Search Results (8,160)

Fe-based medium-entropy alloys (MEAs) are a cost-effective class of multi-principal-element alloys with tunable mechanical behavior. Their key advantage lies in the ability of Fe-rich, non-equiatomic compositions to regulate phase stability, deformation behavior, and strain hardening without relying heavily on expensive Co, Ni, or V. Increasing evidence shows that metastable face-centered cubic (FCC) matrices can provide excellent combinations of strength and ductility when their transformation behavior is properly controlled. Through compositional tuning and microstructural regulation, the phase stability, stacking-fault energy, precipitation behavior, and deformation pathways of Fe-based MEAs can be adjusted to achieve a balance between strength, ductility, and service reliability. This review critically synthesizes the metastability, microstructure, strengthening mechanisms, and service-oriented design principles of Fe-based MEAs. The literature discussed in this review was selected from peer-reviewed studies that report clear links among alloy composition, processing history, microstructure, deformation behavior, and mechanical or service-related properties. Unlike reviews that mainly classify alloy systems or deformation modes, this work emphasizes how metastability engineering and microstructural design can be integrated to guide application-specific alloy development. Representative Fe-rich non-equiatomic alloy systems are compared to clarify how alloying and processing regulate metastability, precipitation behavior, transformation kinetics, and strain partitioning. This review highlights that superior properties arise from the coordinated control of metastability, heterogeneous microstructures, and strengthening mechanisms. A central conclusion is that controlled transformation kinetics, rather than the pursuit of a maximum martensite fraction, is the key design variable for sustaining strain hardening and achieving stable strength–ductility synergy. Remaining challenges include quantitative deconvolution of coupled mechanisms, reliable prediction of local metastability, long-term microstructural stability, manufacturability, cost–performance balance, and integration of high-throughput experiments with computational alloy design. Overall, this review provides a service-oriented design framework for high-performance, low-cost Fe-based MEAs through the integrated control of composition, metastability, microstructure, processing, strengthening mechanisms, and application-specific performance. Full article

Reliable remaining useful life (RUL) estimation is important for the safe and efficient use of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and energy-storage systems. Most data-driven RUL models are trained under controlled laboratory conditions, but their performance can weaken during field operation, where usage, sensing quality, and degradation paths are less predictable. This study proposes a physics-informed laboratory-to-field (L2F) deep learning framework for battery RUL prediction under limited or unavailable target labels. The framework combines three components: a six-channel laboratory cycle representation comprising voltage, current, temperature, cumulative charge throughput, cumulative energy throughput, and voltage derivative; a gated Transformer–Temporal Convolutional Network (TCN) Fusion backbone for modeling long-range and local degradation patterns; and a staged adaptation policy based on paired-view consistency and covariance alignment. The Fusion backbone achieved the lowest held-out XJTU laboratory root mean square error (RMSE) of 46.67, compared with 48.27 for TCN and 48.85 for the Transformer. In the Tsinghua University (Tsinghua) deployment experiment, measured target RUL labels were unavailable after preprocessing and window construction. Therefore, the direct field-side mean absolute error (MAE), RMSE, and coefficient of determination $R^2$ were not computed. The Tsinghua results are interpreted as an unlabeled deployment-credibility and trajectory-regularity assessment, showing operational continuity, finite vehicle-specific predicted trajectories, and reduced local trajectory volatility after staged adaptation. The S2a + S2b policy reduced Fusion RUL volatility from 8.376 to 0.632. In the XJTU laboratory source representation, Integrated Gradients showed that physics-aware channels contributed 30.98% of the attribution mass, increasing from 19.50% in early-life windows to 31.86% in late-life windows. These attributions explain the laboratory six-channel waveform model and are not used as direct evidence of Tsinghua field-feature importance. Full article

The rapid electrification of marine transportation has increased the number of hybrid marine microgrids with the addition of renewables and energy storage. The continuously varying propulsion loads, fluctuating sea states, and renewable intermittency introduce significant challenges in bidirectional power transfer and power quality enhancement in marine vessels. This work presents a power-oriented operational strategy for a hybrid Roll-on/Roll-off (Ro-Ro) ferry-based marine microgrid (FMG) integrating diesel generators (DGs), Solar Photovoltaic (PV) arrays, and battery energy storage systems as the primary power sources. The proposed FMG adopts a hybrid AC/DC bus configuration linked through a bidirectional voltage source interlinking converter (ILC). The ILC facilitates multiple functionalities, including effective load compensation, mitigation of Total Harmonic Distortion (THD), continuous power support through bidirectional energy exchange, maintenance of balanced sinusoidal currents, and unity power factor (UPF) operation, thereby providing an integrated solution for improved power quality and reliable microgrid performance. A supervisory control (SC) is devised to operate the FMG seamlessly under islanded modes depending on the availability of power sources. To achieve the above-mentioned objectives, a power-optimized Dual Power-based Instantaneous Power Theory (DP_IPT) is employed and it involves a Sequential Delay Signal Cancelation (SDSC)-based Phase-Locked Loop (PLL) for the effective extraction of sequence components, so as to address the unbalance and nonlinearities in an effective manner with reduced oscillations. The proposed control strategy reduces diesel generator utilization through the effective integration of Solar PV and battery support during anchoring operation. The integration of renewable energy sources substantially enhances clean energy utilization, resulting in the reduction of overall carbon emissions, accounting for a near-40% decrease in emissions compared with the conventional diesel generator (DG)-based operating mode. The proposed FMG and control framework are validated through the Hardware-in-the-Loop (HiL) approach employing an OPAL-RT (OP4512) real-time controller. The HiL investigations demonstrate the efficacious working of the proposed control in achieving less carbonized and enhanced power quality operation for next-generation electrified hybrid maritime microgrids. Full article

Wearable inertial sensing and markerless vision are increasingly integrated to enable objective assessment of locomotor and postural function for sports telerehabilitation, intelligent physiotherapy, and athlete performance monitoring. Before such multimodal systems can be translated to clinical practice, their fusion, optimization, and temporal modeling strategies require validation under controlled conditions with reliable ground truth. This study presents a unified multimodal framework that hierarchically integrates inertial measurement unit (IMU) signals and RGB visual information through kernelized representation learning, adaptive multimodal fusion, evolutionary feature optimization, and deep temporal classification. The IMU branch employs Kernelized Extreme Learning Machine (KELM) denoising, Kernelized Canonical Correlation Fusion (KCCF), entropy-guided adaptive windowing, and complementary time-series descriptors (MINIROCKET, TS-CHIEF, and r-STSF). Concurrently, the RGB branch combines anisotropic diffusion filtering, HRNet-based silhouette extraction, DensePose R-CNN, Mesh Graphormer, and Multi-Model Pose-Flow Fusion (MPFF) to learn robust visual representations. Both modalities are integrated through Weighted Canonical Feature Fusion (WCFF) and optimized using a Genetic Algorithm for feature selection and adaptive modality weighting before temporal modeling with cluster-based alignment, Gaussian Process Sequence Modeling, and DeepConvLSTM. As the selected benchmarks do not provide complete inertial recordings, the inertial modality is established according to the adopted experimental protocol to support multimodal fusion analysis. Under 5-fold subject-independent cross-validation, the framework achieves accuracies of 86.56 ± 0.31% on SoccerDiffusion and 88.04 ± 0.25% on HumanoidRobotPose. Although evaluated on humanoid robotic benchmarks, the proposed framework provides a methodological basis for future wearable-enabled clinical movement assessment, remote rehabilitation, and athlete monitoring, while validation on synchronized human inertial-visual datasets remains an important direction for future research. Full article

Multi-UAV missions for remote sensing and environmental monitoring under extreme conditions require task allocation and path optimisation to efficiently distribute goals across vehicles. These methods must also be executed reliably inside an autonomous robotics framework. Several methods for the multiple travelling salesman problem (mTSP) show robust offline routeing efficiency. However, system-level validation under realistic operational conditions including waypoint management and inter-UAV separation remains limited. This research transforms the previously proposed Distance Efficient Clustering Kmeans Genetic Algorithm (DECK_GA) from an offline model into a deployment-focused multi-UAV remote sensing framework implemented in ROS2, Aerostack2, and Gazebo. A uniform waypoint management interface integrates planning, Rviz visualisation, and autonomous execution. The system combines Dynamic Centroid Kmeans (DCKmeans) for spatially coherent waypoint allocation with a Distance Efficient Genetic Algorithm (DEGA) for individual UAV route optimisation. The evaluation is conducted in a high-fidelity Antarctic environment where waypoints represent survey desired objectives in moss regions, and altitude is managed using terrain-referenced control involving two to five UAVs and 30 to 120 waypoints. The framework was evaluated against two baselines under identical mission configurations, with 10 trial runs for each: a Traditional GA Divide & Conquer planner and a Classical Kmeans DEGA planner, which utilises the same route optimisation method and differentiates the outcomes of the allocation stage. DECK_GA showed reduced mean planned and executed distances compared to the Traditional GA Divide & Conquer baseline across all configurations, achieving planned distance reductions ranging from 15.99% to 75.36%. Additionally, it produced shorter path than Classical Kmeans DEGA in 14 out of 16 configurations. The average minimum inter-UAV separation was greater than the Traditional GA Divide & Conquer baseline in 15 of the 16 configurations and higher than Classical Kmeans DEGA in 14 of the 16, which demonstrates that the DCKmeans allocation improves spatial separation. This research focuses on the framework for planning to execution instead of the introduction of a new optimisation method, as DECK_GA was proposed in previous research and is now incorporated and tested within an autonomy framework. This evaluation is simulation only. Real world flying, hardware in the loop testing, wind, communication latency, and location error prediction tend to be future developments. Full article

The adoption of artificial intelligence (AI) has been steadily growing across various fields in recent years. AI can have significant advantages for industries, including automation, reliable prediction, enhanced process control, and improved efficiency. Particularly, the field of academic research has embraced AI as a tool to address global challenges. Yeast strains, especially Saccharomyces cerevisiae, serve as key model organisms and industrial cell factories, enabling fundamental biological discoveries and advanced biotechnological applications through conserved eukaryotic pathways and modern metabolic engineering tools. Yeast cell biology can benefit from AI through its optimization of the fermentation process via an advanced control system, accelerated identification and classification of yeast strains through deep learning, predictive analysis of multi-omics biological data, strain engineering, and as a powerful approach in the evolution of the yeast genotype–phenotype map to explore novel biology. This review evaluates the benefits of using AI in biological studies leveraging yeast strains, discusses the challenges and limitations, and outlines the prospects. Full article

Torsional vibrations represent a significant dynamic phenomenon in rotating mechanical systems and are often associated with increased dynamic loading, fatigue damage, noise generation, and reduced operational reliability. Conventional vibration mitigation techniques are generally effective only within a limited frequency range, which restricts their applicability in modern drivetrains operating under variable loading conditions. Consequently, increasing attention has been devoted to nonlinear vibration control concepts based on the principle of targeted energy transfer. This paper presents the development and experimental investigation of a novel TET system with variable torsional stiffness intended for torsional vibration mitigation in rotating mechanical systems. The proposed concept combines the vibration energy redistribution capability of a nonlinear absorber with adaptive stiffness tuning achieved through pneumatic elements. The torsional stiffness of the secondary subsystem can be continuously adjusted by regulating the pressure within air bellows, enabling adaptation of the system dynamics to varying operating conditions. A dedicated experimental test rig with kinematic excitation was developed to investigate the dynamic response of the coupled mechanical system and evaluate the influence of variable stiffness on the TET mechanism. The study focuses on the analysis of vibration energy redistribution, the identification of optimal operating conditions, and the assessment of the potential of variable-stiffness TET systems for wide range torsional vibration control in rotating machinery. Full article

The increasing penetration of wind and photovoltaic (PV) generation introduces frequent operating-point variations into transmission networks, while missing supervisory control and data acquisition/phasor measurement unit (SCADA/PMU) measurements and bad data may further weaken the reliability of online state estimation. Conventional weighted least squares (WLS) estimators have a clear physical interpretation, but repeated online matrix solutions may become burdensome in large-scale rolling estimation. To address this issue, this paper proposes a graph–physics-constrained fast state estimation method with bad data detection (BDD) and filtering. Wind/PV-load operating scenarios are constructed on standard test systems, and mixed SCADA/PMU measurements are represented with missing masks and bad data perturbations. The filled measurements, measurement availability mask, and residual anomaly scores are used as input features, while the network topology is converted into a graph Laplacian prior. A regularized fast mapping, graph Laplacian smoothing, and threshold-calibrated residual screening are combined to obtain online state estimates and bad data labels. Five-seed case studies compare the proposed method with WLS and Huber robust WLS. In case300, the average online time is reduced from $66.063\pm 3.054$ ms for WLS to $1.922\pm 0.374$ ms for the proposed method, corresponding to a speedup of about 35.08 times. The results indicate that the proposed linearized prototype is most promising as a fast large-scale rolling estimator or abnormality screener, rather than as a full replacement for model-based estimators in all scenarios. Full article

Exercise and sports metabolomics provide a systems-level approach to characterizing how acute exercise, training adaptation, nutrition, recovery, and environmental stress reshape human metabolism. By profiling metabolites related to substrate utilization, mitochondrial function, redox balance, inflammation, muscle stress, and recovery kinetics, these approaches can reveal pathway-level responses that conventional single biomarkers cannot capture. However, many exercise-responsive features remain difficult to interpret because of incomplete chemical identification, uncertain annotation confidence, limited quantitative reproducibility, variable pre-analytical control, inconsistent data processing, and insufficient biological validation. This narrative review examines recent advances in exercise and sports metabolomics, with emphasis on LC–MS, GC–MS, NMR spectroscopy, IMS–MS, and CE–MS workflows; platform selection; metabolite annotation and identification; pathway-level interpretation; and evidence requirements for candidate-panel development. Exercise-responsive metabolites should be interpreted as context-dependent pathway signals rather than isolated indicators of fatigue, recovery, adaptation, or performance. The review consolidates requirements for sampling, quality control, metadata capture, repeated-measures analysis, and external validation within an evidence-readiness roadmap. Wearable biochemical monitoring, AI-assisted analysis, and multi-omics integration may support future applications, but their value depends on analytical robustness, external validation, and physiological interpretability. Exercise and sports metabolomics should therefore progress from descriptive feature discovery toward reproducible, quantitatively reliable, and biologically validated pathway-level interpretation. Full article

The digital transformation of manufacturing associated with Industry 4.0 and the human-centric paradigm of Industry 5.0 requires advanced predictive tools that can improve the performance, reliability, and sustainability of tribological systems. In this context, accurate prediction of friction behavior in rolling contacts is crucial for intelligent monitoring and optimization of bearing components. This study presents a machine learning-based methodology for predicting the coefficient of friction in rolling contact of 100Cr6 steel bearing discs as a function of surface roughness and rolling distance parameters. Experimental studies were conducted using discs with different surface topography under controlled rolling contact conditions. Surface roughness characteristics and rolling distance data were correlated with experimentally measured friction coefficients to create a comprehensive dataset for artificial intelligence (AI) modeling. Several dozen machine learning (ML) algorithms, including random forest, support vector regression, and artificial neural networks, were developed and comparatively evaluated to capture nonlinear relationships between operational and surface parameters. The predictive ability of the models was assessed using statistical metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The obtained results demonstrate that ML methods provide high prediction accuracy and effectively identify the combined effects of surface roughness and rolling distance on rolling contact friction. Feature importance analysis revealed that roughness parameters dominate friction behavior during the run-in phase, while rolling distance becomes increasingly important under stabilized operating conditions. The proposed approach supports the development of intelligent tribological systems, predictive maintenance strategies, and data-driven decision-making frameworks aligned with Industry 4.0 and Industry 5.0 concepts. The presented methodology can contribute to the implementation of intelligent manufacturing solutions, the sustainable operation of bearing systems, and AI-assisted monitoring of machine components in modern industrial environments. Full article

This paper presents an experimental evaluation of side-channel leakage in an FPGA-based implementation of the AES-256 encryption algorithm operating in ECB mode, with a focus on identifying data-dependent information leakage through power consumption and electromagnetic emissions. The implementation was deployed on a Xilinx Artix-7 FPGA using the ChipWhisperer CW305 platform, while measurements were acquired with the ChipWhisperer Husky system under controlled laboratory conditions. The analysis follows the Test Vector Leakage Assessment methodology in accordance with ISO/IEC 17825, using Welch’s t-test to compare trace sets obtained from fixed and random input data. A dataset of 20,000 traces per configuration was collected, with careful synchronization, interleaving, and preprocessing to ensure statistical reliability. The results show multiple instances where the t-statistic exceeds the threshold of |t| > 4.5 within the defined region of interest, indicating significant leakage. In particular, 84 leakage points were detected in the power consumption channel and 10 in the electromagnetic channel. These findings demonstrate that the evaluated implementation does not satisfy the resistance criteria defined by the standard and remains vulnerable to side-channel analysis, highlighting the need for appropriate countermeasures in FPGA-based cryptographic designs. Full article

Wireless power transfer (WPT) is becoming an attractive solution for charging electric vehicles (EVs). It offers greater convenience, reduces mechanical wear, and supports the development of automated and dynamic charging systems. However, the effectiveness of WPT-based EV chargers does not depend only on power electronics, coil design, and energy efficiency. Reliable wireless communication between the vehicle and the charging infrastructure is also essential. This communication link allows the system to identify and authenticate the vehicle, support coil alignment, regulate power transfer, monitor battery conditions, supervise safety, and manage possible faults. This review discusses the role of wireless communication technologies in the control of WPT EV charging systems. It also presents the main EV charging methods, the basic principles of WPT, its applications in electric vehicles, and key standards such as IEC 61980 and ISO 15118. In addition, the review compares Wi-Fi, Bluetooth, NFC, Zigbee, V2X, and cellular communication in terms of their suitability for WPT control. It also highlights major challenges, including electromagnetic interference, latency, cybersecurity, coil misalignment, system complexity, and standardization issues. Overall, future WPT EV chargers will need secure, reliable, and low-latency communication integrated with control and power transfer systems to achieve safe, efficient, and intelligent wireless charging. Full article

Surface water monitoring often requires frequent and reliable sampling at locations that are difficult or unsafe to access using conventional methods. To address these constraints, unmanned aerial vehicles (UAVs) have increasingly been used as platforms for automated water collection. This paper presents a UAV-assisted water sampling system designed to provide a high degree of operational flexibility through control of sampling depth and collected volume. The proposed system can collect up to 3 L of water distributed across six individual containers, enabling a variety of sampling strategies, including discrete, composite, and multi-depth sampling. Sampling depths of up to 3.25 m can be achieved, supporting depth-resolved investigations such as stratified water-column analyses. Compared with existing UAV-based samplers, the proposed system combines configurable sampling depth, configurable sampling volume, and a multi-bottle architecture within a single platform. To support sample integrity, an automated cleaning sequence is executed before each collection step to reduce the risk of cross-contamination between samples. Experimental validation demonstrated repeatable depth deployment under controlled conditions and consistent volume-control performance, with relative errors generally within ±3% and a maximum observed deviation of 5%. In addition, a task-based analysis provided an initial characterization of the energy demand associated with the sampling process. Overall, the results indicate that the proposed architecture can support flexible and protocol-oriented water sampling operations while extending the sampling capabilities of existing UAV-based systems. Full article

Accurate 24 h photovoltaic (PV) power forecasting is essential for day-ahead scheduling, storage operation, reserve planning, and market participation. However, published deep learning comparisons are often difficult to reproduce and interpret because they use inconsistent datasets, forecasting horizons, baselines, evaluation metrics, and leakage-control procedures. From a software engineering perspective, this limits the trustworthiness, comparability, and practical adoption of AI-based forecasting systems. This paper presents a controlled and reproducible benchmarking framework for evaluating AI-driven forecasting software. The framework is applied to nine deep learning architectures, three non-deep learning reference models, and two persistence baselines for hourly PV-power forecasting at a 350 kWp rooftop installation near Edinburgh, Scotland. All models were evaluated under a consistent experimental protocol, including the same chronological train–validation–test split, a 32-feature meteorological and solar-geometry input set, a 24-step forecasting horizon, capacity-normalised mean absolute error (NMAE), and Bayesian hyperparameter optimisation. The results show that TCN-LSTM achieved the best aggregate H24 performance with 7.22% NMAE, narrowly outperforming CPWformer-DEC at 7.28% and CT-PatchTST at 7.31%. LightGBM ranked fourth at 7.35% with fixed hyperparameters, outperforming six of the nine deep learning models. The top three models differed by only 0.09 percentage points, indicating that architectural superiority cannot be established reliably without significance testing and operational diagnostics. Per-horizon analysis showed that CT-PatchTST and S-Mamba performed best at the nearest forecast steps, whereas TCN-LSTM provided the most stable far-horizon profile. Peak-power diagnostics further revealed that aggregate NMAE can mask operational shortcomings, as Naive Persistence outperformed all deep learning models in high-output peak detection. The findings highlight the importance of reproducible benchmarking, leakage safeguards, horizon-aware evaluation, and operationally meaningful diagnostics in trustworthy AI software evaluation. The novelty of this work lies not in proposing a new architecture but in a controlled, reproducible framework that benchmarks fourteen forecasters under identical conditions, with explicit leakage safeguards, per-horizon reporting, and operationally meaningful peak diagnostics, enabling claims of architectural superiority to be made trustworthy rather than merely favourable. Architecture selection for PV forecasting should therefore consider not only aggregate accuracy but also reliability, interpretability of evaluation outcomes, and deployment-relevant performance behaviour. Full article

Remaining useful life (RUL) prediction of power semiconductor devices plays a crucial role in reliability design and predictive maintenance of power control system. This article introduces a data-driven methodology on predicting the RUL of the gate oxide layer in silicon carbide (SiC) MOSFETs. Firstly, a power cycling platform is established to collect the time-varying curves of threshold voltage and construct an aging dataset. Then, the successive variational mode decomposition (SVMD) algorithm is employed to adaptively decompose the signal of gate threshold voltage, helping suppress measurement noise and fluctuations caused by operating conditions while retaining degradation features. Subsequently, a Temporal Convolutional Network (TCN) is adopted to capture temporal dependencies in the degradation sequence, thereby improving the characterization of gate oxide health status assessment. Finally, the extended Kalman particle filter (EKPF) is employed to estimate the degradation state and quantify the associated uncertainty by recursively fusing model predictions with real-time measurements. The proposed method integrates the adaptive signal decomposition capability of SVMD, the temporal feature extraction capability of TCN, and the uncertainty quantification capability of EKPF. Their complementary integration improves prediction accuracy and robustness in gate oxide degradation evaluation for SiC MOSFET. Full article