Search Results (146)

This paper introduces a cutting-edge, real-time fault detection and classification method powered by artificial neural networks (ANNs), designed to significantly boost the reliability and sustainability of medium voltage (MV) underground cable distribution systems. The research analyzes the electrical and physical properties of MV underground cables and common fault types, including line-to-line, line-to-ground, and double line-to-ground faults. A simulation model is developed using MATLAB/Simulink R2025b to generate fault scenarios under various operating conditions. Raw data in the form of Voltage and current signals are generated and processed to extract significant features, which are then fed into the ANN model. The ANN is trained using a supervised learning approach, using a dataset of labeled fault instances. Key parameters like hidden layers, activation functions, and learning rates are optimized to improve the model’s performance. The results show that the proposed ANN-based fault detection technique achieves over 95% accuracy in detecting and classifying faults in real-time, with minimal computational delay. Comparative analysis with conventional fault classification techniques demonstrates the superiority of the ANN model in handling noisy and non-linear data. Full article

Cooperative multi-UAV path planning under dynamic and adversarial conditions demands simultaneous satisfaction of safety, efficiency, and coordination constraints, yet existing swarm-intelligence and RL–swarm hybrids rely on deterministic switching rules, tabular states, and ad hoc training schedules. This paper proposes RL-JSO, a hybrid framework in which a dueling double deep Q-network with prioritized experience replay adaptively selects among the drift, passive, and active phases of a jellyfish search optimizer, replacing the deterministic time-control rule with a learned policy. The framework integrates a five-layer hierarchical safety control mechanism, a mastery-gated nine-stage curriculum, and a shared reward module that architecturally enforces fairness between RL-JSO and a paired RL-PSO counterpart. Evaluation across four progressive campaigns with 160 independent runs per algorithm shows that, within the evaluated JSO/PSO family, RL-JSO is the only method that sustains a 100% collision-free rate across all four progressive difficulty campaigns, its Cliff’s delta over standard JSO grows monotonically with difficulty from medium to large, and under a composite cooperation metric its coordination score remains nearly invariant while comparators degrade by 17–23%. A paired inference-time ablation on the trained checkpoint provides controlled inference-time evidence that adaptive phase switching is a principal contributor to the observed test-time performance within the trained system, rather than the heuristic fallback layers. Full article

With the rapid development of new energy vehicles, fast and reliable prediction of power battery collision damage has become increasingly important. Traditional finite-element analysis is computationally expensive and difficult to deploy for rapid prediction under varying conditions. Although learning-based methods are faster, they usually rely on single-fidelity data: high-fidelity data is accurate but scarce and costly, while low-fidelity data is abundant but less reliable. Existing multi-fidelity methods alleviate this issue, yet often suffer from imbalanced sample allocation and weak cross-fidelity modeling. Moreover, current adaptive sampling strategies cannot dynamically determine the appropriate fidelity for different regions of the design space. To address these challenges, we propose HNGP-LCA, a multi-fidelity active learning framework for battery pack collision damage prediction. Our method consists of two components: (1) an Ensemble Nested Gaussian Process module that integrates single-layer and double-layer nested Gaussian process regression to better capture high–low fidelity correlations; and (2) a Location Information Cost-aware Active Learning strategy that leverages positional information to reconstruct expected improvement under different fidelities, enabling dynamic fidelity selection during sampling. Experiments on multiple synthetic benchmarks and a real battery pack engineering case demonstrate that HNGP-LCA achieves a better trade-off among accuracy, efficiency, and cost than strong baselines such as NARCO and MFBO. In the engineering case, it improves prediction accuracy by 0.6% over NARCO and 1.29% over MFBO, while reducing dependence on expensive high-fidelity data. These results show that HNGP-LCA provides an effective and practical solution for battery collision damage prediction. Full article

Unmanned aerial vehicles (UAVs) for urban and clinical–logistics missions operate under severe constraints in onboard energy, computation, and payload integrity. Addressing these challenges requires not only advanced algorithms but also a tight integration between embedded hardware, energy management, perception, and decision-making. This paper presents a unified UAV platform based on a system-level hardware–software co-design. First, a compact six-layer PCB (85 mm × 55 mm) integrates an NVIDIA Jetson Orin for on-edge artificial intelligence and a dedicated microcontroller for real-time flight control, with explicit power-domain separation, thermal management via arrays, and physical isolation of sensitive sensors. Second, a hybrid energy system combines LiPo batteries with perovskite photovoltaic cells and an MPPT stage with experimentally measured efficiency (94.5%), enabling stable operation under variable irradiance conditions. Third, an autonomous navigation strategy based on a Dueling Double Deep Q Network with Prioritized Experience Replay learns energy-efficient trajectories while explicitly incorporating payload thermal deviation (ΔT) and mechanical jerk into the reward function, thereby supporting clinically safe transport. Experimental validation on the physical platform includes onboard power and latency measurements, statistical evaluation across training and deterministic execution, and mission-level key performance indicators. Results show an average reduction of 18.4% in total energy consumption and a 12.1% increase in operational coverage under representative urban scenarios, with end-to-end decision latency below 50 ms. These findings demonstrate that a tightly integrated design of embedded hardware, hybrid energy management, and clinical-aware reinforcement learning enables robust, efficient, and application-ready UAV systems for urban and healthcare missions. Full article

This study aimed to develop and validate a physics-informed neural network (PINN) framework for data-efficient and physically consistent process optimization in the laser powder bed fusion (LPBF) of Inconel 718 (IN718) superalloy. Laser powder bed fusion (LPBF) is widely adopted for fabricating Inconel 718 (IN718) components in aerospace and energy applications; however, navigating its high-dimensional, nonlinear process parameter space remains a central challenge. High-fidelity finite element simulations are computationally prohibitive for extensive parameter sweeps, whereas purely data-driven machine learning (ML) models are limited by data scarcity and unphysical extrapolation behavior. This study presents a physics-informed neural network (PINN) framework that embeds the transient heat conduction equation and Goldak double-ellipsoidal heat source model directly into the neural network training loss, enforcing thermophysical consistency simultaneously with data fidelity. The model was trained on a curated, multi-source dataset of LPBF IN718 parameter combinations drawn from peer-reviewed experimental studies and validated finite element simulation outputs, spanning the laser power (70–400 W), scan speed (200–2000 mm/s), hatch spacing (50–140 µm), and layer thickness (20–50 µm). The PINN predicted the melt pool width, depth, peak temperature, and relative density with mean absolute percentage errors (MAPE) of 3.8%, 4.7%, 3.1%, and 1.9%, respectively, outperforming a baseline artificial neural network (ANN) with an identical architecture. The framework correctly identified the optimal volumetric energy density (VED) window of 55–105 J/mm³, yielding relative densities ≥99.5%, consistent with the published experimental thresholds for IN718. A data efficiency analysis demonstrated that the PINN with 25% training data achieves a performance equivalent to that of the fully trained ANN with 100% data, confirming an approximately four-fold data efficiency improvement attributable to physics-informed regularization, consistent with theoretical predictions. Sensitivity analysis via automatic differentiation confirmed that laser power and scan speed were the dominant parameters (~85% combined variance), which is in agreement with previous studies. This study provides a computationally efficient, interpretable, and physically consistent ML pathway for the accelerated process qualification of IN718 components for aerospace and energy applications. Full article

Background: Nanoporous anodic alumina (NAA) has emerged as a promising platform for localized drug delivery in biomedical implants owing to its tunable nanoscale pore structure and biocompatibility. However, achieving the desired pore characteristics currently relies on time-consuming trial-and-error adjustments of anodization parameters. Methods: We developed a comprehensive data-driven machine learning framework using a feed-forward artificial neural network (ANN) with three hidden layers (64-32-16 neurons) trained on 77 samples from a compiled dataset of 99 anodization experiments spanning 1995–2025. The model predicts the NAA pore diameter based on anodization conditions (electrolyte type, concentration, voltage, temperature, and time). Results: The ANN achieved R² = 0.803, root mean square error (RMSE) = 25.83 nm, and mean absolute error (MAE) = 17.05 nm on training data; however, 5-fold cross-validation revealed moderate generalization (CV R² = 0.471 ± 0.078). Multiple linear regression showed comparable training performance (R² = 0.804) but superior cross-validation (CV R² = 0.729 ± 0.083). Feature importance analysis identified anodization voltage (29.15% ANN importance) and electrolyte type (30.23%) as the most influential factors. Coupling ANN-predicted pore dimensions with Higuchi diffusion modeling demonstrated that the pore diameter increased from 50 to 100 nm, nearly doubling the initial release rates (8 to 11 h⁻¹) and reducing the time to 50% release from 39.1 to 20.7 h. Conclusions: This data-driven approach offers a powerful tool to reduce experimental iteration and accelerate the development of advanced drug-delivery implants by enabling the rational design of NAA pore structures for optimized drug loading and release kinetics. Full article

The global energy transition and the implementation of carbon capture, utilization, and storage (CCUS) strategies require energy-efficient and scalable CO₂ separation technologies. Mixed-matrix membranes (MMMs), combining polymer matrices with functional inorganic or hybrid nanofillers, have emerged as advanced separation platforms capable of surpassing the conventional permeability–selectivity trade-off observed in neat polymer membranes. This review critically evaluates recent developments in modern hybrid membranes for CO₂ separation from synthetic and industrial gas mixtures, including CO₂/N₂ (flue gas), CO₂/CH₄ (natural gas and biogas upgrading), and syngas systems. Particular emphasis is placed on MMMs incorporating covalent organic frameworks (COFs), metal–organic frameworks (MOFs), graphene oxide (GO), MXenes, transition metal dichalcogenides (TMDs), carbon nanotubes (CNTs), g-C₃N₄, layered double hydroxides (LDH), zeolites, metal oxides, and magnetic nanoparticles. Reported performance ranges include CO₂ permeability (P_CO2) typically between 100 and 800 Barrer, CO₂/N₂ selectivity up to 319, and CO₂/CH₄ selectivity up to 249, depending on filler chemistry, loading, and interfacial compatibility. The mechanisms governing gas transport—molecular sieving, selective adsorption, facilitated transport, and diffusion-pathway engineering—are systematically discussed. Key challenges addressed include filler dispersion, polymer–filler interfacial defects, physical aging, moisture sensitivity, oxidation (particularly in MXenes), and scalability toward industrial membrane modules. Future perspectives focus on sub-nanometer pore engineering, surface functionalization to enhance CO₂ affinity, controlled alignment of 2D nanosheets to promote directional transport, multifunctional core–shell and hollow structures, and the integration of computational modeling and machine learning for accelerated material design. Modern hybrid MMMs are identified as strategically important materials enabling high-efficiency CO₂ separation processes aligned with decarbonization and energy transition objectives. Full article

Surface ozone monitoring remains challenging due to sparse ground networks and limited satellite boundary-layer sensitivity. This study evaluates, for the first time, China’s Environmental Trace Gases Monitoring Instrument II (EMI-II) for estimating surface ozone over the Beijing–Tianjin–Hebei (BTH) region. EMI-II total ozone columns (TOCs) are retrieved using the differential optical absorption spectroscopy (DOAS) algorithm and validated against the TROPOspheric Monitoring Instrument (TROPOMI) (R = 0.96), Geostationary Environment Monitoring Spectrometer (GEMS) (R = 0.97), and the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ground measurements (R > 0.92, bias < 4%). TOCs are then combined with ERA5 meteorology, satellite NO₂/HCHO, and surface observations within machine learning models, achieving cross-validated R² of 0.94 and RMSE of 12.05 μg/m³ for surface ozone estimation. EMI-II estimates show strong agreement with independent observations (R = 0.91, RMSE = 10.83 μg/m³) and reproduce seasonal gradients, with summer concentrations (131 μg/m³) more than double winter levels (61 μg/m³). Estimation skill is regime-dependent: performance comparable to TROPOMI occurs under strong photochemical activity, while reduced sensitivity occurs under weak radiation and stable boundary layers—consistent with averaging kernel diagnostics. This first comprehensive validation demonstrates that EMI-II, despite vertical sensitivity limitations, provides meaningful surface ozone constraints under favorable atmospheric conditions. The framework is potentially applicable to other regions and sensors under similar conditions, providing a case study for integrating national satellite products into multi-source surface ozone estimation. Full article

Single-event effects (SEEs) present a significant challenge to the radiation reliability of integrated circuits. Conventional SEE analysis methods for single-photon avalanche diode (SPAD) devices primarily rely on Sentaurus Technology Computer-Aided Design (TCAD) numerical simulation, which is computationally intensive and time-consuming. In this study, we propose a generalized deep learning (DL) model, using a silicon-based SPAD device with a double-junction double-buried-layer (DJDB) structure fabricated in 180 nm CMOS process as the research subject. By incorporating key parameters that influence SEEs as model inputs, the proposed approach enables rapid prediction of critical parameter metrics, including transient current peaks and dark count rates. Experimental results show that the DL model achieves a prediction accuracy of 97.32% for transient current peaks and 99.87% for dark count rates, demonstrating extremely high prediction precision. To further validate the generalization capability of the proposed network, the model is applied to predict the detection performance of the DJDB-SPAD device. The prediction accuracies for four key performance parameters all exceed 97.5%, further confirming the accuracy and robustness of the developed model. Meanwhile, compared with the conventional Sentaurus TCAD simulation method, the proposed method achieves a 336-fold improvement in computational efficiency. Overall, this method realizes the dual advantages of high precision and high efficiency, which provides an efficient and accurate technical solution for the rapid characteristic analysis and reliability evaluation of SPAD devices under single-event effects (SEEs). Full article

In size, weight, and power (SWaP)-constrained optical systems, such as spaceborne LiDAR, high-precision centroid localization often relies on focal-plane measurements without dedicated wavefront sensors. Under such conditions, the nonlinear coupling between optical aberrations and sensor noise introduces systematic bias that is difficult to mitigate using conventional centroiding methods. To address this issue, we propose a physics-conditioned feature correction framework based on an aberration-conditioned attention mechanism. A hybrid CNN–Transformer architecture is employed to predict and compensate for systematic centroid bias. Specifically, convolutional layers encode the degraded spot morphology, while a multi-head attention mechanism leverages Seidel aberration coefficients to adaptively modulate spatial features for precise regression. Given the unavailability of absolute ground-truth coordinates in empirical scenarios, a physics-consistent simulation framework based on scalar diffraction theory is constructed to generate synthetic data for supervised learning. Simulation results indicate that the proposed method objectively reduces anisotropic systematic bias, achieving a localization root-mean-square error (RMSE) of 0.011 to 0.021 pixels, and maintains stable sub-pixel accuracy even under a 10% empirical prior perturbation. To evaluate generalization performance and engineering reliability, a wedge-based double-spot platform is developed to verify physical consistency via geometric invariance. Experimental results demonstrate a measured spacing standard deviation (SD) of 0.015 to 0.039 pixels. This validates the framework’s transferability from theoretical simulation to controlled physical measurements, providing an algorithmic foundation for precision optical metrology in hardware-constrained environments. Full article

Quantum reinforcement learning (QRL) is often evaluated under idealized, noiseless assumptions, yet realistic quantum devices inevitably introduce noise that can severely degrade performance. This paper improves the robustness of quantum deep Q-learning (QDQN) by redesigning the variational quantum circuit (VQC) used in its value-function approximator. Motivated by recent advances in quantum convolutional neural networks (QCNNs), we construct four QCNN-inspired VQC variants (Models A–D) by combining representative QCNN two-qubit building blocks with an explicit fully connected (all-to-all) layer. Using a 10-fold evaluation protocol at a fixed noise level p = 0.005, Model D achieves the best robustness, reducing the mean number of episodes required to reach a target reward from 1981 (baseline) to 1243. Under a stricter success criterion, Model D also doubles the empirically observed noise-tolerance boundary from 0.002 to 0.004. These results indicate that carefully chosen QCNN-style circuit components and connectivity can significantly improve the noise robustness of QDQN-like QRL agents. Full article

This paper presents a novel Double Buried-Window Junctionless Field-Effect Transistor (DBW-FET) designed for high-sensitivity, label-free biosensing applications. The proposed device integrates two buried windows, one N-type and one P-type, beneath the active channel within the buried oxide layer, along with two nanocavities serving as biomolecular recognition sites. The dual buried windows form two depletion regions that enhance electrostatic coupling, suppress short-channel effects, and improve biomolecular sensitivity. Numerical simulations using Silvaco TCAD Atlas were performed to investigate device performance under various biomolecular binding conditions. Results show that the DBW-FET exhibits higher drain current, lower subthreshold swing, and improved sensitivity compared with a conventional junctionless FET (C-FET). Furthermore, a machine-learning-assisted optimization framework employing Gaussian Process Regression (GPR) and Bayesian Optimization (BO) was implemented to identify optimal buried window parameters. The optimized design achieved a 20–25% improvement in current sensitivity while maintaining low leakage. These findings demonstrate that the proposed DBW-FET offers a promising and Complementary Metal-Oxide-Semiconductor (CMOS)-compatible architecture for next-generation nanoscale biosensors. Full article

Partial demagnetization of multiple in-wheel motors changes torque distribution characteristics and can reduce vehicle stability, which poses a challenge for in-wheel motor drive electric vehicles (IWMDEVs) to maintain a balance between safety and efficiency. To address this issue, a hierarchical multi-objective adaptive fault-tolerant control (FTC) strategy based on wheel terminal torque compensation is developed. In the upper layer, a nonlinear model predictive controller (NMPC) generates the desired total driving force and corrective yaw moment according to vehicle dynamics and driving conditions. The lower layer employs a quadratic programming (QP) scheme to allocate the wheel torques under actuator and tire constraints. Two adaptive coefficients—the stability–efficiency weighting factor and the current compensation factor—are updated through a randomized ensembled double Q-learning (REDQ) algorithm, enabling the controller to adaptively balance yaw stability preservation and energy optimization under different fault scenarios. The proposed method is implemented and verified in a CarSim–Simulink–Python co-simulation environment. The simulation results show that the controller effectively improves yaw and lateral stability while reducing energy consumption, validating the feasibility and effectiveness of the proposed strategy. This approach offers a promising solution to achieve reliable and energy-efficient control of IWMDEVs. Full article

Empirical debates about a “crisis of trust” highlight long-lived pockets of high trust and deep distrust in institutions, as well as abrupt, shock-induced shifts between the two. We propose a probabilistic model in which such phenomena emerge endogenously from social learning on hierarchical networks. Starting from a discrete model on a directed acyclic graph, where each agent makes a binary adoption decision about a single assertion, we derive an effective influence kernel that maps individual priors to stationary adoption probabilities. A continuum limit along hierarchical depth yields a degenerate, non-conservative logistic–diffusion equation for the adoption probability $u(x,t)$, in which diffusion is modulated by $(1-u)$ and increases the integral of u rather than preserving it. To account for micro-level uncertainty, we perturb these dynamics by multiplicative Stratonovich noise with amplitude proportional to $u(1-u)$, strongest in internally polarised layers and vanishing at consensus. At the level of a single depth layer, Stratonovich–Itô conversion and Fokker–Planck analysis show that the noise induces an effective double-well potential with two robust stochastic phases, $u\approx 0$ and $u\approx 1$, corresponding to persistent distrust and trust. Coupled along depth, this local bistability and degenerate diffusion generate extended domains of trust and distrust separated by fronts, as well as rare, Kramers-type transitions between them. We also formulate the associated stochastic partial differential equation in Martin–Siggia–Rose–Janssen–De Dominicis form, providing a field-theoretic basis for future large-deviation and data-informed analyses of trust landscapes in hierarchical societies. Full article

This paper reviews the research progress of supercapacitors (SCs), including the influence of electrode materials on energy storage mechanism and performance, and life prediction. Supercapacitors show application potential in many fields due to their high-power density, fast charge–discharge capability, long cycle life, and environmental protection characteristics. In this paper, the energy storage mechanism of the double-layer capacitor, pseudocapacitor, and asymmetric supercapacitor are discussed. New electrode materials, such as carbon-based materials, metal oxides, and conductive polymers, are reviewed based on the performance optimization measures that are involved in the microstructure design of electrode materials, and integrate the rule prediction of supercapacitors into comprehensive learning. When designing and using supercapacitors, we should not only pay attention to their life but also pay attention to their remaining service life in real time. The paper also mentions the progress of life prediction technology, which is of great significance to improve the reliability and maintenance efficiency of energy storage equipment, and ensure the long-term stable operation of energy storage systems. Future research directions include increasing energy density, extending life, adapting to extreme environments, developing flexible and wearable devices, intelligent management, and reducing costs. Full article