Accurate battery state-of-temperature estimation is essential for battery management systems (BMS) to ensure reliable operation, longevity, and safety. A fast, predictive model with an optimal grid search is needed to develop a data-driven battery model for future model-predictive control BMSs. This work presents a multiphysics-augmented feedforward neural network based on the feedforward multi-layer perceptron (MLP) to predict lithium-ion battery temperature (Tb) under driving conditions. A novelty in this work is the quality of the training data generated from an electrochemical NCA/Gr.SiOx battery model, validated against various driving data from Tesla Model Y experiments. The vehicle tests are performed under multiple battery self-cooling-heating modes (25–40 °C), SOC = 20–46%, and drive cycles (WLTC, FTP75, HWFET). The global sensitivity analysis and genetic algorithm are combined to optimize the cell model parameterizations, comprising geometric, transport/concentration, and kinetic parameters. The MLP grid search (hyperparameters) is optimized for the neural network model. The overall trends in the battery’s thermal states are predictable using the well-trained MLP model, with R2 values of 0.9–0.99. The true and predicted Tb maps as functions of cell geometric, transport, concentration, and kinetics parameters of the NCA/Gr.SiOx cell also match reasonably well. Due to its simple structure and ability to predict real-time battery thermal states, this work can serve as a guideline for designing data-driven battery models for future data-driven BMS applications.
Vehicle–pedestrian crashes with similar predicted fatal-crash risk may arise from different combinations of contributing conditions, while the distinction between risk magnitude and risk composition has received limited attention. This study investigated whether reproducible multivariable contribution architectures exist among vehicle–pedestrian crashes with comparable model-predicted fatal-crash risk. Nationwide police-reported crash data from Japan were analyzed using temporally separated 2023 and 2024 cohorts, comprising 74,002 vehicle–pedestrian crashes, including 1,838 fatal crashes. Four classification models were evaluated using the 2023 cohort, with the 2024 cohort reserved for independent temporal validation. XGBoost was selected as the nonlinear base model and achieved a PR-AUC of 0.203 and ROC-AUC of 0.877 in the 2024 test cohort. SHapley Additive exPlanations (SHAP) were then used to decompose model predictions, and signed SHAP contributions were conditioned on overall predicted risk, aggregated across seven domains, normalized, and clustered. Two contribution profiles were identified. One showed relatively greater contributions from road-user characteristics, whereas the other showed greater contributions from environmental conditions, roadway geometry, and traffic control and speed regulation. The profiles were highly reproducible in 2024, with high centroid cosine similarities and an adjusted Rand index of 0.975 relative to an independently fitted 2024 solution. Both profiles occurred across the full predicted-risk distribution and were not well reproduced by simple partitions based on predicted risk, pedestrian age, or day/night conditions. Profile-specific SHAP interaction analysis further revealed systematic differences in nonlinear predictor combinations, particularly the interaction between pedestrian age and day/night conditions. These findings demonstrate that similar predicted fatal-crash risk can arise through distinct and temporally reproducible contribution architectures, suggesting that predicted risk magnitude alone does not fully characterize the multivariable circumstances underlying fatal vehicle–pedestrian crashes.
Where should Indonesia prioritize decentralized food-waste recovery to reduce landfill methane? This study develops a district-level screening framework that combines public waste records with decentralized infrastructure data to identify where food-waste methane-mitigation potential is high and where local system conditions make near-term intervention more plausible. Using 2024 SIPSN waste-generation, composition, and management records together with district-level Info3R infrastructure records, we estimate food waste exposed to low-management conditions, translate that burden into scenario-based methane-mitigation potential, and compare it with a decentralized system-presence proxy. To address the limits of facility-count data, we also rebuild the Info3R panel and test an organics-oriented sensitivity proxy based on TPS3R, composting, and energy-recovery records. The results show that mitigation opportunity is spatially concentrated and that broad decentralized infrastructure is not equivalent to organics-treatment readiness. The framework is intended as a transparent public-data screen for prioritizing follow-up validation, not as a direct measure of facility capacity or realized emissions.
This paper is concerned with the existence and stability of a type of blowing-up positive steady states for a shadow system of the Shigesada-Kawasaki-Teramoto (SKT) two-species competition model and for the perturbed SKT system with a sufficiently large cross-diffusion parameter and bounded random diffusion parameters. The asymptotic behavior of coexistence steady states of the SKT model as one of the cross-diffusion parameters tends to infinity was studied by Lou and Ni [1], who showed that almost all coexistence states can be characterized by one of three shadow systems. In [2], the first author of the present paper studied one of these shadow systems with one cross-diffusion term in one space dimension and proved that one component along a bifurcation branch blows up as the bifurcation parameter approaches the least positive eigenvalue of-Delta under the homogeneous Neumann boundary condition. Using a different approach from that in [2], based on suitable transformations and the Lyapunov-Schmidt reduction, we establish the existence and detailed asymptotic structure of several branches of positive steady states near the blow-up points for shadow systems with one or two cross-diffusion terms in one-and multidimensional domains. We further prove that all the large steady states obtained near the blow-up points are spectrally unstable. Finally, by perturbation arguments, we establish the existence and instability of corresponding branches of positive steady states for the original SKT system when one of the cross-diffusion parameters is sufficiently large.
This study aims to quantify the contributions of atomic motion in jump, gradual-motion, and stay sections to the self-diffusion coefficient in liquid metals. A computational framework is proposed to reconstruct the self-diffusion coefficient from the average squared displacement and frequency of each section type. Molecular dynamics simulations of liquid Pb at 773K were performed using a polynomial machine-learning potential. The trajectory of each atom was classified into the three section types. A correction derived from localized oscillatory motion was introduced to relate the calculated section displacements to the effective translational displacements. The self-diffusion coefficient reconstructed from the classified atomic motions differed by less than 1% from that independently obtained from the long-time mean-squared displacement. At 773K, gradual motion contributed approximately 65% of the reconstructed self-diffusion coefficient, while jump and stay motions contributed approximately 25% and 10%, respectively. These results demonstrate that the framework can quantitatively relate classified microscopic atomic motions to macroscopic self-diffusion. An exact decomposition of the calculated displacement vectors further revealed a net negative contribution from correlations among these vectors. Analysis of temporally adjacent displacement vectors belonging to the same atom showed that shared section boundaries generate negative correlations through local oscillatory fluctuations. An important part of this correlation effect is therefore approximately incorporated into the oscillation correction rather than being simply neglected in the motion-resolved formulation. At 773K, the scalar oscillation correction was close to the magnitude of the negative calculated-vector cross contribution. The framework thus provides a quantitative connection between macroscopic self-diffusion and classified microscopic atomic motions while allowing the assumptions and applicability of the motion-resolved representation to be examined from the calculated trajectories.
