Search Results (16,356)

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

Operational risk assessment in complex industrial systems like mineral processing plants is hindered by inherent uncertainty and incomplete information. This study presents an integrated grey system theory-based framework to address this challenge. Combining Grey Multi-Criteria Decision-Making (GST-MCDM) and Grey Relational Analysis (GRA), the methodology enables a systemic analysis that prioritizes risks, quantifies interdependencies, and measures cumulative burden across four key objectives: time, cost, quality, and safety. Applied to a case study at the Gohar Zamin iron ore processing plant (Iran), the model analyzed 26 operational risks, classifying them into Critical (8 risks), Significant (9), and Controllable (9) tiers. Electrical power shortage (RPS: 0.19) and raw material supply delay (RPS: 0.21) were identified as the most critical risks. The analysis quantified that the safety objective bears the highest cumulative risk burden at 32%, primarily due to human factor vulnerabilities, while cyber-physical threats ranked among the top 8 critical risks. Strong interdependencies were revealed, notably a quality cascade (relational grade: 0.84) between poor consumable materials and final product failure. Sensitivity analysis confirmed high model robustness (Spearman’s p = 0.91). The framework provides managers with an actionable tool for strategic, cluster-based mitigation and evidence-based resource allocation, emphasizing investment in human capital as a core risk reduction strategy. This research contributes a replicable, quantitative methodology for enhancing operational resilience under uncertainty in capital-intensive industries. Full article

Firms in traditional manufacturing clusters face lock-in situations when crises disrupt established routines. The study analyzes how firms embedded in a mature industrial cluster responded to COVID-19, focusing on location, firm, and relation-based indicators in the Denizli textile district in Türkiye, which remains under-represented in resilience research. A mixed-methods analysis was conducted using primary and secondary data. With secondary data, the textile industry was determined as the mature industrial cluster in Denizli by conducting a three-star analysis (which was used as the cluster analysis method for showing mature, potential, and candidate clusters) within the region’s manufacturing industries. By using primary data with a purposive sampling approach, a logistic regression model is employed to analyze a survey administered to 118 textile firms. It is found that, even though locating in a specialized cluster, partnering with NGOs, and product diversification contribute positively to resilience in adapting to crises, no new growth path is created by firms either locating in the specialized cluster or in other parts of the city. Due to the cognitive and political lock-in and power of local institutional production systems in the textile industry in Türkiye, our case conforms more to the engineering type of resilience to crises than to the adaptive type. Full article

Wearable and intelligent bioelectronic systems are emerging as a key enabling technology for continuous, non-invasive health monitoring, coupling physiological sensing with data-driven inference. Within this paradigm, electroencephalography (EEG) provides a wearable-compatible biosensing modality for capturing the pre-sleep neurophysiological dynamics linked to emotional regulation and sleep onset. Insomnia affects approximately 10–15% of adults worldwide and is often associated with dysregulated emotions and pre-sleep hyperarousal. Existing EEG-based affective and sleep-onset processing pipelines often rely either on deep-learning architectures with limited interpretability or on hand-crafted spectral descriptors with weak theoretical motivation. This study presents an exploratory proof-of-principle bioelectronic processing framework in which EEG sensing features are organized according to ANT-7 (artificial neurotransmitter seven-dimensional model), a neurotransmitter-inspired computational taxonomy introduced as a heuristic feature-design prior rather than as a validated neurochemical theory. The proposed feature set includes the alpha/theta power ratio, sample entropy, Higuchi fractal dimension, and phase-locking value extracted from the public DREAMER and DEAP datasets (23 and 32 subjects, respectively). SVM, Random Forest, and 1D-CNN classifiers are trained under subject-independent leave-one-subject-out cross-validation with strict within-fold normalization to prevent data leakage, and interpretability is assessed through SHAP values and permutation importance (PI). To stress-test whether this feature organization transfers beyond the affective benchmarks on which it is trained, classifier outputs are then related to sleep-onset latency in Sleep-EDF Expanded through a deliberately cautious cross-dataset transfer analysis. Within this protocol, the best model reaches 88.4% accuracy in three-class affective-state recognition (stress/neutral/relaxed; AUC-ROC = 0.93). As an exploratory secondary analysis, classifier-derived relaxation estimates show a statistically significant negative association with polysomnographic sleep-onset latency and improve over a single alpha/theta-ratio baseline; this cross-dataset result is reported as a proof of concept, not as a validated sleep-onset predictor. Interpretability analyses (SHAP and permutation importance) indicate that the learned feature rankings are internally consistent with the neurotransmitter-inspired feature design, a property we interpret as internal coherence rather than as independent confirmation of the taxonomy. Together, these elements outline a complete sensor-to-AI processing chain—from EEG biosensing, through neurotransmitter-inspired signal-feature extraction, to interpretable and computationally lightweight inference—designed for compatibility with low-density wearable EEG devices and edge deployment. However, EEG does not measure neurotransmitter concentrations, the study does not benchmark ANT-7 directly against competing taxonomies such as valence-arousal/circumplex or RDoC-inspired feature organizations, and the Sleep-EDF analysis should not be interpreted as evidence that the model measures a validated latent construct of sleep readiness. Accordingly, the manuscript should be read as a framework-validation study of one interpretable feature taxonomy, not as a theory-validation study of ANT-7 or as a clinical validation study. Full article

Real-time monitoring of operator fatigue is critical for ensuring operational safety and reliability in dynamic industrial environments, especially under swing conditions such as offshore floating nuclear power platforms. This study proposes a multimodal fatigue monitoring framework based on surface electromyography (sEMG) and heart rate variability (HRV) sensors for real-time fatigue recognition. Experiments were conducted on a six-degree-of-freedom motion platform with three swing levels, involving 23 participants performing simulated emergency operation tasks. Four machine learning models (Naive Bayes, K-Nearest Neighbor, Multilayer Perceptron, and Random Forest) were employed for fatigue state classification. The results show that the Random Forest model achieves the best performance, with an overall accuracy of 98.2%, 100% true positive rate for the normal state and fatigue, and 66.7% true precision for severe fatigue. The proposed multimodal fusion method effectively suppresses motion artifacts and improves recognition robustness under swing interference. Rigorous subject-level stratified cross-validation eliminates sample leakage risks; bootstrap confidence intervals and pairwise significance tests statistically verify model performance differences; class imbalance mitigation strategies are deployed to quantify uncertainty for the scarce severe-fatigue category; literature-supported Borg CR-10 grading thresholds are validated via retrospective cutoff sensitivity analysis to guarantee reliable fatigue labeling. This sensor-based intelligent monitoring system provides a reliable solution for real-time fatigue detection of operators in dynamic digital industrial scenarios, supporting accident prevention and sustainable operation of high-risk industrial systems. Full article

As an important infrastructure for the construction of a new power system, pumped-storage hydropower stations may exert certain impacts on the surrounding fractional vegetation cover (FVC) during their construction. Based on Landsat remote sensing imagery and China Land Cover Dataset (CLCD) land use data from 2013 to 2024, this study investigated the dynamic changes in FVC and the spatial extent of engineering disturbance associated with the Zhen’an Pumped-Storage Hydropower Station in Shaanxi Province, China. The analysis integrated the Pixel Dichotomy Model, Theil-Sen trend analysis, Mann–Kendall significance test, coefficient of variation, landscape pattern indices, and correlation analysis. The results showed that: (1) FVC in the study area exhibited distinct stage-dependent evolution characteristics that were highly consistent with the construction timeline of the project. (2) The spatial influence of engineering disturbance on FVC was mainly concentrated within 1250 m, with the 0–250 m zone identified as the core impact area. Landscape fragmentation in this zone was higher than in other distance ranges, and vegetation degradation gradually weakened with increasing distance from the project. (3) Land use change within the study area was primarily characterized by the conversion of forest to cropland and impervious surfaces, resulting in a reduction in high coverage vegetation areas. Landscape patterns exhibited pronounced buffer-gradient characteristics. Within 500 m, the Largest Patch Index (LPI) decreased while the Shannon Diversity Index (SHDI) increased, indicating weakened continuity of dominant landscape patches. Beyond 500 m, LPI generally increased and SHDI decreased, suggesting a trend toward a more stable landscape structure. (4) Both air temperature and precipitation exhibited interannual fluctuations, but neither showed a significant long-term trend. The correlations between climatic factors and FVC were relatively weak, and the multiple regression model demonstrated limited explanatory power for FVC variation. Full article

Agricultural carbon emissions represent a major source of greenhouse gases and play a critical role in achieving global climate mitigation and sustainable agricultural development targets. In China, the rapid transformation of agricultural production systems has led to substantial spatial heterogeneity in emission patterns and driving mechanisms of agricultural carbon emissions, while the underlying processes at the county scale remain insufficiently understood. This study investigated the spatiotemporal evolution and potential influencing factors of agricultural carbon emissions at the county level from 2000 to 2022 in Guangdong Province, China. First, agricultural carbon emissions were estimated based on a multi-source accounting framework covering land management, crop cultivation, animal production, and straw burning based on internationally recognized emission accounting methods and IPCC global warming potentials. Then, spatial clustering characteristics were analyzed using local spatial autocorrelation (LISA) to identify heterogeneous emission patterns. Finally, an interpretable machine learning framework combining Random Forest (RF) and SHapley Additive exPlanations (SHAP) was employed to quantify the nonlinear effects and relative contributions of multiple socioeconomic and agricultural drivers. The results showed that agricultural carbon emissions in Guangdong Province exhibited a fluctuating but overall decreasing trend, declining from 50.89 Mt CO₂-eq in 2000 to 39.24 Mt CO₂-eq in 2022, with an overall reduction of 22.9%. High-emission clusters were primarily concentrated in western and northern Guangdong, while low-emission areas were mainly located in the Pearl River Delta (PRD). The RF models demonstrated satisfactory predictive performance, with spatial cross-validated R² values ranging from 0.75 to 0.91 across different years. SHAP analysis suggested that ploughing area, fertilizer and pesticide usage, agricultural machinery power, and primary industry GDP were the dominant factors associated with agricultural carbon emissions, whereas urbanization consistently showed a negative association. Furthermore, these drivers exhibited pronounced nonlinear responses and distinct regional heterogeneity, particularly between the PRD and the western and northern parts of Guangdong Province. These findings suggested that agricultural carbon emissions are jointly influenced by agricultural production intensity, mechanization, and socioeconomic transition and can provide a scientific basis for developing region-specific low-carbon agricultural policies and promoting the sustainable transformation of agricultural systems. Full article

Zebrafish (Danio rerio) are widely used in biomedical research due to their genetic similarity to humans and rapid development. Efficient management of large embryo populations is essential for experimental reproducibility and high-throughput screening, yet manual counting and sorting methods are labor-intensive, time-consuming, and pose ergonomic risks including musculoskeletal disorders. We evaluated a commercially available AI-powered automated embryo sorting system against manual methods across 117,956 embryos to assess efficiency, accuracy, and reliability in operational facility conditions. The automated system performed simultaneous counting and quality classification at 17.3 embryos/min, while manual counting alone achieved 24.5–25.9 embryos/min (p < 0.001). The automated system provided one-pass processing convenience, though at lower instantaneous throughput than manual counting. With significant ergonomic benefits. However, performance analysis revealed 13.41% systematic undercounting (p = 0.004), limited correlation between automated classifications and developmental outcomes (survival, hatching, deformity rates p > 0.05), and 50.8% false negative rate for GFP fluorescence detection in transgenic embryos. These limitations reflect the current state of machine learning algorithm training rather than fundamental technological constraints. The system’s AI architecture enables continuous improvement as algorithms encounter diverse embryo populations and incorporate user feedback through customizable training platforms. Our large-scale operational evaluation provides quantitative performance benchmarks for facilities considering automation adoption and contributes valuable real-world training data for algorithm refinement. We recommend context-dependent implementation: automated processing for high-volume routine workflows where speed and ergonomic benefits are prioritized, combined with manual validation for transgenic line maintenance and accuracy-critical applications. As an early-generation commercial platform with machine learning at its core, the system demonstrates clear improvement pathways through expanded training datasets and user-contributed algorithm optimization. Full article

With the increase in voltage levels, higher requirements are imposed on the energy-dissipation capability of high-voltage direct current (HVDC) networks. Existing energy-dissipation schemes cannot satisfy the demands of future HVDC systems. In this paper, a composite energy-dissipation branch circuit based on liquid metal, zinc oxide varistors, and damping resistors is proposed for HVDC circuit breakers. First, the self-constricting arc initiation mechanism and energy-dissipation characteristics of gallium–indium–tin liquid metal are studied. The results show that the energy-dissipation process exhibits an obvious stage-wise characteristic. Subsequently, an energy-dissipation topology incorporating liquid metal elements is established. A simulation model for the liquid-metal module is developed using the Mayr arc theory, and the conductance evolution during arc initiation is simulated. The model is combined with a hybrid HVDC circuit breaker model for analysis. Finally, a composite energy-dissipation branch circuit is constructed. The energy allocation among different components and the corresponding power density are evaluated. In the case of connecting three liquid-metal components in series, the energy density reached 0.248 kJ/cm³, representing a 22.2% increase compared to the original. The results support the coordinated application of liquid-metal modules and conventional absorption units in HVDC circuit breakers. Full article

Sustainable aviation fuel is among the most practical near-term routes for aviation decarbonization because it can be used in existing aircraft, engines, and airport fuel systems with limited infrastructure changes while minimizing disruption to the aviation fuel supply chain. This review examines SAF applications in aerospace propulsion systems, focusing on production pathways, aero-engine compatibility, property prediction, and fuel heat sink potential. It compares hydroprocessed esters and fatty acids (HEFA), Fischer–Tropsch (FT), alcohol-to-jet (ATJ), synthesized iso-paraffins (SIP), and power-to-liquid (PtL) fuels in terms of feedstock type, process complexity, product composition, and blending constraints. It also assesses how molecular composition governs density, cold-flow behavior, thermal stability, coking propensity, seal compatibility, and emissions. Recent advances in molecular dynamics, machine learning, spectroscopic analysis, and uncertainty quantification show a shift from empirical estimation toward composition-based prediction, prescreening, and fuel design. For high-thermal-load propulsion systems, SAF is further evaluated as a fuel heat sink in active regenerative cooling. Current evidence points to advantages in thermal stability and low coking tendency, but important gaps remain in transcritical and supercritical heat transfer, pyrolytic heat absorption, wall-material effects, coke deposition, and heat sink capacity modeling across wide operating ranges. 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

To address the supply–security pressures and hosting capacity assessment challenges arising from the high penetration of renewable energy into power systems, this paper proposes a comprehensive evaluation method for grid renewable energy hosting capacity based on the entropy weight method, technique for order preference by similarity to ideal solution, and grey relational analysis (EWM-GRA-TOPSIS). First, a multi-dimensional comprehensive evaluation index system is established, encompassing security and stability, operational economy, flexible power supply security, and renewable energy penetration. Subsequently, a comprehensive hosting capacity evaluation model is formulated. Within the operational simulation layer, key evaluation metrics across diverse renewable energy deployment alternatives are quantified based on time-series production simulation and power flow calculations. In the data processing layer, the EWM is employed to determine index weights, while GRA measures the morphological similarity between alternatives and the ideal sequence. Concurrently, TOPSIS evaluates their geometric proximity, enabling a coordinated trade-off among multi-dimensional metrics and the final ranking of the alternatives. Finally, the result output layer yields the comprehensive assessment outcomes and the prioritized ranking of the proposed scenarios. Simulation results demonstrate that the proposed method effectively evaluates the hosting capacity of various development scenarios, thereby providing a robust decision-making basis for power system planning and operation that balances high grid capacity with optimal comprehensive benefits. Full article

Electrospun nanocomposite membranes have attracted considerable interests for flexible energy-storage devices; however, the influence of graphene derivative surface chemistry on the electrochemical behavior of cellulose acetate-based nanocomposites remains insufficiently understood. In this work, cellulose acetate (CA) nanocomposite membranes containing graphene oxide (GO), reduced graphene oxide (rGO), and octadecylamine-functionalized reduced graphene oxide (rGO-ODA) at concentrations of 0.1 and 0.2 wt% were fabricated by electrospinning and evaluated as electrode materials for supercapacitor applications. The interfacial interactions and morphology of the membranes were investigated by Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), respectively, while electrochemical performance was assessed by cyclic voltammetry in three-electrode and symmetric two-electrode configurations. FTIR analysis confirmed effective interactions between the graphene derivatives and the CA matrix, whereas SEM observations revealed that nanofiller chemistry influenced fiber morphology and structural homogeneity. Among the evaluated systems, GO-containing membranes exhibited the best electrochemical performance, reaching a specific capacitance of 5.982 F/g in the three-electrode configuration. Analysis of the charge-storage mechanism using the power-law relationship revealed distinct electrochemical behavior associated with the surface chemistry of each graphene derivative. The results demonstrate that graphene-derivative chemistry governs the structure-property relationships, electrochemical response, and charge-storage behavior of electrospun CA nanocomposites, providing fundamental insights for the design of lightweight and flexible electrodes for energy-storage applications. Full article

Embedded Modular Multilevel Converter-Based Multi-Terminal Direct Current (MMC-MTDC) systems have become an important solution for enhancing transmission capacity and operational flexibility in urban hybrid AC/DC power grids. However, the coexistence of Grid-Following (GFL) and Grid-Forming (GFM) MMC stations introduces complex dynamic interactions through both AC and DC networks. Existing small-signal stability studies often neglect MMC internal dynamics, such as submodule capacitor voltage fluctuations and circulating current-related states, or simplify the AC network as an ideal voltage source, which may lead to inaccurate stability assessment and limited control parameter optimization performance. To address these issues, this paper proposes a coordinated small-signal stability enhancement strategy for an embedded heterogeneous MMC-MTDC system considering AC/DC bilateral coupling. First, a system-level full-order small-signal state-space model is established by incorporating the internal dynamics of both GFL-MMC and GFM-MMC stations, non-ideal AC networks, and DC transmission links. Then, eigenvalue analysis and participation factor-based sensitivity evaluation are performed to identify weakly damped oscillation modes and screen the key variables and control parameters associated with dominant oscillations. Furthermore, a quadratic performance index is constructed by weighting the sensitivities of key control parameters, and particle swarm optimization is employed to obtain coordinated optimized parameters for heterogeneous MMC stations. Comparative case studies and PSCAD/EMTDC time-domain simulations verify the effectiveness of the proposed strategy under different scenarios. The quantitative active-power indices show that, compared with the unoptimized parameters, Strategy 2 reduces the settling time by 49.1% in the power step response, suppresses the power step overshoot from 6.4% to 0%, shortens the settling time by 53.8% under grid-strength variation, and reduces the active-power peak and settling time by 28.0% and 74.8%, respectively, under the fault ride-through scenario. Full article

In the context of escalating implementation of electric vehicles (EVs), further research is required to investigate the impact of empirical driving conditions on energy demand and battery performance. In spite of the fact that microscopic traffic simulation and EV energy modelling are already used extensively, their use is still limited in studies focusing on batteries. Specifically, in most existing approaches, the effect of traffic-induced variability on battery stress is not explicitly accounted for. The study presented here examines a systematic framework that combines energy demand, traffic dynamics and battery behaviour. Using the SUMO simulator, vehicle trajectories are converted into electric vehicle (EV) energy profiles via a physics-based longitudinal model, thereby estimating battery power, energy consumption, regenerative effects and changes in state of charge (SOC). Next, a variety of indicators related to the battery management system (BMS) are introduced, including the Battery Stress Index (BSI), a traffic–energy severity (TES) indicator and event-based measures for transient conditions. The results show that traffic variability leads to significant fluctuations in battery load, which are not fully captured by conventional energy metrics. The proposed indicators provide additional information on cumulative and dynamic battery solicitation while remaining physically interpretable. Taken together, this framework links traffic conditions and battery solicitation in a coherent approach, thereby creating a scalable approach to traffic-aware energy analysis. Full article