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Squall lines are some of the most common types of mesoscale cloud systems in tropical and subtropical regions. Thunderstorms associated with these systems are among the major causes of weather-related disasters and socio-economic losses in many regions across the world. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating squall line features over the South African Highveld region. Two squall line cases were selected based on the availability of South African Weather Service (SAWS) weather radar data: 21 October 2017 (early austral summer) and 31 January–1 February 2018 (late austral summer). The European Centre for Medium-Range Weather Forecasts ERA5 datasets were used as observational proxies to analyze squall line features and compare them with WRF simulations. Mid-tropospheric perturbations were observed along westerly waves in both cases. These perturbations were coupled with surface troughs over central interior together with the high-pressure systems to the south and southeast of the country creating strong pressure gradients over the plateau, which also transports relative humidity onshore and extending to the Highveld region. The 2018 case also had a zonal structured ridging High, which was responsible for driving moisture from the southwest Indian Ocean towards the eastern parts of South Africa. Both ERA5 and WRF captured onshore near surface (800 hPa) winds and high-moisture contents over the eastern parts of the Highveld. A well-defined dryline was observed and well simulated for the 2017 event, while both ERA5 and WRF did not show any dryline for the 2018 case that was triggered by orography. While WRF successfully reproduced the synoptic-scale processes of these extreme weather events, the simulated rainfall over the area of interest exhibited a broader spatial distribution, with large-scale precipitation overestimated and convective rainfall underestimated. Our study shows that models are able to capture these systems but with some shortcomings, highlighting the need for further improvement in forecasts. Full article

Compressor blades are subjected to multiaxial loads during operation. Using uniaxial life prediction formulas to predict their fatigue life can result in significant errors. Therefore, by analyzing the loading conditions of the blades, a fatigue life prediction model suitable for compressor blades was developed. This model was established by applying the load of a specific engine type to a notched bar specimen and considering the gradient and strengthening effects. Firstly, the parameters of the SWT model were used as the damage parameters to determine the critical plane location based on the principle of coordinate transformation, and these results were compared with the actual fracture angles. Additionally, the physical mechanisms of multiaxial fatigue crack initiation and propagation were investigated at the microscopic level. Secondly, the non-uniform stress field on the critical plane was obtained using the finite element method. The stress distribution from the critical point to the specimen’s principal axis was extracted and normalized to calculate the equivalent stress gradient factor. Finally, the results of the comprehensive fatigue life prediction model were computed. Comparisons between the calculated results of the proposed model, the SWT model, and the Shang model with the experimental fatigue life showed that the prediction accuracy of the proposed model is higher than that of the SWT model and the Shang Deguang model. Full article

This paper presents a thermal study of a lithium-ion traction battery with different cooling configurations during simulated city driving and high-power charging. Four liquid cooling configurations—single or triple plates with straight or U-shaped tubes—were evaluated using finite element models in the Q-Bat Toolbox for MATLAB. Simulations were conducted using the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and a high-current charging profile based on the CHAdeMO standard (up to 400 A). The results indicate that while cooling is not strictly necessary under typical driving conditions, it significantly improves thermal stability and reduces peak temperatures. The best configuration reduced peak cell temperatures by 1.96% during driving and by 16% during fast charging. The cooling system also minimized temperature gradients within the battery, reducing the risk of degradation. Box-plot analysis confirmed that an efficient cooling system stabilizes the temperature distribution and smooths out extreme values. The results highlight the importance of thermal management for extending battery life and ensuring safe operation, particularly during fast charging conditions. Full article

To foster the performance of wireless communication while saving energy, the integration of Intelligent Reflecting Surfaces (IRS) into autonomous vehicle (AV) communication networks is considered a powerful technique. This paper proposes a novel IRS-assisted vehicular communication model that combines Lagrange optimization and Gradient-Based Phase Optimization to determine the optimal transmission power, optimal interference transmission power, and IRS phase shifts. Additionally, the proposed model help increase the Signal-to-Interference-plus-Noise Ratio (SINR) by utilizing IRS, which leads to maximizes energy efficiency and the achievable data rate under a variety of environmental conditions, while guaranteeing that resource limits are satisfied. In order to represent dense vehicular environments, practical constraints for the system model, such as IRS reflection efficiency and interference, have been incorporated from multiple sources, namely, Device-to-Device (D2D), Vehicle-to-Vehicle (V2V), Vehicle-to-Base Station (V2B), and Cellular User Equipment (CUE). A Lagrangian optimization approach has been implemented to determine the required transmission interference power and the best IRS phase designs in order to enhance the system performance. Consequently, a one-dimensional convolutional neural network has been implemented for the optimized data provided by this framework as training input. This deep learning algorithm learns to predict the required optimal IRS settings quickly, allowing for real-time adaptation in dynamic wireless environments. The obtained results from the simulation show that the combined optimization and prediction strategy considerably enhances the system reliability and energy efficiency over baseline techniques. This study lays a solid foundation for implementing IRS-assisted AV networks in real-world settings, hence facilitating the development of next-generation vehicular communication systems that are both performance-driven and energy-efficient. Full article

Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning (BC) and long short-term memory (LSTM) networks. This method can achieve autonomous learning of high-precision control policy without establishing an accurate system dynamics model. Motivated by the memory and prediction functions of biological neural systems, an LSTM module is embedded into the policy network of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This structure captures temporal state patterns more effectively, enhancing adaptability to trajectory variations and resilience to delays or disturbances. Compared to memoryless networks, the LSTM-based design better replicates biological time-series processing, improving tracking stability and accuracy. In addition, behavior cloning is employed to pre-train the DRL policy using expert demonstrations, mimicking the way animals learn from observation. This biomimetic plausible initialization accelerates convergence by reducing inefficient early-stage exploration. By combining offline imitation with online learning, the TD3-LSTM-BC framework balances expert guidance and adaptive optimization, analogous to innate and experience-based learning in nature. Simulation experimental results confirm the superior robustness and tracking accuracy of the proposed method, demonstrating its potential as a control solution for autonomous UAVs. Full article

Carbonate fracture–cavity reservoirs are significant oil and gas reservoirs globally, and their efficient development is influenced by the connectivity between fracture–cavity units within the reservoir. These reservoirs primarily consist of large caves, dissolution holes, and natural fractures, which serve as the primary storage and flow spaces. The S91 unit of the Tarim Oilfield is a karstic fracture–cavity reservoir with shallow coverage. It exhibits significant heterogeneity in the fracture–cavity reservoirs and presents complex connectivity between the fracture–cavity bodies. The integration of static and dynamic data, including geology, well logging, seismic, and production dynamics, resulted in the development of a set of static and dynamic connectivity evaluation processes designed for highly heterogeneous fracture–cavity reservoirs. Methods include using structural gradient tensors and stratigraphic continuity attributes to delineate the boundaries of caves and holes; performing RGB fusion analysis of coherence, curvature, and variance attributes to characterize large-scale fault development features; applying ant-tracking algorithms and fracture simulation techniques to identify the distribution and density characteristics of fracture zones; utilizing 3D visualization technology to describe the spatial relationship between fracture–cavity units and large-scale faults and fracture development zones; and combining dynamic data to verify interwell connectivity. This process will provide a key geological basis for optimizing well network deployment, improving water and gas injection efficiency, predicting residual oil distribution, and formulating adjustment measures, thereby improving the development efficiency of such complex reservoirs. Full article

The rapid development of wireless communication technology is leading to increasingly scarce spectrum resources, making efficient utilization a critical challenge. This paper proposes a Convolutional Neural Network–Long Short-Term Memory-Integrated Gradient-Weighted Class Activation Mapping (GC-CNN-LSTM) model, aimed at enhancing the accuracy of long-term spectrum prediction across multiple frequency bands and improving model interpretability. First, we achieve multi-frequency long-term spectrum prediction using a CNN-LSTM and compare its performance against models including LSTM, GRU, CNN, Transformer, and CNN-LSTM-Attention. Next, we use an improved Grad-CAM method to explain the model and obtain global heatmaps in the time–frequency domain. Finally, based on these interpretable results, we optimize the input data by selecting high-importance frequency points and removing low-importance time segments, thereby enhancing prediction accuracy. The simulation results show that the Grad-CAM-based approach achieves good interpretability, reducing RMSE and MAPE by 6.22% and 4.25%, respectively, compared to CNN-LSTM, while a similar optimization using SHapley Additive exPlanations (SHAP) achieves reductions of 0.86% and 3.55%. Full article

This study develops a thermo-mechanical damage (TMD) model for predicting damage evolution in heterogeneous rock materials after heat treatment. The TMD model employs a Weibull distribution to characterize the spatial heterogeneity of the mechanical properties of rock materials and develops a framework that incorporates thermal effects into a nonlocal gradient damage model, thereby overcoming the mesh dependency issue inherent in homogeneous local damage models. The model is validated by numerical simulations of a notched cruciform specimen subjected to combined mechanical and thermal loading, confirming its capability in thermo-mechanical coupled scenarios. Sensitivity analysis shows increased material heterogeneity promotes localized, X-shaped shear-dominated failure patterns, while lower heterogeneity produces more diffuse, network-like damage distributions. Furthermore, the results demonstrate that thermal loading induces micro-damage that progressively spreads throughout the specimen, resulting in a significant reduction in both overall stiffness and critical strength; this effect becomes increasingly pronounced at higher heating temperatures. These findings demonstrate the model’s ability to predict the mechanical behavior of heterogeneous rock materials under thermal loading, offering valuable insights for safety assessments in high-temperature geotechnical engineering applications. Full article

In modern intelligent buildings, the control of Building Energy Systems (BES) faces increasing complexity in balancing energy costs, thermal comfort, and operational flexibility. Traditional centralized or flat deep reinforcement learning (DRL) methods often fail to effectively handle the multi-timescale dynamics, large state–action spaces, and strict constraint satisfaction required for real-world energy systems. To address these challenges, this paper proposes an energy policy-aware layered control architecture that combines Virtual Energy Storage System (VESS) modeling with a novel Dynamic Constraint-Aware Policy Optimization (DCPO) algorithm. The VESS is modeled based on the thermal inertia of building envelope components, quantifying flexibility in terms of virtual power, capacity, and state of charge, thus enabling BES to behave as if it had embedded, non-physical energy storage. Building on this, the BES control problem is structured using a hierarchical Markov Decision Process, in which the upper level handles strategic decisions (e.g., VESS dispatch, HVAC modes), while the lower level manages real-time control (e.g., temperature adjustments, load balancing). The proposed DCPO algorithm extends actor–critic learning by incorporating dynamic policy constraints, entropy regularization, and adaptive clipping to ensure feasible and efficient policy learning under both operational and comfort-related constraints. Simulation experiments demonstrate that the proposed approach outperforms established algorithms like Deep Q-Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3). Specifically, it achieves a 32.6% reduction in operational costs and over a 51% decrease in thermal comfort violations compared to DQN, while ensuring millisecond-level policy generation suitable for real-time BES deployment. Full article

To address thermal management challenges in CR400BF high-speed EMU electrical cabinets—stemming from heterogeneous component integration, multi-condition dynamic thermal loads, and topological configuration variations—a dual-metric-driven finite element model calibration method is proposed using ANSYS Workbench. A multi-objective optimization function, constructed via the coefficient of determination ($R^2$) and root mean square error ($RMSE$), integrates gradient descent to inversely solve key parameters, achieving precise global–local model matching. This establishes an equivalent model library of 52 components, enabling rapid development of multi-physical-field coupling models for electrical cabinets via parameterization and modularization. The framework supports temperature field analysis, thermal fault prediction, and optimization design for multi-topology cabinets under diverse operating conditions. Validation via simulations and real-vehicle tests demonstrates an average temperature prediction error $\le 10\%$, verifying reliability. A thermal management optimization scheme is further developed, constructing a full-process technical framework spanning model calibration to control for electrical cabinet thermal design. This advances precision thermal management in rail transit systems, enhancing equipment safety and energy efficiency while providing a scalable engineering solution for high-speed train thermal design. Full article

This study presents a three-dimensional numerical analysis of natural convection during the postharvest storage of corn and wheat in a galvanized steel silo with a conical roof and floor, measuring 3 m in radius and 18.7 m in height, located in the Bajío region of Mexico. Simulations were carried out specifically for December, a period characterized by cold ambient temperatures (10–20 °C) and comparatively lower solar radiation than in warmer months, yet still sufficient to induce significant heating of the silo’s metallic surfaces. The governing conservation equations of mass, momentum, energy, and species were solved using the finite volume method under the Boussinesq approximation. The model included grain–air sorption equilibrium via sorption isotherms, as well as metabolic heat generation: for wheat, a constant respiration rate was assumed due to limited biochemical data, whereas for corn, respiration heat was modeled as a function of grain temperature and moisture, thereby more realistically representing metabolic activity. The results, obtained for December storage conditions, reveal distinct thermal and hygroscopic responses between the two grains. Corn, with higher thermal diffusivity, developed a central thermal core reaching 32 °C, whereas wheat, with lower diffusivity, retained heat in the upper region, peaking at 29 °C. Radial temperature profiles showed progressive transitions: the silo core exhibited a delayed response relative to ambient temperature fluctuations, reflecting the insulating effect of grain. In contrast, grain at 1 m from the wall displayed intermediate amplitudes. In contrast, zones adjacent to the wall reached 40–41 °C during solar exposure. In comparison, shaded regions exhibited minimum temperatures close to 15 °C, confirming that wall heating is governed primarily by solar radiation and metal conductivity. Axial gradients further emphasized critical zones, as roof-adjacent grain heated rapidly to 38–40 °C during midday before cooling sharply at night. Relative humidity levels exceeded 70% along roof and wall surfaces, leading to condensation risks, while core moisture remained stable (~14.0% for corn and ~13.9% for wheat). Despite the cold ambient temperatures typical of December, neither temperature nor relative humidity remained within recommended safe storage ranges (10–15 °C; 65–75%). These findings demonstrate that external climatic conditions and solar radiation, even at reduced levels in December, dominate the thermal and hygroscopic behavior of the silo, independent of grain type. The identification of unstable zones near the roof and walls underscores the need for passive conservation strategies, such as grain redistribution and selective ventilation, to mitigate fungal proliferation and storage losses under non-aerated conditions. Full article

Mechanical models are capable of simulating the deformation and stress distribution of oocytes under external forces, thereby providing insights into the underlying mechanisms of intracellular mechanical responses. Interactions with micromanipulation tools involve forces like compression and punction, which are effectively analyzed using principles of solid mechanics. Alternatively, fluid–structure interactions, such as shear stress at fluid junctions or pressure gradients within microchannels, are best described by a multiphase flow model. Developing the two models instead of a single comprehensive model is necessary due to the distinct nature of cell–tool interactions and cell–fluid interactions. In this study, we developed a finite element (FE) model of porcine oocytes that accounts for the viscoelastic properties of the zona pellucida (ZP) and cytoplasm for the case when the oocytes interacted with a micromanipulation tool. Atomic force microscopy (AFM) was employed to measure the Young’s modulus and creep behavior of these subcellular components that were incorporated into the FE model. When the oocyte was solely interacting with the fluids, we simulated oocyte deformation in microfluidic channels by modeling the oocyte-culture-medium system as a three-phase flow, considering the non-Newtonian behavior of the oocyte’s components. Our results show that the Young’s modulus of the ZP and cytoplasm were determined to be 7 kPa and 1.55 kPa, respectively, highlighting the differences in the mechanical properties between these subcomponents. Using the developed layered FE model, we accurately simulated oocyte deformation during their passage through a narrow-necked micropipette, with a deformation error of approximately 5.2% compared to experimental results. Using the three-phase flow model, we effectively simulated oocyte deformation in microfluidic channels under various pressures, validating the model’s efficacy through close agreement with experimental observations. This work significantly contributes to assessing oocyte quality and serves as a valuable tool for advancing cell mechanics studies. Full article

Time synchronization is critical for task-oriented and time-sensitive Industrial Internet of Things (IIoT) systems. Nevertheless, achieving high-precision synchronization with low communication overhead remains a key challenge due to the constrained resources of IIoT devices. In this paper, we propose a single-timestamp time synchronization scheme that significantly reduces communication overhead by utilizing the mechanism of AP to periodically collect sensor device data. The reduced communication overhead alleviates network congestion, which is essential for achieving low end-to-end latency in synchronized IIoT networks. Furthermore, to mitigate the impact of random delay noise on clock parameter estimation, we propose a noise-robust-based Maximum Likelihood Estimation (NR-MLE) algorithm that jointly optimizes synchronization accuracy and resilience to random delays. Specifically, we decompose the collected timestamp matrix into two low-rank matrices and use gradient descent to minimize reconstruction error and regularization, approximating the true signal and removing noise. The denoised timestamp matrix is then used to jointly estimate clock skew and offset via MLE, with the corresponding Cramér–Rao Lower Bounds (CRLBs) being derived. The simulation results demonstrate that the NR-MLE algorithm achieves a higher clock parameter estimation accuracy than conventional MLE and exhibits strong robustness against increasing noise levels. Full article

The temperature rise in mass concrete structures, caused by the exothermic process of cement hydration and concurrent heat exchange with the environment, results in thermal gradients between the core and outer layers of the structure. These gradients generate tensile stresses that may exceed the early age tensile strength of concrete, leading to cracking. Therefore, reliable prediction of the temperature rise and associated thermal gradients is essential for assessing the risk of early age thermal cracking. Traditional methods for predicting temperature development rely on numerical simulations and simplified analytical approaches, which are often time-consuming and impractical for rapid engineering assessments. This paper proposes a machine learning-based (ML) approach to predict temperature rise and thermal gradients in mass concrete. The dataset was generated using the analytical CIRIA C766 method, enabling systematic selection and gradation of key factors, which is nearly impossible using measurements collected from full-scale structures and is essential for identifying an effective ML model. Three regression models, linear regression, decision tree, and XGBoost were trained and evaluated on simulated datasets that included concrete mix parameters and environmental conditions. Among these, the XGBoost model achieved the highest accuracy in predicting the maximum temperature rise and the temperature differential between the core and surface of the analysed element. The results confirm the suitability of ML models for reliable thermal response prediction. Furthermore, ML models can provide a usable alternative to conventional methods, offering both tools to thermal control strategies and insight into the influence of input factors on temperature in early age mass concrete. Full article

Molecular dynamics (MD) simulations provide atomistic insights into nucleation and crystallization in polymers, yet interpreting their complex spatiotemporal data remains a challenge. Existing order parameters face limitations, such as failing to account for directional alignment or lacking sufficient spatial resolution, preventing them from accurately capturing the anisotropic and heterogeneous characteristics of nucleation or the surface phenomena of polymer crystallization. We introduce a novel set of local order parameters—namely, directional entropy bands— that extend scalar entropy-based descriptors by capturing first-order angular moments of the local entropy field around each particle. We compare these against conventional metrics (entropy, the crystallinity index, and smooth overlap of atomic positions (SOAP) descriptors) in equilibrium MD simulations of polymer crystallization. We show that (i) scalar entropy bands demonstrate advantages compared to SOAP in polymer phase separation at single-snapshot resolution and (ii) directional extensions (dipole projections and gradient estimates) robustly highlight the evolving crystal–melt interface, enabling earlier nucleation detection and quantitative surface profiling. UMAP embeddings of these 24–30D feature vectors reveal a continuous melt–surface–core manifold, as confirmed by supervised boundary classification. Our approach is efficient and directly interpretable, offering a practical framework for studying polymer crystallization kinetics and surface growth phenomena. Full article