Search Results (4,862)

An on-site hydrogen refueling station (HRS) directly supplies hydrogen to vehicles using an on-site hydrogen production method such as electrolysis. For the efficient operation of an on-site HRS, it is essential to optimize the entire process from hydrogen production to supply. However, most existing approaches focus on the efficiency of hydrogen production. This study proposes an optimal operation model for a renewable-energy-integrated on-site HRS, which considers the degradation of electrolyzers and operation of compressors. The proposed model maximizes profit by considering the hydrogen revenue, electricity costs, and energy storage system degradation. It estimates hydrogen production using a voltage equation, models compressor power using a shaft power equation, and considers electrolyzer degradation using an empirical voltage model. The effectiveness of the proposed model is evaluated through simulation. Comparison with a conventional control strategy shows an increase of over 56% in the operating revenue. Full article

Maritime Autonomous Surface Ships (MASSs) raise safety and regulatory challenges that extend beyond technical reliability. This study builds on a published system-theoretic process analysis (STPA) of degraded operations that identified 92 loss scenarios. These scenarios were reformulated into a two-round Delphi survey with 20 experts from academic, industry, seafaring, and regulatory backgrounds. Panelists rated each scenario on severity, likelihood, and detectability. To avoid rank reversal, common in the Risk Priority Number, an adjusted index was applied. Initial concordance was low (Kendall’s W = 0.07), reflecting diverse perspectives. After feedback, Round 2 reached substantial agreement (W = 0.693, χ² = 3265.42, df = 91, p < 0.001) and produced a stable Top 10. High-priority items involved propulsion and machinery, communication links, sensing, integrated control, and human–machine interaction. These risks are further exacerbated by oceanographic conditions, such as strong currents, wave-induced motions, and biofouling, which can impair propulsion efficiency and sensor accuracy. This highlights the importance of environmental resilience in MASS safety. These clusters were translated into five action bundles that addressed fallback procedures, link assurance, sensor fusion, control chain verification, and alarm governance. The findings show that Remote Operator competence and oversight are central to MASS safety. At the same time, MASSs rely on artificial intelligence systems that can fail in degraded states, for example, through reduced explainability in decision making, vulnerabilities in sensor fusion, or adversarial conditions such as fog-obscured cameras. Recognizing these AI-specific challenges highlights the need for both human oversight and resilient algorithmic design. They support explicit inclusion of Remote Operators in the STCW convention, along with watchkeeping and fatigue rules for Remote Operation Centers. This study provides a consensus-based baseline for regulatory debate, while future work should extend these insights through quantitative system modeling. Full article

This study conducted a systematic investigation of the degradation pathway and process optimization of strong acid cation exchange resins subjected to SCWO. Controlled experiments evaluated the effects of operating temperature, oxidant stoichiometry, initial organic concentration, and residence time. RSM was utilized to refine the operating parameters, and a second-order regression model (R² = 0.9951) was established to predict COD removal (R_COD), valid within experimental ranges: reaction temperature 400–500 °C, oxidant stoichiometry 80–150%, initial COD 10,000–100,000 mg·L⁻¹, and residence time 1–10 min. COD-dependent NaOH addition could enhance degradation efficiency. The R_COD was sensitive to operating temperature, oxidant stoichiometry, and residence time. Under the optimized conditions of 472 °C, oxidant stoichiometry of 137%, initial COD of 77,216 mg·L⁻¹, and residence time of 4.9 min with the addition of 1.74 wt% NaOH, the R_COD reached 99.92%, which was in close agreement with model predictions. GC-MS analysis of intermediates revealed that sulfonic groups dissociated early, followed by aromatic compounds, particularly phenol, undergoing ring-opening and oxidation to small carboxylic acids and aliphatic species, which were ultimately mineralized to CO₂ and H₂O. These findings provide mechanistic insight into resin decomposition and offer a scientific basis for the safe treatment of radioactive waste resins using SCWO. Full article

Swarm-based UAV cooperative ground target detection faces critical challenges including sensitivity to small targets, susceptibility to occlusion, and data heterogeneity across distributed platforms. To address these issues, we propose FedGTD-UAVs—a privacy-preserving federated transfer learning (FTL) framework optimized for real-time swarm perception tasks. Our solution integrates three key innovations: (1) an FTL paradigm employing centralized pre-training on public datasets followed by federated fine-tuning of sparse parameter subsets—under severe non-Independent and Identically Distributed (non-IID) data distributions, this paradigm ensures data privacy while maintaining over 98% performance; (2) an Space-to-Depth Convolution (SPD-Conv) backbone that replaces lossy downsampling with lossless space-to-depth operations, preserving fine-grained spatial features critical for small targets; (3) a lightweight Global Context Network (GCNet) module leverages contextual reasoning to effectively capture long-range dependencies, thereby enhancing robustness against occluded objects while maintaining real-time inference at 217 FPS. Extensive validation on VisDrone2019 and CARPK benchmarks demonstrates state-of-the-art performance: 44.2% mAP@0.5 (surpassing YOLOv8s by 12.1%) with 3.2× superior accuracy-efficiency trade-off. Compared to traditional centralized learning methods that rely on global data sharing and pose privacy risks, as well as the significant performance degradation of standard federated learning under non-IID data, this framework successfully resolves the core conflict between data privacy protection and detection performance maintenance, providing a secure and efficient solution for real-world deployment in complex dynamic environments. Full article

As environmental degradation intensifies, governments increasingly subsidize green manufacturing to promote sustainability. This study develops a game-theoretic model of two competing supply chains, comprising original equipment manufacturers (OEMs) and both traditional and green contract manufacturers (CMs), to investigate the impacts of subsidies on green manufacturing adoption. Specifically, we construct a four-stage dynamic game model to examine the interactions among OEMs, CMs, and the government. The main findings are as follows: First, the government subsidy affects OEMs’ adoption decisions only if the production cost of green manufacturing or competition intensity is sufficiently high or if the market sensitivity to green products is relatively low. Second, the optimal subsidy level depends jointly on the production cost of green manufacturing, competition intensity, and market greenness sensitivity: when the production cost of green manufacturing is low (high), the subsidy should rise (fall) with market greenness sensitivity but fall (rise) with competition intensity. Third, while intensified competition reduces OEMs’ profits and overall supply chain performance, its impact on CMs and consumers depends on the production cost of green manufacturing; in contrast, greater consumer sensitivity to green products yields an all–win outcome for all stakeholders. These results yield important managerial implications. For policymakers, when the production costs of green manufacturing are relatively low, green subsidies should be scaled back as market competition intensifies. For manufacturers, it is critical to carefully evaluate the production costs of green manufacturing and the level of government subsidies and to strategically pursue first-mover advantages in advancing sustainable operations, thereby fostering an all-win outcome for stakeholders. Full article

Promoting the green transformation of traditional diesel-powered ships is crucial for achieving carbon peaking and carbon neutrality goals. This study focuses on diesel-engine ships operating in the inland river areas of Anhui Province, China. It proposes two electrification retrofit schemes based mainly on lithium iron phosphate (LIP) batteries: full electrification and diesel-engine redundancy. The economic and environmental impacts of these schemes are analyzed and compared with those of conventional diesel-powered ships. A cost prediction algorithm based on model prediction is proposed, supported by a mathematical model for cost analysis. Results indicate that for electric tankers to become economically viable, battery costs must decrease through yearly improvements in energy density and reduced degradation rates. Additionally, government support is essential, such as raising carbon prices and providing subsidies—either an annual operational subsidy of CNY 80,000 or an initial construction subsidy of CNY 500,000. The study concludes that continued advances in battery technology, together with policy and financial support, will accelerate the large-scale electrification of ships. Full article

Automotive manufacturing quality control faces persistent challenges such as limited defect samples, cross-domain variability, and the demand for interpretable decision-making. This work presents an explainable few-shot anomaly detection framework that integrates EfficientNet-based feature extraction, adaptive prototype learning, and component-specific attention mechanisms to address these requirements. The system is designed for rapid adaptation to novel defect types while maintaining interpretability through a multi-modal explainable AI module that combines visual, quantitative, and textual outputs. Evaluation on automotive datasets demonstrates promising performance on evaluated automotive components, achieving 99.4% accuracy for engine wiring inspection and 98.8% for gear inspection, with improvements of 5.2–7.6% over state-of-the-art baselines, including traditional unsupervised methods (PaDiM, PatchCore), advanced approaches (FastFlow, CFA, DRAEM), and few-shot supervised methods (ProtoNet, MatchingNet, RelationNet, FEAT), and with only 0.63% cross-domain degradation between wiring and gear inspection tasks. The architecture operates under real-time industrial constraints, with an average inference time of 18.2 ms, throughput of 60 components per minute, and memory usage below 2 GB on RTX 3080 hardware. Ablation studies confirm the importance of prototype learning (−4.52%), component analyzers (−2.79%), and attention mechanisms (−2.21%), with K = 5 few-shot configuration providing the best trade-off between accuracy and adaptability. Beyond performance, the framework produces interpretable defect localization, root-cause analysis, and severity-based recommendations designed for manufacturing integration with execution systems via standardized industrial protocols. These results demonstrate a practical and scalable approach for intelligent quality control, enabling robust, interpretable, and adaptive inspection within the evaluated automotive components. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder with substantial socioeconomic impact, characterized by the gradual loss of dopaminergic neurons, dopamine deficiency, and pathological processes such as neuroinflammation, oxidative stress, and α-synuclein aggregation. Monoamine oxidases (MAOs) are enzymes responsible for the degradation of neuroactive amines, including dopamine, a neurotransmitter essential for motor, cognitive, and behavioral functions. Among these, MAO-B plays a central role in dopamine metabolism, producing reactive metabolites and oxidative species that contribute to the oxidative stress associated with PD pathophysiology. In this context, MAO-B inhibition has emerged as a promising therapeutic strategy. However, specific limitations, such as motor complications linked to prolonged levodopa use and the adverse effects of currently available MAO inhibitors, remain significant clinical challenges. Methods: A comprehensive literature search was conducted using PubMed and SciFinder databases. Keywords such as “MAO inhibitors”, “Parkinson’s pathology,” and “Parkinson’s disease” were combined with Boolean operators (AND, OR, NOT). The search covered publications from 2010 to 2025. Results: While previous reviews, particularly those by the groups of Guglielmi and Alborghetti, mainly emphasized the clinical use of MAO-B inhibitors and advances in patents, the present review identified approximately 300 compounds synthesized and evaluated as MAO inhibitors, encompassing diverse chemical classes. Among them, selective MAO-B inhibitors exhibited the greatest pharmacological potential, reinforcing the relevance of this isoform as a strategic target in PD therapy. Conclusion: These findings highlight the advances of Medicinal Chemistry in the development of novel MAO-B inhibitors, both as monotherapies for early-stage PD and as adjuvants to levodopa in advanced disease. Collectively, they emphasize the promise of MAO-B inhibitors as candidates for more effective therapeutic interventions in Parkinson’s disease. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

This study addresses the challenge of cognitive waveform design for multiple-input–multiple-output (MIMO) radar systems operating in cluttered environments. It focuses on the key practical requirements for transmitting time-domain waveforms and proposes a novel approach. This method first determines the optimal frequency-domain waveform and then designs a time-domain waveform that closely approximates the frequency-domain solution. The primary objective is to enable MIMO radar systems to transmit orthogonal waveforms while accommodating various constraints. A frequency-domain waveform optimization model was initially developed using the principle of maximizing dual mutual information (DMI), and the energy spectral density (ESD) of the optimal waveform was derived using the water-filling method. Next, a time-domain waveform approximation model is constructed based on the minimum mean square error (MMSE) criterion, which incorporates constant modulus and peak-to-average power ratio (PAPR) constraints. To minimize the performance degradation of the waveform, an improved adaptive gradient descent genetic algorithm (GD-AGA) was proposed to synthesize multichannel orthogonal time-domain waveforms for MIMO radars. The simulation results demonstrate the effectiveness of the proposed model for enhancing the performance of MIMO radar. Compared with traditional genetic algorithms (GA) and two enhanced GA alternatives, the proposed algorithm achieves a lower ESD loss and better orthogonal performance. Full article

In this study, a nonlinear dynamic model is developed to examine the stability and vibration behavior of a self-balancing electric Segway operating over irregular stochastic terrains. The Segway is treated as a three-degrees-of-freedom cart–inverted pendulum system, incorporating elastic and damping effects at the wheel–ground interface. Road irregularities are generated in accordance with international standard using high-order filtered noise, allowing for representation of surface classes from smooth to highly degraded. The governing equations, formulated via Lagrange’s method, are transformed into a Lorenz-like state-space form for nonlinear analysis. Numerical simulations employ the fourth-order Runge–Kutta scheme to compute translational and angular responses under varying speeds and terrain conditions. Frequency-domain analysis using Fast Fourier Transform (FFT) identifies resonant excitation bands linked to road spectral content, while Kernel Density Estimation (KDE) maps the probability distribution of displacement states to distinguish stable from variable regimes. The Lyapunov stability assessment and bifurcation analysis reveal critical velocity thresholds and parameter regions marking transitions from stable operation to chaotic motion. The study quantifies the influence of the gravity–damping ratio, mass–damping coupling, control torque ratio, and vertical excitation on dynamic stability. The results provide a methodology for designing stability control systems that ensure safe and comfortable Segway operation across diverse terrains. Full article

The accurate estimation of the state of health (SOH) of lithium-ion batteries is critical for ensuring the safety, reliability, and efficiency of modern energy storage systems. Traditional model-based and data-driven methods often struggle to capture complex spatiotemporal degradation patterns, leading to reduced accuracy and robustness. To address these limitations, this paper proposes a novel dynamic graph pruning neural network with self-attention mechanism (DynaGPNN-SAM) for SOH estimation. The method transforms sequential battery features into graph-structured representations, enabling the explicit modeling of spatial dependencies among operational variables. A self-attention-guided pruning strategy is introduced to dynamically preserve informative nodes while filtering redundant ones, thereby enhancing interpretability and computational efficiency. The framework is validated on the NASA lithium-ion battery dataset, with extensive experiments and ablation studies demonstrating superior performance compared to conventional approaches. Results show that DynaGPNN-SAM achieves lower root mean square error (RMSE) and mean absolute error (MAE) values across multiple batteries, particularly excelling during rapid degradation phases. Overall, the proposed approach provides an accurate, robust, and scalable solution for real-world battery management systems. Full article

This paper investigates the transient stability of Grid-Forming (GFM) Permanent Magnet Synchronous Generator (PMSG) systems during grid faults. An analysis demonstrates how a fixed saturated current angle can trap the system in undesirable operating points, while reactive power coupling can degrade performance. Both factors pose a risk of turbine overspeed and instability. To overcome these vulnerabilities, a dual-mechanism control strategy is proposed, featuring an adaptive saturated current angle control that, unlike conventional fixed-angle methods, which risk creating Current Limiting Control (CLC) equilibrium points, dynamically aligns the current vector with the grid voltage to guarantee a stable post-fault trajectory. The effectiveness of the proposed strategy is validated through time-domain simulations in MATLAB/Simulink. The results show that the proposed control not only prevents overspeed trip failures seen in conventional methods but also reduces post-fault recovery time by over 60% and significantly improves system damping, ensuring robust fault ride-through and enhancing overall system stability. Full article

Horizontal vibrations in high-speed elevators induced by building sway degrade ride comfort and compromise operational safety. Developing an accurate and robust dynamic model is essential for effective vibration control. To address this, this study develops a comprehensive dynamic model of the coupled traction rope–car–guide shoe–guide rail system under multi-support excitations, incorporating nonlinear contact between the guide shoe and rail, guide rail vibration characteristics, and the time-varying length of traction rope. Using this model, the dynamic responses of the system under stationary and operating conditions are analyzed in detail. The results demonstrate that the proposed model accurately captures the dynamic behavior of the coupled system. In addition, the traction rope’s dynamics are a dominant factor in the system’s response, particularly when the elevator is stationary at a landing, producing a resonant condition with the building sway. Furthermore, a strong coupling between vertical motion and horizontal vibration is identified, which significantly amplifies the system response. By linking elevator dynamics with the sway characteristics of high-rise buildings, this work provides a robust analytical framework for predicting the dynamic response of high-speed elevators due to building sway and contributes to the safety assessment of high-rise reinforced concrete (RC) structures. Full article

In this paper, the impact of SiN passivation on dynamic-R_ON degradation of AlGaN/GaN HEMTs devices is put in evidence. To this end, samples showing different SiN passivation stoichiometry are considered, labeled as Sample A and Sample B. For dynamic-R_ON tests, two different experimental setups are employed to investigate the R_ON-drift showing up during conventional switch mode operation by driving the DUTs under both (i) resistive load and (ii) soft-switching trajectory. This allows to discern the impact of hot carriers and off-state drain voltage stress on the R_ON parameter drift. Measurements performed with both switching loci shows similar dynamic-R_ON response, indicating that hot carriers are not involved in the degradation of tested devices. Nevertheless, a significant difference was observed between Sample A and Sample B, with the former showing an additional R_ON-degradation mechanism, not present on the latter. This additional drift is totally ascribed to the SiN passivation layer and is confirmed by the different leakage current measured across the two SiN types. The mechanism is explained by the injection of negative charges from the Source Field-Plate towards the AlGaN surface that are captured by surface/dielectric states and partially depletes the 2DEG underneath. Full article