Search Results (804)

Accurate docking of Autonomous Guided Vehicles (AGVs) is a critical requirement for efficient automated production systems in Industry 4.0, particularly for collaborative tasks with robotic arms that have a limited working range. This paper introduces a cost-effective software-upgrade solution to enhance the precision of the final docking phase without requiring new hardware. Our approach is based on a two-stage strategy: first, a switch from a global dead reckoning system to a local navigation scheme, is triggered near the docking station; second, a dedicated Takagi-Sugeno Fuzzy Logic Controller (FLC), guides the AGV to its final position with high accuracy. The core novelty of our FLC is its implementation as a gain-scheduling lookup table (LUT), which synthesizes critical state variables—heading error and distance error—from limited proximity sensor data, to robustly handle positional uncertainty and environmental variations. This method directly addresses the inadequacies of traditional odometry, whose cumulative errors become unacceptable at the critical docking point. For experimental validation, we assume the global navigation delivers the AGV to a general switching point, near the assembly station with an unknown true pose. We detail the design of the fuzzy controller and present experimental results that demonstrate a significant improvement, achieving repeatable docking accuracy within industrially acceptable tolerances. Full article

True wireless stereo (TWS) earbuds are evolving into multifunctional wearable devices, offering opportunities not only for audio streaming but also for health-related applications. A fundamental requirement for such devices is the ability to accurately detect whether they are being worn, yet conventional proximity sensors remain limited in both reliability and functionality. This work explores the use of photoplethysmography (PPG) sensors, which are widely applied in heart rate and blood oxygen monitoring, as an alternative solution for wearing detection. A PPG sensor was embedded into a TWS prototype to capture blood flow changes, and the wearing status was classified in real time using a lightweight k-nearest neighbor (k-NN) algorithm on an edge AI processor. Experimental evaluation showed that incorporating a validity check enhanced classification performance, achieving F1 scores above 0.95 across all wearing conditions. These results indicate that PPG-based sensing can serve as a robust alternative to proximity sensors and expand the capabilities of TWS devices. Full article

This paper presents a multi-problem surrogate-assisted evolutionary multi-objective optimization approach for antenna design. By transforming the traditional antenna design optimization problem into expensive multi-objective optimization problems, this method employs a multi-problem surrogate (MPS) model to stack multiple antenna design problems. The MPS model is a knowledge-transfer framework that stacks multiple surrogate models (e.g., Gaussian Processes) trained on related antenna design problems (e.g., Yagi–Uda antennas with varying director configurations) to accelerate optimization. The parameters of Yagi–Uda antenna including radiation patterns and beamwidth—across various director configurations are considered as decision variables. The several surrogates are constructed based on the number of directors of Yagi–Uda antenna. The MPS algorithm identifies promising candidate solutions using an expected improvement strategy and refines them through true function evaluations, effectively balancing exploration with computational cost. Compared to benchmark algorithms assessed by hypervolume, our approach demonstrated superior average performance while requiring fewer function evaluations. Full article

This paper presents the final implemented design and performance evaluation of the ground-based C-band Cheia radar system, developed to enhance Romania’s contribution to the EU Space Surveillance and Tracking (EU SST) network. All data used for performance analysis are real-time, real-life measurements of true spatial test objects orbiting Earth. The radar is based on two decommissioned 32 m satellite communication antennas already present at the Cheia Satellite Communication Center, that were retrofitted for radar operation in a quasi-monostatic architecture. A Linear Frequency Modulated Continuous Wave (LFMCW) Radar design was implemented, using low transmitted power (2.5 kW) and advanced software-defined signal processing for detection and tracking of Low Earth Orbit (LEO) targets. System validation involved dry-run acceptance tests and calibration campaigns with known reference satellites. The radar demonstrated accurate measurements of range, Doppler velocity, and angular coordinates, with the capability to detect objects with radar cross-sections as low as 0.03 m² at slant ranges up to 1200 km. Tracking of medium and large Radar Cross Section (RCS) targets remained robust under both fair and adverse weather conditions. This work highlights the feasibility of re-purposing legacy satellite infrastructure for SST applications. The Cheia radar provides a cost-effective, EUSST-compliant performance solution using primarily commercial off-the-shelf components. The system strengthens the EU SST network while demonstrating the advantages of LFMCW radar architectures in electromagnetically congested environments. Full article

Viscoelastic relaxation time and frequency spectra are useful for describing, analyzing, comparing, and improving the mechanical properties of materials. The spectra are typically obtained using the stress or oscillatory shear measurements. Over the last 80 years, dozens of mathematical models and algorithms were proposed to identify relaxation spectra models using different analytical and numerical tools. Some models and identification algorithms are intended for specific materials, while others are general and can be applied for an arbitrary rheological material. The identified relaxation spectrum model always depends on the identification method applied and on the specific measurements used in the identification process. The stress relaxation experiment data consist of the sampling times used in the experiment and the noise-corrupted relaxation modulus measurements. The aim of this paper is to build a model of the spectrum that asymptotically does not depend on the sampling times used in the experiment as the number of measurements tends to infinity. Broad model classes, determined by a finite series of various basis functions, are assumed for the relaxation spectra approximation. Both orthogonal series expansions based on the Legendre, Laguerre, and Chebyshev functions and non-orthogonal basis functions, like power exponential and modified Bessel functions of the second kind, are considered. It is proved that, even when the true spectrum description is entirely unfamiliar, the approximate sampling-times-independent spectra optimal models can be determined using modulus measurements for appropriately randomly selected sampling times. The recovered spectra models are strongly consistent estimates of the desirable models corresponding to the relaxation modulus models, being optimal for the deterministic integral weighted square error. A complete identification algorithm leading to the relaxation spectra models is presented that requires solving a sequence of weighted least-squares relaxation modulus approximation problems and a random selection of the sampling times. The problems of relaxation spectra identification are ill-posed; solution stability is ensured by applying Tikhonov regularization. Stochastic convergence analysis is conducted and the convergence with an exponential rate is demonstrated. Simulation studies are presented for the Kohlrausch–Williams–Watts spectrum with short relaxation times, the uni- and double-mode Gauss-like spectra with intermediate relaxation times, and the Baumgaertel–Schausberger–Winter spectrum with long relaxation times. Models using spectrum expansions on different basis series are applied. These studies have shown that sampling times randomization provides the sequence of the optimal spectra models that asymptotically converge to sampling-times-independent models. The noise robustness of the identified model was shown both by analytical analysis and numerical studies. Full article

Axial–torsional coupled impact drilling (ATCID) technology represents a promising solution for overcoming the drilling challenges posed by conglomerate formations, which are characterized by strong heterogeneity, high abrasiveness, and poor drillability. However, the optimal parameter matching relationships and their influence patterns on mechanical specific energy (MSE) remain unclear. This study employed self-developed true triaxial impact rotary rock breaking equipment with conglomerate cores from the Junggar Basin to systematically investigate the effects of weight on bit (WOB), rotational speed (RPM), axial impact frequency, and torsional impact frequency on MSE through orthogonal experimental design. The results demonstrate that the parameter influence ranking on MSE is as follows: torsional impact frequency > WOB > RPM > axial impact frequency, with torsional impact frequency exhibiting the largest range value (87.5 MPa). ANOVA reveals that the interaction between axial and torsional impact frequencies is the dominant controlling factor, contributing 22.8% to MSE variation with high statistical significance. The optimal parameter combination yields the minimum MSE (103 MPa): 19 kN WOB, 20 r/min RPM, 20 Hz axial impact frequency, and 20 Hz torsional impact frequency, representing a 69.1% reduction compared to the maximum value. Response surface analysis revealed that increasing WOB significantly reduces MSE, RPM exhibits positive correlation with MSE, and synergistic effects occur when both impact frequencies reach high values simultaneously. A nonlinear MSE prediction model incorporating main effects, quadratic terms, and interaction effects was established with R² = 0.8240 and a mean absolute percentage error of 9.26%. The research findings provide an essential theoretical foundation for parameter optimization and engineering applications of ATCID technology, offering significant implications for enhancing drilling efficiency in conglomerate and other challenging hard rock formations. Full article

The intensified exposure to high temperature in urban areas, resulting from the combination of heat waves and the urban heat island (UHI) effect, necessitates a deeper understanding of the climate–health relationship. This knowledge directly influences the strategies employed by policy makers and urban planners in their efforts to regenerate cities and protect their population. Nature-based solutions and the widely accepted 15 min city model, characterized by a polycentric structure, should drive the implementation of effective adaptation policies, especially given the persistent delay in mitigation efforts. However, it is less clear whether current or future policies are adequately structured to broadly address the complex forms of social vulnerability. A prime example of this complexity is the demographic shift observed since the mid-20th century, characterized by a relative increase in the elderly population, and a shrinking youth demographic. While extensive literature addresses the physiological impacts of heat wave on human health, evidence regarding the neuro-psychological and cognitive implications for elderly individuals, who frequently suffer from chronic diseases, remains less comprehensive and more fragmented. The purpose of this concise review is to emphasize that crucial findings on the climate–health relationship, particularly concerning the elderly, have often been developed within disciplinary silos. The lack of comprehensive interdisciplinary integration coupled with an incomplete understanding of the full spectrum of vulnerabilities (encompassing both physiological and cognitive) may lead to urban policies that are egalitarian in principle but fail to achieve true equity in practice. This review aims to bridge this gap by highlighting the need for a more integrated approach to urban policy and regeneration. Full article

This study addresses the challenge of accurately detecting agricultural land-use changes in bi-temporal remote sensing imagery, which is hindered by cross-temporal interference, multi-scale feature modeling limitations, and poor large-area scalability. The study proposes the Semantic Change Detection (SCD) with Single-Head Dual-Attention Vision Transformer (SHDAViT) and Multidimensional Collaborative Attention (MCA) Block-Based Network (SMBNet). The SHDAViT module enhances local-global feature aggregation through a single-head self-attention mechanism combined with channel–spatial dual attention. The MCA module mitigates cross-temporal style discrepancies by modeling cross-dimensional feature interactions, fusing bi-temporal information to accentuate true change regions. SHDAViT extracts discriminative features from each phase image, MCA aligns and fuses these features to suppress noise and amplify effective change signals. Evaluated on the newly developed AgriCD dataset and the JL1 benchmark, SMBNet outperforms five mainstream methods (BiSRNet, Bi-SRUNet++, HRSCD.str3, HRSCD.str4, and CDSC), achieving state-of-the-art performance, with F1 scores of 91.18% (AgriCD) and 86.44% (JL1), demonstrating superior accuracy in detecting subtle farmland transitions. Experimental results confirm the framework’s robustness against label imbalance and environmental variations, offering a practical solution for agricultural monitoring. Full article

The Portevin-Le Chatelier (PLC) effect has been studied for many decades, yet the influence of testing modes has received limited attention. In the past 20 years, it has become increasingly recognized that the stiffness of the testing machine can significantly affect the occurrence of jerky flow, particularly the serrations observed during tensile tests. This study addresses this issue by conducting tests on the Al-Mg alloy AA5083H111, which contains a substantial amount of diffusible magnesium in solid solution and exhibits dynamic strain aging, resulting in a pronounced PLC effect. Both electromechanical and servohydraulic testing machines were used in the tests; these machines differ in stiffness and control technology for applied strain rates. The study also explored different control modes, including stroke control for both machines and true strain control for the servohydraulic machine. The findings indicate that machine stiffness has a moderate effect on material behavior, and no single machine or testing mode can precisely control the strain rate in the sample during the PLC effect. However, it was noted that true strain rate control using a servohydraulic machine comes closest to accurately reflecting the material’s behavior during jerky flow. Full article

Privacy risk mining, a crucial domain in data privacy protection, endeavors to uncover potential information among datasets that could be linked to individuals’ sensitive data. Existing anonymization and privacy assessment techniques either lack quantitative granularity or fail to adapt to dynamic, heterogeneous data environments. In this work, we propose a unified two-phase linkability quantification framework that systematically measures privacy risks at both the inter-dataset and intra-dataset levels. Our approach integrates unsupervised clustering on attribute distributions with record-level matching to compute interpretable, fine-grained risk scores. By aligning risk measurement with regulatory standards such as the GDPR, our framework provides a practical, scalable solution for safeguarding user privacy in evolving data-sharing ecosystems. Extensive experiments on real-world and synthetic datasets show that our method achieves up to 96.7% precision in identifying true linkage risks, outperforming the compared baseline by 13 percentage points under identical experimental settings. Ablation studies further demonstrate that the hierarchical risk fusion strategy improves sensitivity to latent vulnerabilities, providing more actionable insights than previous privacy gain-based metrics. Full article

A compact object illuminated by background radiation produces a dark silhouette. The edge of the silhouette or shadow (alternatively, the apparent boundary or the critical curve) is commonly determined by the presence of the photon sphere (or photon shell in the case of rotating spacetime), corresponding to the maximum of the effective potential for null geodesics. While this statement stands true for Kerr black holes, here we remark that the apparent boundary (as defined by Bardeen) forms under a more general condition. We demonstrate that a shadow forms if the effective potential of null geodesics has a positive finite upper bound and includes a region where photons are trapped or scattered. Our framework extends beyond conventional solutions, including but not limited to naked singularities. Furthermore, we clarify the difference between the apparent boundary of a dark shadow and the bright ring on the screen of a distant observer. These results provide a unified theoretical basis for interpreting observations from the Event Horizon Telescope (EHT) and guiding future efforts towards extreme-resolution observations of compact objects. Full article

Cutting CO₂ emissions is crucial to face of climate change, and one of the most tried and true means of post-combustion CO₂ capture is by way of chemical absorption. In this work, the effect of titanium dioxide (TiO₂) nanoparticles in a 25 wt% potassium carbonate (K₂CO₃) solution on solvent regeneration is investigated. This research follows the previous work in which the effect of nanofluids was evaluated on CO₂ absorption. Desorption was studied at three different temperatures (343.15, 348.15 and 353.15 K), using the absorbent fluid with and without 0.06 wt% TiO₂ nanoparticles. The results indicate that the nanofluid enhanced the CO₂ release rates, also reducing energy consumption. The mass transfer was intensified by the presence of nanoparticles, which in turn increased CO₂ diffusivity and influenced the liquid boundary layer, resulting in an enhanced desorption rate, because of the higher diffusivity. These enhancements were achieved with negligible modifications to the fluid properties, i.e., viscosity. In summary, application of TiO₂-enhanced K₂CO₃ solutions is a practical approach to enhance CO₂ removal performance and reduce operating costs such that CO₂ capture is beginning to be environmentally and economically more competitive for the existing system retrofit. Full article

Electroencephalogram (EEG)-based identification offers a promising biometric solution by leveraging the uniqueness of individual brain activity patterns. This study proposes a framework based on a convolutional autoencoder (CAE) along with a traditional classifier for identifying individuals using EEG brainprints. The convolutional autoencoder extracts a compact and discriminative representation from the topographic data cubes that capture both spatial and temporal dynamics of neural oscillations. The latent tensor features extracted by the CAE are subsequently classified by a machine learning module utilizing Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) models. EEG data were collected under three conditions—resting state, music stimuli, and cognitive task—to investigate a diverse range of neural responses. Training and testing datasets were extracted from separate sessions to enable a true longitudinal analysis. The performance of the framework was evaluated using the Area Under the Curve (AUC) and accuracy (ACC) metrics. The effect of subject identifiability was also investigated. The proposed framework achieved a performance score up to a maximum AUC of 99.89% and ACC of 96.98%. These results demonstrate the effectiveness of the proposed automated subject-specific patterns in capturing stable EEG brainprints and support the potential of the proposed framework for reliable, session-independent EEG-based biometric identification. Full article

Vision–Language Models (VLMs) have become key contributors to the state of the art in contextual emotion recognition, demonstrating a superior ability to understand the relationship between context, facial expressions, and interactions in images compared to traditional approaches. However, their reliance on contextual cues can introduce unintended biases, especially when the background does not align with the individual’s true emotional state. This raises concerns for the reliability of such models in real-world applications, where robustness and fairness are critical. In this work, we explore the limitations of current VLMs in emotionally ambiguous scenarios and propose a method to overcome contextual bias. Existing VLM-based captioning solutions tend to overweight background and contextual information when determining emotion, often at the expense of the individual’s actual expression. To study this phenomenon, we created synthetic datasets by automatically extracting people from the original images using YOLOv8 and placing them on randomly selected backgrounds from the Landscape Pictures dataset. This allowed us to reduce the correlation between emotional expression and background context while preserving body pose. Through discriminative analysis of VLM behavior on images with both correct and mismatched backgrounds, we find that in 93% of the cases, the predicted emotions vary based on the background—even when models are explicitly instructed to focus on the person. To address this, we propose a multimodal approach (named BECKI) that incorporates body pose, full image context, and a novel description stream focused exclusively on identifying the emotional discrepancy between the individual and the background. Our primary contribution is not just in identifying the weaknesses of existing VLMs, but in proposing a more robust and context-resilient solution. Our method achieves up to 96% accuracy, highlighting its effectiveness in mitigating contextual bias. Full article

The analysis of earthquake source mechanisms is key for seismotectonic studies, but it is often limited to traditional methods plagued with issues of precision and automation. This is particularly true in low-seismicity areas with deep and/or hidden seismogenic sources, where the identification of precise source mechanisms is a difficult and non-trivial task. In this study, we present a detailed application of TESLA (Tool for automatic Earthquake low-frequency Spectral Level estimAtion), a novel tool designed to overcome these limitations. We demonstrated TESLA’s effectiveness in defining source mechanism analysis by applying it to seismic sequences that occurred near Asti (AT), in the Monferrato area (Southern Piedmont, Italy). Our analysis reveals that the observed clusters consist of two distinct seismic sequences, occurring in 1991 and 2012, which were activated by the same seismogenic source. We relocated a total of 36 events with magnitudes ranging from 1.1 to 3.7, using a 3D velocity model, and computed 12 well-constrained focal mechanism solutions using the first motion polarities and the low-frequency spectral level ratios. The results highlight a relatively small seismogenic source located at approximately 5 km north of Asti (AT), at a depth of between 10 and 25 km, trending SW–NE with strike-slip kinematics. A smaller cluster of three events shows an activation of a different fault segment at around 60 km of depth, also showing strike-slip kinematics. These findings are in good agreement with the regional stress field acting in the Monferrato area and support the use of investigation tools such as TESLA for microseismicity analysis. Full article