Search Results (16,644)

In sandstorm environments, a large number of suspended particles in the air absorb and scatter light, causing strong color bias, low contrast, and blurred details in images. These degradations reduce the reliability of computer vision applications in surveillance systems, intelligent transportation systems, unmanned aerial vehicle monitoring, and outdoor autonomous driving systems. A complete sandstorm image enhancement method was developed in this study by combining sky detection, color correction, contrast enhancement, and adaptive dark channel prior (ADCP) dehazing. The Lab color space was used to correct the color bias. The L channel was enhanced using normalized gamma correction and contrast-limited adaptive histogram equalization to improve brightness and contrast. Then, the sky region is detected to avoid over-processing, preserving the natural appearance of the sky region. Finally, ADCP is applied to non-sky regions for further dehazing. Experiments show that the proposed method provides better subjective and objective performance compared to other algorithms. Full article

Animal-vehicle collisions (AVCs) pose a significant threat to road safety, wildlife conservation, and transportation systems worldwide. Advances in artificial intelligence (AI) and computer vision have enabled intelligent detection and mitigation systems aimed at reducing such collisions. This review synthesizes the current state of AI-powered AVC prevention systems, examining deep learning architectures, multimodal sensor technologies, real-time processing frameworks, and system-level integration strategies. We analyze the transition from traditional computer vision methods to modern deep neural networks, evaluate sensor fusion approaches, and assess existing wildlife detection datasets and benchmarking practices. Key technical challenges are identified, including environmental variability, long-range detection constraints, dataset scarcity, cross-species generalization limitations, and real-time safety requirements. Rather than framing AVC prevention solely as an object detection task, this review conceptualizes it as a safety-critical perception and risk assessment pipeline operating under strict latency and deployment constraints. Persistent gaps in wildlife-specific detection, standardized evaluation protocols, and scalable edge deployment are discussed. To organize these insights, we present WildSafe-Edge as a conceptual reference architecture derived from the literature, synthesizing system-level design considerations and highlighting open research directions. Future research directions include transfer learning, synthetic data augmentation, vehicle-to-everything (V2X) integration, and edge-centric architectures to enable robust, real-world collision mitigation systems. Full article

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects. Full article

The coexistence of emulsified oil, dissolved organics, and heavy metal ions in industrial oily wastewater makes one-step treatment highly challenging. Herein, an organic-inorganic co-modified PVDF composite membrane (MTSP) was fabricated via nonsolvent-induced phase separation, with tea polyphenols, SiO₂, and fibrous MXene synergistically incorporated. The resulting membrane exhibited a superhydrophilic/underwater oleophobic surface, with a water contact angle of 1° and an underwater oil contact angle of ~136°, owing to the optimized surface chemistry and hierarchical pore structure. As a result, the MTSP membrane effectively suppressed oil fouling while enabling rapid water transport. At 0.1 bar, the optimized membrane delivered an oil/water separation efficiency of ~99.5% and a high flux of 2420–2670 L·m⁻²·h⁻¹, while maintaining >99% separation efficiency for various emulsified oils, including kerosene, edible oil, n-hexane, and 1,2-dichloroethane. It also showed excellent recyclability and chemical stability, retaining >98–99% efficiency after five cycles and after 24 h exposure to pH 1 and pH 12 conditions. Notably, for complex simulated wastewater containing emulsified kerosene, phenol, and Fe³⁺, Cu²⁺, Zn²⁺, and Cd²⁺, the membrane maintained ~99% oil/water separation efficiency and simultaneously removed ~79% of phenol and 70–86% of heavy metal ions in a single filtration process. The superior performance is attributed to the synergistic effects of the superhydrophilic/underwater-oleophobic membrane surface, hierarchical transport channels enabling rapid water permeation, and multifunctional sites that adsorb/coordinate dissolved pollutants. This work provides a simple, scalable design strategy for PVDF-based membranes that integrate high-flux separation, antifouling performance, and multi-pollutant remediation for the treatment of complex oily wastewater. Full article

Robust numerical frameworks are essential for the simulation, design, monitoring, and control of membrane-based separation units, particularly under highly nonlinear and industrially relevant operating conditions. In this context, a comprehensive phenomenological and numerical framework is proposed for the simulation of hollow-fiber membrane modules, incorporating coupled mass, momentum (through pressure drop), and energy transport equations. The governing equations are discretized using a rigorous orthogonal collocation formulation, and the performances of two numerical solution strategies are systematically investigated for the first time to allow the in-line and real-time implementation of the model: a steady-state approach based on the Newton–Raphson method with careful treatment of initial estimates, and a pseudotransient formulation. Particularly, an original and consistent numerical treatment is introduced for the energy balance at boundaries where the permeate flow vanishes, enabling the stable incorporation of thermal effects and Joule–Thomson phenomena. The results clearly show that the steady-state Newton–Raphson approach provides the best overall performance in terms of computational efficiency, numerical robustness, and accuracy when physically consistent initial profiles are employed. In particular, the combination of a linear initial guess and a numerical mesh constituted of four collocation points yielded the most favorable balance between convergence speed, numerical robustness, and accuracy for the base-case sensitivity analysis. For monitoring-oriented applications, the numerical choice should be weighted primarily toward computational performance once physical consistency and convergence criteria are satisfied, rather than toward maximum mesh-refinement accuracy. In this context, small differences in internal-fiber profiles can be compensated through real-time permeance estimation and are negligible when compared with measurement uncertainty in real industrial processes. Under extreme operating conditions involving low concentrations, low flow rates, and highly permeable species, the pseudotransient formulation proved to be a reliable auxiliary strategy, enabling robust convergence when suitable initial guesses were not readily available. The proposed framework is validated against experimental data from the literature and subjected to extensive convergence and sensitivity analyses, providing a reliable basis for simulation and for assessing computational feasibility in in-line and real-time monitoring-oriented applications. A full demonstration of digital-twin integration, online parameter updating, reduced-order coupling, and closed-loop control is beyond the scope of the present study and will be addressed in future work. Full article

Subdiffusion on graphs is often modeled by time-fractional diffusion equations; yet, its structural and dynamical consequences remain unclear. We show that subdiffusive transport on graphs is a memory-driven process generated by a random time change that compresses operational time, produces long-tailed waiting times, and breaks Markovianity while preserving linearity and mass conservation. While the subordination representation and complete monotonicity properties of the Mittag-Leffler function are classical, we develop a graph-based synthesis in which Mittag-Leffler dynamics admit an exact convex, mass-preserving representation as a superposition of Laplacian semigroups evaluated at rescaled times. This perspective reveals fractional diffusion as ordinary diffusion acting across multiple intrinsic time scales and enables new structural and dynamical interpretations of graphs. This framework uncovers heterogeneous, vertex-dependent memory effects and induces transport biases absent in classical diffusion, including algebraic relaxation, degree-dependent waiting times, and early-time asymmetries between sources and neighbors. These features define a subdiffusive geometry on graphs, enabling the recovery of global shortest paths, in contrast to the graph exploration of diffusive geometry, while simultaneously favoring high-degree regions. Finally, we show that time-fractional diffusion can be interpreted as a singular limit of multi-rate diffusion, in an appropriate asymptotic sense. Full article

Flow lines in aluminum alloy forgings are not merely post-deformation metallographic features; they are integrated indicators of material transport, microstructural evolution, defect susceptibility, and service performance. This review critically examines the mechanisms controlling flow-line evolution, with emphasis on constitutive flow behavior, dynamic recovery and recrystallization, second-phase redistribution, friction, thermal gradients, and die/preform design. It then evaluates how abnormal flow paths promote key defects, including folding/laps, flow-through discontinuities, vortex-like instability, and exposed flow lines, and distinguishes well-established mechanisms from topics that still rely on indirect evidence. Particular attention is given to the effects of flow-line morphology on anisotropy, notch sensitivity, corrosion-assisted damage, and fatigue life in forged aluminum alloys. Current control strategies, including preform optimization, FE-based backward tracing, multiphysics defect indices, frictional heat management, and isothermal forging, are also assessed. The available literature shows that stable contour-following flow lines are essential for the simultaneous control of defect formation, microstructural homogeneity, and durability, while major research needs remain in in situ validation, quantitative defect criteria, and digitally closed-loop process control. This review is therefore framed as a critical narrative synthesis rather than a formal systematic review; emphasis is placed on forging-centered studies that directly relate flow-path evolution to defect formation, anisotropy, fatigue, and process optimization, while evidence transferred from adjacent processes is treated as mechanistic support rather than equivalent proof. Full article

Global value chains have recently changed structurally (“servicification”)—that is, service sectors’ involvement in global value chain processes has become more intensive. We quantify services trade restrictions’ contribution to underdevelopment of global value chain servicification across Asian economies—an underexplored area. The study applies the “structural” gravity trade model and constructs panel data based on the 2025 Trade in Value Added and the Services Trade Restrictiveness Index database developed by the Organization for Economic Co-operation and Development. The empirical analysis covers five major service sectors—trade, transport, I&C, finance, and professional services. First, global value chain servicification remains relatively underdeveloped in most emerging and developing Asian economies, particularly across several service categories. Second, services trade restrictions’ presence significantly and negatively affects global value chain servicification’s extent in these economies. Third, these restrictive measures account for approximately 30–60% of servicification’s observed underdevelopment. Regarding policy implications, removing or easing such trade restrictions could substantially promote global value chain servicification, enhancing productivity and integration for emerging and developing Asian economies. 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

Amorphous metallic materials have emerged as a promising class of functional materials for energy storage and conversion owing to their disordered atomic structure and unique interfacial properties. This review focuses on amorphous metals and alloys, including metallic glasses and high-entropy amorphous systems, with particular emphasis on their surface- and interface-driven behavior in electrochemical environments. This review analyzes how structural disorder influences key properties such as electronic structure, ion transport, catalytic activity, and mechanical compliance and how these factors govern performance in batteries, supercapacitors, electrolyzers, and fuel cells. Special attention is given to interfacial phenomena, including charge-transfer kinetics, corrosion and passivation processes, and structural evolution during long-term operation. In addition, recent advances in fabrication strategies such as rapid solidification, thin-film deposition, mechanical alloying, thermoplastic forming, and electrodeposition are discussed in relation to their ability to tailor amorphous structures and interfaces. This review also highlights critical failure mechanisms and discusses some strategies to mitigate these effects. Overall, this work provides a focused perspective on the role of amorphous metallic surfaces and interfaces in electrochemical systems, identifying current challenges in scalability, durability, and compositional control, and outlining future directions for their integration into next-generation energy technologies. Full article

The reliability of the characteristics of high-voltage (HV) thyristors is related to the operational safety of the entire HVDC project. In order to investigate the degradation mode of thyristors in HVDC projects more realistically, aging experiments were conducted on HV thyristors under the combined action of sinusoidal half-wave voltage and current in a simulated operating environment. Experimental results show that the on-state voltage, reverse recovery characteristics, and reverse leakage current of thyristors have all degraded to varying degrees during the aging process. The main failure mode of thyristors can be summarized as the failure of the reverse blocking characteristic. Microstructural characterization of failed HV thyristors is conducted to explain the degradation mechanisms, including device surface morphology and elemental composition analysis. Observations have shown that the failed thyristor silicon wafer has been burned and hollowed out, accompanied by copper impurities, and significant thermal breakdown has occurred at the edge of the anode surface of the chip. Defects in chip structure and the invasion of impurities can lead to a decrease in the minority carrier lifetime of materials, which is an important factor in the characteristics of semiconductor devices. On this basis, further simulation research is carried out to conclude that the shortening of the minority carrier lifetime of the thyristor will distort the carrier space distribution, resulting in the rise in the on-state voltage. Meanwhile, the carrier transport capability decreases, leading to a decrease in the reverse recovery speed. The energy released during the rapid generation and recombination of carriers is one of the main reasons for the failure of blocking characteristics. This work provides comprehensive insights into the failure modes and mechanisms of HV thyristors. Full article

Flexible electrolyte-gated synaptic field-effect transistors (EGFETs) have emerged as a promising platform for neuromorphic visual systems, owing to their low-voltage operation, diverse synaptic plasticity, and exceptional mechanical flexibility. In particular, gel-based electrolytes, including hydrogels and ion gels, play a pivotal role as functional gate dielectrics, enabling efficient ion transport and strong ion–electron coupling through electric double-layer (EDL) formation. By leveraging these unique properties at the semiconductor/gel interface, EGFETs can effectively emulate essential biological synaptic behaviors, including short-term and long-term plasticity under optical stimulation. The inherent compatibility of EGFETs with a broad range of semiconductor channels, gel electrolytes, and flexible substrates enables the development of wearable and conformable neuromorphic platforms that seamlessly integrate sensing, memory, and signal processing within a single device architecture. Recent advances in gel material engineering, such as polymer network design, ionic modulation, and nanofiller incorporation, have significantly improved ion transport dynamics, interfacial stability, and device performance. Despite remaining challenges related to ion migration stability, multi-physical field coupling, and large-area device uniformity, these developments have substantially advanced the practical potential of gel-based systems. This review provides a comprehensive overview of the operating mechanisms, gel-based material systems, synaptic functionalities, mechanical reliability, and future prospects of flexible electrolyte-gated visual synapses, highlighting their considerable potential for next-generation intelligent perception and artificial vision technologies. Full article

Modern standards and requirements for ensuring the reliability and safety of transport infrastructure are aimed at shifting from routine maintenance to preventive maintenance, focused on predicting technical conditions and lifecycle management. Modern engineering approaches are based on the logic of state assessment and ensuring structural strength and dimensional stability. Therefore, they focus on recording defects or deviations from acceptable values without revealing the failure mechanism, which limits the ability to identify degradation processes and predict failures. The purpose of this article is to develop a formal conceptual framework for operationalizing process-oriented reliability analysis. Within this methodological framework, state is viewed as a snapshot of a dynamic process, while process stability is defined as the ability of a system to maintain its key behavioral characteristics under changing operating conditions and the geometric and physical–mechanical properties of system elements. The proposed framework expands on classical state-based diagnostics by introducing process invariants as prognostic indicators. The transition to trajectory-based behavior analysis allows monitoring systems to evolve into lifecycle management tools. Full article

Despite a plethora of studies in recent years focusing on the impact of nitrogen source addition on plant responses, there remains a lack of clarity regarding the differential utilization and distribution patterns of various nitrogen sources by Larix olgensis A. Henry seedlings. Specifically, the mechanisms by which ammonium nitrogen, nitrate nitrogen, and urea are differentially absorbed and distributed among different organs within the plant, as well as how these processes couple with rhizosphere soil microbial processes, still await elucidation. This study, conducted under field experimental conditions, employed a combination of ¹⁵N isotopic tracing, soil physicochemical property measurements, enzyme activity analysis, and microbial community functional analysis to investigate the effects of three nitrogen sources (NH₄⁺, NO₃⁻, and urea) and their varying addition levels on nitrogen absorption and distribution in Larix olgensis A. Henry seedlings. The results indicate that nitrogen source type significantly influences the nitrogen absorption rate and internal distribution patterns of plants. Within 24 h, seedlings preferentially absorb ammonium nitrogen and retain a higher proportion of newly absorbed nitrogen in their roots. The high ammonium chloride (GN) treatment group exhibited the highest ¹⁵N abundance in the root region, suggesting rapid root assimilation and short-term underground retention. By 48 h, the ¹⁵N abundance and AT% values in most organs across different treatment groups were significantly higher than those at 24 h, facilitating the transport of nitrate nitrogen and urea to stems and leaves, indicating a gradual shift in nitrogen distribution towards the aboveground parts. Moderate nitrogen addition improved soil nutrient conditions, altered pH and conductivity, enhanced nitrogen transformation processes related to urease and nitrate reductase, and increased microbial diversity and metabolic functions related to carbon metabolism, nitrogen metabolism, and energy metabolism. Soil pH, total nitrogen (TN), ammonium nitrogen (NH₄⁺-N), and organic carbon (OC) are core environmental factors driving the differentiation of soil microbial community structure, with distinct specificity in the response of microbial groups across different taxonomic levels to soil physicochemical properties. Full article

Computer modeling has a strong potential to replace or supplement physical fire testing of building structures. A prepared and applicable model must be simple to prepare, capable of calculating and processing results in the shortest possible time without the need for complex computing technology, thus saving time, resources, and finances and becoming a profitable alternative. The aim of this article is to design a model that would suitably represent a non-standard fire test using the Ansys software package 2025/R2. The model was created by combining the Ansys Discovery, Ansys Mechanical and Ansys Fluent software, connected using Ansys Workbench. Several calculation settings were tested, including the proposed finite mesh. The results showed the suitability of the proposed procedure and the division of calculations in separate software. The Species Transport and Viscous Shear Stress Transport (SST) k-omega model best represented fluid flow in Ansys Fluent. Ansys Mechanical confirmed the accuracy of the model in Ansys Fluent, when the predicted temperatures matched the real medium-scale fire test with an average coefficient of determination R² = 0.93. Full article