Search Results (2,866)

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment. Full article

To support the safe operation and technological promotion of existing line speed-up projects, this paper presents an assessment method for pantograph–catenary contact performance under the 200 km/h speed conditions, using the Guangzhou–Shenzhen Lines I and II speed-up projects as representative case studies. Based on the ANCF method, a refined pantograph–catenary coupling dynamic model is established to accurately characterize the large deformation and geometric nonlinear behavior of the catenary system. Model validation is achieved using actual measurement data from the CR400AF train. Based on this model, systematic simulation analyses were conducted to evaluate the current collection performance of four mainstream train models—CR300AF, CR400BF, CRH380A, and CRH380B—under both single-unit and double-unit operation conditions. Results indicate that dynamic contact force metrics for pantograph–catenary interactions meet all limit requirements specified in the Technical Specifications for Dynamic Acceptance of High-Speed Railway Projects under all operating conditions. This demonstrates that the pantograph–catenary system on the analyzed Guangzhou–Shenzhen Line exhibits excellent dynamic stability and safety under the targeted speed-up scheme, providing simulation-based justification for implementing the speed enhancement project. Full article

Electrical Impedance Tomography (EIT) is a promising non-invasive sensing technique, yet its practical application in resource-constrained environments is often limited by the high computational cost of inverse image reconstruction. To address this challenge, we focus on specific sensing objectives rather than full image recovery. In this study, we propose a lightweight, task-oriented inference framework for object localization in EIT that bypasses the need to solve computationally expensive inverse reconstruction problems. This approach addresses the high computational demands and hardware complexity of conventional iterative methods, which often hinder real-time monitoring in resource-constrained edge computing environments. Training datasets were generated via finite element method (FEM) simulations for Opposite and Adjacent current injection configurations. A feedforward neural network was developed to independently estimate the radial and angular object positions as probability distributions. Our systematic evaluation revealed that the localization performance depends on the injection configuration and model depth; notably, the Opposite method achieved perfect classification accuracy (1.00) for radial estimation with an optimized architecture of four hidden layers, whereas the Adjacent method exhibited higher ambiguity. Results quantitatively evaluated using the Wasserstein distance show that the Opposite configuration produces more localized, unimodal probability distributions than the Adjacent configuration by utilizing current fields that traverse the entire domain. Compared with existing image-based reconstruction methods, including the conventional electrical impedance tomography and diffuse optical tomography reconstruction software (EIDORS ver.3.12), the proposed framework reduced energy consumption from 3.09 to 0.96 Wh, demonstrating an approximately 70% improvement in energy efficiency while maintaining a high localization accuracy without the need for iterative Jacobian updates. This task-oriented framework enables reliable, high-speed, and energy-efficient localization, making it well-suited for low-power EIT applications in mobile and embedded sensor systems. Full article

This study proposes a comprehensive methodology for the experimental characterization and non-linear dynamic modelling of Polycrystalline Diamond (PCD) bearings, establishing a high-fidelity digital twin approach for the condition monitoring of rotating machinery. The research addresses complex rotor–stator interactions through the development of a multibody numerical framework. A structural 1D Finite Element (FE) model of the stator assembly was first calibrated via experimental modal analysis, achieving a high correlation with the first four bending modes and a maximum frequency discrepancy of only 1.4%. This validated structure was integrated into a non-linear multibody environment to simulate transient rub-impact events at rotational speeds up to 5500 rpm across varying clearance configurations. The model successfully captures the transition from stable periodic orbital motion to the stochastic and chaotic regimes observed in high-clearance setups. Frequency-domain validation further confirms the model’s accuracy in identifying supersynchronous harmonics and energy distribution patterns. Quantitative analysis shows that high-clearance configurations generate impact forces exceeding 6000 N, providing critical data for structural health assessment. These results demonstrate that the proposed digital twin serves as a robust physical foundation for diagnostic systems, enabling the identification of contact-induced vibrational signatures that are essential for training prognostic algorithms. This approach facilitates the autonomous monitoring of critical rotating machinery in demanding industrial and subsea applications, supporting the transition toward active balancing and model-based vibration control strategies. Full article

Inertial measurement units (IMUs) present promising avenues for performance diagnostics in skateboarding, yet systematic validation of their accuracy and applicability remains limited. This study validates the commercially available Spinnax Freak IMU system in the context of skateboarding, with a focus on selected trick detection and classification, distance measurement, maximal horizontal speed, maximal vertical height of the skateboard and airtime during a jump trick. A total of 23 skateboarders (4 females, 19 males; 27.4 ± 10.9 years) participated in this study. Validation methods included comparisons with established reference systems such as laser ranging for maximal horizontal speed (LAVEG), 2D video analysis for maximal vertical height of the skateboard (Kinovea), light barrier measurements for airtime detection (OptoJump Next), and a fixed metric reference (10 m) for rolling distance measurements. The evaluation was supported by statistical analyses including mean absolute error (MAE), root mean-square error (RMSE), mean absolute percentage error (MAPE), t-tests, Bland–Altman plots, linear regression, and ICC(3,1). The Spinnax Freak system demonstrated high validity in detecting trick events and in providing distance measurements that were statistically equivalent to the reference. Trick classification, maximal horizontal speed, maximal vertical height of the skateboard and airtime showed substantial errors, indicating that these outputs are not reliable for biomechanical interpretation at this point. These findings highlight both the potential and the current constraints of single-sensor setups for field-based motion capture in skateboarding. Future developments should prioritize algorithmic refinement, improved temporal resolution, and optimized event classification to enhance measurement accuracy and expand applicability in biomechanical analysis and automated training documentation in skateboarding. Full article

In this study, an on-site installation type track slab using glass fiber-reinforced polymer (GFRP) reinforcing bars was developed and analyzed for its structural response to high-speed train loading. Concrete track slabs have the most severe deterioration in track circuit characteristic values due to the conduction influence of existing steel bars. Therefore, a track slab applying an insulator and lightweight GFRP reinforcement by replacing the existing steel bar was proposed from a design perspective. In order to present the validity of the proposed method, a full-size specimen was manufactured and a structural performance test was conducted, and the results were compared and verified through three-dimensional numerical analysis. The results showed that the new orbital slab applying the GFRP reinforcement has satisfactory insulation and provides sufficient structural performance that can replace the existing steel bar. Full article

Metallothionein (MT) is a multifunctional metal-binding protein with broad applications in medicine, healthcare, and food industries, but its large-scale use is limited by inefficient industrial synthesis. To address this and obtain optimal fermentation parameters for large-scale MT production, this study used the recombinant marine-derived MT-producing Pichia pastoris strain SMD1168-MT. We first optimized the strain’s growth and induced fermentation conditions, then constructed a Back Propagation (BP) neural network model for in-depth parameter optimization and accurate MT expression prediction. Results showed the optimal growth conditions for SMD1168-MT were: 30 °C, initial pH 8.0, shaking speed 220 r/min, and 4% inoculum size. The BP model exhibited high accuracy (training set: R² = 0.8430, MAE = 0.0129, RMSE = 0.0175; validation set: R² = 0.8337, MAE = 0.0144, RMSE = 0.0174). Combined with Particle Swarm Optimization (PSO), the optimal fermentation conditions were: 7.7% methanol, initial OD₆₀₀ 8.2, 240 r/min, 50 h induction, and 125 μmol/L Zn²⁺. Validation confirmed MT expression reached 0.2141 mg/mL (2.93-fold). This study demonstrates that the BP neural network effectively optimizes recombinant P. pastoris-based marine-derived MT fermentation, improving yield and providing a basis for industrial scale-up. Full article

The aim of the current study was to examine whether weekly dynamic stress load (DSL) volume could be associated with competition internal and external load outcomes in professional soccer players. Weekly DSL volume was recorded across standardized one-match microcycles. Match outcomes included total distance covered (TDC), high-speed running distance (HSRD), sprint distance (SPRD), high-intensity accelerations (HIACC), high-intensity decelerations (HIDEC), high-metabolic-load distance (HMLD), time spent > 85% of maximum heart rate (HRmax), and Edwards training impulse (Edwards’ TRIMP). Analyses of our results revealed that higher weekly DSL volume was associated with greater time > 85%HRmax in the first half (β = 0.00647; p = 0.002) and second half (β = 0.00764; p = 0.026). In the second half, weekly DSL was negatively associated with HSRD (β = −0.3068; p < 0.001) and SPRD (β = −0.0619; p < 0.001), and positively with HMLD (β = 0.3532; p = 0.002). Across the full match, weekly DSL was negatively associated with TDC (β = −0.5080; p = 0.002), HSRD (β = −0.4159; p < 0.001), SPRD (β = −0.0988; p < 0.001), HIACC (β = −0.0265; p = 0.003), and Edwards’ TRIMP (β = −0.2251; p = 0.001). Weekly DSL volume may represent an important monitoring tool providing useful information for practitioners aiming to manage fatigue and support competition performance maintenance; however, these findings should be interpreted cautiously until confirmed in larger samples. Full article

To address the bottlenecks of low sample efficiency and poor control accuracy in traditional single-layer reinforcement learning during autonomous driving overtaking, this paper proposes an overtaking decision and control system based on hierarchical reinforcement learning to decouple complex tasks in spatial and temporal dimensions. A heterogeneous two-layer architecture is constructed, where the upper layer adopts the Proximal Policy Optimization algorithm to generate macroscopic discrete decisions, while the lower layer employs Twin Delayed Deep Deterministic Policy Gradient combined with Long Short-Term Memory to achieve smooth continuous control of steering and acceleration by perceiving temporal features of dynamic obstacles. A composite reward mechanism, integrating hard safety constraints and soft efficiency incentives, is designed to balance safety, efficiency, and comfort. Experimental results in complex scenarios with multiple interfering vehicles and random lane-changing behaviors demonstrate that the proposed system improves the training convergence speed by approximately 30% within 500,000 steps compared to single-layer algorithms. In tests across varying traffic densities, the system achieves a 98.3% success rate in medium-density scenarios with a collision rate of only 0.6%. In high-density challenges, the success rate remains above 95%, with the collision rate reduced by about 80% compared to baseline models. Furthermore, the lateral control deviation is strictly limited to within 0.2 m, and the longitudinal safety distance remains stable above 5 m. This system provides a robust, high-efficiency paradigm for autonomous overtaking. Full article

Pedestrian interaction remains a central safety challenge for autonomous driving, particularly under non-compliant or adversarial pedestrian behavior. Existing research and evaluations predominantly test against rule-following pedestrians, leaving a gap in understanding how learning-based agents handle worst-case interactions. We introduce the Jaywalkers Library, a novel configurable benchmark in CARLA with three adversarial pedestrian archetypes (Intruder, Indecisive Crosser, and Protester). We evaluate a DreamerV3 agent trained with sparse rewards, where the only pedestrian-specific signal is a terminal collision penalty. Evaluation employs a frozen-policy protocol with explicit train–test separation. Safety behavior is decomposed into endpoint outcomes, evasion dynamics, and efficiency costs. Under nominal conditions, the agent achieves high route completion and generalizes to an unseen town, whereas under adversarial exposure, an archetype-sensitive evasion strategy emerges. The agent swerves at speed against dynamic pedestrians but decelerates against the slow-moving Protester. Collision rates reveal a counterintuitive difficulty ordering in which the Protester is the hardest, followed by the Intruder, with the Indecisive Crosser as the most survivable. These findings show that a sparse terminal penalty suffices for emergent pedestrian avoidance in a world-model agent, but that effectiveness is bounded by the world model’s ability to predict pedestrian persistence. Full article

Land cover classification (LCC) is a fundamental task in remote sensing, which enables effective environmental monitoring, agricultural planning, and disaster management. The existing approaches often rely on fine-tuning pre-trained models, which are not specifically designed for LCC, which lead to suboptimal performance in complex scenarios. To address these limitations, we propose the Cross-Module Attention Land Cover Network (CALCNet), a novel architecture developed from scratch. CALCNet follows a contracting and restoration backbone, where the contracting path extracts progressively abstract semantic features while reducing spatial resolution, and the restoration path recovers fine-grained spatial details through upsampling and skip connections. In addition, CALCNet integrates a cross-module attention mechanism that combines spatial attention and multi-scale feature selection to enhance feature representation. Furthermore, we applied a differential evolution-based neuron pruning strategy to create a compressed CALCNet variant, which retains high classification performance while reducing computational cost. The CALCNet is evaluated on four benchmark LCC datasets, AID, UCMerced_LandUse, NWPU_RESISC45, and EuroSAT, demonstrating strong performance across all benchmarks. Specifically, the model achieves classification accuracies of 98.09%, 99.47%, 99.19%, and 99.19%, respectively. The compressed CALCNet variant reduces computational cost to 78.55 million floating point operations (FLOPs) with a model size of 43 MB, while achieving improved inference speeds (38.32 frames/sec on CPU and 118.3 frames/sec on GPU), representing approximately 45–50% reduction in FLOPs and model storage. These results highlight that CALCNet is both highly accurate and computationally efficient, making it well suited for real-world LCC applications. Full article

Laser Powder Bed Fusion (PBF-LB) is currently one of the most versatile and adopted additive manufacturing technologies for printing metals. To take new PBF-LB machines into service, a thorough characterization and calibration is often necessary to get the desired output. This is commonly achieved empirically; however, data-driven methods have become more and more available over the last few years. This research explores the use of transfer learning (TL) to transfer process knowledge from an already-established source machine (Nikon SLM 500) to a target machine (Trumpf TruPrint 5000) with different hardware specifications. To predict the tensile properties of AlSi10Mg0.5 utilizing a minimal data set of merely 25 training samples, eight TL model variants, determined by their degrees of training freedom, were investigated. The results showed that TL is effective in transferring machine learning (ML)-based process models. High prediction accuracy was achieved on the target machine, with coefficient of determination ($R^2$) values reaching 75.5% for yield strength, 82.1% for ultimate tensile strength, and up to 92.0% for elongation at break in testing. Additionally, a weighted mean model ensemble of all eight single models was developed, including all eight TL variants, to enable higher prediction robustness. Validation trials for three different use cases confirmed the capability of the approach to optimize processing conditions, like increasing hatch scan speed by 167% to 292% while maintaining high mechanical performance. Additional microstructure analysis was given to support the findings. The results demonstrate a time- and resource-efficient approach for rapid industrialization of PBF-LB machines, combining ML-based process modeling with machine-specific data. Full article

Leakage identification in district heating networks is challenging because leakage-induced transient pressure waves often overlap with pressure disturbances triggered by routine operations such as valve regulation, pump speed variation, and emergency shut-off. In addition, the scarcity of high-quality labeled leakage samples limits the robustness of data-driven models under small-sample conditions. To address these issues, this study proposes a compact and moderately interpretable framework for multi-condition identification from transient pressure-wave signals, integrating signal preprocessing, handcrafted statistical feature extraction, multiclass ReliefF-based feature selection, and class-wise generative adversarial network augmentation in the selected feature space. A dataset containing four representative conditions, namely leakage, valve regulation, pump speed regulation, and emergency valve shut-off, was constructed using an integrated indoor district heating network testbed. After Hampel-based spike suppression and zero-phase Butterworth band-pass filtering within 0.5 to 300 Hz, time- and frequency-domain statistical features were extracted, and a compact subset was selected by multiclass ReliefF. A class-wise generative adversarial network was then used to augment the training set in feature space, while all evaluations were performed strictly on real samples. The results show that feature-space augmentation improves robustness and generalization under operational disturbances and noise. Using random forest as the representative classifier, Accuracy and Macro-F1 increased from 0.960 to 0.985, while leakage recall improved from 0.920 to 0.980. Further comparisons confirmed that the ReliefF-selected subset outperformed representative alternatives such as LASSO and mRMR. Overall, the proposed framework provides an effective solution for distinguishing leakage events from operational disturbances and offers practical support for online monitoring and intelligent operation of district heating networks. Full article

Objectives: Post-activation performance enhancement (PAPE) has been explored for its potential to improve performance in combat sports. This part II of the systematic review and meta-analysis investigated the acute effects of PAPE protocols on sport-specific performance outcomes and evaluated the influence of moderating variables, specifically competitive level and training experience. Methods: A PRISMA-guided search (2010–2024) identified 13 studies examining PAPE in combat sports athletes. Inclusion criteria required human trials using defined PAPE protocols and evaluating sport-specific tests, primarily the Frequency Speed of Kick Test (FSKT-10) and the Taekwondo-Specific Agility Test (TSAT). A random-effects meta-analysis (Hedges’ g) was conducted on data from 176 athletes. Results: The meta-analysis revealed a profound moderating effect of training status on PAPE responsiveness. For the FSKT-10, amateur athletes demonstrated large, significant improvements (g = 1.28, p < 0.001), whereas elite athletes showed trivial, non-significant changes (g = 0.11, p = 0.357). Similarly, athletes with <6 years of training experience exhibited substantially larger enhancements in both FSKT-10 (g = 1.60) and TSAT agility (g = −1.64) compared to their more experienced (>6 years) counterparts (g = 0.42 and g = −0.65, respectively). Furthermore, dynamic and biomechanically specific conditioning activities (e.g., repeated high-intensity techniques) were most effective at driving sport-specific potentiation. Conclusions: PAPE protocols may enhance acute sport-specific performance when utilizing dynamic, highly specific conditioning activities. However, a possible “ceiling effect” may blunt this potentiation in elite and highly experienced athletes, suggesting a potential need for highly individualized priming strategies at the top competitive levels, specifically in taekwondo. Full article

To investigate the influence of train operating speed on the transient characteristics of the pantograph–catenary arc, this paper establishes an integrated simulation model encompassing the traction network, electric locomotive, and arc. In this model, the traction network adopts a chain circuit model based on multi-conductor transmission line theory. The electric locomotive model considers the train body and the on-board transformer. For the pantograph–catenary offline arc, an improved Habedank model is employed, which takes the train operating speed and arc current as variables. Based on this model, this paper systematically investigates the variation patterns of arc electrical parameters and transient currents in each line of the traction network with train operating speed under pantograph–catenary offline. The simulation results indicate that as train speed increases, both the steady-state arc voltage and the maximum voltage at arc ignition rise, and the arc extinction time at current zero-crossing is prolonged. The peak arc currents on the contact wire, feeder, protective wire, and rails decrease, while the transient current on the ground wire increases. This study can provide a reference for the electromagnetic compatibility design, insulation coordination optimization, and electromagnetic protection of high-speed railway traction power supply systems. Full article