Eng

The rapid development of Generative Adversarial Networks (GANs) has transformed medical image processing, enabling realistic image synthesis, augmentation, and restoration. This study presents a comparative evaluation of three representative GAN architectures, Pix2Pix, SPADE GAN, and Wasserstein GAN (WGAN), across multiple medical imaging tasks, including segmentation, image synthesis, and enhancement. Experiments were conducted on three benchmark datasets: ACDC (cardiac MRI), Brain Tumor MRI, and CHAOS (abdominal MRI). Model performance was assessed using Fréchet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Dice coefficient, and segmentation accuracy. Results show that SPADE-inpainting achieved the best image fidelity (PSNR ≈ 36 dB, SSIM > 0.97, Dice ≈ 0.94, FID < 0.01), while Pix2Pix delivered the highest segmentation accuracy (Dice ≈ 0.90 on ACDC). WGAN provided stable enhancement and strong visual sharpness on smaller datasets such as Brain Tumor MRI. The findings confirm that no single GAN architecture universally excels across all tasks; performance depends on data complexity and task objectives. Overall, GANs demonstrate strong potential for medical image augmentation and synthesis, though their clinical utility remains dependent on anatomical fidelity and dataset diversity. Full article

The design philosophy of a new smart gas meter is presented, based on an ultrasonic sensor employing LoRa and/or NB-IoT protocols and blockchain technologies to overcome the data integrity and security issues with a completely modular design. The architecture is organized into two separate blocks, the former for measurement and the latter for communication, and it presents original characteristics with respect to the state of the art. The accuracy of measured data is studied, paying attention to the fluid dynamic effects of the geometrical layout on the flow rate ultrasonic sensor and the environmental temperature and pressure for variable gas flow rate values. As for data security issues, the proposed solution is critically analyzed with reference to the data string organization and the procedure by which the data are stored and prepared for transmission into the blockchain. Finally, a local network of counters is designed and simulated in order to check the compliance of the provided hardware and software solutions with the predicted computational load. Full article

The hysteresis model can be used to accurately predict the magnetic hysteresis characteristics of ferromagnetic materials. Incorporating the hysteresis model into finite element calculations enables precise prediction of field distributions, voltage or current variations in circuits, and losses, which is essential for electromagnetic transient analysis involving remanent magnetization. When incorporating the hysteresis model into finite element analysis, prohibitively small time-steps are required to resolve hysteresis loops, leading to excessive simulation times compared to simplified BH curve approaches. Furthermore, numerical instabilities arise near zero-crossing points of magnetic flux density, where erroneous negative differential reluctivity values may lead to the divergence of the nonlinear solving process. A finer time resolution needs to be utilized to ensure the convergence of the nonlinear solver. This leads to more time-steps and longer computational time. This work proposes a localized stabilization strategy for regulating the differential reluctivity in instability-prone regions of the hysteresis loop, which can stabilize the nonlinear iteration while avoiding the local refinement of time resolution and thus reduce the overall computation time. Full article

Objective: Stress recognition is a widely investigated and debated area in biomedical research. Physiological monitoring has gained increasing attention as one of the methodologies used to assess an individual’s stress level. In this study, we investigated the effectiveness of a novel normalization technique applied to multi-domain physiological data for the objective classification of stress levels using a feature extraction approach. Methods: Electrocardiographic (ECG) and respiratory data from a publicly available database, collected from drivers experiencing various stress levels, underwent a novel inter-subject normalization procedure. This method involved adjusting the time scale of the original data to a common scale across subjects according to fixed resting heart and respiratory rates. Subsequently, a feature-based stress state classification procedure was conducted using the Support Vector Machine (SVM) algorithm. The efficacy of this inter-subject normalization procedure was assessed by comparing the classification results obtained using features from the original signals with those obtained from the inter-subject-normalized signals. Additionally, the inter-subject normalization procedure was compared with two common feature normalization approaches: standardization and scaling. Results: Features derived from the subject-normalized signals yielded improved performance, significantly enhancing accuracy from 68% to 73%, as well as precision and sensitivity. Conclusions: The novel inter-subject normalization procedure proves to be an effective technique for highlighting differences in features among various stress states and for mitigating basal physiological variability across subjects. Significance: Using inter-subject normalization on multi-domain physiological signals holds promise as a method to improve multilevel stress classification through feature extraction, ensuring that the features maintain their correspondence even after the normalization process. Full article

The advancing speed of the coal mining face has a significant impact on the mining-induced stress and energy accumulation of the surrounding rock. To explain the influence mechanism from a mesoscopic perspective, this study conducted a uniaxial compression test on the coal–rock combination body under different quasi-static loading rates, and analyzed their mechanical properties, failure characteristics, acoustic emission characteristics and energy evolution characteristics. The main findings are as follows: The uniaxial compressive strength and elastic modulus of the coal–rock combination body show a variation law of first increasing and then decreasing with the increase in loading rate, while the degree of impact failure significantly increases gradually as the loading rate rises. With the increase in loading rate, there is a tendency that the AE parameters concentrate from the first two stages to the latter two stages. The post-peak residual elastic energy density of the coal–rock combination body increases gradually with the increase in loading rate. The formation of the advancing speed effect of mining-induced stress concentration and elastic energy accumulation in coal–rock masses is caused by the “competitive” interaction between fracture propagation and coal matrix damage when the coal component in the coal–rock combination is deformed under stress. Full article

In radio frequency (RF) systems, the mixer is a critical component for achieving frequency conversion in electromagnetic sensor backends. This paper proposes a mixer design methodology aimed at improving noise figure and conversion gain specifically for sensor signal processing applications. This design employs a process incorporating high-quality bipolar junction transistors (BJTs) and adopts a mixer-first architecture instead of a conventional low noise amplifier (LNA). By optimizing the layout and symmetry of the BJTs, the input impedance can be flexibly adjusted, thereby simplifying the receiver front-end while simultaneously improving local oscillator (LO) feedthrough. Design and simulation were completed using Advanced Design System (ADS) 2020 software. Simulation results demonstrate that the proposed mixer exhibits significant advantages in suppressing noise and interference while enhancing conversion gain, making it particularly suitable for electromagnetic sensor backend applications. Full article

To address the engineering challenges of powertrain excitation noise and aggravated low-frequency interior noise caused by armored structures in special-purpose vehicles, this study proposes an in-vehicle acoustic response analysis method based on vibro-acoustic coupling theory. This study presents a method for analyzing in-vehicle acoustic response under engine excitation, integrating Panel Acoustic Contribution Analysis (PACA) with a vibro-acoustic coupling model tailored for armored vehicles. The framework experimentally reveals a condition-independent resonance at 26.5 Hz and reproduces engine-order peaks at 40 Hz, 93.3 Hz, and 140 Hz. Quantitative comparison shows ΔSPL ≤ 2.5 dB and RMSE ≤ 2.2 dB between simulation and experiment, confirming model robustness. Based on these results, conceptual Dynamic Vibration Absorber (DVA) placement guidelines are proposed for dominant panels, providing practical engineering insights for NVH mitigation in armored vehicles. Full article

The persistent contamination of water bodies by organic compounds, heavy metals, and pathogenic microorganisms represents a critical environmental and public health concern worldwide. In this context, polymer composite materials have emerged as promising multifunctional platforms for advanced water purification. These materials combine the structural versatility of natural and synthetic polymers with the enhanced physicochemical functionalities of inorganic fillers, such as metal oxides and clay minerals. This review comprehensively analyzes recent developments in polymer composites designed to remove organic, inorganic, and biological pollutants from water systems. Emphasis is placed on key removal mechanisms, adsorption, ion exchange, photocatalysis, and antimicrobial action, alongside relevant synthesis strategies and material properties that influence performance, such as surface area, porosity, functional group availability, and mechanical stability. Representative studies are examined to illustrate contaminant-specific composite designs and removal efficiencies. Despite significant advancements, challenges remain regarding scalability, material regeneration, and the environmental safety of nanostructured components. Future perspectives highlight the potential of bio-based and stimuli-responsive polymers, hybrid systems, and AI-assisted material design in promoting sustainable, efficient, and targeted water purification technologies. Full article

Taxonomies provide essential hierarchical structures for classifying information, enabling effective retrieval and knowledge organization in diverse domains such as e-commerce, academic research, and web search. Traditional taxonomy construction, heavily reliant on manual curation by domain experts, faces significant challenges in scalability, cost, and consistency when dealing with the exponential growth of digital data. Recent advancements in Large Language Models (LLMs) and Natural Language Processing (NLP) present powerful opportunities for automating this complex process. This paper explores the potential of LLMs for automated taxonomy generation, focusing on methodologies incorporating semantic embedding generation, keyword extraction, and machine learning clustering algorithms. We specifically investigate and conduct a comparative analysis of two primary LLM-based approaches using a dataset of eBay product descriptions. The first approach involves fine-tuning a pre-trained LLM using structured hierarchical data derived from chain-of-layer clustering outputs. The second employs prompt-engineering techniques to guide LLMs in generating context-aware hierarchical taxonomies based on clustered keywords without explicit model retraining. Both methodologies are evaluated for their efficacy in constructing organized multi-level hierarchical taxonomies. Evaluation using semantic similarity metrics (BERTScore and Cosine Similarity) against a ground truth reveals that the fine-tuning approach yields higher overall accuracy and consistency (BERTScore F1: 70.91%; Cosine Similarity: 66.40%) compared to the prompt-engineering approach (BERTScore F1: 61.66%; Cosine Similarity: 60.34%). We delve into the inherent trade-offs between these methods concerning semantic fidelity, computational resource requirements, result stability, and scalability. Finally, we outline potential directions for future research aimed at refining LLM-based taxonomy construction systems to handle large dynamic datasets with enhanced accuracy, robustness, and granularity. Full article

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

This paper presents Rain-Cloud Condensation Optimizer (RCCO), a nature-inspired metaheuristic that maps cloud microphysics to population-based search. Candidate solutions (“droplets”) evolve under a dual-attractor dynamic toward both a global leader and a rank-weighted cloud core, with time-decaying coefficients that progressively shift emphasis from exploration to exploitation. Diversity is preserved via domain-aware coalescence and opposition-based mirroring sampled within the coordinate-wise band defined by two parents. Rare heavy-tailed “turbulence gusts” (Cauchy perturbations) enable long jumps, while a wrap-and-reflect scheme enforces feasibility near the bounds. A sine-map initializer improves early coverage with negligible overhead. RCCO exposes a small hyperparameter set, and its per-iteration time and memory scale linearly with population size and problem dimension. RCOO has been compared with 21 state-of-the-art optimizers, over the CEC 2022 benchmark suite, where it achieves competitive to superior accuracy and stability, and achieves the top results over eight functions, including in high-dimensional regimes. We further demonstrate constrained, real-world effectiveness on five structural engineering problems—cantilever stepped beam, pressure vessel, planetary gear train, ten-bar planar truss, and three-bar truss. These results suggest that a hydrology-inspired search framework, coupled with simple state-dependent schedules, yields a robust, low-tuning optimizer for black-box, nonconvex problems. Full article

Four tertiary amines (tributylamine, tripentylamine, trihexylamine, triethanolamine) were investigated with a 25 μm platinum disc microelectrode in more organic solvents. In the commonly used inert solvent acetonitrile, the sigmoidal-shaped curves used were recorded, except for triethanolamine, which showed two current plateaus close to each other, indicating temporal blocking in this case. The really surprising results arose from studies in acetic acid and ethyl acetate. Due to the complete protonation in acetic acid, the significant shifts of oxidation potentials led to the acquisition of lower currents only with the rising parts also using the same potential window as in ethyl acetate, where the voltammograms had a sigmoidal shape. Triethanolamine exhibited significant electrode deactivation in ethyl acetate, leading to the appearance of peak-shaped curves, and the difference between the first and second voltammograms was large. The current difference between the first and second scans allowed consequences for acetic acid content in cases where it was small, as the choice of this parameter proved to be the best for the analytical task. On the other hand, the differences in the shape of voltammograms allowed for quantitative approximations. The observed phenomenon could be utilized only for the estimation of acetic acid content. Full article

Background: Operational cycle detection underpins a range of important tasks, such as predictive maintenance, energy consumption prediction, and energy management for mobile equipment in mining. Yet, no review has investigated the landscape of methods that segment mobile mining vehicle telemetry into discrete operating modes—a task termed operational cycle detection. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Scoping Review extension (PRISMA-ScR) framework, we searched The Lens database on 27 June 2025, for records published between 2000 and 2025 that apply cycle detection to mobile mining vehicles. After de-duplication and two-stage screening, 20 empirical studies met all criteria (19 diesel, 1 electric-drive). Due to the sparse research involving battery electric vehicles (BEVs) in mining, three articles performing cycle detection on heavy-duty vehicles in a similar operational context to mining are synthesized. Results: Early diesel work used single-sensor thresholds, often achieving >90% site-specific accuracy, while recent studies increasingly employ neural networks using multivariate datasets. While the cycle detection research on mining BEVs, even supplemented with additional heavy-duty BEV studies, is sparse, similar approaches are favored. Conclusions: Persisting gaps in the literature include the absence of public mining datasets, inconsistent evaluation metrics, and limited real-time validation. Full article

Virtual Machine Introspection (VMI) is a powerful technology used to detect and analyze malicious software inside Virtual Machines (VMs) from the outside. Asynchronous access to the VM’s memory can be insufficient for efficient monitoring of what is happening inside of a VM. Active VMI introduces breakpoints to intercept VM execution at relevant points. Especially for frequently visited breakpoints, and even more so for production systems, it is crucial to keep performance overhead as low as possible. In this paper, we present an empirical study that compares the performance of four VMI breakpoint-implementation variants—EPT switching (SLAT view switching) with and without fast single-stepping acceleration, instruction repair, and instruction emulation—from two VMI applications (DRAKVUF, SmartVMI) with the XEN hypervisor on 20 Intel Core i processors ranging from the fourth to the thirteenth generation. Instruction emulation was the fastest method across all 20 tested platforms. Modern processors such as the Intel Core i7 12700H and Intel Core i9 13900HX achieved median breakpoint-processing times as low as 15 µs for the emulation mechanism. The slowest method was instruction repair, followed by EPT switching and EPT switching with FSS. The order was the same for all measurements, indicating that this is a strong and generalizable result. Full article

The article presents the results of uniaxial fatigue tests for the high-cycle regime on a polymer matrix composite material reinforced with Kevlar and carbon fibers, fabricated with material extrusion (MEX) technology. The samples were manufactured according to the ASTM D638 type-I standard, and the tests were performed under a load inversion factor of 0.1 at room temperature, measuring the number of cycles to failure. Based on previous results, in which different configurations were tested, tests were carried out on specimens subjected to loads ranging from 40% to 91% of the rupture stress for Kevlar and 25.5% to 80.7% for carbon, obtaining a maximum life of 2.5 M cycles for Kevlar and 4.06 M cycles for carbon. The observed failure modes included fiber tearing, matrix cracking, and fiber–matrix pull-out. Full article

The increasing adoption of electric vehicles has driven the development of charging technologies that meet growing demands for power, efficiency, and grid compatibility. This review presents a comprehensive analysis of the EV charging ecosystem, covering Level 3 DC charging stations, power converter topologies, and the role of energy storage systems in supporting grid integration. Commercial solutions and academic prototypes are compared across key parameters such as voltage, current, power, efficiency, and communication protocols. The study highlights trends in charger architectures—including buck, boost, buck–boost, LLC resonant, and full-bridge configurations—while also addressing the integration of stationary storage as a buffer for fast charging stations. Special attention is given to wide-bandgap semiconductors like SiC and GaN, which enhance efficiency and thermal performance. A significant gap persists between the technical transparency of commercial systems and the ambiguity often observed in prototypes, highlighting the urgent need for standardized research reporting. Although converter efficiency is no longer a primary constraint, substantial challenges remain regarding infrastructure availability and the integration of storage with charging stations. This paper seeks to offer a comprehensive perspective on the design and deployment of smart, scalable, and energy-efficient charging systems, with particular emphasis on cascaded and bidirectional topologies, as well as hybrid storage solutions, which represent promising pathways for the advancement of future EV charging infrastructure. Full article

This study reviews and experimentally investigates critical logistics and transport models in stainless-steel (SS) fluid storage tanks, focusing on stainless steel grades 316 and 304L. Conceptual vessel schematics emphasize hygienic drainability, refill uniformity, and thermal control, supported by representative 316L properties for heat-transfer, stress, and fluid–structure analyses. At the logistics scale, modelling integrates component-level simulations, computational fluid dynamics (CFD), and Finite Element Method (FEM) with network-level approaches, such as Continuous Approximation, to address facility location, refilling schedules, and demand variability. Experimental characterization using EDS and XRF confirmed the expected Cr/Ni backbone and grade-consistent Mo in 316, while unexpected C, Mn, and Cu readings were attributed to instrumental limits or statistical variance. Unexpected detection of Europium in 304L highlights the need for further mechanical testing. Overall, combining simulation, logistics modelling, and compositional verification offers a coherent framework for safe, efficient, and thermally reliable stainless-steel tank design. Full article

Deepfake technology, which utilizes advanced AI models such as Generative Adversarial Networks (GANs), has led to the proliferation of highly convincing manipulated media, posing significant challenges for detection. Existing detection methods often struggle with the low-quality or compressed press, which is prevalent on social media platforms. This paper proposes a novel Deepfake detection framework that leverages No-Reference Image Quality Assessment (NRIQA) techniques, specifically, BRISQUE, NIQE, and PIQUE, to extract quality-related features from facial images. These features are then classified using a Support Vector Machine (SVM) with various kernel functions. We evaluate our method under both intra-dataset and cross-dataset settings. For intra-dataset evaluation, we conduct K-fold cross-validation on two benchmark datasets, DFDC and Celeb-DF (v2), including downsampled versions to simulate real-world degradation. The results show that our method maintains high accuracy even under significant quality loss, achieving up to 98% accuracy on the Celeb-DF (v2) dataset and outperforming several state-of-the-art methods. To improve the transferability of the detection models, we introduce an integrated filtering strategy based on NR-IQA thresholding, which enhances performance in cross-dataset transfer scenarios. This approach yields up to 7% improvement in detection accuracy under challenging cross-domain conditions. Full article

Titanium and, in particular, its alloys are widely used in biomedical applications due to their favorable combination of mechanical properties, such as high strength, low density, low elastic modulus, and excellent biocompatibility. In this study, novel titanium-based alloys were developed using powder metallurgy techniques. The chemical composition of the studied alloys was 93%Ti-7%Fe, 90%Ti-7%Fe-3%Al, and 88%Ti-7%Fe-5%Cr. The metallic powders were processed in a planetary mill, uniaxially compacted, and subsequently sintered at 1300 °C during 2 h under an inert atmosphere. The primary objective was to evaluate the corrosion behavior of these alloys in simulated body fluid solutions, as well as to determine some of the properties, such as the relative density, microhardness, and elastic modulus. The resulting microstructures were homogeneous, with micrometer-scale grain sizes and the formation of intermetallic precipitates generated during sintering. Mechanical tests revealed that the Ti-Fe-Cr alloy exhibited the highest microhardness and Young’s modulus values, followed by Ti-Fe and Ti-Fe-Al. These results confirm a strong correlation between hardness and stiffness, showing that Cr enhances mechanical and elastic properties, while Al reduces them. Corrosion tests demonstrated that the alloys possess high resistance and stability in physiological environments, with a low current density, minimal mass loss, and strong performance even under prolonged exposure to acidic conditions. Full article