Search Results (6,637)

Pavement energy harvesting has been investigated as a means of converting traffic loading, solar radiation, and pavement thermal gradients into usable electricity or heat. This paper reviews 135 publications available through March 2026 and evaluates the field from a pavement engineering perspective. The literature is organized into six technology families: piezoelectric systems, mechanical-electromagnetic systems, triboelectric systems, thermoelectric systems, hydronic/geothermal/solar-thermal pavements, and photovoltaic or pavement-integrated photovoltaic-thermal systems. The review considers not only reported energy output, but also structural compatibility, durability, constructability, maintenance requirements, safety, and deployment conditions. The synthesis shows that the most credible near-term roles of piezoelectric and triboelectric systems are self-powered sensing and other localized low-power functions rather than bulk electricity generation. Mechanical-electromagnetic systems can produce larger event-level output, but their practicality is limited to low-speed and highly controlled settings because they rely on deliberate surface displacement. Thermoelectric systems are mechanically compatible with pavements, yet their performance remains constrained by weak and transient temperature gradients. Hydronic and solar-thermal pavements are presently the most infrastructure-compatible option for large-area energy recovery because they deliver useful heat and align with snow-melting, seasonal storage, and adjacent building-energy applications. Photovoltaic and photovoltaic-thermal pavements offer direct electrical generation, but continued challenges with transparent cover layers, surface friction, durability, fouling, and maintenance still limit broad roadway deployment. Overall, the review indicates that future progress will depend less on maximizing peak output in isolated prototypes and more on integrated pavement-energy design, standardized performance reporting, durability assessment, techno-economic evaluation, and corridor-scale demonstration. Full article

Electric field distortion remains a fundamental challenge to the operational reliability of HVDC cable accessories, where localized stress intensifies space charge injection and accelerates insulation degradation. While nonlinear conductive composites incorporating functional fillers such as ZnO have shown potential for adaptive field grading, their dynamic interaction with space charge under non-uniform fields has yet to be fully resolved. This study experimentally examines the spatiotemporal evolution of space charge in double-layer dielectric structures comprising linear low-density polyethylene (LLDPE) and ZnO-based nonlinear composites, using the laser-induced pressure pulse (LIPP) technique. Localized field enhancement is introduced via metallic pin defects embedded on the cathode side. Comparative analysis reveals that composites with 40 vol% ZnO microvaristors markedly suppress charge injection compared to conventional semiconductive ethylene-vinyl acetate (EVA) layers. Specifically, interfacial charge accumulation during polarization is reduced by 71%, and residual charge density after depolarization decreases by 88%, leading to a more uniform internal field distribution. These findings provide direct experimental evidence of the field-regulating mechanism of nonlinear composites from the perspective of charge dynamics, supporting their application in intelligent HVDC insulation systems. Full article

Radiation pneumonitis (RP), one of the most common and severe complications in locally advanced non-small cell lung cancer (LA-NSCLC) patients following thoracic radiotherapy, presents significant challenges in prediction due to the complexity of clinical risk factors, incomplete multimodal data, and unavailable slice-level annotations in pre-radiotherapy CT images. To address these challenges, we propose a multimodal fusion framework based on Dual-Layer Attention-Based Adaptive Bag Embedding Multiple-Instance Learning (DAAE-MIL) for accurate RP prediction. This study retrospectively collected data from 995 LA-NSCLC patients who received thoracic radiotherapy between November 2018 and April 2025. After screening, Subject datasets (n = 670) were allocated for training (n = 535), and the remaining samples (n = 135) were reserved for an independent test set. The proposed framework first extracts pre-radiotherapy CT image features using a fine-tuned C3D network, followed by the DAAE-MIL module to screen critical instances and generate bag-level representations, thereby enhancing the accuracy of deep feature extraction. Subsequently, clinical data, radiomics features, and CT-derived deep features are integrated to construct a multimodal prediction model. The proposed model demonstrates promising RP prediction performance across multiple evaluation metrics, outperforming both state-of-the-art and unimodal RP prediction approaches. On the test set, it achieves an accuracy (ACC) of 0.93 and an area under the curve (AUC) of 0.97. This study validates that the proposed method effectively addresses the limitations of single-modal prediction and the unknown key features in pre-radiotherapy CT images while providing significant clinical value for RP risk assessment. Full article

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

Thermal–oxidative coking of aviation fuel remains a critical limitation for fuel-cooled aero-engine systems operating under high heat loads. This study systematically investigates the oxidative coking behavior of RP-3 aviation kerosene, focusing on the coupled evolution of deposit morphology, composition, and operating conditions. Experiments were conducted in an electrically heated stainless-steel tube while independently varying dissolved oxygen concentration, fuel temperature, temperature gradient, operating pressure, and heating duration. Deposit layers were characterized by SEM and XPS, and residual fuel chemistry was analyzed using GC/MS. The results show that dissolved oxygen governs both the extent and mechanism of coking in the autoxidation regime (150–450 °C). Normal and elevated oxygen levels promote autoxidation of straight-chain alkanes, generating oxygen-containing intermediates that form flocculent, oxygen-rich deposits, whereas near-deoxygenated conditions suppress autoxidation but sustain sulfur-dominated, needle-like deposits. Temperature primarily controls deposition rate and morphology, with steep temperature gradients inducing localized coke formation, while pressure exerts only a minor indirect influence. Prolonged operation leads to deposit densification and non-linear accumulation behavior. These findings clarify the links between fuel chemistry, thermal conditions, and deposit architecture, providing a basis for morphology-aware coking models in fuel-cooled aero-engine systems. Full article

Three-dimensional printed polymers produced using Fused Deposition Modelling (FDM) exhibit directional microstructures resulting from filament paths, layer interfaces, and cellular infill, leading to mechanical and tribological responses distinct from those of homogeneous bulk materials. This study presents a comparative tribomechanical evaluation of polypropylene (PP) bulk inserts and 3D-printed polyethylene terephthalate glycol (PETG) inserts with a 30% hexagonal infill, relevant for robotic gripping applications. Progressive scratch tests were performed under loads from 5 to 100 N (150 N for PP), and profilometry was applied to quantify groove morphology, ridge formation, and displaced-volume ratios. An elasto-plastic conical indentation model was used to derive indentation pressures and elastic–plastic transition radii from groove geometry. The PETG inserts exhibited heterogeneous groove depth, intermittent ridge tearing, and friction fluctuations associated with the internal infill structure, consistent with previous findings on anisotropy and architecture-dependent behaviour in additively manufactured polymers. In contrast, bulk PP demonstrated smoother friction profiles and more stable plastic flow under increasing loads. Two functional indices—specific frictional work and ridge-to-trace volumetric ratio—are introduced to support material selection for robotic gripping systems. The results show that local contact mechanics in 3D-printed inserts are governed by print-induced structural features and can be effectively evaluated through a scratch-based elasto-plastic analysis. The methods and results presented in this work support the rational selection and design of polymer inserts for robotic gripper fingertips. The proposed scratch-based elasto-plastic evaluation framework enables manufacturers and automation engineers to compare 3D-printed and conventional materials based on friction stability, wear response, and deformation resistance. This approach can be directly applied to optimise gripping performance in industrial handling, packaging, and collaborative robotics. Full article

The present study investigates the morphology, chemical composition, and phase constitution of oxide scales formed on the Fe40Al5Cr0.2TiB intermetallic alloy after long-term oxidation at 700 °C for 2000 h in air and water vapor environments. The results demonstrate the formation of an extremely thin oxide scale (≈300 nm), composed predominantly of α-Al₂O₃, which provides effective protection against further oxidation. The oxide layer exhibits locally heterogeneous morphology, including whisker-like structures and fine crystallites. Due to the very limited thickness of the oxide scale, significant challenges arise in the interpretation of microanalytical data. It is shown that the accelerating voltage strongly influences the effective information depth in SEM-EDS analysis, leading to a substantial contribution from the substrate even at low voltages. Monte Carlo simulations were used to support the interpretation of electron–matter interactions and to explain the observed discrepancies in chemical analysis. The study demonstrates that reliable characterization of ultrathin oxide scales requires careful optimization of SEM parameters and the combined use of complementary techniques, including EDS/WDS, XRD, and EBSD. The findings highlight the importance of methodological considerations in the analysis of thin oxide layers and provide guidance for the correct interpretation of experimental data in similar systems. Full article

Ocean temperature and salinity structures are crucial in understanding ocean circulation and heat–salt transport processes. However, the high cost and limited spatiotemporal coverage of in situ observations make it difficult to reconstruct high-resolution three-dimensional temperature–salinity (T-S) fields. To address these limitations and the strong spatiotemporal heterogeneity of T-S structures in the South China Sea (SCS), the Smart Synthetic Ocean Profile (SSOP) model is proposed, which is a two-stage machine learning-based inversion framework for reconstructing subsurface T-S profiles from satellite surface data. The framework integrates localized training, adaptive model selection, and an error correction strategy. Using climate-state grids with a consistent spatiotemporal resolution as a baseline, multiple candidate regression models are independently trained for each grid point–depth layer–month combination, and the optimal model is selected through performance validation to generate initial T-S profiles. An error correction module is then introduced to refine temperature profile deviations, improving profile consistency and overall accuracy. Experiments using three independent observational periods from the SCS show that SSOP reliably reconstructs vertical T-S structures, particularly in the upper ocean and thermocline. Comparisons with in situ observations indicate that SSOP achieves improved accuracy relative to the Modular Ocean Data Assimilation System and climatology. Full article

A bottom-up fabrication approach for flexible piezoelectric micromachined ultrasound transducer (PMUT) arrays on stainless-steel substrates was developed. Devices were fabricated using chemical solution deposition of a 700 nm-thick layer of Pb_0.99□_0.01(Zr_0.52Ti_0.48)Nb_0.02O₃, where □ denotes a vacancy on the Pb site, on 50 μm-thick LaNiO₃/HfO₂/stainless-steel foils. Lithography for definition of the electrode and piezoelectric layers was completed on the front of the wafer. Ni electroplating on the back side of the foil was used to create locally stiff areas to define the deflection area. PMUT devices were successfully fabricated using this method. The permittivity and loss tangent of the fabricated device at 1 kHz were 283 ± 9 and <1.5%, respectively. The remanent polarization was measured to be 38 ± 0.3 μC/cm². Full article

The transition of hypersonic boundary layers remains a significant unresolved challenge in fluid mechanics, particularly regarding the influence of expansion corners on three-dimensional boundary layer instability. The present work investigates a hypersonic swept wing configuration with an expansion corner using linear stability theory (LST) and direct numerical simulations (DNSs). A high-order shock-fitting method provides the laminar base flow for sweep angles of ${30}^{\circ }$, ${45}^{\circ }$ and ${60}^{\circ }$ and expansion corner angles of $0^{\circ }$, $3^{\circ }$ and $6^{\circ }$. As the sweep and expansion angles increase, both the favourable pressure gradient and crossflow intensity are strengthened. LST reveals that, while the expansion corner suppresses disturbance growth locally, it promotes the development of subharmonic modes downstream, with the dominant spanwise wavelength doubling across the corner. Crossflow instability intensifies with increasing sweep and expansion angles. DNSs accounting for non-parallel effects confirm a sharp reduction in growth rate at the corner itself, while upstream and downstream trends remain consistent with LST predictions. Nonlinear simulations with finite-amplitude perturbations show saturated crossflow vortex structures. The subharmonic mode develops into mushroom-shaped vortices distinct from those in conventional studies. The expansion corner weakens the vortex intensity for both spanwise wavelengths, exerting a complex effect on the transition process. Full article

Semantic segmentation for autonomous driving demands balancing high-fidelity perception with real-time latency. While Transformers achieve state-of-the-art results, their quadratic complexity bottlenecks high-resolution processing. State Space Models (SSMs) like Mamba offer linear complexity but often suffer from local detail loss and inefficient scanning strategies. We introduce AutoMamba, a tailored Hybrid-SSM architecture. We propose a Hybrid-SSM block incorporating Depthwise Convolutions to inject local spatial priors and a Stage-Adaptive Mixed-Scanning strategy. This strategy prioritizes horizontal context in early stages for road layouts while only activating vertical scanning in deep layers to preserve anisotropic structures like poles. Furthermore, we reveal that unlike Transformers, Mamba architectures require Auxiliary Supervision and Online Hard Example Mining (OHEM) to address “long-tail forgetting.” Experiments on Cityscapes and BDD100K under a training-from-scratch setting demonstrate AutoMamba’s superiority. Notably, AutoMamba-B0 achieves 67.79% mIoU on Cityscapes with 31.3% fewer FLOPs than SegFormer-B0. Moreover, while the larger SegFormer-B2 fails with Out-Of-Memory errors at $2048\times 2048$ resolution, AutoMamba-B2 scales efficiently, validating its linear complexity advantage for next-generation perception systems. Full article

Autonomous guided vehicle (AGV) navigation in high-density warehouses faces significant challenges due to narrow aisles and complex U-shaped traps. In such environments, traditional sampling-based path planning algorithms often converge slowly and produce suboptimal paths. To solve these issues, a novel hierarchical hybrid planning framework named MEG-RRT* (Multi-objective Evolutionary Guided RRT*) is proposed in this study. The proposed MEG-RRT* integrates an optimization engine based on NSGA-II into the sampling process. It guides exploration direction away from local minima by jointly optimizing convergence efficiency and safety-related objectives. Furthermore, a geometry-aware execution layer is introduced to improve motion through narrow passages and to refine the path structure. This layer includes radar-guided steering, adaptive step-size control, and ancestor shortcut operations. Comparative experiments were conducted in simulated scenarios of complex narrow passages and high-density warehouses to verify the superiority of the proposed MEG-RRT*. In complex narrow passages, the proposed algorithm achieves a 100% success rate; it also reduces convergence time by 43.5% compared to standard RRT* and by 44.9% compared to Informed-RRT*. In warehouse environments, it generates smooth, kinematically favorable paths that are 39% shorter than those produced by RRT-Connect. These results demonstrate that MEG-RRT* balances exploration efficiency and solution optimality, making it well suited for automated logistics applications. Full article

In the fabrication process of pipelines for petrochemical and other industries, weld reinforcement is often excessive and adversely affects subsequent processes such as anticorrosion treatment and surface coating. Weld reinforcement must be removed through a grinding process. Welding deformation and fit-up errors often lead to highly irregular weld geometries, which makes robotic grinding difficult and causes the task to still heavily rely on manual operation. To address this issue, this study proposes an automatic weld recognition and grinding trajectory planning method based on 3D visualization and deep learning. A weld recognition network, termed WSR-Net, has been developed based on an improved PointNet++ architecture with a cross-attention mechanism, achieving a segmentation accuracy of 98.87% and a mean intersection over union of 90.71% on the test set. An intrinsic shape signature (ISS) key point selection algorithm with orthogonal slicing-based pruning optimization is developed to robustly extract key weld ridge points that characterize the weld trend on rugged weld surfaces. According to the height differences between the weld and the adjacent base metal surfaces, the grinding reference surface is fitted using the weld contour through the moving least-squares method. The ridge line points are projected onto the grinding reference surface along the local normal to generate the expected grinding trajectory points. The grinding trajectory that meets the process constraints is generated through reverse layer slicing. Grinding experiments demonstrate that the proposed WSR-Net achieves robust segmentation performance for both planar and curved surface welds. With the reverse layered trajectory planning method, the proposed method enables high-precision automatic grinding of complex spatially curved surface welds. The results show that the final grinding mean error is 0.316 mm, which satisfies the preprocessing requirements for subsequent processes. The proposed method provides a feasible technical method for the intelligent grinding of spatially curved surface welds. Full article

This paper develops an intelligent Enhanced Starfish Optimization (ESFO) algorithm for optimizing a secure wireless communication infrastructure. The Starfish Optimization (SFO) algorithm is inspired by starfish biology, using the integrated modeling of the arm-based exploration, preying, and regeneration behaviors of starfish. To further enhance the exploitation capability of the standard Starfish Optimization (SFO), the proposed Enhanced Starfish Optimization (ESFO) integrates a fitness-based interacting mechanism within the exploitation phase. This innovative modification improves local search accuracy, preserves population diversity, and mitigates premature convergence without introducing additional control parameters. Moreover, the proposed Enhanced Starfish Optimization (ESFO) is designed for secure wireless transmission, which is considered one of the main topics in next-generation wireless network infrastructure. The investigated network addresses the use of Simultaneously Transmitting and Reflecting RIS (STAR-RIS) in the security of the physical layer. This implemented STAR-RIS has a coupled phase shift to create reflected and transmission links, unlike traditional Reconfigurable Intelligent Surface (RIS). In this regard, we create a safe beamforming architecture that optimizes both Base Station (BS) precoding vectors and STAR-RIS transmission/reflection coefficients. In order to validate the efficiency of the proposed Enhanced Starfish Optimization (ESFO) algorithm, it is compared to several benchmark optimizers such as standard Starfish Optimization (SFO), Dhole Optimizer (DO), Neural Network Algorithm (NNA), Crocodile Ambush Optimization Algorithm (CAOA), and white shark Optimizer (WSO). These comparisons include several scenarios based on the transmitted power threshold which is varied in the range of 20 to 70 dBm with step of 5 dBm. The simulation results show that the proposed Enhanced Star Fish Optimization (ESFO) algorithm consistently outperforms existing benchmark approaches. This study supports future intelligent communication infrastructures in terms of secrecy and achievable rates over a range of transmit power levels. In particular, ESFO improves performance by up to 20–25% while converging 40–50% faster than traditional optimization algorithms, demonstrating its usefulness and resilience in STAR-RIS-assisted secure communication systems. The suggested ESFO-enabled architecture outperforms standard RIS-based systems in terms of secrecy capacity, according to numerical studies, and low-resolution STAR-RIS phase-shifters are sufficient to ensure robust secrecy performance. Full article

Cold-mercury gilding uses mercury as an adhesive to bond gold foil onto the surface of copper and silver artifacts. This technique and mercury gilding (fire gilding) both belong to the Au-Hg system and are closely related in technology. Clarifying the technical differences between them is of great significance for revealing the developmental sequence of ancient gilding technologies. On the basis of reconstructing traditional fire gilding, simulated cold-mercury-gilded samples were successfully prepared using experimental archeological methods, and multi-scale characterization was performed using SEM-EDS, XRD, and XPS. The results show that the surface of cold-mercury-gilded samples displays a micromorphology of folded and overlapped gold foil accompanied by locally dense particle aggregation. The cross-section of the gold layer exhibits a multilayer stacked structure, in which mercury is enriched at the gold layer/substrate interface and forms an AuHgCu/Ag diffusion layer. Room-temperature-stable Au-Hg and Ag-Hg phases such as Au₂Hg and AgHg are present in the gold layer, reflecting complex phase transformation behavior of the Au-Hg/Ag-Hg system at room temperature. During cold-mercury gilding, liquid mercury first adheres to the gold foil, and then interdiffusion and phase reactions occur between mercury, gold, and copper/silver atoms at room temperature. Intermetallic compounds and diffusion layers formed at the interface achieve firm bonding between the gold layer and the substrate. Both cold-mercury gilding and mercury gilding achieve metallurgical bonding through atomic interdiffusion. However, affected by differences in the initial state of mercury and operating temperature, the phase transformation and atomic diffusion behaviors of the system differ significantly, which are ultimately reflected in the cross-sectional structure of the gold layer, the composition of the interfacial diffusion layer, and the types of phases. Therefore, mercury-gilded artifacts show superior gold layer durability and bonding strength with the substrate compared with cold-mercury-gilded artifacts. Both techniques pioneered the application of mercury in metallic gilding and represent important innovations in ancient surface decoration technology. Full article