mechanical and chemical attack remains poorly understood. This study examines the electrochemical and mechanical degradation of NiTi under sustained tensile loading (40% UTS) in carbonated concrete pore solution (CCPS, pH 9.0–9.5), with and without combined 0.5 M NaCl and anodic polarization (Eapp = Ecorr + 200 mV). Open-circuit potential measurements show that stressed specimens in chloride-free CCPS stabilize near −0.29 V vs. SCE, approximately 60 mV more negative than unstressed controls, reflecting persistent repassivation instability rather than steady-state film growth; in the presence of NaCl, the stressed OCP shifts further to −0.31 V. Electrochemical impedance spectroscopy reveals a stress-induced transition from a single-barrier to a dual-layer response, with the corrosion-product resistance (Rcp) declining 61% from 8321 ± 1264 Ω·cm2 to 3261 ± 518 Ω·cm2 over 48 h; the CPE exponent n falls to 0.61, indicating severe interfacial disorder. Mott–Schottky analysis confirms that the passive film is an n-type TiO2-based semiconductor whose total ionized-donor density (ND) increases by approximately 69%, from (8.3 ± 0.4) × 1016 cm−3 to (1.4 ± 0.1) × 1017 cm−3, under load. This elevation is consistent with stress-driven accumulation of point defects and, as one plausible contributing pathway, localized near-surface B2 → B19′ transformation at stress concentrations, though Mott–Schottky analysis alone does not identify the specific donor species. XPS depth profiling corroborates this picture structurally: stressed films display a broadened oxide–metal transition zone, an anomalous spatial distribution of metallic nickel inconsistent with the sharp compositional boundary expected beneath a well-passivated surface, and a premature onset of the metallic Ti signal—collectively indicative of a structurally compromised interface. Slow-strain-rate testing quantifies the mechanical toll: fracture strain drops from 10.2 ± 0.6% in air 6.1 ± 0.4% in CCPS and 4.5 ± 0.5% under combined chloride and anodic polarization (approximately 56% total reduction vs. air), whereas UTS declines by only approximately 15%, a disproportionate diagnostic of localized SCC. The susceptibility index ISCC(UTS) rises from 0.11 in CCPS at OCP to 0.15 under the combined chloride–anodic condition, consistent with progressive passive-film degradation driven by the superposition of mechanical, chemical, and electrochemical attack. These results reframe passive film degradation as a defect-engineering process in which mechanical load restructures the TiO2 point-defect population, converting a protective barrier into a mechanochemical gateway for stress corrosion cracking.

Technologies doi: 10.3390/technologies14110713

Authors: Lauren Genith Isaza Dominguez Sonia Marcela Moreno Gracia Alfonso A. Portacio

Crop diseases, pest infestations, environmental stress, and resource-management limitations challenge effective crop management within precision agriculture. Augmented reality (AR) offers opportunities to integrate crop observations with digital agricultural information, yet limited attention has been given to synthesizing limitations affecting AR-assisted systems in disease detection, pest monitoring, and agricultural decision-making. Across these areas, 24 AR-assisted systems were comparatively analyzed alongside the supporting technological, biological, and conventional-method literature. Cross-area synthesis examined the challenges addressed, AR’s role, and technological limitations. The findings informed a development framework comprising five considerations: reliable agricultural information generation, field-capable system architecture, computation, and connectivity, spatial and contextual information presentation, human-interpretable information and decision support, and task-integrated field interaction and operational robustness, complemented by cross-cutting validation. Illustrated through an AR-assisted rice disease-assessment prototype, the framework provides a structured basis for developing and evaluating future AR-assisted crop-management systems. The review highlights that AR’s practical value in crop management depends not only on information visualization but also on task integration, trade-offs between analytical detail and operational feasibility, reliable sensing and processing, field-compatible architectures, and validation of its contribution to agricultural decision-making.

Technologies doi: 10.3390/technologies14110712

Authors: Alexander Shao-Rong Pang Hao Min Cheo Jia Wen Amelia Ong Qihuang Xie Raymond Daniel Alix Lu Dinesh Kumar Srinivasan Zheng Jye Ling Glen Zi Qiang Liau

The integration of artificial intelligence (AI) into healthcare represents a paradigm shift in both clinical practice and administrative operations. This narrative review examines the transformative role of large language models (LLMs) and generative AI across two intersecting domains. Firstly, clinical applications, including early disease detection, medical imaging, personalized medicine, and chronic disease monitoring. Secondly, administrative functions, encompassing automated documentation, electronic health record (EHR) enhancement, conversational agents, and patient flow optimization. Through a health informatics lens, oncology is used as an illustrative setting, rather than a formal case study, to examine how AI systems engage with clinical reasoning, workflow integration, and enterprise-scale deployment. The trajectory from IBM Watson for Oncology to contemporary platforms such as Tempus and PathAI reflects both the progress achieved and the persistent challenges of scalability, interpretability, and equitable data representation. Emerging LLM-based tools, including ambient documentation systems, have been reported to reduce administrative burden in some studies, though the evidence base remains nascent and context-dependent. Critical limitations are identified across ethical, regulatory, and practical dimensions. Some of these limitations include algorithmic opacity, automation bias, dataset inequity, and the risk that automated note generation may attenuate the cognitive rigor inherent to clinical documentation. Future directions emphasize the development of multimodal and human-centered AI systems evaluated through external and prospective validation and reported in line with guidelines like CONSORT-AI and SPIRIT-AI, alongside robust post-deployment monitoring. Ultimately, oncology’s experience illustrates potentially transferable principles for responsible AI adoption across healthcare specialties, suggesting that sustainable integration depends not solely on technical performance, but on transparency, governance, and meaningful clinician–AI collaboration.

Technologies doi: 10.3390/technologies14110711

Authors: David Álvarez-Martínez Daniel Orlando Rivera-Royero

The convergence of e-commerce growth, thin margins, and tight service windows has turned the warehouse into the critical node where supply-chain performance is won or lost, and where a wave of Industry 4.0 technologies—computer vision, automatic identification, collaborative and mobile robotics, mixed reality, and optimization coupled with digital twins—is now being deployed. Yet adoption in Latin American distribution centers remains uneven, and the literature rarely connects the survey of available technologies to the problems that operators actually prioritize on the floor. This paper presents a state-of-the-art review of technology integration in storage and dispatch operations, grounded in a structured field diagnostic of five distribution facilities visited in Colombia and the United States across grocery hard-discount, automotive/motorcycle distribution, motorcycle assembly, textile distribution, and cross-border third-party logistics. Combining the field evidence with the academic literature, we organize the domain into three integration layers: perception (E1: computer vision and automatic identification), decision (E2: optimization and simulation-based decision support, including digital twins), and orchestration (E3: yard, dock, and internal-transport management). For each layer, we synthesize the literature, map it to observed operational pain points through a documented coding path, and identify gaps. We find that vision is the most reusable asset across heterogeneous operations, that digital twins deliver value only above a scale threshold, and that four cross-cutting determinants—interoperability with incumbent warehouse-management systems, the human practices of the workforce, software and data integration, and an explicit adoption threshold—govern whether any deployment succeeds. We close with testable propositions and a ten-year research-and-development agenda for the region.

Technologies doi: 10.3390/technologies14110710

Authors: Soemsak Yooyen Tanawit Suteewong Pattarapong Phasukkit Pattharin Wichittrakarn Nongluck Houngkamhang Popphon Laon Supavee Pourbunthidkul Tanawit Sahavisit Chusit Pradabpet Yoshikazu Miyanaga

What does a short sensor record support, and what do its faults cost? We retained 762 h of a 919-h wet-season window (40 calendar dates) on a 12-m Ku-band dish in Bangkok; Ku-band carries Internet-of-Things backhaul. The novelty is quantifying, on this record, the limits deciding which statistics hold: sampling uncertainty, the clear-sky reference, noise floor, receiver ceiling, time base, and the exclusion of 51% of gauge-wet against 15% of gauge-dry time. Claim-bearing statistics carry day–block bootstrap intervals, with four named exceptions. Level uncertainty is 22–52%, and ceiling-censored 0.1–0.2% levels are lower bounds. A key null result: over 16 rain days, ridge regression and gradient boosting on twelve weather-station and time-of-day inputs did not improve on a rain-rate-only power law; paired 95% intervals exclude gains above 0.58 dB. Fades above 3 dB last from one 30-s sample to ≥87 min. A 3-dB margin is met for 98.4% of retained time and 81.6–98.7% of wall-clock time, depending on how excluded time is counted. Late-afternoon 3-dB exceedance tops overnight by 3.85 percentage points [1.34, 6.91]. Time base, level uncertainty and censoring leave rain-attenuation prediction error unmeasurable. Practitioners get planning inputs for comparable Ku-band gateways; absolute levels need adjustment.

Technologies doi: 10.3390/technologies14110709

Authors: Nelson Chambi Celso Sanga Alejandra Sanga

Medical equipment is essential for patient diagnosis and treatment; however, its maintenance strategies remain heterogeneous. This systematic review aimed to characterize the maintenance strategies applied to medical equipment and analyze their effects on reliability, availability, safety, and costs. Following PRISMA 2020 guidelines, original studies were searched in Scopus, ScienceDirect, PubMed, and Web of Science, published between January 2015 and July 2026. Studies were included if they applied maintenance strategies (preventive, corrective, predictive, condition-based, risk-based, or reliability-centered) and evaluated their effects on reliability, availability, safety, or costs. Seventy-four studies were selected. Eleven equipment categories were identified, with diagnostic imaging, life support, and infusion therapy being the most studied. Hybrid strategies—particularly preventive combined with corrective—were the most frequent (14 studies), followed by predictive maintenance (13 studies). Advanced analytics, IoT, and machine learning were the most applied technologies. A gap persists between model performance metrics and real maintenance indicators: several studies reported high predictive accuracy, but limited evidence of improvement in MTBF, downtime, availability, or cost. This review offers an integrative framework for healthcare technology managers and identifies priorities for future research.

Technologies doi: 10.3390/technologies14110705

Authors: Luis Antonio Yataco Pastor Yoisdel Castillo Alvarez Reinier Jiménez Borges Marcos Aviles Luis Angel Iturralde Carrera Juvenal Rodríguez-Reséndiz

Archimedes screw turbines (ASTs) are a well-established technology for low-head micro-hydropower generation, whose performance is highly sensitive to the radial clearance between the helical blade and the casing. In engineering practice, manufacturing and assembly tolerances frequently result in realized (as-built) clearances larger than the optimal design (as-designed) value; however, the literature lacks a quantitative assessment of how this discrepancy affects the performance curve and the position of the best efficiency point (BEP). This study numerically investigates a 1.0 m3/s, 2.1 m head AST in a closed tubular configuration, adopting a 15.00 mm radial clearance that exceeds the analytical optimum (6.36 mm) by 136% as a worst-case construction scenario, representative of both relaxed manufacturing tolerances and severe diametral wear. A three-dimensional, transient, multiphase CFD model (RANS SST k–ω with a volume-of-fluid free-surface formulation) is applied over six flow-rate conditions (0.55–2.00 m3/s) at constant rotational speed, with mesh independence verified using the Grid Convergence Index. At the nominal design flow rate, the overall efficiency decreases to 65.0%, 18 percentage points below the leakage-limited prediction of the one-dimensional model for the same clearance (83.3%) and 28 points below the ideal design (93.0%), i.e., a penalty nearly three times the one-dimensional estimate. The fitted best efficiency point shifts to 1.44±0.27 m3/s (95% CI), approximately 44% above the design flow rate, with an interpolated maximum efficiency of 66.0%; the efficiency plateau (η≥ηmax−1 pp) spans 1.16–1.72 m3/s and excludes the design flow rate, whereas the fixed-speed one-dimensional model predicts a shift of only +10% by volumetric compensation of the leakage. The discrepancy with the analytical model is attributed to two mechanisms: turbulent dissipation of the leakage jet and, more importantly, the internal nature of overfilling in a closed casing, where excess flow recirculates above the shaft and is retained by the machine rather than being treated as a terminal loss. The discharge also exhibits pulsatile behavior at the rotational frequency (0.52 Hz), with a relative amplitude that decreases from 43% to 29% of the mean torque over the simulated range, with a secondary local maximum near the best efficiency point, carrying direct implications for shaft fatigue design. The study is an exploratory numerical investigation whose predictions constitute testable hypotheses for experimental validation; these findings demonstrate that radial-clearance manufacturing tolerances require an operational reclassification of the intake flow rate and provide criteria for incorporating clearance effects into the design and long-term planning of low-head micro-hydropower projects.

Technologies doi: 10.3390/technologies14110708

Authors: Yenlik Begimbayeva Aida Mukazhanova Gulzada Mussapirova Azhar Analiyeva Gulnar Mamatova Nurzhigit Smailov Nurzhamal Kashkimbayeva

Indirect prompt injection can redirect web security agents, yet an omitted check alone cannot establish attacker influence. We evaluate an application-specific contract integrating exposure chronology, HTTP evidence provenance, application-state verification, and report fidelity. Matched placebo comparisons and controlled analyses examine background failure, verification error, and stopping. Across 420 Qwen3/HarborDesk executions, two payloads yielded six-check placebo-minus-attack losses of 15.28 and 12.50 percentage points. Secondary three-target losses were 10.56, 16.67, and 6.11 points; target coverage under attack matched or exceeded untreated S0. Conditional goal analysis retained only 13 of 60 baseline blocks per payload, limiting precision and generalizability. A 560-session Gemini/GPT-OSS experiment yielded a mean loss of 1.32 points (95% block-bootstrap interval 0.97–1.67), entirely from 19 GPT-OSS/A2 pairs losing C4. After interface correction, 120 stable-tool sessions on independently developed OWASP crAPI confirmed every authorized action, giving zero observed differences across 60 pairs. Strong and simple evaluators agreed on unchanged traces; designed faults differentiated rejection behavior without establishing natural-trace accuracy superiority. The contribution is the operational integration and reproducible testing of this evidence chain using established outcome measures.

Technologies doi: 10.3390/technologies14110707

Authors: Mahziar Parsapour Muhamad Fahezal Ismail Mohammad Hadi Ghasemi Engku Ahmad Azrulhisham Engku Mohamed Yusof Bin Mohd Ekhsan Noor Ayuni Che Zakaria

Model Predictive Control (MPC) performance depends on prediction and control horizons, particularly for experimentally identified inverse-response processes with noise, actuator constraints, and plant–model mismatch. This study evaluates an offline reinforcement learning (RL)-assisted supervisory framework for horizon selection in constrained MPC of a membrane-filtration process. Pump-speed-command and pump-line-flow data identified a second-order state-space model using Numerical Algorithms for Subspace State-Space System Identification (N4SID), achieving 92.16% estimation fit and 90.90% independent one-step-ahead validation fit, although long-horizon fidelity remained limited. The model was embedded in target-tracking MPC with input and input-move constraints, while RL selected only the horizons. Nominally, the representative RL-selected pair reduced the prespecified composite score from 2.5700 to 2.4562 (4.43%) with zero simulated quadratic-programming failures. Exhaustive evaluation of 146 feasible pairs identified 1.2214, confirming that RL was not globally optimal. Under a 120-iteration budget, Bayesian optimisation reached the global optimum in 20/30 runs versus 3/30 for epsilon-greedy. Extending the domain to 208 pairs reduced the score to 1.1838, indicating sensitivity to the lower prediction-horizon bound. Across 11 model-based robustness scenarios, RL-tuned MPC achieved an average 3.13% score reduction versus baseline, with individual-metric trade-offs. Overall, horizon selection improved the MPC trade-off, but results remained dependent on the search domain and identified-model environment.

Technologies doi: 10.3390/technologies14110706

Authors: Mario Bakota Lea Vojković Jelena Žanić Mikuličić Toni Meštrović

Cognitive workload and stress are key factors affecting human performance and safety in maritime navigation operations. This research proposes a composite gaze-tracking index, the Stress-Induced Gaze Pattern Score (SGPS), which integrates three complementary oculomotor metrics: pupil entropy, gaze dispersion, and gaze shift frequency. Single-blind research was conducted on a maritime navigation simulator with 65 participants of different ages, educational backgrounds, and maritime experience, who were exposed to an unexpected crisis scenario while eye movements and heart rate (HR) were simultaneously recorded. A two-layer interpretative model was introduced, in which SGPS serves as a quantitative measure of workload intensity, while FSR (Fixation-to-Saccade Ratio) acts as a qualitative moderator. Results showed that SGPS significantly correlated with HR in students (r = 0.77 in the acute phase, r = 0.74 in the sustained phase), thereby confirming its validity. At the whole-sample level, a significant correlation was observed in the sustained phase (r = 0.26, p = 0.05), suggesting broader applicability of the index. SGPS represents a promising objective tool for assessing cognitive workload in maritime simulation training and education, enabling early recognition of maladaptive stress responses, such as panic searching and cognitive tunnelling.

Technologies doi: 10.3390/technologies14110704

Authors: Diana Bratić Miroslav Mikota Suzana Pasanec Preprotić Denis Jurečić

Multi-scale registration and adaptive feature fusion provide established mechanisms for combining spatial information, but their effectiveness does not establish the benefit of combining drift estimates from independently generated reference patterns. This study proposes a multi-scale latent reference pattern model and its mathematical formulation for geometric drift estimation in printing. Coarse, medium, and fine Poisson-disk patterns provide separate observations for estimating translation, small-angle rotation, and fixed-frequency sinusoidal deformation. Three scale-specific estimators are compared with equal-weight and adaptive multi-scale combinations. The main experiment comprises 250 independent synthetic realizations, each evaluated under 16 combinations of Gaussian noise and blur. All three scales produced valid estimates in all 4000 scenario realizations. The fine scale had the lowest individual estimation errors, while equal weighting achieved the lowest mean errors among the five estimators in the main experiment. Mean deformation RMSE was 0.056848 px for equal weighting and 0.058735 px for adaptive weighting. Additional synthetic experiments examine threshold sensitivity, rigid and thin-plate-spline baselines, pooled estimation, and the relationship between adaptive weights and estimation error. Rigid registration was most precise in the rigid-only control, while pooled estimation achieved the lowest mean exact-field error under sinusoidal deformation. The contribution is a mathematically specified reference-pattern model and a controlled evaluation that identifies scale-dependent precision and limitations of the tested reliability indicator. The results support computational feasibility under the specified synthetic conditions. They do not establish an accuracy advantage for adaptive weighting or measurement performance on physically printed and captured patterns.

Technologies doi: 10.3390/technologies14110703

Authors: Yaser Shahvari Mohammadreza Sharbaf Shekoufeh Kolahdouz-Rahimi Kevin Lano

Multi-view engineering uses heterogeneous models to represent complementary system concerns, but independent model evolution can introduce cross-view inconsistencies. This paper presents MVCL (Multi-View Constraint Language), a domain-specific language for specifying cross-view constraints over heterogeneous model configurations. MVCL separates deterministic inconsistency detection from context-dependent tolerance classification: validated constraints are evaluated over composed model states, and violations are represented as typed inconsistency instances. The executable realization uses Xtext and EVL for language processing and validation, structured JSON for violation exchange, and gpt-5.6-luna as one downstream realization of the abstract tolerance layer. A controlled automotive benchmark evaluates 30 rules across three primary detection configurations and one larger stress configuration. Pooled detection results are 0.920 precision, 0.900 recall, and 0.910 F1, while 75 tolerance cases yield 0.867 accuracy and 0.853 repeated-run consistency. The results support the feasibility of separating formal inconsistency detection from context-dependent tolerance decisions while highlighting the need for broader industrial validation and systematic threshold calibration.

Technologies doi: 10.3390/technologies14110702

Authors: Cesar H. Valencia Laura M. Valencia Alfredo E. Sanabria Ospino

Strategic logistics network management requires decision support that anticipates demand and disruption uncertainty and evaluates candidate decisions before implementation. This paper presents GATrESN, a simulation-based predictive–prescriptive framework that couples Echo State Networks, a fixed parameter-free multi-head attention mechanism and a genetic algorithm over a robust scenario objective into a single decision loop over capacity, cost, inventory, workforce, resilience, and supplier redundancy. The case study is a panel of 24 sites, 16 product families, and 640 periods with 1800 matched decision instances. Against a linear-programming benchmark corrected so that the evaluator’s quality threshold is visible to it, and scored against realized rather than forecast states, the framework improves the strategic score by 0.0236 (95% bootstrap CI 0.0222–0.0250); the apparent margin against the original, weaker benchmark specification was 0.0596. Four external optimizers of the same objective reach the same operating region, and replacing the predictive core with a ridge readout changes ex post decision quality by 0.0001, locating the advantage in the robust objective rather than in the optimizer or the forecasting architecture. A closed-loop rolling-horizon experiment, in which decisions alter installed capacity, inventory, and risk at the next period, widens the advantage to 0.0315 and raises the worst-period service level from 0.52 to 0.76. Without automated exchange with a physical counterpart, the implementation is a digital model; the paper specifies the data acquisition required to close that loop.

Technologies doi: 10.3390/technologies14110701

Authors: Igor Wolf Ozren Bukovac Mateo Kirinčić Paolo Blecich

Physics-informed neural networks (PINNs) have been increasingly used for thermal-fluid problems. This study evaluates the performance of the PINN approach for reconstructing a laminar forced flow field inside a rectangular channel with four staggered wall-mounted squares. Air is used as the working fluid, at a Reynolds number of Re = 100 and a Prandtl number of Pr = 0.71. The four squares are isothermal at 80 °C while the inlet fluid temperature is 20 °C. The PINN is used to predict velocity, pressure and temperature fields, as well as local and average Nusselt numbers. The PINN solutions are verified against the reference finite volume solution with 262,400 control volumes. The finite volume method is validated against exact solutions for laminar flow between isothermal plates (Poiseuille flow and Graetz problem). The PINN was trained on 100, 250, 1000 and 4000 velocity and temperature sample points. Each case was trained five times with different random seeds. Without samples the PINN fails, with streamwise velocity and temperature errors equal to 49% and 137%, respectively. With 1000 uniformly placed samples throughout the domain, the streamwise and cross-stream velocity errors are 0.5% and 2.9%, respectively. The error of the pressure predicted by the network is 0.9% while the error in the temperature field is 1.6%. The average Nusselt number is underpredicted by 11% and increasing the sample count to 4000 points does not reduce this error. A significant improvement is obtained by placing sample points inside the thermal boundary layer of the isothermal squares. When 35% of the 1000 sample points are placed in the thermal boundary layer, the error in the average Nusselt number decreases to 8% and its scatter between the training runs is reduced by four times. The reconstructed fields are more accurate than linear, radial basis function and Kriging interpolation of the same samples.

Technologies doi: 10.3390/technologies14110700

Authors: Johan J. Estrada-López Vladimir Barrón-Briceño Omar Cocom-Sulú Helbert Canché Ramón Atoche-Enseñat Javier Vázquez-Castillo Francis Avilés Alejandro Castillo-Atoche

Ensuring the structural integrity of safety-critical systems, from aerospace to civil infrastructure, demands continuous, real-time strain monitoring under stringent security and power constraints. The integration of cryptographic circuits with Internet of things connectivity and nanostructured sensors offers a promising path toward enhancing system safety. However, challenges including noise, hysteresis, signal reproducibility, modeling, calibration, and data security risks during wireless transmission make signal processing difficult. This study presents a secure, energy-efficient smart strain sensing system for piezoresistive nanocomposite films, implemented on a field-programmable gate array (FPGA). The system integrates a deep neural network model to approximate the average piezoresistive response and mitigate static nonlinearities, alongside a 128-bit Advanced Encryption Standard (AES) cryptographic engine to secure Bluetooth data transmissions. This hardware-accelerated approach enables continuous structural strain monitoring, providing reliable data for early failure warning and predictive maintenance models. Experimental results demonstrate that the secure smart strain sensing system integrated with AES-encryption achieves a bit-error rate of 1.2×10−7, a packet delivery ratio of 99.7%, and a mean square error of 2.4×10−6, confirming the fidelity of the encrypted Bluetooth transmission under the tested conditions (0 dBm, 10 m range). In addition, a 99.4% dice similarity coefficient is achieved between transmitted and received/decrypted data, with a normalized mean absolute error of 12.5% over the elastic range. The system achieves a simulated core dynamic power consumption of 0.74 mW while utilizing only 8936 logic cells and 953 registers on an Altera 5CSEMA5F31C6N FPGA. Compared to existing approaches, the proposed system offers significant advantages in security, energy efficiency, and hardware resource utilization, making it a promising solution for real-time embedded monitoring in reliability-critical applications.

Technologies doi: 10.3390/technologies14110699

Authors: Ilze Vircava Liga Proskina Andris Karpovics Sandijs Meskis Daina Kairisa Lilija Degola

Resource-efficient conversion of biomass and organic residues into humic substances represents an important pathway for waste valorization within the circular bioeconomy. This review systematizes feedstock sources for humic substance extraction and compares their characteristics, sustainability, processing requirements, and assessment approaches. The scientific literature was analyzed together with regulatory and statistical sources relevant to feedstock classification and resource availability. The review synthesized evidence from 156 sources and shows that humic substance potential cannot be evaluated from resource volume or origin alone; it also depends on humification, organic-matter stability, physicochemical composition, contamination risks, and extraction technology. Primary resources such as peat and sapropel provide naturally humified fractions but are constrained by slow regeneration and environmental considerations. Secondary resources, including agricultural and forestry residues, fresh manure, and food waste, require flow- and coefficient-based assessment and may undergo further processing or stabilization. Tertiary resources, such as compost, digestate, treated sewage sludge, and processed or stabilized manure, are evaluated by linking material flows with quality, humification, safety, and recovery-yield indicators. For selected feedstocks, reported humic-substance content varied substantially, ranging from 2–20% dry matter in sapropel and 15–40% dry matter in peat, with different source-specific ranges reported for other organic materials. Because these values also depend on the analytical and extraction procedures applied, they should not be interpreted as directly comparable without harmonized analytical and reporting criteria. The quantitative assessment framework distinguishes theoretical, technical, and sustainably available resource potential and incorporates residue-to-product ratios, availability coefficients, material composition, and humic-substance content or recovery factors. The proposed differentiated framework supports more consistent comparison of renewable and waste-derived feedstocks and provides a methodological basis for assessing resource suitability, while highlighting the need for standardized analytical procedures, uncertainty assessment, contaminant-fate evaluation, and validation using regional or national case studies.

Technologies doi: 10.3390/technologies14110698

Authors: Óscar A. González-Sánchez Beatriz A. Rivera-Aguilar Erik Cuevas Marco Perez-Cisneros Janeth G. Rivera-Aguilar

High-quality images are essential for several computer-vision applications. Hence, low-contrast images often pose significant challenges because they can cause failures in automated detection and analysis processes. This study presents an innovative strategy to improve image quality by segmenting an image into interleaved cell groups and processing each group independently using equalization. For each group, we construct a mesh of images with local equalization, then integrate it using a set of fusion functions. The method enables equalization not only based on the intensity histogram but also on the spatial properties of the elements, ensuring overall contrast enhancement across the entire image. Likewise, because the algorithm is deterministic, it is possible to estimate its latency and accelerate it reliably through hardware implementation. The performance of the proposed method was validated qualitatively and quantitatively against other state-of-the-art algorithms, demonstrating outstanding results.

Technologies doi: 10.3390/technologies14110697

Authors: Abdul Aabid Muneer Baig Asraar Anjum

Piezoelectric transducers have attracted considerable attention in structural repair and health monitoring because of their dual actuation and sensing capabilities. This review critically examines their application in repairing and monitoring damaged engineering structures, particularly those containing cracks, notches, delamination, and debonding. First, analytical, numerical, and experimental approaches for piezoelectric-based structural repair are evaluated, with emphasis on crack suppression, stress intensity factor reduction, stress concentration control, and structural response improvement. The effects of actuator location, applied voltage, patch geometry, adhesive prop-erties, loading conditions, and optimization techniques are also discussed. The review then examines piezoelec-tric-based structural health monitoring techniques, including guided Lamb waves, electromechanical impedance, vibration-based sensing, sensor placement, and machine-learning-assisted damage assessment. The critical analysis shows that numerical investigations dominate the existing literature, whereas experimental validation, mixed-mode fracture studies, and long-term performance assessments remain limited. Finally, current research gaps, challenges, and future opportunities for intelligent and adaptive engineering structures are identified.

Technologies doi: 10.3390/technologies14110696

Authors: Patricia Acosta-Vargas Luis Suarez Tomas Cuadrado Luis Salvador-Ullauri

Industry 5.0 promotes human-centric industrial safety, yet it remains unclear whether AI-based risk-prevention systems support meaningful human decision-making or merely leave humans nominally in control. Following PRISMA-ScR, this scoping review examined 65 studies published between 2021 and 2026 (20 from an initial search and 45 from an updated search). Human authority was almost universally retained: 98.4% of the 61 implemented systems left the final decision to humans, and none operated autonomously. However, only 24.6% specified the human role, and 34.4% asked humans to decide on black-box outputs. The evidence was predominantly retrospective or simulated: only three studies were evaluated in operating workplaces, one assessed users’ responses, and none measured preventive effect comparatively. A strong reported preventive contribution was associated with operationally grounded evidence (Fisher’s exact p = 0.004; Holm-adjusted p = 0.019), whereas explainability was not (p = 0.602). AI acted mainly at the hazard source (53.8%, mostly through retrospective analytics) or the receptor (32.3%), rarely on the exposure pathway (4.6%). Only 3.1% of studies cited ISO 45001, and 75.4% did not embed any safety-management standard or process, revealing a reporting gap between technical oversight and formal safety governance. We propose a nine-item minimum reporting set to address these gaps.

Technologies doi: 10.3390/technologies14110695

Authors: Jailene Marlen Jaramillo-Perez Bárbara A. Macías-Hernández Edgar Tello-Leal

Rapid urbanization and climate change increase heat exposure in large cities, and reproducible baselines are needed to compare their long-term thermal behavior. We derive 1994–2024 near-surface air temperature (T2m) climatologies and trends for eight American megacities from hourly ERA5 reanalysis. The multiannual mean, hourly 90th percentile, interannual standard deviation, Mann–Kendall test, and Sen’s slope were computed on the native 0.25° grid and weighted by area, with 2km bilinear regridding used only for boundary-conformal cartographic visualization. Compared with one NOAA ISD airport station per city, ERA5 achieved CC=0.92–0.99 and RMSE=1.24–2.04 °C in seven cities, with values of 0.84 and 2.66 °C in Bogotá. Multiannual means ranged from 12.2 (Bogotá) to 23.6 °C (Rio de Janeiro). Warming was robust to serial dependence and the analysis period in New York, Buenos Aires, Mexico City, and Bogotá (+0.032 to +0.045 °C/year), conditional in Los Angeles (+0.048 °C/year; not significant after the Hamed–Rao correction), and not detected in São Paulo, Rio de Janeiro (+0.017 and +0.014 °C/year, p≈0.10), and Lima (+0.001 °C/year, p=0.92), where the 31-year record cannot distinguish these slopes from zero. Robust within-city slope contrasts reached 0.4–0.8 °C per 30 years in Mexico City and Bogotá. Because ERA5 resolves at about 28 km, the results describe city-scale baselines, not intra-urban variability or urban effects.

Technologies doi: 10.3390/technologies14110694

Authors: Shubrajit Bhaumik Byreddy Lakshmi Manohar Reddy Viorel Paleu William Woei Fong Chong

In the original publication [...]

Technologies doi: 10.3390/technologies14110693

Authors: Cuiya Liu Xiaojun Xu Min Wang Guojun Qin Junhong Yang Qing Guo

Hydraulic actuation can deliver substantial force or torque within a restricted robotic limb, yet installed joint capability emerges from more than actuator pressure. This critical engineering review connects mechanical output mechanism, hydraulic source placement, installation site, and runtime hydraulic capacity in a multidimensional architecture taxonomy. Representative hardware spans legged and wheel-legged robots, humanoids, wearable systems, and manipulators. Pressure–area, flow–speed, and virtual-work relations are interpreted together with containment stress, fluid and structural compliance, leakage, friction, and heat rejection to explain the usable output envelope. Integration is treated as a redistribution of fluid-path volume, moving mass, losses, thermal load, and service interfaces across shared sources, embedded passages, valve-integrated modules, and electro-hydrostatic actuators. Task-usable joint capability is defined through hardware, operating-condition, energy-conversion, and validation boundaries. A source-specific evidence matrix shows that pressure and output are commonly reported, whereas matched output, efficiency, installed-boundary clarity, and duty qualification remain substantially less complete. The review therefore links architecture-conditioned physics to evidence-aware benchmarking and proposes measurable research priorities for high-pressure tribology, thermal management, lightweight integration, reconfiguration, sensing, and robot-level validation.

Technologies doi: 10.3390/technologies14110692

Authors: Zahra Rajaei Shekoufeh Kolahdouz-Rahimi Massimo Tisi

Graph learning leverages deep neural networks to analyze and infer from graph-structured data, to extract meaningful patterns and insights from graph data for various classification and prediction problems. The community is exploring ways to exploit graph learning to address several tasks within the field of model-driven engineering (MDE). However, effectively using graph learning requires MDE practitioners to have a strong grasp of the technical details behind the graph-learning process, for instance, to properly encode models in an input format that best suits the given graph neural network. In this work, we propose MoEGL, a domain-specific language designed to specify and perform the encoding process. The user names, in the metamodel’s own terms, which classes, references, and attribute values enter the graph. The tool writes NetworkX 3.4.2 graphs and PyTorch Geometric 2.6.1 heterogeneous graphs, and it can keep the type information of the model. On the graphs published by López et al., one configuration reproduces every graph we checked: 500 Ecore, 500 RDS, and 369 Yakindu. On the 500 Ecore models, a first run is faster than their Java encoder, and each task is 14 to 29 lines of YAML.

Technologies doi: 10.3390/technologies14110691

Authors: Calvin Ser Thong Seah Wai Leong Eugene Wong Harry Sze Li Lim Hiong Yap Gan

Additive manufacturing (AM) is increasingly considered for deployment in dynamic operating environments where environmental vibration and motion may influence fabrication quality and process stability. However, understanding of vibration effects remains largely process-specific, limiting comparison across AM technologies. This structured comparative review examines vibration–process interactions across the seven AM process families using three dimensions: disturbance coupling, material state and consolidation behaviour, and the temporal distribution of vulnerability. The final evidence base comprised 85 records, including 46 providing direct vibration–process evidence and 39 providing supporting mechanistic or contextual evidence. Only five studies directly investigated external or environmental vibration, indicating that deployment-relevant evidence remains limited and uneven across AM process families. Cross-process synthesis identified two principal vulnerability archetypes: continuously sensitive processes, where opportunities for disturbance coupling persist throughout fabrication, and stage-dependent processes, where vulnerability is concentrated within discrete process stages. The available evidence demonstrates multiple process-specific pathways through which mechanical excitation can interact with AM fabrication but does not support universal vibration tolerance thresholds or a general ranking of process sensitivity. The resulting framework provides a basis for vibration-aware qualification and future experimental investigation of AM systems intended for dynamic deployment environments.

Technologies doi: 10.3390/technologies14110690

Authors: Dmytro Mamchur

Electrical-signature methods offer a non-invasive basis for induction-motor condition monitoring, but multimodal fusion does not necessarily improve prognosis when modalities have unequal reliability. This simulation-based study combines Motor Current Signature Analysis (MCSA), Instantaneous Power Signature Analysis (IPSA), reliability-weighted fusion, an interpretable health indicator, low-data Remaining Useful Life (RUL) estimation, measurement-noise analysis, and computational timing. A verified phase-coordinate induction-motor model was evaluated for healthy operation, progressive stator asymmetry, and reduced-circuit broken-rotor-bar (BRB) cases. For stator asymmetry, the reliability-weighted health trajectory was strictly directional in all 72 evaluated states. Reliability weighting improved over equal fusion and IPSA-only prognosis but did not outperform MCSA-only RUL accuracy. Across all diagnostic inputs, a free-intercept linear model outperformed the exponential model; MCSA-LIN achieved the lowest clean normalized-RUL MAE and RMSE of 0.082690 and 0.084853, respectively. Under synthetic electrical noise, p05–p95 intervals contained the clean estimate in all 360 cells but the synthetic truth in none, showing that measurement-noise dispersion was much smaller than deterministic bias. For BRB diagnosis, slip-tracked current sidebands were primary and low-frequency power signatures were complementary. The worst complete stator-diagnostic p95 latency was 6.350665 ms for a 2 s acquisition window. These results provide a reproducible simulation-only baseline for subsequent experimental validation.

Technologies doi: 10.3390/technologies14110689

Authors: Le Ding Xuan Zhu Zhichao Xu Yu Mao

Subgrades are vulnerable to hydrological effects, repeated loading, and material degradation, which may induce settlement, voids, loosening, and differential deformation and ultimately threaten the safety and serviceability of transportation infrastructure. With the growing demand for continuous and intelligent condition assessment, conventional single modality inspection methods are increasingly insufficient for complex field environments. This paper reviews recent advances in intelligent subgrade inspection and distress identification following a “sensing–identification–application” framework. Multi-physics sensing technologies, including ground-penetrating radar, elastic-wave testing, intelligent compaction, and embedded sensing, are summarized with emphasis on their applicable distress types, sensing characteristics, and life-cycle stages. AI-based methods for distress identification and condition prediction are then discussed, covering image recognition, object detection, semantic segmentation, time-series forecasting, and physics-informed data fusion. Engineering applications involving vehicle, rail, UAV, and robot-based inspection platforms are also examined. Future research should focus on multisource sensing fusion, stronger integration of data-driven models with engineering mechanics, improved cross-scenario adaptability, and closer links between inspection results and maintenance decisions. These advances are expected to support a transition from passive distress detection toward proactive prediction and intelligent infrastructure management.

Technologies doi: 10.3390/technologies14110688

Authors: Taha Ben-Abbou Mouhcine El Garej Khalid El Fazazy Mohamed Adnane Mahraz Hamid Tairi Jamal Riffi

Feature preprocessing directly shapes the geometry seen by distance- and similarity-based classifiers. This study introduces DART (Discriminative Adaptive Robust Transformation), a supervised preprocessing method combining median/MAD normalization, bounded nonlinear saturation, and continuous Fisher-mutual-information relevance weighting. DART is compared with supervised feature weighting and metric learning. Across the original conventional-preprocessing 41-dataset benchmark, DART achieved an overall balanced accuracy of 0.8118. In a separate supervised-control experiment, kNN results were NCA 0.8003, FisherWeight 0.7966, DART 0.7960, MIWeight 0.7855, and StandardScaler 0.7742; for RBF-SVM, DART obtained the highest aggregate BA (0.8308). Training-only RBF gamma selection retained a smaller DART advantage (0.8320 vs. 0.8286), and five independent subsamples of the datasets affected by the 20,000-sample cap produced stable conclusions. In US Accidents, default Full DART improved repeated-CV BA from 0.2774 to 0.2906 relative to StandardScaler (paired difference +0.01316; approximate dependence-corrected 95% CI 0.00327–0.02304), while equally budgeted nested k selection yielded 0.3011 versus 0.2797. State-disjoint BA was 0.2814 versus 0.2746 at fixed k and 0.2915 versus 0.2747 for the nested pair. Default Full DART did not rank first among every fixed-setting US control, and rare-class recall remained poor. STATS19 and India likewise show that gains are method-, class-, and validation-dependent; India additionally exhibited a substantial chronological generalization gap. Overall, DART is best interpreted as a competitive supervised geometry transformation whose benefit is classifier- and representation-dependent and comes with additional training cost.

Technologies doi: 10.3390/technologies14100687

Authors: Cesar Hernando Valencia Niño Lina R. Cuellar Davinson D. Lopez

Finite-element analysis (FEA) is a widely used computational method for predicting the biomechanical response of implant-supported prostheses, but its computational cost limits its use in large-scale parametric exploration or chairside decision support. This study evaluated whether supervised machine learning models trained on finite-element simulations can act as accurate, near-instantaneous surrogates, whether their predictions are driven by physically plausible input variables, and whether their residual error is small enough to preserve the verdict of a downstream, illustrative design-screening protocol. A finite-element dataset was generated for 100 independently meshed and solved parent configurations of a fixed, four-implant (“All-on-4”-type) implant-supported prosthesis, varying seven geometry-level input variables (bone quality, bar material, implant diameter and length, implant tilt angle, cantilever length, and occlusal load angle) using a custom three-dimensional finite-element solver implemented in Python; each parent solution was then linearly scaled to 71 occlusal-load magnitudes, yielding a 7100-row modeling dataset with four biomechanical outputs per row: 95th-percentile (P95) cortical bone von Mises stress, P95 trabecular bone von Mises stress, maximum prosthesis displacement, and P95 bone principal strain. All 100 parent simulations converged and passed internal quality-control checks on solver residuals and force-balance equilibrium. Because rows sharing a parent geometry are not statistically independent, all partitioning and cross-validation were performed at the geometry level: 20 geometries (1420 rows) were held out a priori as a fixed test set, and the remaining 80 geometries (5680 rows) formed a development pool on which Random Forest, XGBoost, and support-vector regression (SVR) models were compared using grouped cross-validation GroupKFold keyed on parent geometry, 5 folds × 5 repeats, 25 matched folds), with model selection performed independently for each output based on the lowest mean cross-validated RMSE. This yielded a different winning model per output rather than a single globally best algorithm: target-scaled SVR for both P95 cortical stress (1.919±0.501 MPa) and P95 trabecular stress (0.168±0.039 MPa), Random Forest for maximum displacement (0.190±0.079 mm), and XGBoost for P95 principal strain (166.3±77.8 με). Friedman and Holm-corrected pairwise Wilcoxon signed-rank tests corroborated that this ranking was consistent across resampled folds, but the more practically relevant evidence is the magnitude of the differences themselves, which is reported primarily as percentage RMSE reduction and matched-pairs rank-biserial effect size: 14.5% (cortical stress), 1.5% (trabecular stress, a practically negligible difference), 9.4% (displacement), and 15.0% (principal strain). Each selected model was refit on the full 80-geometry development pool and evaluated once, without further tuning, on the fixed 20-geometry (1420-row) test set. On this held-out test set, the selected models achieved coefficients of determination (R2) of 0.985 (95% bootstrap CI 0.976 to 0.993) for cortical stress, 0.968 (0.946 to 0.982) for trabecular stress, 0.986 (0.977 to 0.993) for maximum displacement, and 0.931 (0.883 to 0.980) for principal strain, with mean absolute percentage errors between 7.99% and 14.88%. SHAP (SHapley Additive exPlanations) analysis of the actual selected model for each output (TreeExplainer for Random Forest and XGBoost; KernelExplainer for SVR) showed that occlusal load magnitude was the dominant driver of both stress outputs and of principal strain (39 to 45% of attributed importance), bone quality contributed materially to trabecular stress and strain (19% and 31%, respectively), and bar material was the leading driver of maximum displacement (50%), whereas implant diameter and tilt angle together contributed well under 10% of attributed importance for every output. Applying an illustrative biomechanical design-screening protocol (combining Frost’s mechanostat strain zones with Misch bone-quality-adjusted safety margins, with structural veto thresholds calibrated on the 80-geometry development pool only) independently to the true and surrogate-predicted outputs of all 1420 held-out test rows yielded an identical screening flag in 1301 rows (91.6%), with discordant rows concentrated close to a decision-zone boundary (89.9% of discordances within 20% of the boundary) rather than distributed uniformly across the design space, and this agreement rate was stable (91.5 to 92.3%) across alternative Misch discount-factor magnitudes. These results indicate that, within the sampled design space of this parametric finite-element model, output-specific machine learning surrogates can reproduce its biomechanical outputs with good numerical accuracy and physically interpretable feature attributions at a fraction of the computational cost, supporting their exploratory use as surrogate models for rapid design screening rather than as a substitute for clinical judgment or experimental validation.

Technologies doi: 10.3390/technologies14100686

Authors: Rosa Gómez-Martínez Amanda Camacho-Illana Noemí García-Padilla Alberto Ruiz-Ariza

This systematic review examined three complementary lines of intervention evidence in children and adolescents aged 6–18 years: (1) mobile app-supported physical activity (PA) interventions and learning-relevant cognitive, motivational, and psychosocial outcomes; (2) PA/physical education (PE) interventions and bullying/cyberbullying outcomes; and (3) mobile app-based interventions and bullying/cyberbullying outcomes. Twenty-two intervention studies identified across four databases between September 2016 and September 2026 were included. The results were organized into two main domains: bullying/cyberbullying and learning-relevant cognitive, motivational, and psychosocial outcomes. These outcomes were examined across emotional–motivational, sociobehavioral, and cognitive dimensions. In the emotional–motivational dimension, favorable outcomes were observed in self-efficacy, motivation, and emotional well-being, although effects varied across studies. In the sociobehavioral dimension, benefits were identified in prosocial behavior, social relationships, and peer support. Some studies also reported improvements in attention, concentration, and other cognitive functions related to learning. Regarding bullying/cyberbullying, an evidence gap was identified. No identified study evaluated the effects of mobile app-based PA interventions on bullying/cyberbullying-related outcomes. Interventions were also classified according to whether mobile applications were used independently or in combination with wearable devices or other digital technologies. Overall, the findings suggest that mobile applications may extend the benefits of PA beyond physical outcomes and promote learning-relevant cognitive, motivational, and psychosocial outcomes. Finally, the main evidence gaps were identified to guide the design of future interventions targeting children and adolescents.

Technologies doi: 10.3390/technologies14100685

Authors: Jon Ider Andon Lafreniere Nathan Edwards Joshua Hagen Samantha Krening

The integration of multimodal athletic data, including wearable monitoring, self-reports, and training schedules presents a growing opportunity for machine learning to support individualized recovery decision-making. We present a feasibility study that evaluated the capability of large language model (LLM) pipelines to generate personalized athlete recovery recommendations using multimodal inputs. A prototype system (RecoveryGPT) was implemented across three technical environments: (1) Azure OpenAI with Heroku web deployment, (2) Azure AI Foundry, and (3) Microsoft Copilot Studio with trigger-based multimodal integration. All systems employed Retrieval-Augmented Generation (RAG) to ground responses to a Literature Corpus. We expanded the Copilot Studio implementation to incorporate user-triggered surveys, wearable data, and structured training schedules to deliver personalized, evidence-based guidance. Systems were evaluated using nine standardized recovery queries and fifteen simulated athlete case scenarios. Results indicated comparable literature grounding across platforms but revealed that Copilot Studio offers a more intuitive environment and a more capable deployment approach, making it the most operationally viable for embedding into existing team workflows like Microsoft Teams. This feasibility study offers a proof-of-concept pathway for integrating generative AI into high-performance recovery planning, with implications for use across other domains.

Technologies doi: 10.3390/technologies14100684

Authors: Muhammad Hamza Zafar Phil Green

Printer color characterization maps colorant amounts to colorimetry. We compare 13 machine learning methods with polynomial regression on nine characterization datasets with three to seven colorants, and 13 newsprint press runs, using the CIEDE2000 color difference (ΔE00). The planned (pre-specified) comparison fits every model in XYZ. A cube-root (CIELAB-like) fitting space was added later and applied to every method. In the main comparison, Gaussian process regression has the lowest median on all nine datasets. On the three five- and seven-ink datasets, a degree-4 polynomial fitted in cube-root space comes within a factor of 1.1 to 1.7 of it. On the chart with the largest margin, where 84% of patches use at most four inks, a degree-5 cube-root polynomial has a lower median but twice the maximum error. A cube-root support vector regressor tuned inside each training fold has a lower median on two of the three, but a worse error tail on both. Machine learning characterizes n>4 colorant systems well, and so does polynomial regression, given the right fitting space and degree. The cube-root Gaussian process is the most reliable fixed configuration: close to the best in median, 95th percentile and maximum error, but not always the best.

Technologies doi: 10.3390/technologies14100683

Authors: Lesti Setianingrum Sasono Rahardjo Muhammad Suryanegara

Submarine cables offer valuable potential for environmental and geophysical sensing. However, technical feasibility alone does not establish the broader conditions required for adoption. This study examines the structural relationships among technological, organizational, and environmental factors influencing sensor-integrated submarine communication cable systems (SCCS) in Indonesia. An exploratory expert-based design was employed, combining a previously developed optical communication simulation as the technical basis with Technology–Organization–Environment (TOE)-based factor identification, expert elicitation, Interpretive Structural Modeling (ISM), and Matrix of Cross-Impact Multiplication Applied to Classification (MICMAC). Fifteen experts from government institutions and the submarine cable industry assessed the contextual relationships among seven predefined factors. The ISM-MICMAC analysis produced a single-level structure in which all seven factors had a driving power of 7 and a dependence power of 7, indicating that each factor was reachable from every other factor through direct and/or indirect relationships in the final reachability matrix. The results highlight the structural interconnectedness of technological, organizational, and environmental factors. From a Responsible Innovation (RI) perspective, the findings highlight organizational capability, stakeholder inclusion, regulatory responsiveness, and standardization as governance considerations for sensor-integrated SCCS in Indonesia.

Technologies doi: 10.3390/technologies14100682

Authors: Sherif Samy Sorour Areej El Mufti Mohamed Ahmed Nabawy Anwar Sahbel

Industrial safety question answering requires access to the exact source material that supports an answer, not merely a related document. This study evaluates the retrieval stage of a local retrieval-augmented generation (RAG) system at the source page level. Eighteen documents on lockout/tagout, machine guarding, and personal protective equipment were prepared in raw, cleaned, and metadata-enriched forms. Best Matching 25 (BM25), latent semantic analysis (LSA), and reciprocal rank fusion (RRF) hybrid retrieval were tested on 54 questions: 36 answerable, 12 safety-related but unsupported, and 6 outside the covered topics. Raw BM25 achieved the highest mean reciprocal rank (0.492) and Hit@5 (0.722), cleaned BM25 achieved the highest Hit@3 (0.556) and answerability AUROC (0.765), and metadata-enriched BM25 achieved the highest Hit@10 (0.833). For raw text, the retrieval method affected reciprocal rank (Friedman χ2 = 7.13, p = 0.028). Hybrid retrieval exceeded LSA after Holm correction (p = 0.011), whereas BM25 and hybrid retrieval did not differ (p = 0.302). Text preparation had no overall effect within BM25 (p = 0.220). The results show that document-level evaluation can overstate access to exact evidence. The study provides a repeatable procedure for source tracking, page-level relevance labeling, separate retrieval and generation evaluation, and refusal analysis. These design principles are also relevant to construction safety knowledge systems that combine regulations, accident reports, project data, and BIM-linked information. Because the relevance set is a model-assisted silver standard, the findings remain exploratory rather than expert-certified absolute retrieval accuracy. A controlled same-run extension with all MiniLM L6 v2 and BM25 + MiniLM RRF is reported separately; its observed fusion gains did not reach statistical significance on the 36 answerable questions. The work is intended to support safety engineers, researchers, and developers designing evidence-grounded industrial and construction safety information systems.

Technologies doi: 10.3390/technologies14100681

Authors: Dulat Daurenbekov Yerkebulan Koshkinbayev Rakhima Daurenbekova Keleshek Zhangylyssov Sergey Dorofeev Sergei Bubenov

Post-synthetic doping provides a route to modify the excited-state dynamics of semiconductor nanoplatelets without coupling dopant incorporation to their initial nucleation and growth. Here, we investigate post-synthetic Mn incorporation into preformed CdSe nanoplatelets using manganese (II) diethyldithiocarbamate followed by thermal treatment under inert conditions. Structural and compositional analyses confirm the association of Mn with the nanoplatelets while preserving their platelet-like morphology and zinc-blende CdSe structure. Under the present reaction conditions, DPE-based treatments yield substantially higher Mn incorporation than HB-based treatments, demonstrating the strong sensitivity of the incorporation process to the reaction environment. Importantly, undoped control experiments show that broad visible-to-NIR emission can also arise from pristine CdSe nanoplatelets and can be strongly enhanced by thermal treatment, demonstrating that the spectral appearance of the broad band alone is not a unique signature of Mn-related states. The decisive difference is instead found in the emission dynamics: pristine and thermally treated undoped nanoplatelets, as well as low-Mn HB samples, exhibit broad-band decay on the few-microsecond timescale, whereas the main Mn-rich DPE samples display exceptionally long-lived broadband emission extending into the hundred-microsecond regime. PLE measurements further show that the low-energy emission is populated following excitation of the CdSe host. These results identify the emission kinetics, rather than the presence of broadband photoluminescence itself, as the clearest signature of additional Mn-associated relaxation pathways and demonstrate that post-synthetic reaction conditions provide a means of accessing long-lived excited-state dynamics in CdSe nanoplatelets. A representative sample also retained a comparable PLQY after more than two years of ambient storage, supporting the long-term stability of the optical response.

Technologies doi: 10.3390/technologies14100680

Authors: Gulden Khairusheva Akezhan Sabibolda Gulzada Yerkeldessova Nurzhigit Smailov Gulzada Mussapirova

Distributed antenna layouts optimized for nominal demand may deteriorate under changing hotspots and correlated shadowing. We present SA-MOBO-DAS, a scenario-aware multi-objective Bayesian optimization framework for placing four shared LTE/5G remote antenna units in a synthetic 500 × 500 m urban area. Its contribution is a placement and validation protocol combining worst-band coverage and throughput objectives, frozen operating scenarios, a fixed evaluation budget, and independent post-search risk assessment. A reproducible simulator incorporates 3GPP TR 38.901-inspired path loss, obstruction footprints, correlated shadowing, strongest power association, full-frequency reuse, and load-aware throughput at 2.1 GHz LTE and 3.5 GHz NR. Independent Matérn-5/2 Gaussian processes and a finite-pool approximation of q-noisy expected hypervolume improvement guide an 80-evaluation search. Thirty paired runs used 64 development and 160 held-out scenarios. SA-MOBO-DAS achieved held-out lower-tail hypervolume of 0.006619 (SD 0.000822), with no statistically detected difference from ParEGO (+0.7%; adjusted p = 0.968). Hypervolume exceeded NSGA-II and Sobol by 27.2% and 24.4%, respectively (both adjusted p < 0.001). Direct CVaR optimization yielded 7.6% and 0.9% higher point estimates of stress tail coverage and throughput, respectively, but reduced overall hypervolume by 10.6%. Paired 95% confidence intervals for both stress tail differences included zero.

Technologies doi: 10.3390/technologies14100679

Authors: Aizhan Yessirkepova Talgat Azykanov Kuanysh Kadirkulov Aisulu Ismailova Dzmitry Likhacheuski Marina Baizharikova Aliya Beissegul Iskander Baizhanov

This study presents PAGER-Life, an adaptive hybrid framework for the early prediction of lithium-ion battery service life under heterogeneous operating conditions. The system integrates physically interpretable degradation features and standardized charge–discharge curves using linear regression, decision tree-based models, and neural networks. An error-aware routing mechanism combines expert predictions based on out-of-fold errors, while conformal prediction enables uncertainty quantification. The framework was evaluated using the MATR and HUST datasets with observation horizons of 20, 50, and 100 cycles, following a protocol that accounts for the grouped data structure and prevents information leakage. A comparative analysis involving 114 physical cells and 14 alternative methods demonstrated the following performance metrics for PAGER-Life: Mean Absolute Percentage Error (MAPE) of 12.827%, Mean Absolute Error (MAE) of 166.766 cycles, Root Mean Square Error (RMSE) of 210.715 cycles, and a macro-averaged coefficient of determination (R2) of 0.495. Ablation studies indicated that removing the physically grounded degradation features substantially degraded predictive performance. The results indicate that the adaptive integration of complementary prognostic models offers a promising approach for the early estimation of battery service life under heterogeneous operating conditions.

Technologies doi: 10.3390/technologies14100678

Authors: Ayoub Boudlal Abderahim Khafaji Jamal Elabbadi

We introduce PhASE-RTAQC, a phase-aware multi-scale encoding framework for risk-aware twin-actor quadcopter control with Proximal Policy Optimization (PPO). A Gated Recurrent Unit (GRU) summarizes recent state history to infer a bounded continuous control-regime index, ϕt∈(0,1). This index is not defined by prescribed physical thresholds and should not be interpreted as a calibrated probability of instability. Instead, it represents a learned continuous coordinate capturing control-relevant temporal regimes from recent state history. Temporal information is further encoded using dilated 1D convolutions at multiple scales. Control actions are generated by twin Gaussian actors corresponding to nominal and recovery-oriented behaviors and are smoothly combined through a regime-dependent routing mechanism. The control-regime estimator is trained jointly through the reinforcement-learning objective, without explicit regime labels or auxiliary supervision. A distributional critic provides risk-sensitive value estimation, while PPO uses lower-tail Conditional Value at Risk (CVaR)-style Generalized Advantage Estimation (GAE) to place greater emphasis on low-return trajectories during training. Under the final simulation and actuator configuration, PhASE-RTAQC achieves, over the final 100 training episodes, a mean reward of 2345.16, a mean terminal altitude of 75.37 m, and a mean terminal position error of 0.39 m for the 75 m target. A matched comparison with an adapted Game-Theoretic Constrained Policy Optimization (GCPO) controller is performed under nominal, mild, and strong perturbations using the same simulation dynamics and evaluation protocol. The two controllers show scenario-dependent trade-offs under nominal and mild perturbations, while both degrade under strong perturbations, with GCPO maintaining stronger constraint preservation. Sensitivity tests show higher sensitivity to mass and thrust-model mismatch than to the tested observation noise and delay. These results characterize PhASE-RTAQC as a simulation-based, risk-aware control framework whose conclusions are limited to the evaluated conditions. The controller has not yet been validated through hardware-in-the-loop or physical-flight experiments, and the study does not establish formal guarantees of constraint satisfaction, closed-loop stability, policy convergence, robustness, or safe operation on a physical Unmanned Aerial Vehicle (UAV).

Technologies doi: 10.3390/technologies14100677

Authors: Peng Li Heng Zhang Mengyao Wang Liexiong Da Huan Li Qunmin Yan

Temperature-oriented long-horizon forecasting requires stable extrapolation of slowly evolving targets in the presence of local fluctuations and instance-level distribution shifts. This paper presents FADLinear, a target-only linear forecasting architecture evaluated on temperature-oriented tasks. The term “temperature-oriented” describes the empirical scope; the architecture itself remains domain-generic. FADLinear applies reversible instance normalization to the designated target, extracts a constrained Gaussian-like low-pass trend before stride-2 compression, constructs a full-resolution residual path, and combines two linear branch forecasts through bounded energy-contrast calibration. The evaluation compares eight models over 30 public dataset–horizon tasks and uses three random seeds to stabilize each task estimate. Task means for the statistical analyses are calculated from seed-level metrics before four-decimal table display. Friedman and paired Wilcoxon tests with Holm correction summarize the observed, partially dependent benchmark suite: lower FADLinear error is detected relative to four baselines for MSE and six baselines for MAE, while no difference is detected relative to PatchTST for either metric. Descriptively, FADLinear is best or tied for best on 8 MSE tasks and 10 MAE tasks, with mean ranks of 2.433 and 2.233. In the controlled ETTh1 efficiency benchmark at L=336 and H=720, FADLinear uses 0.365 million parameters and 0.750 million FLOPs per target forecast, with 0.890 ms median batch-1 latency and 9.58 MiB peak inference memory. These results support a top-tier accuracy–efficiency trade-off rather than universal accuracy superiority. An ETTm2-derived stress test is retained only for exploratory characterization because its construction does not satisfy split-first causal isolation.

Technologies doi: 10.3390/technologies14100676

Authors: Duy Thuan Vu Tung Linh Nguyen Quynh Anh Nguyen Ngoc Khoat Nguyen

High photovoltaic (PV) penetration can produce voltage rise, reverse power flow, feeder congestion, and less effective use of battery energy storage in radial distribution networks. This study develops a hybrid safe reinforcement learning–second-order cone programming (Safe RL–SOCP) framework for coordinated distribution network reconfiguration (DNR) and BESS dispatch. The end-to-end workflow was implemented as in-house research code. MATPOWER and OpenDSS were used for network simulation and AC power-flow verification, while YALMIP/CVX with MOSEK supported the conic projection stage. A constrained proximal policy optimization policy generates candidate topology and BESS actions; branch-exchange repair restores radiality, the SOCP layer corrects the candidate operating point, and AC power flow provides the final acceptance check. The framework is assessed on modified IEEE 33-bus and IEEE 69-bus feeders at 30%, 60%, and 90% PV penetration. Across the implemented benchmarks, the proposed controller reports lower daily energy loss and reverse power flow, tighter voltage ranges, and shorter online decision times. Monte Carlo evaluation over 365 unseen operating days further shows lower mean loss and dispersion. Residual post-projection voltage-violation probabilities of 0.3–0.5%, however, confirm that conic feasibility must be followed by AC verification rather than treated as an unconditional AC-feasibility guarantee.

Technologies doi: 10.3390/technologies14100675

Authors: Angélica Mercedes Nivia-Vargas Carlos R. Baier Oscar Danilo Montoya Walter Gil-González Nelson Leonardo Díaz-Aldana

Constant power loads (CPLs) introduce nonlinear voltage–current characteristics that can restrict the feasible operation of power-electronic converters. This paper presents a nonlinear steady-state framework for characterizing the feasible operating region of a single-phase PWM current-source converter (PWM-CSC) supplying a CPL. The averaged converter model is transformed into the synchronous dq reference frame and reduced to a nonlinear algebraic system that explicitly relates converter states, modulation variables, and active–reactive power demands. The resulting equilibrium set is mapped onto the P–Q and P–Q–m spaces to identify admissible operating points and modulation requirements. The numerical procedure evaluated 50,000 operating points with a 100% convergence rate, an average solution time of 4.168 ms per point, and residual norms below 10−6. Time-domain simulations in PSIM v9 validated representative operating points across the feasible region, with maximum deviations of approximately 0.83% in active power and 0.74% in reactive power relative to the prescribed references. Simulations also showed degraded voltage regulation near the feasibility boundary and loss of regulation for demands outside the identified region. The proposed approach provides a quantitative tool for feasibility-aware operation of PWM-CSCs supplying CPLs.

Technologies doi: 10.3390/technologies14100674

Authors: Olusegun Aanuoluwapo Oguntona Chijioke Emmanuel Emere Clinton Ohis Aigbavboa

The transition toward smart mining technologies (SMTs), spanning automation, artificial intelligence, robotics, the Internet of Things and digital twins, is reshaping safety, productivity and sustainability outcomes across the global extractive sector. Yet, their adoption within South Africa’s hard-rock mining industry remains slow, fragmented and empirically under-theorised. Drawing on diffusion of innovation and socio-technical systems perspectives as interpretive lenses, this exploratory study investigates the barriers to SMT adoption as perceived by mining professionals. A quantitative research design was adopted, using a structured questionnaire administered to 80 mining professionals across South Africa’s Northwest, Gauteng and Limpopo provinces. Data were analysed through a three-stage process comprising reliability and validity testing, descriptive statistics, and principal component analysis (PCA). PCA of the twenty-one barriers identified in the literature extracted four components that jointly explained 62.05% of the total variance. Nineteen barriers met the loading criteria for assignment to these components: Adoption Risk and Workforce Barriers; Organisational and Stakeholder Readiness Barriers; Technological Awareness and Change Barriers; and Institutional and Environmental Sustainability Barriers. The findings suggest that, as perceived by the respondents, barriers to SMT adoption extend beyond technological limitations to include regulatory ambiguity, financial constraints, workforce readiness and weak institutional support. Given the modest, non-probability sample, the component structure is exploratory and preliminary. It requires confirmatory testing in larger, independent samples before it is extended to other capital-intensive, safety-critical and regulation-heavy industries. The study concludes with practical, policy, and theoretical implications for mining firms, technology providers, regulators, and researchers seeking to accelerate safe, sustainable, and intelligent digital transformation.

Technologies doi: 10.3390/technologies14100673

Authors: Jeremiah O. Abolade Pradeep Kumar Dominic B. O. Konditi

A novel hybrid metaheuristic optimization algorithm for antenna design optimization is presented in this paper. The proposed algorithm, called the Hybrid Grey Wolf–Whale Particle Swarm Optimization Algorithm (HGWWOA-PSO), combines the exploration capability of Grey Wolf Optimizer (GWO), the exploitation mechanism of the Whale Optimization Algorithm (WOA), and the adaptive search strategy of Particle Swarm Optimization (PSO) to solve a multi-objective antenna design problem. The proposed hybrid algorithm is evaluated and validated based on its convergence characteristics and distribution of cost function value in comparison with GWO and WOA. The proposed HGWWOA-PSO is used to optimize the reflection coefficient, gain, and radiation efficiency of a terahertz antenna with a footprint of 0.24λo × 0.24λo at 361 GHz. The optimization targets three resonant frequencies (361 GHz, 391.6 GHz, and 611.2 GHz). The optimized antenna has a reflection coefficient of −51.35 dB, −16.7 dB, and −20 dB at 361.0 GHz, 391.6 GHz, and 611.2 GHz, respectively. The resonances at 361.0 GHz and 391.6 GHz combine to form a lower operating band, hence a dual-band antenna. The lower operating band achieves a −10 dB impedance bandwidth of 56.21 GHz (347.34–403.55 GHz), while the upper band impedance bandwidth is 183.21 GHz (524.60–707.81 GHz). The peak gain and radiation efficiency achieved are 4.5 dBi and 95%, respectively, while maintaining an omnidirectional radiation pattern in the E-plane and H-plane across both operating bands. The combination of miniaturization, multiband capability, high gain, and high radiation efficiency makes the proposed antenna an attractive candidate for terahertz communication systems.

Technologies doi: 10.3390/technologies14100672

Authors: Montree Kumngern Sattawat Kumchoo Fabian Khateb Tomasz Kulej

This paper introduces a nano-power, voltage-mode versatile first-order universal filter that employs a single differential difference transconductance amplifier (DDTA). Ultra-low-voltage and low-power operation is achieved by biasing the DDTA transistors in the subthreshold region, while employing a multiple-input bulk-driven (MI-BD) technique to handle multiple signals on a single differential pair, thereby eliminating the need for redundant differential stages. Acting as a multiple-input multiple-output (MIMO) topology, the proposed circuit simultaneously delivers six fundamental filtering functions—covering non-inverting and inverting variants of low-pass, high-pass, and all-pass responses—while offering independent electronic control over the pole frequency. To demonstrate practical utility, two non-inverting all-pass filter blocks are combined with a voltage-gain amplifier to construct a functional quadrature oscillator. SPICE simulations in a 0.18-µm CMOS process demonstrate that the filter operates reliably from a 0.5-V supply, consuming only 97 nW of power while achieving a dynamic range of 59.41 dB at a pole frequency of 250 Hz. Finally, experimental results from a commercially available LM13700-based prototype demonstrate the functional topology and tuning capabilities, while full validation of the ultra-low-power operation is reserved for integrated CMOS realization.

Technologies doi: 10.3390/technologies14100671

Authors: Roberto Moya-Jiménez Mario Rivera-Valenzuela Alberto Bustillos Bolívar Chávez-Ortiz Andrés Medina-Moncayo

Distributed digital fabrication training links computer-aided design (CAD), machine operation, electronics, and physical infrastructure. This study demonstrates a documentary evidence framework using 19 assignment pages and one final-project page from the first author’s Ecuadorian Fab Academy portfolio. Five dimensions organize the record: digital-to-physical translation, troubleshooting and iteration, technical assistance, equipment and material access, and reuse or integration. Translation was coded on 17 of 20 pages (85%), reuse or integration on 13 (65%), troubleshooting on 9 (45%), assistance on 8 (40%), and alternative access on 4 (20%). These non-additive percentages describe page-level evidence coverage, including reported intentions where explicitly identified; they do not measure competence. A doctoral thesis by the same author supplies cross-record documentary traceability of CAD, rapid prototyping, Arduino programming, printed circuit board (PCB) design, and computer numerical control (CNC) fabrication. Common authorship prevents treating this comparison as independent validation. The contribution is a source-linked classification of representations, processes, assistance, access, and task dependencies with explicit inference limits. The single-case, artificial intelligence-assisted coding demonstrates an auditable analytical procedure. Coding reliability remains unvalidated; independent recoding and additional cases are required before comparative assessment.

Technologies doi: 10.3390/technologies14100670

Authors: Peter Anuoluwapo Gbadega Kabulo Loji

The increasing integration of photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) is transforming residential electricity networks into flexible community microgrids. However, intermittent renewable generation and mismatches between generation and demand require effective energy management strategies for reliable and economical operation. This paper presents a dynamic pricing-based Energy Management System (EMS) for a residential PV microgrid comprising 19 households with a total rooftop PV capacity of 76 kW, distributed battery storage rated at 9.6 kWh per household, and 19 Vehicle-to-Grid (V2G)-enabled EVs with 50 kWh battery capacity each. The proposed EMS integrates PV generation data obtained from PVsyst, residential load profiles, dynamic electricity pricing, and coordinated control of household batteries, EV charging/discharging, V2G operation, and grid interactions. A rule-based scheduling framework is employed to improve renewable energy utilization, reduce electricity procurement costs, and enhance overall microgrid performance under predefined operational and price-based decision rules. Hourly-resolution simulations were conducted over an extended operating period to evaluate the effectiveness of the proposed strategy. The results achieved a PV self-consumption rate of 86.9%, community energy self-sufficiency of 63.6%, and PV energy coverage of 87.9%. The coordinated storage operation resulted in 34,732 kWh of battery charging energy and 30,018 kWh of discharged energy, with an average battery efficiency of 86.4%. Grid imports and exports were 27,671 kWh and 10,015 kWh, respectively, yielding an export-to-import ratio of 0.36. Transformer loading was maintained at 12.8% of its rated capacity, indicating secure transformer operation with loading well below the rated capacity. Overall, the proposed rule-based EMS improves renewable energy utilization, grid flexibility, and operational performance, providing a practical approach for managing distributed energy resources in residential community microgrids.

Technologies doi: 10.3390/technologies14100669

Authors: Valentina Vit Luca Liparulo Ayub Alam Riccardo Pecori Nicola Coppedè

Wearable sensors offer promising solutions for continuous gait monitoring in healthcare, rehabilitation, and sports. In this paper, a novel, soft, flexible, and easily made wearable sensor to detect the pressure generated by human gait was developed. The sensor consists of a soft polyurethane foam functionalized with a PEDOT:PSS aqueous solution designed to integrate the active material and develop a piezoresistive device. Eight soft sensors have been integrated into an insole, with dedicated readout electronic circuitry, including an accelerometer, a gyroscope, and a Bluetooth communication tag. Seven participants’ gaits were analyzed, and for each one, three types of walks were recorded: normal, prone, and supine. The data were analyzed with Machine Learning (ML) techniques, combining plantar pressure, accelerometer, and gyroscope features, to validate the device in discriminating among the three types of gaits. Ten supervised classifiers were assessed, such as Random Forest (RF), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), K-Nearest Neighbors, and Gradient Boosting, using a sliding-window feature extraction pipeline and a chronological, per-recording train-test split designed to avoid information leakage between overlapping windows. Hyperparameter optimization was carried out via Randomized Search with a stratified 5-fold cross-validation protocol. The device has turned out to be suitable for detecting the three types of gaits thanks to the good results obtained by leveraging ML algorithms, reaching a classification accuracy of 90.38% on held-out data in a user-specific (subject-dependent) setting. In a stricter Leave-One-Subject-Out evaluation on participants never seen during training, accuracy reached 67.66%, about twice the chance level, without any labeled data from the new user. These results demonstrate the proposed flexible pressure system’s capability for continuous gait monitoring and support its use in healthcare diagnostics, rehabilitation evaluation, and sports biomechanics.

Technologies doi: 10.3390/technologies14100668

Authors: Nazanin Rahmati Yeganeh Baghi Yaser Rahmati Saman Shojae Chaeikar Mojtaba Alizadeh

Blockchain systems face critical security vulnerabilities across all architectural layers, with over $1.8 billion stolen in 2023 alone. This systematic review analyzes 140 peer-reviewed studies (2018–2025) to provide a comprehensive taxonomy of blockchain vulnerabilities, detection methods, and mitigation strategies. We categorize 30+ distinct attack types across five architectural layers (Hardware, Data, Network, Consensus, Application) and three critical applications (Smart Contracts, Decentralized Exchanges, Supply Chains). Our quantitative analysis reveals severe research imbalance: the Application layer receives 58% of research attention with 45+ detection tools, while foundational Hardware (9%) and Data layers (6.7%) remain critically underexplored. We systematically compare 25+ vulnerability detection tools, identifying their strengths, limitations, and deployment contexts. Unlike existing surveys focusing on isolated aspects, we provide integrated detection mitigation mappings and analyze vulnerability propagation across layers. We identify nine security paradigms including AI-powered agents, zero-knowledge proofs, and post-quantum cryptography with assessment of maturity levels and adoption timelines. This work provides researchers and practitioners with an actionable roadmap for understanding current vulnerabilities, selecting appropriate tools, and anticipating future challenges in blockchain ecosystems.

Technologies doi: 10.3390/technologies14100667

Authors: Xiaoyong Li Wei Liang Zhiqiu He Dongkai Qiao Yongzhen Gong Xingang Wang

Multichannel vibration from a planetary gearbox exhibits operating-condition-dependent interactions across both time scale and lag. Static undirected graphs cannot represent ordered temporal dependence, while repeated graph aggregation can attenuate localized impacts. This paper presents PG-MTCDGNet, a physics-grounded multi-scale time-lagged dynamic graph network for structured evidence reporting in planetary-gearbox fault diagnosis. Phase-synchronous X/Y vibration, transient residuals, Hilbert envelopes, cross-channel coupling, and encoder micro-speed are represented as eight physical nodes. Sparse directed graphs are induced using the angular lags of one, four, and 16 samples from lagged predictive associations and are regularized by label-independent mesh and modulation priors. A time-lagged graph branch is fused with an unsmoothed local-evidence branch, and the complete model is trained with a fold-internal noise-consistency curriculum spanning −6 to 10 dB. The WT dataset comprises 80 continuous raw files and 5120 windows from five health states, two installations, and eight speeds. Under raw-file-isolated, class-installation-speed-balanced five-fold validation, PG-MTCDGNet achieved 99.61% ± 0.33% accuracy, 99.63% ± 0.31% macro-F1, and 0.99999 macro-AUC. Accuracy at 0 dB feature noise was 96.78%. A depth-reduced variant, PG-MTCDGNet-Lite, separates comparable signal descriptors from heterogeneous physical tokens and uses a regularized linear readout. In exploratory installation and speed holdouts, its four-condition file-level accuracy was 88.125%, versus 86.250% for the matched MLP; likewise, macro-F1 was 88.044% versus 84.171%. Removing graph propagation tied its overall accuracy, so this improvement does not establish a unique graph contribution. The 80 recordings were reused during model development, and the results require confirmation on new independent recordings.

Technologies doi: 10.3390/technologies14100666

Authors: Petros Pistofidis Anastasia Petrou Pantelis N. Botsaris

Large language models (LLMs) are increasingly used to interpret mechanical engineering drawings, yet the main risk is a wrong answer that reads as correct. We report an exploratory empirical study of how prompt design and evaluation shape the reliability of this interpretation, in two connected stages over the same twenty drawings. Stage 1 runs one prose prompting protocol on two model families and finds that progressive enrichment does not improve accuracy; beyond a threshold, it raises confident fabrication. Stage 2 compares six structured prompt architectures at thirty repetitions per cell, producing 3540 generations scored under four judge conditions. Across both stages, identification accuracy does not improve, while the rate of claims the drawing does not support varies by nearly an order of magnitude with prompt architecture. Only a judge that sees the drawing can measure it; a text-only judge inflates confident fabrication three to four times, making evaluator modality a first-order variable. Without a ground-truth answer, an image-only check can test only whether claims are visible; it recovers about half of the reference flags, enough for human-in-the-loop triage. A judge from another model family reproduces the ranking. We distill five practical recommendations for this setting.

Technologies doi: 10.3390/technologies14100665

Authors: Brittany S. Samulski Ashwini Kulkarni Neda Moghim Ross Gore Soumya Banerjee Christopher K. Rhea

Multifactorial falls assessment is recommended for older adults at elevated risk but can generate numerous potentially overlapping measurements, increasing testing burden despite potentially diminishing informational returns as correlated measures are added. Characterizing how measures covary may inform subsequent efforts to evaluate more efficient assessment procedures. Community-dwelling older adults (n = 298) completed a single-visit assessment including the Physiological Profile Assessment (PPA), preferred- and maximal-speed walking, Montreal Cognitive Assessment (MoCA), and Modified Falls Efficacy Scale (MFES). Hierarchical agglomerative clustering was applied to 51 variables. Dendrogram fidelity, cluster separation, and resampling stability were evaluated using the cophenetic correlation coefficient (CCC), silhouette width, Jaccard coefficients, and adjusted Rand index (ARI). The dendrogram showed strong fidelity to the correlation-distance structure (CCC = 0.937). Among the examined partitions, k = 2 had the highest mean silhouette width (0.333), indicating weak separation, and demonstrated high resampling stability. This partition grouped MoCA and MFES items with visual, strength, and maximal balance-range measures, separately from gait, postural sway, reaction-time, sensory, and coordinated-stability measures. At k = 3, the latter grouping remained unchanged while MoCA items separated from the MFES and physiologic measures; however, this finer, hypothesis-guided partition had lower separation and lower stability for the MoCA-dominant cluster. These findings describe the covariance organization of this multifactorial assessment battery and provide candidate groupings for future research on measurement redundancy, information retention, and reproducibility across datasets.

Technologies doi: 10.3390/technologies14100664

Authors: Nilthon Arce Fernández José Luis Piedra Tineo Luis Rafael Tinedo Saavedra Flabio Gutiérrez Segura Jose Manuel Palomino Ojeda

Estimating construction costs constitutes a strategic component of project management, because it provides fundamental information for assessing its economic feasibility. The present research aims to develop artificial intelligence models for estimating the construction cost of reinforced concrete housing. The database was constructed from 276 technical files corresponding to the period 2019–2025. Random Forest achieved the highest average R2 across the three input-variable groups (R2 = 0.8520), followed by XGBoost and Multiple Linear Regression, both with an average R2 of 0.8473, whereas MLP Regressor obtained a lower value (R2 = 0.8197). Likewise, it presented the lowest average standard deviation across the three groups (σ = 0.0530), compared with XGBoost (σ = 0.0547), Multiple Linear Regression (σ = 0.0719), and MLP Regressor (σ = 0.1023). Compared with Groups 1 and 2, the Random Forest model, using Group 3, presented lower MAE values by S/ 3174.91 and S/ 3185.34, respectively, while the MAPE values were lower by 4.19 and 4.40 percentage points, respectively. The main scientific contribution of this research lies in establishing a reproducible methodology, which made it possible not only to identify the model with the highest predictive capacity but also to characterize the conditions under which its accuracy decreases.

Technologies doi: 10.3390/technologies14100663

Authors: Mohammed Rabie Atia Khalifa

Concentrated photovoltaic cooling and membrane distillation channel enhancement are commonly investigated as separate design problems, without considering the quality of the recovered heat available to the desalination unit. This study presents a thermally coupled CPV–DCMD system in which the cooling and desalination components are systematically co-designed. Validated numerical models were used to compare six internally finned microchannel heat sinks and eight baffled DCMD configurations with their respective blank channels. The recovered CPV heat was transferred to the saline feed through a heat exchanger while accounting for a 5% heat loss. The selected CPV configuration, comprising a 10 × 10 fin arrangement with a height of 60% of the channel, reduced the average and maximum cell temperatures at 1500 suns from over 101 and 112.4 °C to 63.8 and 70.56 °C, respectively, while increasing electrical efficiency from 37.5% to 39.2%. The selected DCMD design employed uniform height baffles in both channels, enhancing near-membrane mixing while limiting hydraulic losses. It increased permeate flux by 6% compared with the blank module and required 88% less hydraulic power than the uniform tall baffle configuration. Under the coupled operating conditions, increasing the coolant flow rate improved CPV cooling and electrical performance but reduced the recovered-heat temperature and DCMD productivity. Therefore, the coolant flow rate should be selected by balancing the allowable cell temperature with electrical efficiency and freshwater production.

Technologies doi: 10.3390/technologies14100662

Authors: Xianlong Shen Shu Zhang Ziyi Bao Yan Zhang

Large-language-model (LLM) layers are increasingly appended to electroencephalography (EEG) decoders, although their decision contribution is often unclear. We evaluate an uncertainty-driven architecture in which a calibrated EEG classifier predicts participants’ own four-level occupational-risk keypress ratings, a deterministic rule either retains or defers a prediction, and a template or optional LLM renders an auditable report. The objective is not to replace a previously published classifier benchmark on the shared EEG archive, but to evaluate downstream decisions from a fixed frozen-representation decoder. The analysis used 2634 response-inclusive 3 s epochs from 31 retained workers. Subject-grouped three-fold evaluation yielded balanced accuracy of 0.6336 (95% CI 0.5745–0.6622) and accuracy of 0.6875. Peak probability distinguished correct from incorrect predictions only moderately (AUC 0.6529), and the coverage-risk relationship was non-monotonic. At tau = 0.5, 71.49% of trials were retained (balanced accuracy of 0.6829); with illustrative under-estimation/over-estimation/deferral costs of 5/0.5/1, normalized cost fell from 0.5816 to 0.5363, but sensitivity analysis showed that benefit depends on the cost setting and threshold. A rule engine reproduced all evaluated LLM risk/deferral decisions, while a deterministic template matched the prespecified reporting checks; therefore, no decision-performance advantage of the LLM was demonstrated. The study supports cross-participant prediction within the shared experimental setting, not stimulus-disjoint generalization or objective workplace-risk prediction. Because response timestamps and actual supervisor decisions were unavailable, motor/visual confounding cannot be excluded and the human handoff remains a simulated policy rather than a human–AI team evaluation.

Technologies doi: 10.3390/technologies14100661

Authors: Maros Smondrk Branko Babusiak Ladislav Janousek Lucia Bednarcikova Monika Michalikova Branko Stefanovic Jozef Zivcak

This paper presents the design, characterization, and preliminary evaluation of a portable wireless pressure measurement system for monitoring the socket-limb interface of a transtibial prosthesis. The system employs custom-fabricated, fluid-filled silicone bladders flush-mounted within an additively manufactured prosthetic socket to capture loading data from multiple anatomical locations. Benchtop testing under quasi-static conditions confirmed the sensors’ suitability for this application, showing high linearity and minimal drift and ensuring signal stability. Preliminary feasibility was assessed in a single transtibial prosthesis user during standing and walking tasks as an initial proof-of-concept evaluation. The system successfully detected distinct site-specific loading patterns, identifying localized high pressure at the fibular head. Furthermore, gait analysis revealed non-intuitive loading shifts at the fibular head during unsupported walking, highlighting the importance of dynamic monitoring. These preliminary findings demonstrate the system’s technical capability to provide objective interface loading data that may help prosthetists evaluate socket design. Future work will focus on expanding participant recruitment, performing dynamic sensor characterization, incorporating additional sensing locations, and studying long-term sensor performance. This proof-of-concept study establishes the technical viability of the proposed sensing approach; however, generalization to the broader clinical population requires multi-participant validation.

Technologies doi: 10.3390/technologies14100660

Authors: Liang Xuan Haowei Yu Tengfei Jia Zhishen Yu

Time-varying interaction loads and uncertain actuator dynamics complicate isokinetic velocity regulation. This study evaluates a hierarchical controller combining nominal compensation, measured-load feedforward, posture-aware radial basis function (RBF) residual compensation, and adaptive sliding-mode feedback. The RBF network uses offline initialization, bounded online updates, and domain-gated output. Closed-loop boundedness is discussed under stated disturbance and actuator assumptions. Under a 40 N·m, 0.5 Hz periodic load, the complete controller achieved a velocity RMSE of 0.9938°/s, with reductions of 55.75% and 86.88% relative to the tested fixed-gain SMC and ASMC configurations, respectively. Independent validation showed a decrease in mean robust gain from 9.0492 to 3.8409 rad/s2. In five prototype trials per controller and load, C3 achieved RMSE values of 1.380 ± 0.049°/s at 12.5 kg and 1.984 ± 0.067°/s at 25 kg. Repeated-load tests revealed peak-error deterioration in five of nine conditions and substantial out-of-domain exposure at higher loads. The results support the complete compensation module under the tested conditions but do not isolate the incremental benefit of online updating or establish patient-level safety.

Technologies doi: 10.3390/technologies14100659

Authors: Huma Sikandar Nohman Khan Mohd Amran Mohd Daril Ishamuddin Mustapha Khadija Sikandar

Artificial intelligence (AI) and AI-enabled digital technologies are increasingly proposed as enablers of circularity in construction and demolition (C&D) waste management, yet existing reviews are organised around technology and lifecycle stage and rarely examine the organisational conditions under which technically capable applications move from pilot to sustained practice. This review addresses that gap through a PRISMA 2020-guided systematic review of 217 recent Scopus records (2022–2026), interpreted through dynamic capabilities theory, diffusion of innovation theory, and the circular economy value-retention hierarchy. Abstract-level coding of capability enablers, AI mechanism clusters, and circularity outcomes is triangulated with latent Dirichlet allocation topic modelling, bibliographic coupling, co-citation and keyword co-occurrence analyses, and statistical tests of association. The central finding concerns where research attention has concentrated: digital infrastructure and AI/machine learning technical capability appear in 64–67% of studies, whereas governance and human/organisational capability appear in only 13–18%, and traceability/material security is the least-examined outcome (11.1%). These patterns describe the published literature rather than organisational practice. They are integrated into a four-layer framework that separates observed associations from theoretical inferences, and into six propositions for organisational-level testing. Substantive policy instruments appear in only 4.1% of studies, so the policy implications are presented as exploratory.

Technologies doi: 10.3390/technologies14100658

Authors: Lingfeng Li Wei Li Qin Zhang Zhenshuo Zhang Hanzhi Peng Jiaheng Wei

Wind turbines of the same design develop different thermal and mechanical margins during long-term operation, requiring active power allocation to consider individual health conditions. This study proposes a supervisory control and data acquisition (SCADA)-based method for determining a health-constrained power boundary for aging wind turbines. A normal behavior model establishes references for key thermomechanical variables matched to current operating conditions. Standardized residuals are combined using the Mahalanobis distance and a trimmed temporal window to construct a turbine-specific health index and reduce the influence of isolated extremes. Short-horizon state prediction evaluates candidate power levels. The maximum feasible candidate power is determined separately under statistical, temperature, and mechanical constraints, and their minimum defines the health-constrained power boundary. Combined with available power and local operating limits, this boundary enters the wind farm power allocator as a turbine-level inequality constraint. Field data from 30 turbines rated at 1.5 MW are used to evaluate representative events and farm-wide performance. Turbines show different operating margins under similar wind conditions. In the evaluation sample, the health-constrained operation group had a shorter non-generating duration and a higher energy yield normalized by wind speed frequency; group differences remained statistically uncertain.

Technologies doi: 10.3390/technologies14100657

Authors: Amit J. Rangari Lalit Narayan Mishra Biswaranjan Senapati

DevOps toolchains record 164 metrics across eight software development lifecycle (SDLC) phases, yet no public dataset links these pre-deployment signals to production runtime outcomes. We present PRESTO, a framework for predicting software system performance from multi-phase SDLC metrics, plus a Gaussian copula-based synthetic data generator—both evaluated as a proof of concept, not a validated prediction method. Under nested cross-validation that selects the algorithm and hyperparameters within the training partition, non-autoregressive SDLC process features reach R2=0.108 to 0.620 across three synthetic domains; a target-correlation ablation shows that this is largely recovery of the generator’s specified structure, shared identically across all three domains, so the results show that the framework recovers relationships embedded in the generator. Across four real-world datasets, the primary validation analyses are Mozilla Perfherder’s autoregressive persistence on a real production-performance signal (R2=0.17 to 0.92) and SQuaD’s CVE-count (R2=0.23) and static-analysis-enriched defect-fix (R2=0.50) results; the confidence intervals are wide throughout, and most remaining comparisons include zero. The central limitation is that no available dataset joins eight-phase SDLC telemetry to production uptime, so each real dataset tests only a narrower proxy outcome (build duration, browser-performance regression, CI workflow duration, defect and CVE counts). PRESTO is therefore a framework and benchmark whose central proposition remains untested.

Technologies doi: 10.3390/technologies14100656

Authors: Goodness Ayanda Z. Dlamini Pitshou N. Bokoro

Metal oxide arresters (MOAs) are protective devices widely used in power transmission and distribution systems to clamp transient overvoltages caused by lightning strikes and switching events, thereby protecting electrical equipment and enhancing power system reliability. However, failure of an MOA could significantly compromise the insulation coordination and overvoltage protection of power systems, thus exposing critical assets to potentially damaging voltage stresses. Accurate and early assessment of the health condition of MOAs is essential for maintaining power system reliability and minimising the risk of catastrophic insulation failure. Owing to several limitations associated with model-based condition-monitoring techniques of MOAs, this paper investigates whether combining the temporal learning capability of Long Short-Term Memory (LSTM) with the discriminative strength of Support Vector Machine (SVM) can provide a more effective and computationally feasible approach for MOA health assessment. Therefore, a dataset comprising 5157 observations extracted from historical inspection records of 20 kV distribution surge arresters was used to train predictive models for assessing the degradation condition of MOAs. The historical inspection measurements reflect the operational condition and degradation characteristics of MOAs. The results demonstrate that the hybrid LSTM–SVM model achieved the highest predictive performance, with an R2 of 0.916 and an F1-score of 0.9711, outperforming the standalone LSTM and SVM models. However, this improvement is achieved at the expense of increased computational requirements, with the hybrid model requiring 367.8 s of training time and 548 MB of peak memory, which are higher than that observed for LSTM and SVM. Despite its higher training cost, the LSTM–SVM model achieved an inference time of 1.45 ms/sample, which is lower than that of LSTM, although higher than that of SVM. Statistical validation of the LSTM–SVM model based on the Diebold–Mariano (DM) test yields a p-value < 0.001 and negative DM statistic, providing statistical evidence of improved predictive performance. These findings suggest that the LSTM–SVM model could enable earlier identification of deteriorating MOAs, thereby supporting timely maintenance interventions and reducing the likelihood of unexpected failures and associated power supply disruptions.

Technologies doi: 10.3390/technologies14100655

Authors: Karla A. Camarillo-Gómez Juvenal Rodriguez-Resendiz Marcos Aviles Saul Tovar-Arriaga Gerardo I. Pérez-Soto

The convergence of artificial intelligence (AI) and biomedical engineering is transforming the development of neurological health technologies [...]

Technologies doi: 10.3390/technologies14100654

Authors: Marina Konuhova Valerijs Bezrukovs Vladislavs Bezrukovs Agris Berzins Maksym Buryi Kaspars Liepins Anatoli I. Popov

Hydrogen compression is a critical interface between production, storage and end use in decentralized green hydrogen infrastructure, where fluctuating electrolyzer output and intermittent demand require operation over variable pressure and flow conditions. This review provides a cross-technology and application-oriented assessment of conventional mechanical, liquid-piston, electrochemical, metal-hydride and hybrid compression systems using common criteria including pressure ratio, specific energy consumption, thermal behavior, hydrogen purity, dynamic flexibility, reliability, scalability and technology readiness. The comparison shows that mechanical compressors remain the reference choice when high pressure, high throughput and commercial maturity dominate. Liquid-piston systems may offer advantages where thermal management and near-isothermal operation are priorities, but their performance depends on working-fluid properties, interface stability, chamber scaling and hydraulic efficiency. Electrochemical compressors are attractive for compact, high-purity and moderate-flow applications, although membrane durability, hydrogen crossover and water management remain key barriers. Metal-hydride systems are most relevant where low-grade or waste heat is available, while hybrid systems can distribute complementary compression functions between stages. Overall, no single technology is optimal; selection should consider the complete operating envelope and integration with electrolyzer operation, buffer storage and downstream demand. Future development requires standardized reporting, long-term cyclic validation, and realistic lifetime and cost data.

Technologies doi: 10.3390/technologies14100653

Authors: Valentin Mollov Panayot Petrov

Here, we propose an adaptive and easy extendable approach for collecting large-scale sensor data streams and processing the stored data with standard dataset algorithms. The practice shows that the long-term sensor monitoring produces time-ordered logs that usually exceed the practical spreadsheet and in-memory processing limits. This work continues our previous measurements with an LMT70EVM ambient-air temperature system, which generates app. 1.48 GB per log over period of 20 days, with about 35 million samples at a 50 ms period. Now, we extend that method into a reusable Python-based library tailored as a processing architecture for large sensor logs. Incremental file and CSV iterators are combined with closest-stream mapping, automatic small-shift detection, synchronization checks, direct duration aggregation, typed error samples, and SVG plotting, which are implemented as applicable processing options. The implementation presets 12.8 s detection buckets and 60 s detailed data averages are now grouped into 30 min snapshots. During processing, the stable snapshots are reduced to one weighted point, whereas the snapshots classified as containing a detected temperature drift retain 60 s detailed data values. There is a CSV output option, allowing direct reduced-sample-to-SVG processing. To reduce the overall dataset reconstruction time of the applied library in case of reuse with data from other sources, we construct a small-log equivalence test bench that produces 29 rows in both readable and direct paths, 261 compared values, and zero differences. The proposed implementation establishes reusable streaming, verification, and visualization layers required for any long-term measurement. We also demonstrate a second data stream logging from a three-axis gyroscope with CSV data through motion detection, bias compensation, quaternion integration, tabular export, and XY SVG visualization. Although large-scale gyroscope streaming is not yet demonstrated, it will be part of the future sensor fusion implementation of the library.

Technologies doi: 10.3390/technologies14100652

Authors: Modesto Herrera-González Jaime Martínez-Castillo Enrique Delgado-Alvarado Pedro Mabil-Espinosa Agustín L. Herrera-May Edmundo Antonio Gutiérrez Domínguez Pedro J. García-Ramírez

We present SimulH, a semiclassical Monte Carlo framework developed to model carrier transport in monolayer graphene field-effect transistors (GFETs), where achieving both physical accuracy and computational efficiency remains a key challenge for high-frequency device applications. The model is based on the Boltzmann transport equation and incorporates stochastic Monte Carlo simulations to describe carrier dynamics, including electron–phonon, electron–impurity, and electron–electron scattering processes, as well as quantum capacitance and contact resistance effects. To ensure a consistent description across operating conditions, three complementary approaches are used to evaluate the drain current. The results show strong agreement with reported experimental data, with prediction accuracies above 90% for mobility, capacitance behavior, and charge distribution. Simulations involving more than 40,000 carriers provide detailed insight into transport behavior and scattering mechanisms, while also capturing key features near the Dirac point and predicting cut-off frequencies up to 143.5 GHz. Overall, SimulH offers a reliable and computationally efficient framework that can support the design and optimization of graphene-based RF and nanoelectronic devices. In this regard, SimulH contributes to computational nanotechnology by enabling scalable transport analysis in graphene-based nanoelectronics.

Technologies doi: 10.3390/technologies14100651

Authors: Ericmoore Ngharamike Li-Minn Ang Mingzhong Wang Kah Phooi Seng

The electric network frequency (ENF) signal embedded in recordings made under grid-powered lighting is widely used to verify recording time and assess authenticity. Its reliability under deliberate anti-forensic manipulation, however, requires further study. This paper demonstrates a signal-processing pipeline that suppresses the original ENF component in a target video and introduces the ENF-bearing luminance component of a donor video recorded at another time. We also examine ENF injection into videos with no usable ENF trace. In eight matched experiments, the ENF re-extracted from the manipulated videos produced normalized cross-correlation values within the range observed for authentic donor videos. Frame-averaged PSNR and SSIM were used to quantify visual fidelity, and two reference-free tests examined artifacts introduced by the pipeline. A batch-boundary phase-discontinuity test was not statistically significant, whereas a notch-shoulder spectral-energy measure separated the original and manipulated videos in this small sample (AUC = 1.00). These results show that ENF substitution can mislead NCC-based timestamp matching but is not necessarily undetectable, supporting the use of complementary forensic checks when ENF evidence is evaluated.

Technologies doi: 10.3390/technologies14100650

Authors: Valter Carvelli Toru Fujii Kazuya Okubo

This study investigates the effects of thermal treatment, high-humidity exposure, and tensile loading during heating on the mechanical properties of kenaf fiber bundles and kenaf fiber-reinforced polypropylene (kenaf-PP) composites. Kenaf fiber bundles were thermally treated at 140, 160, 180, and 200 °C for one hour, and their tensile properties were evaluated before and after exposure to a high-humidity environment. The crystallinity index (CrI) was determined by X-ray diffraction using Segal’s empirical method to investigate the relationship between cellulose crystallinity and the mechanical performance of the fibers. Heating at 140 °C increased the average tensile strength by approximately 11%, which coincided with the highest crystallinity index measured by X-ray diffraction, whereas higher treatment temperatures resulted in reduced mechanical performance, consistent with the onset of thermal degradation. The application of tensile loading during thermal treatment further enhanced the tensile strength of the kenaf fiber bundles, with the greatest improvement observed for fibers treated at 140 °C under a 48 N load. Microwave-assisted treatment (600 W) under tensile loading provided comparable improvements in tensile properties while reducing the treatment time from one hour to 60 s. Kenaf-PP composite sheets reinforced with thermally treated fibers exhibited enhanced flexural performance, with fibers treated at 140 °C producing the best overall results, including increases of approximately 15% in specific flexural strength and 31% in specific flexural modulus. These findings demonstrate that moderate thermal treatment, particularly when combined with tensile loading, is an effective strategy for improving the mechanical performance of kenaf fibers and kenaf-PP composites. Furthermore, microwave-assisted heating represents a promising alternative to conventional thermal treatment by significantly reducing processing time while maintaining the mechanical benefits.

Technologies doi: 10.3390/technologies14100649

Authors: Pavel Bazhin Ivan Nazarko Arina Bazhina Svetlana Agasieva Andrei Chizhikov Varvara Avdeeva Kirill Khvostunkov

This work systematically examines the effect of zirconium additions on the phase transition behavior and microstructural evolution of TiAl(Nb,Mo)B systems subjected to shear deformation under unconstrained self-propagating high-temperature synthesis (SHS). The study further evaluates the direct correlation between Zr doping levels, microstructural arrangements, and the resulting mechanical behavior of the densified compacts. The experiments focused on a precursor powder blend with the nominal composition of 51.85Ti–43Al–4Nb–1Mo–0.15B (at %) supplemented with 1–3 at % of zirconium. The results demonstrate that introducing Zr during SHS processing promotes the precipitation of a secondary Al2Zr intermetallic compound, which selectively segregates at the boundaries of the lamellar γ and α2 colonies. This alloying strategy yields a pronounced enhancement in room-temperature improved crack resistance under contact/indentation conditions and structural damage tolerance. These mechanical enhancements are associated with a synergistic effect, phase transformations, and the precipitation of an Al2Zr phase, whose volume fraction is governed by the initial Zr concentration. We assumed that the resulting microstructure effectively impedes macrocrack advancement through multi-directional branching and interfacial energy dissipation, thereby effectively preventing brittle failure under indentation loads up to 100 N at room temperature.

Technologies doi: 10.3390/technologies14100648

Authors: Mariano Vargas Santiago Diana Assaely León-Velasco Sergio Quezada-García Ricardo Isaac Cázares-Ramírez Érick G. Espinosa-Martínez

The analysis of heterogeneous tabular datasets remains challenging for non-specialist users because data preparation, statistical diagnostics, algorithm selection, model evaluation, and result interpretation are frequently distributed across separate software environments. This manuscript presents a reproducible web-based technology that integrates data ingestion, automated data-quality assessment, statistical validation, transparent rule-based analytical guidance, supervised and unsupervised machine learning, and interactive visual analytics within a unified workflow. The platform supports commonly used tabular formats and employs a modular, containerized architecture to support portability and reproducible analytical execution. Rather than autonomously selecting a supposedly optimal model, the recommendation mechanism identifies the analytical task, screens incompatible methods, detects semantic ambiguity, generates methodological warnings, and prioritizes compatible candidate models using explicit and inspectable rules. Final supervised-model selection is based on empirical cross-validation within the training data. The technology is evaluated through functional tests on heterogeneous health and financial datasets, predictive and clustering experiments, controlled recommendation-engine and schema-robustness experiments, computational benchmarks, and a usability study involving users with limited programming experience. In a COVID-19 dataset containing 95,839 records, the platform supported two-code and three-code patient-classification experiments, descriptive clustering, and data-quality analysis while transparently identifying class imbalance, predictor missingness, repeated predictor profiles, and performance limitations. A grouped-profile sensitivity analysis further showed that the principal classification results did not deteriorate when identical selected predictor configurations were prevented from occurring in both the training and held-out partitions. Controlled schema-robustness experiments correctly identified all prespecified unambiguous analytical tasks, detected all prespecified semantic ambiguities, rejected incompatible supervised configurations, and produced no silent acceptance of ambiguous or invalid cases. The recommender-specific evaluation also showed that the highest-ranked rule-based candidate does not necessarily coincide with the cross-validation-best model, reinforcing its role as a decision-support mechanism rather than an autonomous model-selection procedure. An exploratory laboratory usability evaluation with twelve non-specialist participants produced an average task-success rate of 95.3% and a mean System Usability Scale score of 86.7±5.2. These values correspond to aggregate results recorded during the original laboratory experiment. Because the participant-level observer logs and individual SUS responses were not retained in the available study materials, the usability results are interpreted descriptively and are not presented as population-level estimates. The results indicate that the proposed architecture can reduce technical barriers while preserving analytical traceability, methodological transparency, and user control. The platform therefore provides an extensible foundation for human-supervised, reproducible analysis of heterogeneous tabular data rather than an autonomous statistical or machine-learning decision-making system.

Technologies doi: 10.3390/technologies14100647

Authors: Mohsen S. Alsaadi Muhammad Imran Noora Fadhel Malik Al-Essa Iulian Sorin Munteanu Wasim A. Ali

Adversarial evaluation of machine-learning-based network intrusion detection systems (NIDSs) requires an explicit bound on attacker perturbations, yet fixed bounds are often reused across datasets without verifying scale comparability. We evaluate two differentiable detectors on CICIDS2017, TON_IoT, and UNSW-NB15 using a constrained, control-surface-aware evasion procedure in which perturbation cost is measured in interquartile-range units. The measured constrained feature-space evasion cost differs by more than two orders of magnitude across benchmarks. On UNSW-NB15, changing only the perturbation bound from a fixed reference value of 50 to a dataset-calibrated P95 value changes measured evasion success from 2.6% to 84.0%, with the conclusion remaining stable across P90, P95, and P99 calibration. We therefore propose a dataset-specific quantile calibration procedure and a monotonicity-based internal validity test exploiting nested attacker action spaces. Calibration removes the severe fixed-bound monotonicity violations observed on the two most poorly scaled datasets, although the test remains diagnostic rather than certifying. A secondary attribution-based robustness estimator does not generalise consistently across the three benchmarks. These results show that perturbation-budget calibration is an essential component of credible cross-dataset adversarial NIDS evaluation.

Technologies doi: 10.3390/technologies14100646

Authors: Yun Cheng Zhongbin Luo Xiangyu Yu

Traffic sign recognition is a fundamental perception task in intelligent transportation and autonomous driving systems. Despite the strong performance of modern deep neural networks under matched training and testing conditions, their recognition accuracy can degrade substantially when deployed in unseen environments due to variations in illumination, viewpoint, motion blur, weather conditions, image acquisition devices, and regional traffic sign appearances. To address this challenge, we propose an information-theoretic framework for unsupervised domain adaptation in traffic sign recognition. Unlike conventional approaches that mainly align marginal feature distributions, our method explicitly aligns the joint distributions of multi-level latent representations and model predictions between the source and target domains. Specifically, we introduce a Joint Cauchy–Schwarz (JCS) divergence based on tensor-product kernels, which enables the statistical dependencies among representations extracted from multiple network layers and prediction outputs to be jointly characterized and aligned. In addition, an information-bottleneck regularization is incorporated to suppress domain-specific nuisance information while preserving task-relevant representations, thereby complementing the proposed joint distribution alignment. The overall framework integrates supervised source-domain classification, joint source–target distribution alignment, information compression, and target-domain entropy minimization within a unified objective. Experiments are conducted on multiple cross-domain traffic sign recognition settings constructed from GTSRB, BelgiumTSC, and TT100K to evaluate the effectiveness and generalization ability of the proposed method under diverse domain shifts.

Technologies doi: 10.3390/technologies14100645

Authors: Razieh Rastgoo Kourosh Kiani Sergio Escalera

Sign Language Recognition (SLR) has gained increasing attention, while Continuous Sign Language Recognition (CSLR) remains substantially more challenging than Isolated SLR (ISLR) due to the difficulty of accurately detecting sign boundaries within continuous signing streams. This challenge is intensified by co-articulation effects, temporal ambiguity, and the coexistence of slow hand trajectories with rapid and fine-grained finger articulations. To address these issues, this paper presents a diffusion-enhanced, skeleton-based multi-view Wavelet Transformer framework for boundary-aware sign recognition in continuous videos. The proposed model is trained using isolated sign samples and is subsequently extended to continuous sequences through an uncertainty-aware boundary modeling strategy. Hand motion is represented using 3D skeletal keypoints projected onto multiple orthogonal planes, namely the XY, XZ, and YZ views, enabling complementary perspectives that capture both global motion patterns and subtle articulatory variations. Wavelet decomposition is employed to jointly encode temporal and frequency-domain characteristics, facilitating the detection of important motion cues such as pauses, rapid finger movements, and inter-sign transitions. To explicitly model uncertainty in transitional regions, a Boundary-Aware Temporal Diffusion module is introduced to stochastically refine wavelet-domain motion features and to estimate frame-level uncertainty that is particularly informative near sign boundaries. The refined representations are subsequently processed by a Transformer encoder to capture long-range temporal dependencies. A diffusion-augmented post-processing mechanism then integrates classification confidence, wavelet energy, and diffusion-based uncertainty to achieve precise boundary localization within continuous signing streams. Extensive experiments on four benchmark datasets against multiple methods demonstrate competitive performance, confirming robust diffusion-enhanced transfer between isolated and continuous recognition.

Technologies doi: 10.3390/technologies14100644

Authors: Dan Wang Shengsheng Xie Yuanxiao Li Lisha Zhu Yun Lin Cong Yuan

Steel surface defect detection is a critical component of industrial quality inspection based on artificial intelligence (AI). In recent years, deep learning has substantially improved the level of automation in this task. However, model performance in real production lines remains jointly affected by evaluation metrics, data organization strategies, inference and post-processing settings, and deployment conditions, resulting in limited comparability across studies, poor reusability of conclusions, and difficulties in engineering transfer. Focusing on AI-based industrial inspection scenarios, this paper presents a problem-driven narrative review of steel surface defect detection research, providing a systematic analysis centered on industrial-grade evaluation metrics, public datasets and data risks, key model mechanisms, and their industrial applicability. Particular attention is paid to recall evaluation under fixed false-positive constraints, tiling- and batch-level leakage, long-tail distributions, and annotation noise as well as issues related to input fidelity, representation against complex backgrounds, end-stage decision-making, and lightweight deployment. This review provides a problem-oriented reference framework for result interpretation, method comparison, and industrial deployment evaluation in steel surface defect detection research.

Technologies doi: 10.3390/technologies14100643

Authors: Daria Makarova Sergey Shadrin Andrey Keller Yury Furletov

The transition of highly automated vehicle (HAV) steering to a by-wire architecture (SbW) eliminates the backup mechanical link between the steering wheel and the road wheels and places a failure of the actuation subsystem in the highest safety integrity category, automotive safety integrity level D (ASIL D) under ISO 26262. Most known solutions achieve the required integrity level through duplication of steering angle sensors and electromechanical actuators, which increases cost and complicates certification. The aim of this work is the development and experimental validation of an independent algorithm for indirect kinematic verification of the steering actuation system that requires no additional sensors and relies exclusively on the standard wheel speed sensor signals of the anti-lock braking system/electronic stability program (ABS/ESP system). The computed steering angle of the road wheels is determined from the two-track Ackermann kinematic model based on the difference between the linear speeds of the non-steered axle wheels. An empirical speed-dependent correction is introduced to compensate for lateral tyre slip. The dynamic tolerance threshold for the angular disagreement is derived from the geometric condition that the vehicle must not depart from its lane within the reaction time. Risk accumulation is implemented by a hybrid algorithm combining cumulative sums with a weighted integrator whose growth aggressiveness depends on vehicle speed. The algorithm was implemented in MATLAB/Simulink (The MathWorks, Inc., Natick, MA, USA) and verified both on synthesized data and on telemetry from a real vehicle recorded via the Unified Diagnostic Services (UDS) diagnostic protocol. No false positives occurred throughout the entire experimental run. An artificially injected fault was reliably detected by the algorithm, with a subsequent correct decay of the risk indicator after the fault was removed. The proposed approach establishes a diagnostic channel that is physically independent of the steering actuation system and is based on already certified standard sensors, which reduces the implementation cost of the safety function without loss of sensitivity to the characteristic failure modes of SbW. The algorithm was validated on a production vehicle equipped with conventional electric power steering (EPS) using standard ABS/ESP telemetry; it is therefore presented as a candidate diagnostic channel, and extension to a dedicated SbW platform with architecture-specific fault injection—including a hardware-in-the-loop study—is identified as future work.

Technologies doi: 10.3390/technologies14100642

Authors: Fereshteh Ghorbani Anilou Pooria Pasbakhsh Zipeng Zhang Brodie Houghton Kevin Otto

Worldwide transmission-grid expansion requires extensive construction of new tower foundations across diverse geological, environmental, and logistical conditions, while conventional bored piling faces increasing cost, schedule, constructability, and adaptability constraints. Steel helical pile groups have been proposed as an alternative, but a systematic evaluation of their suitability for Australian transmission infrastructure has been lacking. This narrative review synthesizes Australian and international case studies, current design standards, and published installation-productivity models to compare helical and bored piles on installation logistics, quality assurance, structural and lateral load performance, environmental footprint, and the under-examined implications of foundation type on tower earthing and lightning performance. The comparison finds that helical piles offer real-time, torque-based capacity verification, immediate load-bearing capacity, and lower labor intensity under favorable ground conditions. However, shallow competent bedrock, dense gravels or boulder-affected ground, and very high lateral-load structures favor bored piles or hybrid solutions, and helical-pile grounding performance for tower earthing has not yet been independently verified. These findings indicate that helical piles are a resilient, logistically efficient alternative to bored piles for much of Australia’s transmission foundation demand when applied on a site-specific basis, and the review recommends Australian design guidance and further grounding-performance research.

Technologies doi: 10.3390/technologies14100641

Authors: Satra Tor. Wattanaphol Palit Lerdsrisuwattana Natthakan Rueangnetr Kittipon Kankhunthod Simon J. Greaves Chanon Warisarn

Dual-layer recording doubles the effective areal density of bit-patterned magnetic media, but because both layers are sensed by a common reader, interlayer interference (ILI) is the dominant impairment. This paper presents a two-reader detection method for staggered, dual-layer bit-patterned magnetic recording at 4.0 Tb/in2, in which both layers are read in a single pass by oversampling the outputs of each reader at half the bit period. Detection proceeds in two stages. The log-likelihood ratios from a between-track reader and an on-track reader, with the latter equalized by a fractionally spaced equalizer (FSE), are first combined by a sum soft-information technique to recover the bottom layer. The enhanced soft information then reconstructs and cancels the ILI that corrupts the top layer, where a convolutional neural network (CNN) predicts the residual interference left by a fixed-weight reconstruction. The FSE lowers the bottom-layer bit-error rate (BER) by more than an order of magnitude, combining soft-information gains of about 2 dB SNR at the same BER, and the CNN cancellation adds a further 2 dB. Under 10% media noise, a fixed-weight canceller develops an error floor, whereas the CNN does not, without retraining. This gives the bottom layer larger islands than the top layer, lowering the overall BER from 7.5 × 10−7 to below 10−7 at 18 dB SNR.

Technologies doi: 10.3390/technologies14100640

Authors: Shuping Xue Yuanyuan Wang Danyang He Shuwei Zhang Lei Jia Yun Yang

The multilayer composite structure of waste toothpaste packaging hinders its effective recovery through conventional sorting and recycling processes. In this study, a flexible single-electrode triboelectric sensor based on waste toothpaste tubes (WTT sensor) was fabricated by functionally repurposing and integrating different components of toothpaste packaging waste. The aluminum–plastic composite film and aluminum foil recovered from discarded toothpaste tubes constituted the core triboelectric functional unit, with the aluminum foil also serving as the conductive electrode, while cardboard from the outer toothpaste box was used to construct an elastic spacer. At an excitation frequency of 1 Hz, increasing the applied force from 10 to 300 N raised the open-circuit voltage from approximately 15 to 40 V and the short-circuit current from approximately 0.1 to 0.3 μA. The output voltage exhibited a piecewise linear relationship with the applied force, with sensitivities of 0.391 VN−1 below 50 N and 0.024 VN−1 above 50 N. The output signal remained relatively stable over 1000 consecutive contact–separation cycles. The WTT sensor was attached to the heel region of an athletic shoe to acquire distinct electrical signals corresponding to five types of human motion: tiptoe standing, jumping, squatting, walking, and running. Subsequently, 15 features encompassing time-domain, peak-related, and frequency-domain characteristics were extracted and used in conjunction with an optimized ExtraTrees model for motion pattern recognition. To minimize the risk of data leakage, Complete motion recordings were first assigned exclusively to either the training or test set at a ratio of 7:3; all action samples segmented from a given recording were kept in the same subset to avoid data leakage. The optimized model achieved an overall classification accuracy of 96.18% on the independent test set. In addition, the WTT sensor was used as a self-powered input unit to wirelessly control multiple movements of a remote-controlled car. This work provides a simple and feasible upcycling strategy for converting difficult-to-recycle toothpaste packaging waste into value-added flexible smart sensing devices.

Technologies doi: 10.3390/technologies14100639

Authors: Hafiz Abdul Wajid Ali Tufail Aabid A. Mir

Transparent and efficient water management is becoming a necessity for smart cities worldwide. Recently, technological advancements in water system infrastructure are helping address modern challenges for sustainable water management, particularly in densely populated cities experiencing rapid urbanization. Smart water metering (SWM) systems are increasingly deployed because they generate detailed consumption data and enable early identification of system-wide water losses. However, privacy and security risks, including unauthorized ownership, data manipulation, and tampering, can create mistrust between utilities and citizens, especially when combined with issues such as system-wide water loss, unhealthy physical water infrastructure, and billing inaccuracies. This study introduces a conceptual framework that explores how non-fungible tokens (NFTs) might contribute to more transparent and trustworthy SWM operations. In this approach, each meter is assigned a unique NFT-based digital identity, allowing installation records, maintenance logs, ownership status, and usage history to be represented in a structured and traceable way. The framework is compared with existing identity and asset-representation mechanisms, including PKI, DIDs, and non-tokenized blockchain registries. A simple data flow model illustrates how NFTs could function as digital identities for physical meters, and the role of digital water governance in authorizing token issuance is discussed. These ideas are conceptual rather than operational; they outline how meter-related metadata could become more auditable and interoperable across stakeholders. This paper also proposes a phased pilot pathway as a future validation plan, together with an illustrative evaluation framework to guide pilot design, along with potential security challenges and conceptual mitigation strategies. Moreover, the framework highlights how NFT-enabled identities and smart contracts may support improvements in transparency, accountability, and user trust in SWM systems. It is intended as a foundation for future empirical evaluation in smart-city contexts, not as evidence of a deployed or validated solution.

Technologies doi: 10.3390/technologies14100638

Authors: Anawin Khametong Chanon Warisarn Simon J. Greaves Pornchai Supnithi

This research presents a non-uniform track configuration in which the main track is twice as wide as its adjacent tracks, enabling high-quality soft information and enhancing adjacent-track detection via low-density parity-check (LDPC)-based turbo decoding. We then propose an iterative joint a priori probability (JAPP) detection method for turbo decoding in non-uniform track systems, in which highly-reliable information from the wider track is fed back to improve the reliability of adjacent track detection in the branch metric calculation step. Moreover, to maximize robustness against inter-track interference (ITI), the reader offset is also optimized to achieve a better bit-error rate (BER) performance. The simulation results demonstrate that non-uniform iterative JAPP configurations significantly outperform conventional uniform configurations, even at higher areal densities, achieving substantial BER improvements at an optimal reader offset of 20%.

Technologies doi: 10.3390/technologies14100637

Authors: Emmanuel Ahatsi Oludolapo Olanrewaju

Telecommunication base transceiver stations (BTS) require power-source switching and thermal-management logic that remains predictable and checkable under grid disturbances, generator operation and thermal excursions. Existing microcontroller-based BTS site-management implementations, including the ATmega32-based MBSMECS architecture that motivates this study, provide the basis for the present formal analysis, describe this logic operationally as narrative relay behaviour rather than as an explicit verifiable control model. This paper reformulates the switching and thermal-control logic of that implemented system as a two-layer formal control framework: a finite-state model that represents grid-supply, generator-start-permissive, generator-supply, restoration, transfer and shutdown conditions as explicit states and transition conditions and a Boolean interlock layer that expresses three-phase grid availability, the generator-start permissive and source exclusivity as logical expressions. A symbolic lumped-parameter thermal model with hysteresis switching is developed to represent the thermostat-driven air-conditioner allocation logic and is treated as a discrete-event process coupled to, rather than separate from, the power-switching automaton. Component-level sizing calculations from the original design are retained as supporting engineering-consistency checks rather than as the paper’s central contribution. The framework is exercised through eleven structured operating and fault scenarios and summarized in a nine-property formal verification table that distinguishes properties satisfied by construction (source exclusivity, generator lockout, thermal-threshold activation) from properties the source design leaves unresolved (partial-phase-loss continuity, thermostat-interrupt exit behaviour, and alarm/thermal concurrency). No new experimental or simulated hardware data are introduced; all quantitative illustrations are explicitly labelled as hypothetical. The contribution is a transparent and reusable formal representation of the switching and thermal-control logic, distinct from the underlying hardware implementation that supports design review, verification and future extension of this class of BTS control system.

Technologies doi: 10.3390/technologies14100636

Authors: Jamal Boussouf Ismail Lamaakal Ibrahim Ouahbi Khalid El Makkaoui Mounia Zaydi Belhassen Zouari

Backdoor attacks pose a serious threat to small language models (SLMs) because compromised models can behave normally on benign inputs while producing attacker-specified outputs when a hidden trigger is activated. Existing defenses commonly require model retraining, operate only before deployment, rely on input-level signals, or incur excessive inference overhead. This paper presents AEGIS (Activation Evaluation and Guardrail for Inference Security), a non-invasive runtime framework for detecting backdoor-induced anomalies in transformer representations. AEGIS dynamically monitors equidistant internal layers, mean-pools and concatenates their hidden states, and compresses the resulting high-dimensional representation into a 64-dimensional latent space. A hybrid compression mechanism uses zero-shot principal component analysis for predominantly linear text representations and a lightweight autoencoder for nonlinear or multimodal representations. Detection is then performed entirely on the GPU using cosine distance from a centroid calibrated on clean data, without modifying or retraining the protected model. We evaluate AEGIS across Mistral-7B, Qwen2.5-7B, a 4-bit QLoRA-backdoored Qwen2.5-1.5B model, and a BadNets-backdoored Vision Transformer, covering simulated latent and PEFT-style attacks, genuine fine-tuned backdoors, FP16 inference, and 4-bit NF4 quantization. Across the main optimized shield-overhead experiments, AEGIS achieves AUROC values between 0.95 and 1.00, true-positive rates between 86% and 100%, and prefill-stage detection latency between 0.47 and 2.40 ms. In the dedicated T4 deployment stress tests, which measure the full guardrail overhead under edge and real QLoRA settings, latency ranges from 5.20 to 7.77 ms with combined model-plus-guardrail memory between 2.59 and 4.02 GB. AEGIS obtains AUROC = 1.00 and 100% true-positive rate against the genuinely fine-tuned QLoRA backdoor, while achieving AUROC = 0.997 against the fine-tuned visual BadNets attack. Under 4-bit quantization, it retains an AUROC = 0.95 with a 4.02 GB memory footprint. These results show that a compact GPU-native implementation of activation-space monitoring can provide effective, retraining-free backdoor detection for real-time transformer inference.

Technologies doi: 10.3390/technologies14100635

Authors: Hamza Reguieg Essaid Sabir Mohamed El Kamili

Federated learning must accommodate statistical heterogeneity, costly client participation, and distinct fairness objectives for model performance and participation frequency. We introduce Fair Bayesian Stackelberg Federated Learning (FBS-FL), in which a server maintains beliefs over private client cost types and selects stochastic inclusion probabilities and incentives under an expected client-inclusion constraint. The framework combines an analytical cost-threshold response model with a scalable PPO–EXP3 behavioral implementation using approximate belief tracking. We evaluate FBS-FL over five seeds on CIFAR-10, FEMNIST, and Shakespeare. FBS-FL obtains Jain indices from 0.91 to 0.93 and KL divergences to uniform participation from 0.07 to 0.09, compared with 0.81 to 0.86 and 0.16 to 0.22 for the strongest baseline values on the same participation metrics. The evaluation jointly examines participation fairness, mean accuracy, and worst-client performance. The results indicate improved participation balance with small accuracy variation under the reported protocol.

Technologies doi: 10.3390/technologies14100634

Authors: Andres Valle-Gonzalez Maryam Carrillo-Reales Adalberto Ospino-Castro Diego Restrepo-Leal Carlos Robles-Algarín

High-resolution atmospheric modeling over topographically complex coastal zones requires multi-source spatial data fusion to bridge the scale gap between satellite-assimilated global reanalysis and localized boundary layer dynamics. This study presents a spatial data fusion framework coupling convection-permitting Weather Research and Forecasting (WRF) simulations (3 km grid spacing) with machine learning calibration and GIS multi-criteria decision analysis across continuous computational grids (24,871 cells). Focusing on a high-resolution seasonal case study during the peak Caribbean Low-Level Jet (CLLJ) and Trade Winds regime along the Colombian Caribbean coast, predictive algorithms were benchmarked across day-grouped and Leave-One-Station-Out (LOSO) spatial transfer protocols against classical meteorological baselines. Support Vector Regression (SVR) with an RBF kernel achieved superior residual calibration (R2=0.9523, RMSE=0.4601 m/s) under day-grouped cross-validation, and an average Leave-One-Station-Out (LOSO) spatial transfer RMSE of 1.1030 m/s under blind geographic transfer to unmonitored stations, corresponding to a >76% RMSE reduction relative to raw, uncalibrated WRF output in that same spatial-transfer setting. Chronological forward-forecasting tests further showed that this seasonally augmented kernel model does not extrapolate reliably to calendar months absent from training (R2=−0.09), so a reduced 8-variable configuration is recommended for genuine forward forecasting into unseen seasons. Scalar calibrations were transferred to horizontal wind vectors preserving simulated flow direction, and vertically scaled to 80 m hub height using WRF prognostic shear. Calibrated fields were integrated into a Boolean GIS-MCDA model incorporating wind power density, slope limits, environmental/indigenous reserves, and infrastructure proximity. The framework delineated an 8.9% high-feasibility macro-corridor (2214 cells; ≈19,926 km2) in northern La Guajira, while substantially reducing uncalibrated mesoscale overestimation in the sheltered wake of the Sierra Nevada de Santa Marta. The methodology provides a robust screening-level tool for coastal renewable resource assessment.

Technologies doi: 10.3390/technologies14100633

Authors: Fei Chen Bing Guo Yan Shen Jian Xu Mingjie Zhao Xin Chen Junnan Li

Temporal knowledge graph forecasting aims to predict future missing facts from continuously evolving relational data in dynamic information systems. Such forecasting tasks are increasingly important for intelligent applications involving complex interactions among entities, events, and evolving environments. Existing approaches typically employ unified architectures that process all queries through the same representation pipeline, despite substantial variations in structural connectivity, historical availability, and semantic information among different queries. Such a homogeneous modeling strategy may limit the ability to effectively handle diverse forecasting scenarios, including highly connected entities, recurrent temporal patterns, and cold-start cases with insufficient historical evidence. To address this challenge, we propose a Heterogeneous Modality Mixture-of-Experts (H-MoE) framework that adaptively selects specialized reasoning pathways according to query characteristics. Unlike conventional mixture-of-experts architectures with homogeneous subnetworks, our framework incorporates three complementary experts with distinct inductive biases: a time-aware graph neural network for structural relational reasoning, a temporal transformer for historical sequence modeling, and a semantic representation adapter based on pretrained language models for knowledge transfer in sparse scenarios. A query-aware gating network is introduced to dynamically allocate computational resources among different experts according to the available structural, temporal, and semantic evidence. Furthermore, we employ entropy-based routing regularization to encourage confident expert selection while maintaining balanced expert utilization, together with a representation diversity constraint to promote complementary feature learning among heterogeneous reasoning pathways. Experiments on three benchmark temporal knowledge graph datasets indicate that the proposed framework can improve forecasting performance under the evaluation setting used in this study. The largest observed gains occur for queries with limited historical evidence. These findings support heterogeneous expert specialization as a useful design direction, while the scope of the conclusions remains limited to the tested ICEWS benchmarks and implementation.

Technologies doi: 10.3390/technologies14100631

Authors: Jorge Ortíz-Marín Guillermo Adolfo Anaya-Ruiz Gilberto Alejandro Herrejón-Pintor Vicente Venegas-Rebollar Edgar L. Moreno-Goytia

Current applications in photovoltaic power generation and energy storage require isolated, efficient, and reliable DC-DC converter topologies. However, the design of the nanocrystalline-core medium-frequency transformer (MFT), as a key element of these isolated topologies, often overlooks the impact of specific operating conditions, such as non-sinusoidal input signals and variations in total harmonic distortion (THD). This work introduces an innovative design method customized for a high-efficiency Central-Tapped medium-frequency transformer (CT-MFT) integrated into a SiC-based central-tapped phase-shift full-bridge (CT-PSFB) converter. Unlike earlier proposals, this work considers key parameters in the transformer design, including non-sinusoidal excitation waveforms and changes in THD during converter operation. After the CT-MFT is designed, the CT-MFT/CT-PSFB structure is simulated and validated using a tailor-made prototype. The experimental results confirm the structure’s effectiveness: the CT-MFT achieves 99.65% efficiency, and the CT-PSFB achieves 94.7% average efficiency, with THD up to 22.5% and 97.66% peak efficiency.

Technologies doi: 10.3390/technologies14100632

Authors: Dandan Wang Yanping Lu Hongyan Liu Jingzheng Dong

Carbon emission prediction of coastal port-industrial zones integrates industrial production, maritime logistics and spatial environmental governance. Existing models fail to simultaneously capture multi-scale temporal fluctuations and spatial correlations of industrial units under meteorological and policy interference. This paper constructs an interpretable dual-branch framework combining Ensemble Empirical Mode Decomposition–XGBoost (EEMD-XGBoost) and a Temporal Graph Neural Network (T-GNN). The EEMD-XGBoost branch extracts multi-scale temporal features from non-stationary emission sequences, while the T-GNN models spatiotemporal dependencies of industrial sub-units; weighted fusion integrates the two branches, and the framework outputs interpretable indicators including feature importance, spatial contribution and temporal attention weights to analyze emission driving factors. Two 2023 datasets are adopted: a coastal-port prefecture-level subset derived from the national regional carbon dataset (28 coastal port-related prefectures), split chronologically into 70% training and 30% test sets; the Yangtze River Delta ship dataset integrating AIS, ship properties and fuel data is partitioned via tonnage-based stratified sampling into 7:2:1 training–validation–test subsets, with an independent test subset for short-term ship-type forecasting. On the independent ship test set for maritime greenhouse gas prediction, the proposed model achieves an R2 of 0.96, 0.95 and 0.93 for container, bulk and oil ships respectively, which only applies to ship-scale forecasting rather than regional carbon prediction. Special ablation experiments show that single T-GNN converges within 72 epochs, single EEMD-XGBoost has a high-frequency fitting error of 6.82%, and the complete dual-branch model reaches a spatial feature capture rate of 94.65% with only a 3.47% fitting error, despite a 27.52 ms single-sample inference time, proving the synergy of the two branches. The model outperforms baselines under abnormal and sparse data conditions. This interpretable framework supports traceable refined carbon management and provides quantitative engineering implications for port zoning control, differentiated ship emission reduction and regional low-carbon policy implementation.

Technologies doi: 10.3390/technologies14100630

Authors: Ronnie Concepcion Jeanette Pao Rhen Anjerome Bedruz Ronnel Agulto Jared Jan Abayan Edza Aria Wikurendra

Natural and anthropogenic disasters are rising in frequency and severity, and reactive, single-technology response models are now the principal bottleneck in safeguarding planetary health. This article develops Hyperautomated Hazard Mitigation (HHM), in which robots, unmanned aerial vehicles (UAVs), and artificial intelligence (AI) services operate as one integrated, self-optimizing system rather than as isolated tools. Hazard mitigation is treated as a specific resilience behavior of a multi-agent engineering system, building on established proactive-resilience theory rather than presenting that framing as new. This framework-oriented review synthesizes evidence and proposes an architecture. It reports no new experimental validation. It maps hazard-mitigation functions onto the six layers of the Reference Architectural Model Industrie 4.0 (RAMI 4.0) with the standards and interfaces that realize each layer; classifies robotic and UAV functions across natural and anthropogenic hazards using one set of four capability classes; expresses ethical and regulatory requirements as quantified engineering constraints; operationalizes health- centered evaluation metrics; and appraises the maturity of the evidence in each hazard domain. It concludes that the architecture is tractable with existing standards, that the actuation stage is the least mature and least evaluated, and that the decisive gap is evaluative: the field has no shared benchmark and rarely reports health outcomes.