Search Results (11,146)

This study investigates the transient wheel–rail contact mechanics of welded joints in heavy-haul rails via a validated 3D finite element model, and analyzes the stick-slip behavior, dynamic response and elastoplastic characteristics in the base material zone, heat-affected zone and weld bead zone. Results show a distinct contact state transition from stick-slip in the base material to predominant slip within the welded zones, indicating higher wear susceptibility. Dynamic response analysis reveals the highest and lowest contact-point acceleration amplitudes in the base material and heat-affected zone, respectively, due to material heterogeneity. Plastic deformation consistently initiates at the rail surface, where stress and strain concentrate, establishing it as the primary site for damage nucleation. A systematic parametric study shows that plastic deformation can be effectively mitigated by increasing the yield strength and elastic modulus of the welded joint material, or reducing the wheelset velocity, unsprung mass and wheel–rail friction coefficient. In contrast, adjusting the primary suspension and fastener parameters exerts a negligible influence on plastic deformation control. These findings provide a mechanistic basis for optimizing the performance and maintenance of welded joints in heavy-haul rail operations. This study reveals the coupling law of multiple mechanisms among contact behavior, dynamic response and material failure during the damage initiation process of rail welded joints from the mechanistic perspective, which provides a theoretical basis for the structural optimization, condition assessment and maintenance of rail welded joints in heavy-haul railways. Full article

This study investigates the thermal performance of a flat-plate phase-change thermal energy storage system, focusing on two structural innovations: a cascaded arrangement of multiple phase-change materials (PCMs) with varying melting points, and the implementation of series/parallel flow configurations. A combined numerical and experimental approach is employed to analyze dynamic charging/discharging behavior. Quantitative results indicate that the cascaded configuration (three PCMs) reduces phase-change completion time by 13% and increases cooling energy storage power from 2.00 kW to 2.43 kW during charging compared to single-PCM systems. Flow configuration significantly impacts thermal response: the parallel layout delivers more stable cooling output, while the series layout achieves faster initial cooling (reaching 6.24 °C within 1200 s, 31% faster than the parallel layout). Experimental results reveal that inlet water temperature is the most critical operating parameter, with each 2 °C increase significantly prolonging charging time. This work offers practical guidance for the design and optimization of efficient cascaded PCM thermal storage systems. Full article

Effective thermal management is critical for high-power lithium-ion batteries to mitigate excessive heat generation and ensure operational reliability. Failure to maintain a uniform temperature distribution can lead to accelerated capacity fading and severe safety risks, such as thermal runaway. In this study, a ferrofluid-based magnetohydrodynamic (MHD) microchannel cooling system was numerically investigated to elucidate the influence of magnetic intensity, magnet geometry, and electrical boundary conditions on flow behavior and heat transfer performance for battery cooling applications. A fully coupled multiphysics model incorporating electromagnetic, fluid flow, and heat transfer phenomena was developed and validated against experimental and numerical data from the literature. The results show that increasing the applied voltage enhances current density and Lorentz force almost linearly, leading to significant flow acceleration and improved convective heat transfer. Electrical insulation effectively suppresses current leakage into the channel walls, increasing the average current density by up to 222% and the Lorentz force by more than 300%. Compared with a cylindrical magnet, a rectangular magnet provides a more uniform magnetic field distribution and stronger near-wall Lorentz forcing, resulting in superior cooling performance. Under a 4C discharge condition, the insulated rectangular magnet reduces the maximum battery temperature by approximately 30% and increases the average Nusselt number by up to 103% relative to the non-insulated case. The findings reveal the critical roles of magnetic-field-controlled flow symmetry and near-wall forcing in MHD-driven microchannels, and provide practical design guidelines for battery cooling systems with no moving mechanical parts and active electromagnetic flow control. Full article

With the widespread adoption of electric vehicles, predicting user charging consumption can enhance the operational efficiency of charging infrastructure. However, differences in user charging habits result in charging station operators obtaining data that is non-continuous and event-driven, lacking internal battery state information. This makes traditional methods difficult to apply directly. This paper explores how to accurately predict user charging consumption based on non-continuous observation data from charging stations. To this end, we propose a three-stage solution: (1) Design a method for segmenting the temporal sequence of users’ internal charging behavior based on statistical significance testing, enabling unsupervised recognition of homogeneous sequences of user behavior patterns; (2) establish a continuous-time reconstruction mechanism based on a physics-inspired power decay model to convert discrete homogenous sequences into equidistant daily sequences of charging consumption; (3) utilize seasonal and trend decomposition using Loess (STL) time-series decomposition to extract the component from the reconstructed sequence and input it as a feature into the Long Short-Term Memory (LSTM) prediction model. Through experimental validation using real charging data, the proposed method significantly enhances prediction performance, providing an effective solution for forecasting user charging consumption in actual charging stations. Full article

To address global carbon reduction demands, oxy-fuel combustion in circulating fluidized beds (oxy-CFB) has emerged as a highly promising carbon capture technology, offering extensive fuel flexibility and facilitating bioenergy with carbon capture and storage (BECCS). However, its commercialization is hindered by significant energy penalties and complex scale-up challenges. This review comprehensively analyzes the fundamental multiphase mechanisms, heat transfer behaviors, and multi-pollutant emission characteristics of oxy-CFB systems, drawing upon multiscale modeling advancements and operational data from pilot to 30 MW_th industrial demonstrations. Replacing air with an O₂/CO₂/H₂O mixture fundamentally alters gas–solid hydrodynamics and char conversion pathways, necessitating active fluidization state re-specification. Despite shifting optimal desulfurization temperatures and introducing recarbonation risks, the technology demonstrates inherent advantages in synergistic pollutant control, including the complete elimination of thermal NO_x. While atmospheric oxy-CFB is technically viable, transitioning to pressurized operation is critical to minimizing system efficiency penalties. Furthermore, integrating oxygen carrier-aided combustion (OCAC) and developing advanced predictive control strategies are essential to managing multi-module thermal inertia and enabling rapid dynamic responsiveness for modern power grids. Full article

In polar environments, the thermoviscous behavior and heat dissipation characteristics of deck hydraulic systems are severely affected, resulting in response delays and increased failure risk during high-load operations such as anchor retrieval. To address the limited availability of polar field test samples and the multi-scale nature of oil-temperature responses—featuring short-term abrupt variations and slow-varying hysteresis—this study proposes a Multi-Scale Attention Transformer (MSA-Transformer). Through parallel multi-scale attention branches, the model collaboratively captures both transient and gradual dynamics, thereby improving prediction robustness under polar extreme cold conditions. Based on anchor-retrieval test data collected in Genhe, China’s Cold Pole, at −30 °C, −35 °C, and −40 °C, a dataset containing 18 load cycles was constructed. Experimental results based on 5-fold stratified cross-validation results show that the MSA-Transformer achieves the best performance across evaluation metrics, attaining an average coefficient of determination (R²) of 0.9119 along with the lowest error rates (MAE, RMSE, MSE) on the test set, thereby outperforming LSTM, CNN-LSTM, and the standard Transformer. This work provides an effective tool for state prediction, maintenance optimization, and anomaly early warning in polar deck hydraulic systems, supporting the intelligent health management of hydraulic equipment. Full article

This study aimed to develop a 100 mg immediate-release (IR) tablet containing dasatinib monohydrate, a tyrosine kinase inhibitor, using a Quality by Design (QbD) framework at laboratory scale. The development strategy focused on systematic identification and control of critical process parameters (CPPs) affecting tablet quality during wet granulation. Preformulation studies were conducted to evaluate key physicochemical properties of the active pharmaceutical ingredient (API), including solubility, particle size distribution, and crystallinity, which may influence dissolution behavior. A risk assessment approach based on preliminary hazard analysis (PHA) and failure mode and effects analysis (FMEA) was applied to identify high-risk process variables. Based on the risk assessment results, chopper speed during wet granulation and compression force during tableting were identified as critical process parameters. These factors were further investigated using a Design of Experiments (DoE) approach based on Define Custom Design (DCD) and response surface methodology (RSM) to evaluate their effects on critical quality attributes (CQAs), including dissolution performance, disintegration time, and tablet friability. Response surface analysis established a design space in which chopper speed ranged from approximately 2300–2500 rpm and compression force ranged from 11 to 13 kN, ensuring consistent tablet quality within the investigated operating range. The optimized process conditions produced tablets that satisfied predefined quality targets. Comparative dissolution studies demonstrated dissolution profiles comparable to the reference product across pH 1.2, 4.0, and 6.8 media, with similarity factor (f₂) values ranging from 51.18 to 85.23. The experimentally established design space demonstrated reproducible in vitro performance and physicochemical stability under accelerated storage conditions. Overall, this study demonstrates the practical application of a QbD-based development strategy integrating risk assessment and response surface optimization to improve process understanding and manufacturing robustness in wet granulation-based tablet production. Full article

Push–pull exhaust systems are widely applied for controlling industry-processing fumes, and their performance is fundamentally governed by the coupling interaction among the air-curtain jet (“push”), the buoyant thermal plume generated by the heat source, and the converging flow induced by the exhaust hood (“pull”). However, the dynamic characteristics and design criteria of this coupled flow field under large temperature differences remain insufficiently explored. Here, a series of scaled experiments combined with numerical simulations is conducted to systematically investigate the coupling behavior of the air-curtain jet and the thermal plume, and two quantitative performance indicators, namely plume deflection height and flow rate along the plume deflection path, are proposed to evaluate flow control effectiveness and energy dissipation. An orthogonal experimental design is further employed to analyze the sensitivity of heat-source and air-curtain parameters with respect to these indicators. The results demonstrate that the air temperature reaches its maximum at approximately 0.8 m downstream of the air-curtain outlet, and that both the supply velocity and outlet width of the air curtain are dominant parameters exerting statistically significant influences on plume deflection height and flow rate along the path (p < 0.01). Furthermore, the Archimedes number effectively characterizes the competition between jet inertia and plume buoyancy in the coupled flow field, with its appropriate value preliminarily recommended to be controlled below 40. This study provides quantitative insights for the engineering design of push–pull exhaust systems operating under high thermal load conditions. Full article

Security organizations increasingly rely on cyber threat intelligence (CTI) sharing to enhance their resilience against cyberattacks. Indicators of Compromise (IoCs) play a critical operational role in CTI by providing malicious artifacts that support threat detection, incident response, and facilitate proactive defense. However, the rapid growth of social media as CTI sources, characterized by short-text content, poses significant challenges to automated IoC extraction, contextual interpretation, operational integration, and reliable verification. To address these challenges, this study proposes a comprehensive framework that integrates Large Language Models (LLMs) across multiple stages of the CTI pipeline. The framework leverages LLM-driven data augmentation, a hybrid classification model, and contextual summarization to enhance short-text understanding while supporting expert-in-the-loop validation for operational reliability. Extensive experimental evaluations demonstrate that LLM-driven data augmentation substantially improves model robustness and generalization while reducing false-positive alerts, achieving a precision of $98.87$%. Quantitative diversity analysis and expert-based human evaluation further confirm the linguistic quality and correctness of the generated augmented samples. In addition, IoC reports are validated using both reference-based and reference-free evaluation metrics that show strong alignment and high semantic adequacy. Moreover, a technology acceptance model was integrated with cybersecurity domain constructs to assess the acceptance factors of the proposed framework. Regression analysis showed that perceived usefulness, behavioral intention, trust in automation, and risk were the strongest predictors of actual use. These predictors are commonly interpreted as indicators of technology acceptance. Full article

To mitigate the compliance deviation risk induced by wind power output fluctuations, this paper proposes a two-stage joint bidding model for cascaded hydropower–wind systems within the electricity spot market framework from a price-taker perspective, explicitly accounting for the decision maker’s risk preferences. To capture the impacts of hydropower vibration zones on joint bidding decisions, the feasible output range of hydropower units is divided into multiple safe operating sub-intervals, and vibration zone avoidance is modeled using binary decision variables; meanwhile, penalty terms are incorporated into the objective function to suppress vibration zone crossing behaviors. From a risk-aware decision-making perspective, Conditional Value-at-Risk (CVaR) is adopted to quantify the downside tail risk of bidding revenues, and a risk factor is introduced to flexibly adjust the decision maker’s risk attitude. Finally, a case study based on a cascaded hydropower system and an associated wind farm in Southwest China is conducted to demonstrate the effectiveness of the proposed joint bidding strategy and to examine the impacts of risk preferences and vibration zone considerations on joint bidding outcomes. Full article

Resource allocation in sixth-generation (6G) networks must meet throughput, latency, and reliability targets while network conditions keep changing. At the same time, the telemetry needed to train good models is distributed across many devices and edge nodes, so sending it to a central server can violate privacy or data-sharing constraints. Federated learning (FL) helps, but two practical concerns usually determine whether it works in practice: how much communication is needed to achieve strong performance, and whether weaker (tail) clients benefit-not only the average client. In this study, we run large-scale FL on 6G telemetry with 200 clients and quantify the communication fairness trade-off. We evaluate FedAvg and FedProx under multiple settings and benchmark them against a strong centralized model and a local-only baseline. Results are reported as mean ± 95% confidence intervals over five random seeds. We measure the accuracy, macro-F1, AUC, and AP, and we also focus on tail behavior using the worst eligible client accuracy, p10 client accuracy, and fairness gap. By plotting the accuracy/macro-F1 against cumulative communication (bytes), we show that some configurations match the average performance while transmitting far fewer data. Finally, we find that the worst client performance improves early and then stabilizes, and a sensitivity study suggests that FedProx’s $\mu$ has a limited impact in this setup. These findings offer actionable guidance for 6G operators and system designers by quantifying how participation and dropout policies translate into concrete communication budgets and tail client behavior. Full article

The increasing complexity and uncertain system of modern discrete event system (DES) challenge traditional model-based control approaches, while artificial intelligence (AI) techniques offer powerful data-driven decision-making capabilities but lack formal guarantees. This review surveys recent research on the integration of AI with DES and supervisory control theory. Following a systematic literature mapping methodology, the literature is organized using a taxonomy based on three orthogonal perspectives: control and decision paradigm, system capability and property, and application and operational objectives. The review highlights how learning-based methods enhance adaptability and performance in DES, while also exposing persistent challenges related to safety, nonblocking behavior, data efficiency, and interpretability. By structuring existing approaches and identifying open issues, this review provides a coherent overview of the current research landscape and outlines key directions for future work on AI-enabled DES. Full article

Modern computing platforms increasingly rely on random number generators (RNGs) for modeling probabilistic processes in simulation, probabilistic computing, and system validation. They are also essential for cryptographic operations such as key generation, authenticated encryption, and digital signatures. Deterministic Random Bit Generators (DRBGs), as specified in the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90A, provides a standardized method for expanding entropy into cryptographically strong pseudorandom sequences. This work presents the design and Field Programmable Gate Array (FPGA) implementation of a hash-based DRBG using Ascon-Hash256, a lightweight, quantum-resistant hash function from the NIST-standardized Ascon cryptographic suite. It implements hash-based derivation, instantiation, generation, and reseeding of the generator via iterative hash invocations and state updates. Leveraging Ascon’s sponge-based structure, the design achieves efficient entropy absorption and diffusion while maintaining an area-efficient FPGA architecture, making it well suited for resource-constrained platforms. The diffusion properties of the proposed DRBG are evaluated through avalanche and reproducibility analyses, confirming strong sensitivity to input variations and secure, repeatable operation. Moreover, Monte Carlo and stochastic-diffusion evaluation of the generated bitstreams demonstrates correct convergence and statistically consistent behavior. These results confirm that the proposed hash-based DRBG provides reproducible, hardware-efficient, and cryptographically secure random numbers suitable for next-generation neuromorphic, probabilistic computing systems, and Internet of Things (IoT) devices. Full article

Theological engagement with artificial intelligence has largely focused on applied ethics, addressing bias, governance, and labor displacement. While indispensable, this framing often presumes that algorithmic systems operate as external instruments acting upon already constituted subjects. This article argues that contemporary predictive architectures intervene at a deeper anthropological level by structuring attention, expectation, and habituation prior to deliberative judgment. It introduces the concept of ordo extractionis to designate a technologically mediated regime of formation characterized by behavioral trace extraction, probabilistic modeling, and recursive projection of statistically inferred continuity. Drawing on Augustine’s account of ordered love and temporality and Aquinas’s doctrine of habitus and the invisible mission of the Spirit, the article distinguishes algorithmic projection from sanctification as divergent pedagogies of temporal formation. Predictive systems stabilize continuity by extrapolating from measurable past behavior; sanctification reorders desire teleologically toward a final end not deducible from prior pattern and grounded in non-competitive divine causality. Algorithmic mediation is therefore interpreted pedagogically rather than metaphysically: it does not rival divine agency but participates creaturely in shaping the ecology within which habituation unfolds. Engagement with contemporary AI research on recommender systems, reinforcement learning, and generative models situates the argument within technological realism and resists determinism. The digital twin is analyzed as a probabilistic representation that acquires institutional authority when operationalized in ranking, profiling, and evaluative systems, without constituting a metaphysical competitor to the imago Dei. In response to anticipatory closure, Eucharistic anamnesis and epiclesis are developed as practices that re-situate memory and expectation within eschatological promise. The article concludes that the central theological question posed by AI is not whether machines can think, but how formative sovereignty over desire is exercised within technologically mediated modernity. Full article

Structural symmetry appears as a natural feature in both optimal solution landscapes and hospital scheduling behaviors, representing an inherent balance that can be deliberately leveraged to improve how quickly algorithms converge and how reliably systems perform in intricate healthcare optimization contexts. Managing hospital resources is a multifaceted challenge that requires simultaneously addressing several competing goals, such as reducing costs, improving patient experiences, making the most of available resources, distributing staff workload fairly, and strengthening readiness for emergencies. Traditional optimization approaches frequently struggle to cope with the complexity and ever-changing nature of modern healthcare environments. To address this gap, this study introduces a novel Multi-Objective Dream Optimization Algorithm (MO-DOA) tailored for smart healthcare resource management, which adapts a biologically inspired optimization framework to meet the specific demands of healthcare settings. The MO-DOA is built around three core mechanisms: a foundational memory component that retains high-quality solutions, a forgetting-supplementation component that maintains a productive balance between exploration and exploitation, and a dream-sharing component that promotes diversity among candidate solutions. Rigorous testing across realistic hospital environments confirms MO-DOA’s outstanding effectiveness, with results showing a 21.86% gain in resource utilization, a 30.95% decrease in patient waiting times, a 19.06% boost in patient satisfaction, and a 29.56% improvement in how evenly staff workloads are distributed. The algorithm’s emergency response capabilities are especially noteworthy, achieving bed assignments within 4.23 min and an equipment deployment success rate of 94.56%. Computationally, the algorithm proves highly efficient, with an average response time of 18.87 s and strong scalability across different operational scales. Collectively, these findings position MO-DOA as a powerful and practical tool for optimizing hospital operations in real time. Full article