Search Results (66,248)

Two-speed transmissions can regulate the motor operating point by changing the transmission ratio of drive systems and are an effective approach to improving both dynamic performance and energy efficiency of battery electric vehicles. However, existing gear shift strategies rarely consider the impact of transmission operating states on shift rationality and system stability, leading to limited adaptability under complex driving conditions. To address this issue, a hierarchical fuzzy evaluation and gear shift strategy matching method based on transmission operating states is proposed. First, three basic strategies are designed. Then, shift frequency and gear duty ratio are introduced to characterize transmission behavior, and a hierarchical decision framework consisting of driving demand evaluation, transmission behavior evaluation, and strategy matching is constructed to enable adaptive selection among different strategies. Furthermore, a fuzzy shift frequency correction strategy is proposed to adjust shift thresholds online, thereby reducing frequent and unnecessary shifting. Finally, simulations are conducted under multiple typical driving cycles based on a vehicle model, and experimental validation is carried out using a high-speed dual motor load test bench. The results demonstrate that the proposed strategy can effectively balance dynamic performance and energy efficiency while reducing unnecessary shifts. Full article

Federated Learning (FL) is a machine learning technique that preserves data privacy and security by training models directly on decentralized edge network devices. This generates substantial communication overhead due to the repeated exchange of model updates across numerous edge network devices. Quantization has tackled this challenge by reducing communication overhead and computational costs by quantizing model updates. Although selecting the most suitable quantization level to balance communication efficiency and model accuracy is challenging, failing to achieve this balance results in excessive compression, leading to accuracy degradation due to the lossy nature of the quantization technique. This challenge was tackled in this paper via a Quantile-based lossless compression method named Pcodec, which implements lossless compression in the FL context. Pcodec is a Quantile-based lossless compression algorithm designed for numerical data that utilizes mode identification with delta encoding and binning, where binning groups similar values into entropy-coded bins and stores the exact offset within each bin, thus achieving high compression ratios and efficient processing speed. Using MNIST and CIFAR-10 datasets and models such as CNN and ResNet18, we demonstrate that Pcodec achieves up to 58.19% size reduction with no accuracy loss compared to standard quantization methods. The experiments showed that the proposed Quantile-based compression approach in FL reduces up to 2.81× the communication overhead between each server and edge network device while maintaining the accuracy. In comparison to quantization, the Quantile approach reduced the communication overhead by 2.74×, tackling the main challenge of FL context by reducing communication overhead with a remarkably high compression ratio while maintaining the model’s accuracy. Full article

Three-dimensional geological structural modelling provides the geometric framework for sub-surface exploration and development. However, conventional workflows, driven primarily by seismic interpretation, often lack explicit constraints from expert knowledge and are difficult to update when interpretations evolve. In particular, the conventional surface-based workflow follows a sequential pipeline—from seismic interpretation through manual intersection editing to surface generation and pillar gridding—in which geological knowledge is embedded only implicitly through operator-dependent parameter tuning, making knowledge transfer and model reproducibility difficult. This study proposes an intelligent modelling methodology guided by a geological structure knowledge graph. The method includes: (i) a three-tier knowledge architecture (TKA) that formalises domain knowledge in entity, relationship and inference layers using RDF/OWL; (ii) a knowledge-driven intersection line generation algorithm (KILGA) coupled with a hierarchical adaptive mesh refinement scheme based on a posteriori error estimation (HAMR-APEE) to integrate geological constraints and mitigate boundary aliasing; and (iii) a bidirectional linkage mechanism between the knowledge graph and 3D models to support incremental updates following knowledge revision. The approach is validated in three petroliferous basins in China (Ordos, Qaidam and Sichuan), representing micro-amplitude, thrust-nappe and deep complex structural styles. Compared with a conventional surface-based workflow, the proposed method reduces modelling RMSE from 15–20 m to 5–8 m, improves geological reasonableness from ~85% to >95%, and shortens modelling cycles from months to weeks. These results demonstrate that explicit integration of formalised geological knowledge into the modelling pipeline can substantially enhance both accuracy and efficiency across a range of structural settings. Full article

Chromatin conformation capture technologies have revealed the complex 3D organization of the genome and its key regulatory role. Single-cell Hi-C (scHi-C) maps this architecture at single-cell level, but its sparse nature makes data interpretation challenging, and tools for their analysis remain limited. Here, we present a physics-based framework that combines polymer modeling with computational methods to reconstruct full 3D genome structures from sparse scHi-C data. Using both artificial and experimental data, we show that our approach imputes missing contacts and recovers accurate structures validated against independent Hi-C and established polymer models. Applied to scHi-C from a 15 Mb region of human HeLa-S3 cells as a case study, the method uncovers distinct structural classes defined by the spatial distribution of chromatin binding domains. The reconstructed models enable robust downstream analyses, including the identification of single-cell topologically associated domains (TADs), which appear highly variable across cells yet tend to accumulate around those observed in bulk. Importantly, the inferred 3D polymer models capture diverse epigenetic signatures, with active chromatin domains exhibiting greater structural variability than repressive ones across single cells. Overall, our study provides a mechanistic and interpretable framework to analyze sparse scHi-C data, highlighting how polymer physics can be leveraged to uncover genome architecture and its functional variability at single-cell resolution. Full article

This paper studies observer-based stabilization of a normalized incommensurate fractional-order discrete-time SI benchmark model for computer-virus propagation. The model is formulated with Caputo-like fractional-difference operators and allows the susceptible and infected compartments to have different memory orders. In contrast with a predictive malware-forecasting model, the proposed system is explicitly treated as a dimensionless benchmark for qualitative analysis and control design. To clarify how the benchmark can be connected to empirical cybersecurity data, the revised formulation includes a calibration and fractional-order selection procedure based on normalized infection telemetry, admissible parameter sets, and loss minimization. The incommensurate orders are therefore interpreted as identifiable modeling parameters, not as arbitrary constants. The plant, observer, and control laws are formulated on the integer update grid, and the memory terms are implemented through the equivalent Volterra-type convolution representation. A nonlinear Luenberger-type observer is proposed under infected-state measurements, which is justified as a detectability-based cyber-monitoring configuration rather than a full observability assumption. The observer gain design, the full-state feedback design, and the observer-based output-feedback design are derived from first-order linearized incommensurate fractional-order models. The resulting criteria are expressed through characteristic-root conditions associated with linear incommensurate Caputo-type fractional-order difference systems. The scope of the theoretical claims is made explicit: the results provide local linearized-design guarantees and do not establish global or semi-global nonlinear stabilization. The nonlinear residuals, measurement-noise channel, incomplete-measurement formulation, and limitations of the linearized characteristic-root approach are stated explicitly so that the numerical section can assess robustness, sensitivity, and the effective region of attraction of the nonlinear closed loop. Full article

Background: Major depressive disorder (MDD) affects not only patients but also their relatives, who often carry substantial emotional and practical responsibilities. Family psychoeducation has shown benefits in several psychiatric conditions, yet its effects on relatives of adults with MDD remain insufficiently documented. Aim: We aimed to examine whether a brief group-based family psychoeducation program improves relatives’ well-being and perceived family functioning compared with an active social-support control condition and to explore whether intervention response varies across caregiver subgroups. Methods: Relatives of patients with MDD were enrolled in a two-center randomized controlled trial and allocated to either a four-week psychoeducation program or a structurally matched social-support group. Outcomes were assessed at baseline, post-intervention, and 9-month follow-up using the WHO-5 Well-Being Index (WHO-5), the Family Attitude Scale (FAS), and the Family Assessment Device (FAD). Repeated-measures ANCOVA models tested time × group interactions, with and without adjustment for age and gender. Results: Eighty-nine relatives were included (n = 43 intervention; n = 46 control). No significant intervention effects were observed on well-being (WHO-5) or family attitudes (FAS). A significant time × group interaction was found only for the FAD affective involvement subscale, with short-term improvement in the intervention group compared with deterioration in the control group. Subgroup analyses suggested a heterogeneous pattern of response, with more consistent patterns of improvement among older relatives (≥50 years), non-partner relatives, and those with a history of psychiatric treatment, while effects appeared more limited among partners and younger participants. Women showed worsening communication in the intervention group, whereas men demonstrated improvements in selected well-being and general functioning outcomes. Conclusions: The intervention showed limited effects at the whole-sample level, but exploratory subgroup analyses suggested that responsiveness to brief family psychoeducation may vary according to caregiver characteristics. These findings support further investigation of more targeted psychoeducational approaches for relatives of adults with MDD. Full article

This paper presents a generalized epidemic modeling framework based on g-tempered Caputo fractional derivatives with discrete time delays. The proposed approach incorporates nonlocal memory effects, nonlinear temporal scaling, and delayed epidemiological responses within a unified mathematical structure. The introduction of the nonlinear time transformation $g\left(t\right)$ and the tempering parameter $\lambda$ eliminates the unrealistic infinite-memory behavior associated with classical power-law kernels while simultaneously introducing new challenges related to parameter identifiability and inverse problems. We investigate the structural properties of the resulting dynamical systems and show that the associated inverse problem is inherently ill-posed. To illustrate the practical implications of these results, the framework is applied to a delayed SIQR epidemiological model. Numerical simulations are performed using a generalized L1-type scheme adapted to delayed fractional histories, and a multi-phase parameter estimation procedure is proposed to address the ill-posedness of the reconstruction problem. The results demonstrate the ability of the model to capture both short- and long-term memory effects in epidemic evolution while highlighting the challenges of statistical identifiability in generalized fractional systems. Full article

This study presents an integrated experimental–numerical approach for evaluating the wear behavior of three non-standardized hypereutectic aluminum–silicon (Al–Si) piston alloys based on the AlSi25CuCr system, namely AlSi25Cu4Cr (M1), AlSi25Cu5Cr (M3), and AlSi25Cu5Cr (M5). The wear coefficient was determined experimentally under boundary-lubrication conditions, while the contact conditions in the piston–cylinder system were evaluated using Finite Element Analysis (FEA) and implemented within the Archard wear model. The results reveal a pronounced inconsistency between hardness and wear resistance. Although hardness increases from 1363 MPa (M1) to 1677 MPa (M5), the corresponding wear depth increases from 13.94 nm to 27.61 nm per engine cycle. This behavior is attributed to differences in microstructural characteristics, particularly the morphology and distribution of silicon particles and intermetallic phases, which significantly influence the tribological performance of hypereutectic Al–Si alloys. The experimentally determined wear coefficient K also shows a significant increase, rising from 12.14 × 10⁻⁵ (M1) to 29.59 × 10⁻⁵ (M5). The lowest wear is observed for alloy M1, whereas M5 exhibits the poorest tribological performance. These findings demonstrate that microstructural characteristics, particularly the morphology and distribution of silicon particles and intermetallic phases, have a dominant influence over hardness in governing wear behavior. The main scientific contribution lies in the direct coupling of experimentally determined material properties with realistically simulated contact conditions, enabling a quantitative and physically consistent comparison of piston alloys under identical operating regimes. The proposed methodology provides a reliable framework for material selection and optimization of piston alloys with enhanced wear resistance. Full article

Against the backdrop of the expansion of sustainable finance and the growing relevance of ESG-related information, disclosure and regulation, this paper examines the dynamic relationship between sustainability-related uncertainty and ESG equity market volatility in a global framework. Sustainability-related uncertainty is proxied by the Global GDP-Weighted ESG-Based Sustainability Uncertainty Index (ESGUI), while ESG market volatility is measured through a monthly proxy constructed from estimated daily conditional variances obtained from GJR-GARCH(1,1) models with Student-t innovations. The paper explicitly distinguishes sustainability-related uncertainty, understood as ambiguity in the ESG information environment, from ESG market volatility, understood as market-pricing instability in ESG equity benchmarks. Empirically, the study combines bootstrap full-sample Granger-causality tests, parameter-stability diagnostics, and rolling-window bootstrap analysis. Robustness and extended analyses use an EGARCH-based volatility proxy, alternative rolling-window lengths, macro-financial controls, an emerging-market ESG benchmark, impulse-response analysis, forecast-error variance decomposition, and out-of-sample forecasting tests. The full-sample results indicate an asymmetric predictive pattern: ESG market volatility contains Granger-causal predictive information for changes in sustainability-related uncertainty, whereas the reverse direction is not supported on average. However, parameter-stability tests reject constancy, and rolling-window evidence shows that predictive effects arise episodically in both directions, with changes in sign, magnitude and significance. The uncertainty-to-volatility channel becomes statistically relevant and locally stronger during stress episodes, especially around 2019–2021, while macro-control results show that broader market stress absorbs part of the volatility-to-uncertainty linkage. The findings indicate a regime-dependent uncertainty–volatility nexus and support dynamic approaches to ESG risk monitoring, portfolio management and regulatory communication. All results are interpreted as predictive evidence, not structural causality. Full article

Rising ambient temperatures and the increasing frequency of training and competition in hot climates have made heat stress a major challenge in football. Under such conditions, players experience greater cardiovascular and thermoregulatory strain, faster glycogen use, higher perceived exertion, and progressive impairment in repeated high-intensity actions and decision-making. These responses have intensified interest in nutritional strategies that might complement heat acclimation, hydration/electrolyte planning, cooling practices, and recovery management. This narrative review critically synthesizes current evidence on nutritional interventions that may be relevant to football performed in the heat, with emphasis on hydration and electrolyte replacement, carbohydrate–protein strategies, taurine, branched-chain amino acids (BCAAs), creatine, menthol, antioxidant- and nitrate-related approaches, and selected multi-ingredient products. Across the available literature, hydration/electrolyte planning and carbohydrate–protein feeding remain the practical foundation, menthol appears most consistently useful for perceptual cooling, creatine seems safe and potentially helpful for repeated-sprint support, and taurine is promising but still supported by relatively few trials. By contrast, evidence for BCAAs, antioxidants, nitrates, and caffeine as stand-alone heat strategies, as well as for many compound supplements, remains inconsistent, context-specific, or too indirect for strong football-specific endorsement. Overall, the evidence base remains heterogeneous in study quality, protocol design, exercise mode, and sport specificity. A substantial proportion of the available data is derived from cycling, endurance, or laboratory heat-exercise models rather than football-specific trials. Accordingly, any practical recommendation should be interpreted cautiously and embedded within broader heat-management strategies. Future work should prioritize ecologically valid randomized controlled trials in football or football-like intermittent protocols, with transparent reporting of dose, timing, perceptual outcomes, and match-relevant performance measures. Full article

To improve the accuracy of cutter wear and service life prediction for disc cutters, an improved normal force model is established based on the traditional CSM model by considering the supporting force and friction acting on the disc cutter from the side crushing zones. By incorporating the micro-mechanism of abrasive wear, an analytical model for the radial wear of the disc cutter and a service life prediction model are derived. Meanwhile, a regression model for cutter wear is established based on field operational parameters and cutter wear data. The mechanistic model is validated using field data from a tunnel project in Guangdong, China, and the results show that the average prediction errors of wear and service life are 8.13% and 8.85%, respectively, which are significantly lower than those of the traditional CSM model. Further comparative analysis between the two types of models is conducted, and the results indicate that the regression model achieves average prediction errors of 7.57% and 7.86% for wear and service life, respectively, showing higher prediction accuracy than the mechanistic model. The results demonstrate that the mechanistic model is suitable for revealing the wear mechanism of the disc cutter, while the regression model is more applicable for engineering prediction, and the two approaches can be used in a complementary manner. Full article

To ensure a reliable electrical isolation point in power systems, the seismic performance assessment of disconnect switches is of critical importance for maintaining operational continuity under earthquake excitations. In this study, a hybrid method combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network is proposed for the seismic intelligent classification and response prediction of disconnect switches. Unlike conventional approaches that rely on finite element simulations or shake table tests with high computational costs, the proposed method learns directly from raw ground motion records. The CNN component is designed to capture local frequency characteristics of input ground motions, enabling automatic classification into low-, medium-, or high-frequency categories. Subsequently, category-specific LSTM models are established to map the ground motion time series to multi-dimensional performance indicators of the disconnect switch. These indicators include top absolute accelerations, bottom shear forces, and relative deformations of porcelain posts. A training set comprising 102 ground motion records is constructed based on numerical simulations of a validated simplified model, while another testing set comparing 21 ground motion records are employed to validate the performance of predicted models. Training and validation results demonstrate that the CNN achieves a great classification accuracy. The LSTM predictions show good agreement with the computed time-history responses, with errors of root-mean-square responses generally within 10%. The proposed method provides a rapid, data-driven alternative to traditional seismic analysis, significantly reducing computational time while preserving prediction fidelity. It also enables the parallel prediction of multiple coupled performance indicators, which is not readily achievable by existing single-output surrogate models. Full article

Post-stroke depression (PSD) is the most prevalent neuropsychological disorder among stroke survivors, affecting over 30% of patients. It significantly impairs patients’ quality of life and imposes a substantial burden on individuals, families, and society. Currently, the primary treatment for PSD focuses on conventional antidepressant therapies, with a lack of innovative approaches. Therefore, there is an urgent need to develop novel targeted therapies for PSD. This review synthesizes PSD pathogenesis as a multi-system network disorder involving monoamine deficits, neuroinflammation, HPA axis dysfunction, and neurotrophic imbalance. Within this framework, mesenchymal stem cells (MSCs) transplantation, as an emerging therapeutic strategy, may exert beneficial effects through anti-inflammatory, neuroprotective mechanisms, and the provision of neurotrophic factors. This review provides a preclinical framework that highlights the potential of MSC-based strategies, while emphasizing the need for further validation in PSD-specific models before clinical translation. Full article

The complex fracture networks, multiphase flow behavior, and nonlinear flow mechanisms induced by hydraulic fracturing in horizontal wells of shale oil reservoirs pose significant challenges to production evaluation. In this study, a semi-empirical productivity evaluation method for multiphase shale oil systems is developed by integrating production dynamics with flow mechanisms. Three-phase productivity equations for oil, gas, and water are established, explicitly incorporating the underlying flow mechanisms. A nonlinear flow index is introduced to characterize both the stress sensitivity of fractures and the threshold pressure gradient in the matrix. Key unknown parameters, including oil saturation, water cut, stimulated reservoir volume, and nonlinear coefficients, are determined through history matching of production data. The impacts of geological properties, fracturing parameters, operating conditions, and nonlinear flow parameters on oil–gas productivity are systematically investigated using the proposed multiphase semi-empirical model. The model is validated against production data from fractured horizontal wells in a field case, demonstrating its accuracy and applicability. Furthermore, the model enables reliable production forecasting based on the derived productivity relationships. The proposed approach provides a practical and efficient tool for rapid post-fracturing productivity evaluation in shale oil reservoirs. Full article

The quality and integrity of aluminothermic rail welds are strongly governed by the thermal conditions involved during preheating, pouring and cooling stages of the process. In this study, a simplified numerical framework is presented, based on the finite volume method and implemented in the open-source software OpenFOAM^® version 7, to predict the heat transfer and solidification processes. Within this framework, the preheating stage is simulated by employing a heat flux profile derived from experimental measurements, while the mould filling stage is neglected under the assumption of instantaneous pouring of the molten metal. The steel–slag multiphase system is treated using the Volume of Fluid method, whereas melting and solidification are captured using the enthalpy-porosity approach on a fixed Eulerian grid. The numerical framework is validated for a rapid welding process with short preheating procedure, consistent with typical industrial practice for rail welding. The predicted temperature histories during the preheating stage show sufficiently good agreement with the experimental measurements. Subsequently, the cooling stage is validated for a molten metal temperature of 2200${}^{\circ }\mathrm{C}$ (≈$2200+273$$\mathrm{K}$). The predicted width of the fusion zone is compared with experimental data, showing reasonably good agreement in the railhead region, while an underestimation is observed in the rail web and rail foot regions. Furthermore, a systematic parametric investigation is conducted by varying two key process parameters, namely the molten metal temperature examined at four distinct levels ranging from 1800${}^{\circ }\mathrm{C}$ (≈$1800+273$$\mathrm{K}$) to 2400${}^{\circ }\mathrm{C}$ (≈$2400+273$$\mathrm{K}$), and the active preheating duration, varied across six values ranging from 90$\mathrm{s}$ ( $90/60$$\min$)– 390$\mathrm{s}$ ( $390/60$$\min$), in order to assess their influence on the cooling stage. The numerical results provide detailed insight into the temporal evolution of the thermal field and its influence on the formation and extent of the fusion zone and heat-affected zone. The results demonstrate that, despite simplifications, the model captures the dominant thermal phenomena of the process and offers a computationally efficient tool for parameter studies and process optimisation. Full article