Search Results (28,625)

Efficient thermal management is critical for maintaining the safety, durability, and performance of lithium-ion batteries used in electric vehicles (EVs). In this study, a comprehensive numerical investigation is conducted to evaluate the heat transfer characteristics of mono- and hybrid-nanofluids in a microchannel-cooled lithium-ion battery module. A three-dimensional computational model of a 5S7P battery module composed of cylindrical 21700 cells is developed. Battery heat generation during 3C high discharge rate operation is predicted using the Newman-Tiedemann-Gu-Kim (NTGK) electrochemical model, while coolant flow and heat transfer are simulated using the governing conservation equations for mass, momentum, and energy. The cooling system consists of six liquid-cooling plates with circular microchannels. The performance of water-glycol (50/50) coolant is compared with several mono nanofluids of Al₂O₃ and Cu, and hybrid nanofluids of Al₂O₃-Cu, Al₂O₃-MWCNT, Al₂O₃-Graphene, Cu-MWCNT, and Cu-Graphene across multiple coolant flow rates from 1–5 LPM. The results demonstrate that nanofluids significantly enhance convective heat transfer and reduce battery temperature compared with the conventional water-glycol coolant. Among the investigated coolants, the Al₂O₃-Cu hybrid nanofluid (0.45–0.45%) operating at 1 LPM achieves the best overall thermo-hydraulic performance with a performance evaluation criterion (PEC) of 1.065. Further analysis of nanoparticle composition ratios shows that a Cu-dominant hybrid mixture (Al₂O₃-Cu: 0.27–0.63%) slightly improves the PEC to 1.0657, indicating marginally superior cooling performance. The findings highlight the potential of hybrid nanofluids as advanced coolants for microchannel-based battery thermal management systems in EVs, particularly under moderate coolant flow conditions. Full article

Hemorrhagic shock remains a leading cause of preventable death in trauma, yet traditional vital signs may fail to reflect early blood loss before physiological compensatory mechanisms are no longer able to maintain hemodynamic stability. The Compensatory Reserve Measurement (CRM) algorithm offers early detection capability using physiological waveforms but requires testing with emerging wearable sensor technologies for operational deployment. This study tested the Epicore Epidermal Patch for Imperceptible Care (EPIC) wearable healthcare device (WHD) for CRM-based hemodynamic monitoring during progressive central hypovolemia induced by lower-body negative pressure (LBNP) to simulate hemorrhage. Twenty participants underwent progressive LBNP while photoplethysmography (PPG) signals were recorded from EPIC sensors placed at the clavicle and triceps alongside a clinical-grade finger pulse oximeter for reference. Signal quality, heart-rate accuracy, and CRM predictions were evaluated across multiple filtering approaches. The triceps placement achieved signal quality comparable to the pulse oximeter reference when Chebyshev Type II filtering was applied, as well as high heart-rate accuracy. CRM derived from the EPIC sensor placed at the triceps tracked compensatory trends during progressive hypovolemia, but prediction magnitudes were inaccurate compared to calculated CRM values. In contrast, the clavicle placement consistently performed poorly across all measurements, regardless of the signal-processing approach. These findings support the feasibility of soft, flexible wearable sensors for continuous hemorrhage monitoring at the triceps location in operational environments where traditional finger-based pulse oximetry is impractical. Full article

The crosslinked binders formed by using glycidyl azide polymer (GAP) as the binder matrix and bis-propargyl succinate (BPS) as the curing agent have good application prospects in the field of solid propellants. Aiming at the shortcomings of traditional experimental research, such as high cost, and molecular dynamics (MD) simulation, which are time-consuming for complex combination problems, this study will realize accurate prediction of the mechanical properties of binders through machine learning (ML) based on the molecular simulation dataset. Firstly, 273 sets of GAP-BPS binder models under different conditions were formed based on 21 crosslinking degrees and 13 temperatures, and MD simulation and mechanical property simulation were carried out. Then, the initial conditions of molecular simulation (crosslinking degree, temperature) and structural parameters (free volume) were taken as features, and the bulk modulus and shear modulus were taken as labels to form the dataset. Three machine learning models were trained and evaluated based on this dataset to test their prediction performance. Based on the cross-validation results, the Tabular Prior Data Fitting Network (TabPFN) exhibits the highest average prediction values (the average R² for bulk modulus and shear modulus were 0.9684 and 0.8827, respectively). But the significance analysis reveals that TabPFN significantly outperforms the RF model only in predicting bulk modulus. In subsequent prediction tasks with smaller datasets, TabPFN achieves superior average prediction values compared with RF and XGBoost. Full article

Metal–organic frameworks (MOFs) are a versatile class of hybrid crystalline materials that have emerged as promising candidates for a broad range of applications. γ-cyclodextrin MOFs (γ-CD-MOFs) represent an innovative subgroup of MOFs constructed from “edible” γ-CD ligands coordinated with biocompatible metal ions to form an extended porous structure. Owing to their unique characteristics such as their “green” origin, biodegradability, and biocompatibility they became a promising platform for drug delivery applications. Structurally, γ-CD-MOF possess a body-centered cubic structure with dual-mode porosity, enabling the simultaneous encapsulation of hydrophilic and hydrophobic drugs. Such structural features contribute to high loading capacity, tunable release behavior, and enhanced stability of incorporated drugs. In this review, we comprehensively discuss the structural features of γ-CD-MOF, synthesis strategies, crystals size and morphology control, activation and drying techniques, and drug encapsulation approaches. We further address computational and simulation approaches used to predict and optimize drug-framework interactions, as well as post- synthetic modifications aimed at enhancing stability and functionality. The diverse pharmaceutical applications of γ-CD-MOFs are examined, including the delivery of small molecules, macromolecules, multi-drug systems, and emerging pulmonary formulations. Additionally, we examine biocompatibility and safety considerations and current limitations related to aqueous stability, industrial-scale production, and reproducibility. Finally, this review highlights recent progress and underlines future perspectives, emphasizing innovations such as fast drug-loaded MOF formation via spray-drying, co-delivery strategies, and vaccine-oriented formulations. Together, these insights highlight the potential of γ-CD-MOFs to shape the next generation of multifunctional drug delivery systems across interdisciplinary fields. Full article

With the increasing application of the Vertical Shaft Machine (VSM) method in ultra-deep shafts, accurate prediction of construction-induced structural stresses is vital for engineering safety. Currently, VSM is predominantly used in soft soils, where structural response analysis still relies on finite element (FE) simulations that are computationally intensive and complex to model. To improve analysis efficiency and understand the structural behavior of VSM shafts in granite composite strata, this study takes the first VSM shaft project in South China—the Guangzhou–Huadu Intercity Railway Shield Shaft—as a case study. A “monitoring-driven, large-sample data, machine learning substitution” framework is proposed for predicting structural stresses during construction. The framework calibrates an FE model using monitoring data. Through full factorial design, key design parameters—including main reinforcement diameter, stirrup diameter, concrete strength grade, and steel plate thickness—are systematically varied. Parametric FE simulations are then conducted to construct large-sample response databases (540 sets for ring 0 and 864 sets for the cutting edge ring). Genetic algorithm is introduced to optimize the hyperparameters of Random Forest, XGBoost, and Neural Network models, and their predictive performances are systematically compared. Results show that the proposed framework effectively substitutes traditional FE analysis and enables rapid multi-parameter comparison. Among the models, GA-XGBoost achieves the highest prediction accuracy across all stress indicators (R² > 0.999, where R² is the coefficient of determination, with values closer to 1 indicating better predictive performance), demonstrating the superiority of its gradient boosting and regularization mechanisms in handling tabular data with strong physical correlations. Moreover, the method exhibits good extensibility to other engineering response predictions beyond construction stresses. Full article

Accurate prediction of syngas composition is essential for process design, optimization, and scale-up, yet it remains challenging due to interactions among operating conditions, biomass properties, and chemical reactions. This study used a database of 450 experimental observations spanning a wide range of biomass feedstocks and operating conditions to compare the predictive performance of Aspen Plus simulations and Machine Learning models in estimating the concentrations of CO, CO₂, H₂, and CH₄ in syngas. Aspen Plus was used to simulate the 4 stages of the biomass gasification process under different operating conditions, with special focus on the three reactor modules (RPlug, RGibbs, and REquil) modeling the last two stages. In parallel, Machine Learning models using four regression algorithms (XGBoost, Support Vector Machines, Random Forest and Artificial Neural Networks), with different preprocessing and data-splitting strategies, were evaluated for predicting syngas composition. The best Machine Learning models achieved R² values of 0.753 (CO), 0.866 (CO₂), 0.879 (H₂) and 0.734 (CH₄) on the test set. These results outperformed the Aspen Plus approach and highlight the potential of Machine Learning models as complementary or alternative tools for modelling biomass gasification. Shapley Additive Explanation analysis identified the most influential input variables, revealing key roles for the steam-to-biomass ratio and the equivalence ratio in predicting syngas composition. This study demonstrates that existing Aspen Plus simulation models require further development to improve performance metrics across a wide range of biomass feedstocks and operating conditions. Full article

Siponimod, a selective sphingosine 1-phosphate (S1P) receptor modulator, represents a next-generation therapeutic drug for active secondary progressive multiple sclerosis. This study conducted in-depth forced degradation studies of siponimod in solid state subjected to acidic, alkaline, oxidative, photolytic, and thermal conditions, in compliance with ICH guidelines Q1A (R2) and Q3A (R2). An HPLC method was developed to quantify siponimod and separate its degradation products (DPs). The DPs were characterized using LC-HRMS/MS and LC-MSⁿ techniques. Moreover, the toxicological profiles of siponimod and its DPs were evaluated through the in silico tools ProTox 3.0 and ADMETlab 3.0, with molecular docking and dynamics simulations assessing their binding to the S1P1 receptor. Siponimod was stable to light but degraded under acidic, alkaline, oxidative, and thermal stress, producing five products: DP-1 (acidic), DP-2/3 (oxidative), DP-4 (hydrolytic), and DP-5 (thermal). The toxicity prediction suggested that neither siponimod nor its DPs exhibited carcinogenic or mutagenic potential, and the molecular modeling analysis revealed that DP-2 and DP-3 demonstrated favorable binding affinities, with stable dynamic profiles and thermodynamic properties that closely resembled those of siponimod. As far as we know, this is the first study on the structural elucidation of the DPs of siponimod by LC-HRMS/MS and LC-MSⁿ. Full article

To achieve the same service life of glass fiber reinforced polymer (GFRP) connectors and precast concrete sandwich panels, ensuring the structural stability and safety of the walls during long-term service, it is necessary to research the durability of GFRP connectors. In accordance with the ACI 440.3R-12 test method, an accelerated aging study was conducted by immersing 90 GFRP connectors in a simulated concrete pore solution at temperatures of 40 °C, 60 °C, and 80 °C for durations of 3.65, 18, 36.5, 92, and 183 days. This investigation aimed to analyze the effects of temperature and exposure time on the shear strength of the GFRP connectors. Scanning Electron Microscopy (SEM) was employed to analyze the micro-morphology of the specimens before and after exposure. The SEM observations revealed that after 183 days at 40 °C, the fiber-matrix interface remained relatively intact without significant debonding. However, at 60 °C, noticeable degradation occurred, characterized by corrosion of fibers and evident debonding from the surrounding matrix. At 80 °C, the GFRP specimens were severely damaged, precluding the extraction of viable samples for SEM analysis. The results further indicated that the most rapid decline in the shear strength occurred within the initial 3.65 days of exposure, with reductions of 8.62%, 10.12%, and 10.77% at 40 °C, 60 °C, and 80 °C, respectively. The degradation rate subsequently decelerated with prolonged exposure. After 183 days, the residual shear strength retention rates decreased by 21.03% and 26.89% at 40 °C and 60 °C, respectively. This behavior is primarily attributed to a high moisture absorption rate driven by a significant humidity gradient between the surface and the interior, leading to rapid swelling and plasticization of the vinyl ester resin matrix, which consequently reduced the stiffness and strength of the GFRP connectors. Finally, a predictive model for the time-dependent shear strength of GFRP connectors under various temperature conditions was developed based on Fick’s law. Full article

The spatiotemporal evolution of ecosystem services and the elucidation of their driving mechanisms constitute a central scientific issue in territorial spatial optimization and regional sustainable development. Taking Gansu Province, a core area of the ecological security barrier in northwestern China, as the study area, this study integrates land-use, natural geographic, and socioeconomic data from 2000 to 2020. Using a land-use transfer matrix, the InVEST model, the Geographical Detector, and the PLUS model, we constructed a comprehensive analytical framework that combines historical evolution analysis, spatial differentiation identification, and multi-scenario simulation and prediction. The framework was used to systematically reveal the spatiotemporal dynamics of four core ecosystem services, namely carbon storage (CS), water yield (WY), habitat quality (HQ), and soil retention service (SDR), and to analyze their natural and socioeconomic driving mechanisms, while also simulating land-use change and ecosystem-service responses under the natural development, ecological protection, and urban expansion scenarios in 2030. The results show that, from 2000 to 2020, land use in Gansu Province was dominated by grassland (average proportion: 33.34%) and unused land (average proportion: 41.35%). Urban land expanded from 660.52 km² to 2227.36 km², with its share increasing from 0.15% to 0.50%, mainly through the conversion of cropland and grassland. Ecosystem services exhibited marked spatial differentiation: CS increased from east to west; WY showed an increasing pattern from northwest to southeast; HQ was lower in the central and southeastern regions and higher in the western and southern regions; and SDR was dominated by low-value areas in the northwest (average proportion: 84.81%). Driving-mechanism analysis indicated that slope was the core natural factor affecting CS, HQ, and SDR (q = 0.18–0.45), while mean annual precipitation dominated the variation in WY (q = 0.31–0.35). The influence of socioeconomic factors such as GDP increased gradually over time, showing an evolutionary trend from natural dominance to coordinated natural–socioeconomic regulation. Multi-scenario simulation further showed that, under the ecological protection scenario, grassland area increased significantly (+0.60%), the proportions of medium-value CS zones and high-value WY zones increased, and ecosystem services were optimized overall; under the urban expansion scenario, cropland and urban land expanded (+0.87% and +0.23%, respectively), imposing potential pressure on part of the ecosystem-service functions. These findings provide a scientific basis for optimizing territorial spatial planning, strengthening the ecological security barrier, and promoting regional sustainable development in Gansu Province. The methodological framework also offers a broadly applicable reference for ecologically sensitive arid and semi-arid regions in northwestern China. Full article

This paper investigates the prediction of unobserved future failure times for the heavy-tailed Log-Logistic distribution under Progressive Type-II censoring. We first develop point and interval estimates for the unknown parameters using both frequentist maximum likelihood and Bayesian approaches. For predicting future failures, we derive three distinct point predictors: the Best Unbiased Predictor (BUP), the Conditional Median Predictor (CMP), and the Bayesian Predictor (BP). Corresponding prediction intervals are constructed using frequentist pivotal quantities, Bayesian Equal-Tailed Intervals (ETIs), and Highest Posterior Density (HPD) methods. The Bayesian procedures are implemented via Markov chain Monte Carlo (MCMC) sampling. We evaluate the finite-sample performance of the proposed methodologies through a Monte Carlo simulation study and further validate them using two real-world datasets, namely bladder cancer remission times and guinea pig survival times. The numerical results indicate that the proposed BP, particularly under the empirical prior, provides the most accurate and stable overall performance for point prediction, while the frequentist predictors become less reliable in extreme heavy-tailed settings. For interval prediction, the Bayesian HPD method consistently outperforms the alternatives, substantially reducing interval lengths for right-skewed data while maintaining the nominal coverage probability. Full article

Two-phase displacement between cement slurry and drilling fluid in wellbore systems is inherently nonlinear, interface-dominated, and strongly affected by geometric confinement, posing substantial challenges to efficient and stable numerical simulation. Conventional CFD approaches rely on mesh discretization and explicit interface tracking, which become computationally demanding and sensitive to grid quality in complex geometries and convection-dominated regimes. To address these limitations, this study develops a unified physics-informed neural network (PINN) framework for directly solving the coupled incompressible Navier–Stokes and Volume of Fluid (VOF) equations governing pressure-driven displacement. The framework is first validated against canonical transient flows and then applied to two-phase displacement in parallel-plate channels, semicircular bends, and a casing–annulus geometry representative of well cementing operations. The predicted velocity, pressure, and volume fraction fields exhibit strong agreement with ANSYS Fluent (2024R1) results, with relative errors generally around 5%, thereby demonstrating physical consistency and numerical stability without mesh generation or pressure–velocity splitting, while also showing favorable computational efficiency for the cases considered. Sensitivity analyses demonstrate that a smoother casing-shoe geometry significantly enhances PINN convergence, while higher Péclet numbers deteriorate training stability by increasing convection-dominated stiffness and optimization difficulty. The results demonstrate that the proposed PINN framework, with its mesh-free and geometrically flexible characteristics, is a promising approach for modeling multiphase displacement in cementing applications. Full article

Scandate cathodes have garnered significant attention for their exceptional low-temperature, high-current-density emission characteristics. However, their widespread deployment in vacuum electronic devices is currently hindered by stringent vacuum requirements and susceptibility to ion bombardment. To enhance the engineering applicability of scandate cathodes, this study employs first-principles density functional theory (DFT) to model the surface microstructures of strontium (Sr)–scandium (Sc) co-doped systems. Guided by simulation predictions regarding surface elemental ratios, corresponding emission active materials and cathode samples were fabricated. A systematic comparison between theoretical calculations and experimental measurements reveals a critical trade-off: while increasing Sr content enhances structural stability (indicated by lower formation energies), it concurrently increases the work function. Consequently, an optimal Sr doping level of approximately 2 wt% is identified, which significantly improves emission current density without compromising stability. Cathodes fabricated with this optimized composition were tested in a practical electron gun configuration. Results demonstrate that under low-temperature conditions (1000 °C) and wide-pulse operation (2 ms), the cathode achieves an emission current density of 21.57 A/cm². These findings validate the efficacy of simulation-guided material design and highlight the potential of Sr-doped scandate cathodes for high-power microwave applications. Full article

Accurate representation of short-term reservoir water-level dynamics is essential for operational analysis and scenario-based assessment under prescribed inflow–outflow conditions. In many practical applications, physically based modelling is limited by incomplete process knowledge, unavailable boundary conditions, or insufficient temporal resolution of input data. This study presents a data-driven framework for hourly conditional simulation of reservoir water level based on a hybrid Conv1D–LSTM architecture. The model learns nonlinear relationships among hydraulic forcing, operational control, and system state from historical observations, and is evaluated in a recursive multi-step simulation (rollout) mode to reflect its intended use and capture error accumulation over time. A systematic analysis of input sequence length and activation function is performed to identify a robust model configuration. On the test set, the selected configuration ($L = 24$, GELU) achieved RMSE = 0.1057 m, MAE = 0.0881 m, and R² = 0.972 in rollout evaluation. The proposed framework is designed for scenario-based simulation rather than one-step deterministic forecasting, enabling rapid operational screening of alternative inflow–outflow regimes. Unlike many previous studies that emphasize one-step predictive accuracy, this work explicitly assesses model stability in recursive multi-step simulation, which is more relevant for reservoir scenario analysis. Full article

A hybrid aquatic–aerial vehicle (HAAV) is a novel type of aircraft capable of both aerial flight and underwater navigation. Inspired by the swan’s gliding and landing motion on water surfaces, this study investigates the dynamic modeling and integrated optimization design of an HAAV equipped with a biomimetic skipping plate. By comprehensively accounting for the aerodynamic, impact, hydrodynamic, and frictional forces during the water entry process, a dynamic model for the HAAV’s gliding water entry is established. The reliability of the model is verified through comparisons between numerical simulations and theoretical predictions. Parametric modeling of the skipping plate’s configuration and layout is performed to analyze the influence of different parameters on the water entry dynamics. With the objectives of minimizing the overload and pitch angle variation, a hybrid infilling strategy based on a radial basis function neural network (RBFNN) surrogate model is constructed to improve optimization efficiency. This is combined with a quantum-behaved particle swarm optimization (QPSO) algorithm to conduct the multi-objective optimization of the biomimetic plate, thereby obtaining its optimal configuration and layout parameters. The results demonstrate that the established dynamic model is effective and can accurately capture the kinematic characteristics of the gliding water entry process. The error between the peak load and the pitch angle variation is less than 5%. Compared with the direct QPSO algorithm, the proposed method reduces the number of model evaluations by 66.7%, the computational time by 52.1%, and the optimal solution response value by 12.01%, demonstrating strong potential for engineering applications. Full article

Existing research primarily focuses on ordinary straight roads or curves; however, there is a notable lack of recent research on continuous curves. This research employs Computational Fluid Dynamics (CFD) dynamic mesh technology to numerically simulate the external flow field during vehicle overtaking on a continuous curve resembling a sine wave. This study conducts a numerical simulation to analyze the external flow field of vehicles during overtaking on a continuous curve, similar to a sine curve, using CFD. Using different initial velocities, the study analyzes lateral force on the vehicle body during overtaking. It investigates how dynamic changes in the external flow field affect vehicle dynamics by employing tetrahedral meshes, the SST k-ω turbulence model, and UDF programming. To address emergency overtaking scenarios during medical vehicle rescues, a four-factor orthogonal experimental design was employed to identify the safest overtaking condition: overtaking a small vehicle (5 m × 1.8 m) at 22 m per second with 1.5 times the vehicle width and no crosswind. Regression lines were fitted to the data, yielding a nonlinear regression equation that can predict road conditions, thereby providing theoretical support for intelligent driving systems. Full article