Search Results (1,684)

Artificial nerve guidance conduits (NGCs) have gained significant attention in the field of peripheral nerve regeneration for the treatment of critically sized nerve defects. Nanotechnology-based NGCs are being explored as potential solutions for repairing and reconstructing peripheral nerve injuries due to their unique structure and topography. In this study, we present a novel core–sheath GelMA/PCL nanofiber construct fabricated through electrospinning and phase separation methods. The core–sheath GelMA/PCL nanofibers replicate the topological morphology of the native extracellular matrix (ECM). The outer layer, composed of GelMA, serves as an “adhesion domain” facilitating direct interaction with surrounding cells and tissues while improving wettability, integrin-mediated cell adhesion/attachment, and degradation. PCL, acting as the “elastic domain” within the nanofibers, enhances mechanical properties, maintains long-term stability of the NGCs, and enables controlled release of GelMA. Histomorphometric analysis along with electrophysiological and behavioral assessments demonstrate that these core–sheath GelMA/PCL nanofiber-based NGCs can activate endogenous mechanisms for peripheral nerve repair while promoting sensory/motor nerve regeneration and functional recovery. Overall, our findings demonstrate that GelMA/PCL nanofibers within the nuclear sheath can effectively remodel the nerve regeneration microenvironment by integrating “mechanical- biochemical” signals, thereby offering a novel strategy for addressing critical-size nerve defects. Full article

Accurately capturing spatiotemporal dependencies and enabling effective decision support are core challenges in Intelligent Transportation Systems (ITS). Existing research often treats traffic prediction and path planning as isolated tasks. Moreover, mainstream prediction models struggle with long-term periodic patterns, while Reinforcement Learning (RL)-based planning often suffers from inefficient exploration in sparse topologies. To address these issues, this paper proposes a unified framework combining a spectral-temporal Graph Neural Network (GNN) and bi-directional decoding RL. Specifically, a time-frequency dual-stream adaptive learning module is introduced for prediction. Fast Fourier Transform (FFT) and Gated Recurrent Unit (GRU) are employed to capture global frequency periodicities and local temporal dynamics, respectively. Their adaptive fusion effectively mitigates the long-sequence information forgetting problem. For path planning, the task is formulated as sequence generation. A graph-aware attention encoder with adjacency masking is designed, and heuristic feature embeddings are incorporated to guide efficient exploration. Furthermore, a bi-directional autoregressive decoding strategy enhances robustness against topological bottlenecks. On PEMSD4 and PEMSD8, the proposed predictor achieves MAE/RMSE/MAPE values of 18.211/30.433/12.006 and 13.587/23.566/8.955, respectively. Path-planning simulations on the PEMSD4-derived sparse topology further demonstrate stable bi-directional RL optimization, faster convergence with heuristic guidance, and a sparsity-aware encoder that reduces redundant attention interactions in sparse road networks. These results validate the effectiveness of the proposed “predict-then-plan” paradigm. Full article

Vehicular ad hoc networks (VANETs) enable direct wireless communication between moving vehicles for safety and cooperative driving. Routing in VANETs is challenging due to high mobility, frequent topology changes, and variable node density. The Greedy Perimeter Stateless Routing (GPSR) protocol maintains only a one-hop neighbor position table through periodic beacon exchanges, making it highly scalable. Each node forwards packets to the neighbor geographically closest to the destination. However, this distance-only criterion leads to a low packet delivery ratio (PDR). Existing improvements, such as Weight-Based Path-Aware GPSR (W-PAGPSR) combining distance progress, velocity direction, neighbor density, and link duration, incorporate multiple factors but complicate parameter tuning and lack a unified neighbor quality metric. This paper proposes Directional Propagation Capacity Index–GPSR (DPCI-GPSR), integrating neighbor information into a single directional metric capturing propagation capacity. Two enhancements are introduced: (1) an eight-direction DPCI computing a composite propagation capacity index per sector, exchanged via Hello packets, and (2) a trapezoidal link quality function treating 30–200 m as optimal while penalizing edge-zone neighbors. Implemented in NS-3 with SUMO-generated mobility, results across four node densities (30–120 vehicles), five concurrent sender–receiver pairs, and 15 random seeds show DPCI-GPSR achieves 63.08–98.39% PDR, outperforming both W-PAGPSR (52.38–80.14%) and standard GPSR (50.23–66.31%). Full article

Background: Growth factor therapy often fails in diabetic foot ulcers (DFUs). The reason remains unclear. Standard differential expression analysis may miss functionally critical genes with modest expression changes. Methods: We performed a secondary computational analysis of a longitudinal DFU transcriptomic dataset (Dryad; 17 patients, 117 serial biopsy samples, 12-week follow-up). Co-expression networks were built separately for healed (n = 37) and non-healed (n = 80) samples. Virtual gene knockout (VGK) was used to rank genes by topological impact on network cohesion. Single-cell analysis (GSE165816) assessed the association between endogenous KIF13A expression and keratinocyte migration-related signatures. A conceptual Hill-equation simulation was used to illustrate the transport-signaling threshold relationship. Drug repurposing used DSigDB enrichment. An independent bulk DFU cohort (GSE134431) was used for external validation. Results: KIF13A showed no differential expression (log₂FC = 0.173, p = 0.263) yet ranked first by VGK topological impact. In keratinocytes, high KIF13A expression correlated with greater migration scores versus zero-detection cells (p = 0.0058). A clear threshold effect emerged: below the 30th expression percentile, EGF, PDGF, and FGF pathway activation scores remained near baseline. In a structural-equation model, transport activity negatively predicted inflammation (standardized β = −0.92, p < 0.001). HIF1A showed the strongest positive correlation with KIF13A in keratinocytes (Spearman ρ = 0.26, p < 0.001), and FOS showed a negative correlation in the single-cell analysis (ρ = −0.16, p < 0.001) and in the bulk longitudinal cohort (ρ = −0.32, p < 0.001, n = 117). Recurrent AKR1B1-related drug signatures nominated the aldose-reductase pathway, and epalrestat was therefore prioritized as a hypothesis-generating candidate compound rather than a direct top-ranked enrichment hit. External validation confirmed consistent upregulation of KIF13A (Fold-Change = 1.58, adj. p = 0.0075), EPN1, and CLIP1 in DFU tissue. Despite population-level upregulation, a subset of cells fell below the functional signaling threshold. Conclusions: These computational findings suggest that KIF13A-associated vesicle transport impairment may represent a candidate systems-level bottleneck for growth-factor responsiveness in DFUs, a network-level pattern not captured by standard differential-expression analysis. Epalrestat, an AKR1B1 inhibitor prioritized through recurrent AKR1B1-related drug signatures, is presented as a candidate compound for further evaluation. As the present analysis is observational and computational, the findings should be interpreted as hypothesis-generating; experimental perturbation studies and prospective clinical validation are required. Full article

Plastic design enables efficient structural systems by exploiting controlled inelastic deformation and force redistribution. While mature in steel structures due to stable ductility and well-defined yielding, its extension to reinforced concrete (RC) remains challenging because cracking, stiffness degradation, confinement dependency, and progressive damage govern deformation capacity and collapse mechanisms. This paper presents a state-of-the-art review of optimal plastic design methodologies for RC structures by tracing the evolution from classical plasticity theory to modern damage-informed, reliability-oriented, and sustainability-driven formulations. A systematic and structured literature review of more than 90 peer-reviewed journal articles (1990–2025) was conducted using Scopus, Web of Science, and ScienceDirect. The selected studies are classified by structural system type, plastic analysis approach, constitutive modeling strategy, and strengthening technique, including CFRP and hybrid fiber systems, optimization framework, and uncertainty treatment. The review highlights how nonlinear elasto-plastic and damage–plasticity models improve the prediction of plastic hinge development, redistribution, and failure-mode transitions, and how metaheuristic optimization, topology optimization, surrogate modeling, and machine learning are increasingly used to manage discrete design variables and computational cost. Reliability-based methods (e.g., FORM/SORM and simulation) are shown to be essential for quantifying deformation-capacity uncertainty and ensuring consistent collapse-prevention performance. A comparative assessment of nine plastic design methodologies is also provided, identifying their core assumptions, limitations, and domains of applicability within a structured evaluative framework. Remaining challenges include robust deformation-capacity prediction, reproducible calibration of damage models, and integration of life-cycle sustainability criteria within reliability-constrained plastic optimization. Future research directions are proposed toward multi-objective reliability-based design, durability-informed plastic modeling, and hybrid physics-informed AI-assisted workflows. Full article

Poor vascularization is one of the basic obstacles to the regeneration of functioning tissues because an oxygen diffusion process and elimination of wastes are essential in preserving the grafts. Recently, biomaterials have allowed the invention of bioinspired polymer scaffolds and replicated the natural extracellular matrix (ECM) due to the mechanical tunability of the synthetic polymers with the biological signals of natural macromolecules. The review uses a mechanistic analysis of the strategies to improve angiogenesis by using surface topography modification, bioactive peptide incorporation and pre-vascularization. Another way to achieve complex, perfusable topologies is by using more sophisticated methods of fabrication, such as electrospinning, 3D/4D bioprinting, or microfluidics. Based on in vitro and in vivo results, we determine angiogenic effectiveness by using cellular assays and animal transfers, pointing towards the translational advances in patents and clinical uses of bone, cardiac, nervous, and skin tissues. In spite of the substantial improvements, large-scale production and high demands of the regulations still exist. The future directions include the incorporation of bioinspired designs and intelligent materials, nanotechnology, and AI-based optimization into developing patient-specific and adaptive scaffolds. The following innovations herald the advent of highly effective constructs that can be used to regenerate tissue and overcome the limitations of present tissue engineering therapies through the introduction of highly effective, vascularized constructs. Full article

Identifying critical stations is fundamental to improving the resilience and operational safety of urban rail transit networks. However, most existing identification methods—especially dynamic node removal approaches—assume that station failures affect only the failed node itself, thereby overlooking the cascading impacts caused by train turnback adjustments under bidirectional service interruptions. This simplification leads to systematic underestimation of stations with strong operational dependencies. To address this gap, this study proposes a framework for identifying critical station that explicitly incorporates bidirectional operational disruptions and the indirect failures they induce within turnback sections. This study is among the first to explicitly model turnback-related failure propagation within operational sections in critical station identification, providing a closer alignment with real-world rail transit operations. A comprehensive evaluation system is then constructed by integrating dynamic network connectivity indicators, network topology characteristics, and station attributes. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), combined with objectively determined indicator weights, is employed to synthesize multidimensional indicators and rank station importance. The method is applied to the Chengdu Metro network (12 lines and 282 stations). Results indicate that considering turnback related indirect failures substantially amplifies the measured impact of station disruptions on network connectivity. Critical stations are highly concentrated at intersections between the loop line and major radial lines, while several non-interchange stations within key turnback sections—such as Lijiatuo Station and Wannianchang Station—exhibit pronounced increases in importance rankings. Comparative analysis shows that the rankings of some stations change by more than 50% relative to the conventional node removal method, indicating that traditional approaches may significantly underestimate operationally critical stations associated with turnback sections. More importantly, the proposed method enables a direct comparison between structurally important stations and operationally critical stations under disruption scenarios. Overall, the proposed framework provides a more realistic and operation oriented identification of critical stations by explicitly accounting for train operation dependencies under bidirectional interruptions, offering practical insights for resilience assessment and emergency management of large scale urban rail transit networks. Full article

Flight dynamics modeling is a fundamental cornerstone of aircraft design, simulation, and control. Traditional approaches rely on aerodynamic look-up tables for numerical integration, which suffer from high data-acquisition costs, poor extrapolation capability, and difficulty in assimilating flight test data. This paper proposes an architectural integration of physics-informed neural networks (PINNs), graph neural networks (GNNs), and known flight mechanics equations for flight dynamics modeling. Without requiring aerodynamic coefficient labels, the method predicts flight state derivatives using state-transition data. The approach encodes the structural knowledge of flight mechanics equations into graph topology and a physics computation layer (PhysicsLayer), so that the neural network only needs to learn the unknown aerodynamic coefficients while all remaining physical relationships are computed by the governing equations. Using an F-16 fighter six-degree-of-freedom model as the verification platform, an ablation study involving Direct-MLP, PINN, PIGNN, and GNN is conducted. Results show that the PIGNN architecture improves single-step derivative prediction accuracy by 86.6% over Direct-MLP, 60.9% over pure PINN, and 90.8% over GNN. In 499-step (approximately 5 s) rollout state prediction, the PIGNN Core RMSE is 1.1554, with approximately linear error growth within the first 100 steps indicating well-controlled short-range error accumulation. The graph-structural prior enables the network to learn aerodynamic coefficients that closely match the F-16 reference aerodynamic database without aerodynamic coefficient supervision. The results demonstrate that combining graph-based dependency modeling with hard physical constraints is effective for interpretable flight dynamics surrogate modeling. Full article

Sustainable power-system operation requires carbon-reduction strategies that are emission-effective, physically deliverable, economically feasible, and compatible with user-side decarbonization claims. As Scope 2 carbon accounting increasingly emphasizes temporal, spatial, and physical consistency, dispatch models need to link user-level carbon claims with network-constrained power delivery. This paper proposes a User-Centric Carbon Cost-Constrained Low-Carbon Dispatch (CCC-LCD) framework that integrates carbon emission flow (CEF), nodal carbon intensity (NCI), network-constrained optimal dispatch, and endogenous demand response. A PTDF-based DC-OPF model represents active-power deliverability, while dual virtual flow variables determine carbon-flow directions endogenously. The model minimizes the target user’s physically traced Scope 2 emissions under a cost-tolerance budget and flexible-load constraints. Case studies on a modified IEEE 14-bus system show that nodal decarbonization is topology-dependent: high-load and high-NCI nodes obtain larger reductions from source-side generation substitution, whereas renewable-adjacent nodes exhibit limited marginal gains. The CEF-DR strategy outperforms single-mechanism cases, indicating the value of coordinating physical carbon-flow constraints with flexible demand. From a sustainability perspective, the proposed framework supports verifiable low-carbon electricity consumption, improves the economic feasibility of user-side decarbonization, and provides a practical dispatch tool for sustainable energy transition and corporate Scope 2 emission reduction. Full article

Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a unified mathematical framework for explainable deep learning which integrates multimodal inputs, graph-theoretic road geometry, uncertainty modeling, and intrinsically interpretable representations. Road-structured priors that include lane topology and spatial constraints are incorporated into learning and optimization processes for ensuring model predictions and explanations to remain physically and semantically grounded. The review synthesizes methods across saliency-based, concept-based, causal, and intrinsic explainability, and extends them to vision-language models. This enables language-grounded, human-interpretable reasoning in autonomous vehicle systems. While vision-language models offer a new paradigm for semantic explainability, their limitations such as hallucinations, misgrounding, and reduced reliability under distribution shifts are also critically examined. Along with the role of road priors in improving alignment and robustness, another key contribution of this work is its quantitative evaluation metrics for road-aware explainability. These evaluation metrics link the explanations to spatial consistency, uncertainty alignment, and graph-structured reasoning. The overall framework connects latent representations, predictions, and explanations within a single formulation, enabling systematic comparison and analysis across models. Based on a PRISMA-guided review of 164 studies, this research identifies gaps in real-world reliability, temporal reasoning, and standardized evaluation, and outlines future directions including human-in-the-loop systems, regulatory readiness, and language-based auditing. Overall, this study advances a mathematically grounded and road-aware perspective on explainable vehicle AI which significantly bridges the gap between high-performance models and transparent, trustworthy autonomous systems. Full article

Abrupt maritime-energy disruption can generate system-wide production losses before firms and policymakers can adjust. Existing assessments usually emphasize direct exposure or long-run equilibrium responses, which makes them less suitable for short-run risk assessment in energy-dependent production systems. We develop a threshold-cascade framework that combines dual-track dependence topology, edge-level inventories, smooth operability bands, and a separate price-validation step to identify the blockade intensity at which a localized chokepoint shock becomes systemic production loss. The framework is evaluated against the March 2021 Suez blockage and the 2022 Russia–Ukraine producer-price episode, and then applied to a 2026 Strait of Hormuz stress scenario using the Organisation for Economic Co-operation and Development (OECD) Inter-Country Input-Output (ICIO) tables, 2025 edition, with the 2022 benchmark year. Under the baseline 150-day horizon, terminal loss first reaches 50% at about 32% blockade intensity, with a broader calibrated threshold band of 32–46%. Losses spread beyond the point of origin and become concentrated in East and Southeast Asian manufacturing supply chains and in downstream consumer markets after inventories at connected hubs are depleted. Policy experiments show that single-channel interventions shift the threshold only modestly, whereas an integrated package that relaxes logistics, inventories, and upstream scarcity moves the threshold to about 46% in this calibration. The analysis targets the weeks-to-months interval before substitution, contract renegotiation, and broader market adjustments dominate. Within that interval, the model identifies when buffers fail, how production losses spread, and which intervention packages delay systemic disruption. Full article

With the increasing demand for flexible electric vehicle charging and grid-interactive energy utilization, wireless power transfer (WPT) systems with high efficiency, bidirectional power flow capability, and controllable charging characteristics have attracted growing attention. However, existing WPT systems for electric vehicles still suffer from challenges including low adaptability to multiple operating modes, difficulty in achieving stable constant-current/constant-voltage output, and limited bidirectional power transfer capability under weak-coupling conditions. To address these issues, two relay-based four-coil WPT topologies, namely S-SS-LCC and LCC-SS-LCC, are proposed for electric vehicle charging and bidirectional energy transfer applications. Based on fundamental frequency analysis, frequency-domain models of the two topologies are established to reveal the relationships among resonant characteristics, output behavior, and power transfer direction. The results show that the S-SS-LCC topology can achieve constant-current and constant-voltage output in the forward grid-to-vehicle charging mode, as well as constant-voltage output in the reverse vehicle-to-grid mode. In contrast, the symmetrical LCC-SS-LCC topology can achieve bidirectional constant-current power transfer, making it suitable for vehicle-to-vehicle emergency charging scenarios. Under weak-coupling conditions (k = 0.1), the S-SS-LCC system delivers an output current of approximately 12 A at 85.2 kHz and an output voltage of about 612 V at 87.7 kHz, with a peak efficiency of 91.63%. The LCC-SS-LCC system achieves bidirectional constant-current output at 87.7 kHz with a maximum efficiency of 92.23%. Low-power experimental results further verify the predicted constant-current and constant-voltage characteristics. The proposed topologies provide a promising solution for efficient electric vehicle wireless charging and flexible bidirectional energy interaction in future smart charging systems. Full article

Dominant flow channels formed during the late stages of waterflooding can severely reduce sweep efficiency and intensify ineffective interwell circulation. Conventional identification approaches, including tracer testing, well testing, and numerical simulation, often suffer from high operational cost, long execution time, or limited adaptability to heterogeneous interwell connectivity. Although ant colony optimization (ACO) is suitable for path-search problems in reservoir networks, its performance depends strongly on hyperparameter settings, and sample-by-sample parameter tuning introduces substantial online computational overhead. This study proposes a structure-informed GIN–ACO framework for adaptive dominant flow channel identification in connection-element reservoir graphs. A physics-constrained benchmark model is first established using Darcy’s law and the connection element method to provide reference flow paths. A geometry-based surrogate model is then developed to approximate flow splitting coefficients efficiently while preserving the main physical trends. Based on graph topology and geometric descriptors, a graph isomorphism network is trained to predict task-specific ACO parameters, replacing iterative online search with direct parameter inference. Experiments on 1000 synthetic reservoir graphs show that the proposed method achieves a 100% success rate with an average online computation time of 143.5 ms, outperforming fixed-parameter ACO, PSO-ACO, and BO-ACO. On 20 semi-realistic SPE10 reservoir models, GIN–ACO achieves a success rate of 92 ± 1% with an average runtime of 160.3 ± 5 ms. Ablation studies further confirm that graph-structure learning, combined topology–geometry features, and GIN-based parameter prediction are essential for robust performance. The proposed framework provides a promising and computationally efficient route for structure-aware dominant channel identification in connection-element reservoir models. Full article

Accurate extrinsic calibration among multiple heterogeneous Light Detection and Ranging (LiDAR) sensors is essential for autonomous vehicle perception systems, yet remains challenging in distributed topologies where overlap exists only between adjacent sensor pairs. Existing methods often assume a central LiDAR with direct field-of-view overlap to all others and suffer from error accumulation in sequential pairwise registration. This paper presents a targetless, motionless multi-LiDAR extrinsic calibration framework that is topology-agnostic and resolves error accumulation through global optimization. The method integrates (1) Random Sample Consensus (RANSAC)-based planar patch extraction with a dual-criterion normal-guided matching strategy, (2) robust coarse alignment via TEASER++, and (3) pose graph optimization with analytically derived edge weights from Generalized Iterative Closest Point (GICP) covariance matrices. The use of structural planar primitives rather than local point descriptors overcomes density-dependent matching failures inherent to heterogeneous sensor pairs, while global pose graph optimization eliminates the cumulative error propagation of sequential pairwise approaches. Validation is performed on three distinct real-world configurations: a six-LiDAR autonomous port truck (ring topology), the four-LiDAR EDGAR research vehicle (distributed topology), and a three-LiDAR benchmark from the OpenCalib toolbox. The proposed method consistently outperforms state-of-the-art baselines, achieving 0.021 m translation Root Mean Square Error (RMSE) and 0.36° rotation RMSE on the port dataset, with full calibration completed in under 2 s on CPU—enabling rapid in-situ recalibration without requiring dedicated facilities or vehicle motion. Full article

Accurate three-dimensional (3D) reconstruction of digital rocks from limited data remains a significant challenge in digital rock physics. While Multiple-Point Geostatistics (MPS) offers a powerful solution, its multi-scale performance, particularly regarding extrapolation from small training images to larger domains, lacks a comprehensive evaluation framework that connects structural fidelity to functional equivalence. This study proposes an integrative multi-dimensional quantitative evaluation system that incorporates macroscopic statistics, microscopic topology, complex morphology, and seepage properties. Utilizing an improved Single Normal Equation Simulation (SNESIM) algorithm and a 60 × 60 × 60 voxel sandstone Training Image, 3D models were reconstructed across five scales ranging from 40 × 40 × 40 to 120 × 120 × 120 voxels. To ensure statistical robustness and mitigate stochastic uncertainty, ten independent realizations were performed for each scale. Quantitative analysis reveals that while SNESIM maintains high accuracy in macroscopic parameters and second-order spatial statistics, it exhibits systematic deviations in microscopic topology and surface complexity. Specifically, as the scale expands, the coordination number decreases while intrinsic anisotropy is progressively lost, yet permeability does not drop proportionally. This paradox is attributed to structural homogenization driven by the loss of long-range directional correlations. These findings indicate that the algorithm tends toward structural homogenization during scale extrapolation, systematically weakening the directional transport properties of the original rock. This study provides a standardized benchmarking methodology that promotes the evolution from visual similarity toward functional equivalence, thereby enhancing the reliability of reservoir characterization and seepage prediction. Full article