Search Results (140)

Gear defect detection directly affects the operational reliability of critical equipment in fields such as automotive and aerospace. Gear defect detection technology based on machine vision, leveraging the advantages of non-contact measurement, high efficiency, and cost-effectiveness, has become a key support for quality control in intelligent manufacturing. However, it still faces challenges including difficulties in semantic alignment of multimodal data, the imbalance between real-time detection requirements and computational resources, and poor model generalization in few-shot scenarios. This paper takes the paradigm evolution of gear defect detection technology as the main line, systematically reviews its development from traditional image processing to deep learning, and focuses on the innovative application of intelligent algorithms. A research framework of “technical bottleneck-breakthrough path-application verification” is constructed: for the problem of multimodal fusion, the cross-modal feature alignment mechanism based on Transformer network is deeply analyzed, clarifying its technical path of realizing joint embedding of visual and vibration signals by establishing global correlation mapping; for resource constraints, the performance of lightweight models such as MobileNet and ShuffleNet is quantitatively compared, verifying that these models reduce Parameters by 40–60% while maintaining the mean Average Precision essentially unchanged; for small-sample scenarios, few-shot generation models based on contrastive learning are systematically organized, confirming that their accuracy in the 10-shot scenario can reach 90% of that of fully supervised models, thus enhancing generalization ability. Future research can focus on the collaboration between few-shot generation and physical simulation, edge-cloud dynamic scheduling, defect evolution modeling driven by multiphysics fields, and standardization of explainable artificial intelligence. It aims to construct a gear detection system with autonomous perception capabilities, promoting the development of industrial quality inspection toward high-precision, high-robustness, and low-cost intelligence. Full article

Transmission line icing poses a significant natural disaster threat to power grid security. This paper systematically reviews recent advances in the understanding of icing mechanisms, intelligent detection, and prevention technologies, while providing perspectives on future development directions. In mechanistic research, although a multi-physics coupling framework has been established, characterization of dynamic evolution over complex terrain and coupled physical mechanisms remains inadequate. Detection technology is undergoing a paradigm shift from traditional contact measurements to non-contact intelligent perception. Visual systems based on UAVs and fixed platforms have achieved breakthroughs in ice recognition and thickness retrieval, yet their performance remains constrained by image quality, data scale, and edge computing capabilities. Anti-/de-icing technologies have evolved into an integrated system combining active intervention and passive defense: DC de-icing (particularly MMC-based topologies) has become the mainstream active solution for high-voltage lines due to its high efficiency and low energy consumption; superhydrophobic coatings, photothermal functional coatings, and expanded-diameter conductors show promising potential but face challenges in durability, environmental adaptability, and costs. Future development relies on the deep integration of mechanistic research, intelligent perception, and active prevention technologies. Through multidisciplinary innovation, key technologies such as digital twins, photo-electro-thermal collaborative response systems, and intelligent self-healing materials will be advanced, with the ultimate goal of comprehensively enhancing power grid resilience under extreme climate conditions. Full article

Severe snowstorms pose multiple threats to high-speed rail systems, including sudden drops in track friction coefficients, icing of overhead contact lines, and reduced visibility. These conditions can trigger dynamic risks such as train speed restrictions, cascading delays, and operational disruptions. Addressing the limitations of traditional scheduling methods in spatio-temporal modeling during blizzards, real-time multi-objective trade-offs, and high-dimensional constraint solving efficiency, this paper proposes a collaborative optimization approach integrating temporal forecasting with deep reinforcement learning. A dual-module LSTM-PPO model is constructed using LSTM (Long Short-Term Memory) and PPO (Proximal Policy Optimization) algorithms, coupled with a composite reward function. This design collaboratively optimizes punctuality and scheduling stability, enabling efficient schedule adjustments. To validate the proposed method’s effectiveness, a simulation environment based on the Lanzhou-Xinjiang High-Speed Railway line was constructed. Experiments employing a three-stage blizzard evolution mechanism demonstrated that this approach effectively achieves a dynamic equilibrium among safety, punctuality, and scheduling stability during severe snowstorms. This provides crucial decision support for intelligent scheduling of high-speed rail systems under extreme weather conditions. Full article

The On-Load Tap Changer (OLTC), as a critical component of transformers, undergoes frequent switching operations that can lead to faults such as contact wear and arc discharge, which are often difficult to detect at an early stage using traditional monitoring methods. In particular, dissolved gas analysis (DGA) in OLTC oil is challenged by the unique oil gas decomposition mechanisms and the presence of background noise, making conventional DGA criteria less effective. Moreover, OLTC oil monitoring data are typically obtained through intermittent sampling, resulting in sparse time series with low resolution that complicate fault prediction. To address these challenges, this paper proposes an integrated framework combining LGOD-based anomaly detection, Locally Weighted Regression (LWR) for data repair, and the ETSformer temporal prediction model. This approach effectively identifies and corrects anomalies, restores the dynamic variation trends of gas concentrations, and enhances prediction accuracy through deep temporal modeling, thereby providing more reliable data support for OLTC state assessment and fault diagnosis. Experimental results demonstrate that the proposed method significantly improves prediction accuracy, enhances sensitivity to gas concentration evolution, and exhibits robust adaptability under both normal and fault scenarios. Furthermore, ablation experiments confirm that the observed performance gains are attributable to the complementary contributions of LGOD, LWR, and ETSformer, rather than any single component alone, highlighting the effectiveness of the integrated approach. Full article

The monkeypox virus (MPXV) has spread globally, posing a severe challenge to global public health. This study systematically evaluated the aerosol shedding dynamics of the epidemic Clade IIb MPXV strain in infected young rabbits, along with its direct contact and airborne transmission potential among them. We found that young rabbits could be experimentally infected with MPXV, exhibiting distinct pathogenic features and viral shedding patterns. Young rabbits infected with MPXV shed the virus through nasal secretions and exhaled aerosols, peaking at 7 dpi. In total, 89–95.8% of virus-laden respiratory particles had a diameter ≥4.7 μm. Notably, MPXV can be efficiently shed and transferred among young rabbits through direct contact and airborne routes. The nasal secretions and exhaled virus particles from donor rabbits can be contacted or inhaled by recipient rabbits. Large amounts of viral DNA were detected in the nasal wash of rabbits exposed to contact or airborne exposure. Furthermore, virus particles invade the lungs, causing pathological changes and disseminating them to multiple organs. However, no infectious virus was successfully recovered from these recipient rabbits, as their exposed or inhaled MPXV dose might have been below the MPXV’s minimum infectious dose for young rabbits. These findings indicate that although the airborne transmissibility of the current MPXV strain is relatively limited, inhalation of viral particles following airborne exposure can still result in bodily damage. Continuous monitoring of MPXV transmissibility and mutation evolution is imperative to prevent efficient respiratory aerosol transmission, which guides global monkeypox prevention and control strategies. Full article

Tire contact imprint characteristics and pressure distribution directly affect their lateral mechanical characteristics under rolling conditions, which are the key influencing factors for vehicle handling stability. Based on the nonlinear finite element method, an explicit dynamic model of radial tires is established using Abaqus, and its contact process is simulated through phased load transfer and kinematic inversion. The modified mathematical model of contact pressure distribution is introduced from the geometric evolution law of contact imprint and the nonlinear characteristics of contact pressure distribution. The corrected lateral force and aligning torque and contact imprint behavior are analyzed. The results show that in the low roll-angle range, with the increase in the roll angle, the contact imprint shrinks asymmetrically, the pressure center shifts to the outer shoulder of the roll direction, and the lateral force and aligning torque show linear growth characteristics. At the critical value ±8°, the growth rate is significantly slowed down due to the stress saturation effect of the shoulder area. The research analyzes the evolution mechanism of the lateral mechanical characteristics of the contact imprint geometry and pressure distribution drive tires under roll conditions, providing theoretical support for vehicle handling stability optimization and tire structure design. Full article

Carbide-bonded graphene (CBG) coating, with its unique 3D cross-linked network structure, shows significant potential for protecting silicon substrates. However, a comprehensive understanding of its macroscale tribological properties remains lacking. This study investigated the macroscale friction and wear behaviors of CBG-coated silicon wafers using reciprocating sliding tests against steel balls under various loads and sliding cycles. The CBG coating exhibited excellent friction-reduction and anti-wear performance, reducing the steady friction coefficient from 0.80 to 0.17 and wear rate by an order of magnitude compared to those of bare silicon. Higher loads slightly decreased both friction coefficients and wear rates, primarily due to the formation of denser tribofilms and transfer layers. Re-running experiments revealed three distinct wear stages—adhesive, abrasive, and accelerated substrate wear—driven by the evolution of tribofilms, transfer layers, and unabraded flat areas. Furthermore, comparative experiments confirmed that these “unabraded flat areas” on the wear track play a critical role in sustaining low friction and prolonging coating life. The findings identify CBG as a robust solid lubricant for high-contact-pressure applications and emphasize the influence of tribo-layer dynamics and wear debris behavior on coating performance. Full article

Closing resistors in ultra-high-voltage (UHV) gas-insulated circuit breakers (GCBs) are critical components designed to suppress inrush currents and transient overvoltages during switching operations. However, in practical service, these resistors are subjected to repeated mechanical impacts and transient electrical stresses, leading to degradation of their electrical contact interfaces, fluctuating resistance values, and potential failure of the entire breaker assembly. Existing studies mostly simplify the closing resistor as a constant resistance element, neglecting the coupled electro-thermal–mechanical effects that occur during transient events. In this work, a comprehensive modeling framework is developed to investigate the dynamic electrical contact characteristics of a 750 kV GCB closing resistor under transient closing impacts. First, an electromagnetic transient model is built to calculate the combined inrush and power-frequency currents flowing through the resistor during its pre-insertion period. A full-scale mechanical test platform is then used to capture acceleration signals representing the mechanical shock imparted to the resistor stack. These measured signals are fed into a finite element model incorporating the Cooper–Mikic–Yovanovich (CMY) electrical contact correlation to simulate stress evolution, current density distribution, and temperature rise at the resistor interface. The simulation reveals pronounced skin effect and current crowding at resistor edges, leading to localized heating, while transient mechanical impacts cause contact pressure to fluctuate dynamically—resulting in a temporary decrease and subsequent recovery of contact resistance. These findings provide insight into the real-time behavior of closing resistors under operational conditions and offer a theoretical basis for design optimization and lifetime assessment of UHV GCBs. Full article

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations. Full article

The array of regular square pyramid microstructures with zero-spacing features is an ideal structural topology for building superhydrophobic functional surfaces due to its excellent anti-wetting performance and low surface adhesion properties. In the framework of existing studies, this microstructured array is usually considered to exist only in two typical wetting states, the stable Cassie state and the Wenzel state. In this study, a third type of wetting state, the incomplete Wenzel state, was discovered for the first time using experimental characterization, and the evolution mechanism of this new wetting state was revealed based on critical contact angle theory and numerical simulation. It is revealed that the faces and edges of the square pyramid microstructures exhibit different tilting angles, and this unique geometrical design endows them with dual critical contact angles. When the intrinsic contact angle of the microstructure is between the critical contact angles for the edges and faces, the wetting behavior of the droplet contact line in the directions parallel to the edges and faces will generate spontaneous and non-spontaneous competition effects, which lead to the formation of the incomplete Wenzel state. The dual-critical-angle theoretical model constructed in this study provides a new perspective for improving the theoretical system of wetting dynamics on pyramid arrays. Full article

To address the wear life prediction challenge of Guide Cones in passive compliant connectors under dynamic loads within specialized equipment, this study proposes a dynamic wear modeling and life assessment method based on the improved Archard model. Through integrated theoretical modeling, finite element simulation, and experimental validation, we establish a bidirectional coupling framework analyzing dynamic contact mechanics and wear evolution. By developing phased contact state identification criteria and geometric constraints, a transient load calculation model is established, revealing dynamic load characteristics with peak contact forces reaching 206.34 N. A dynamic contact stress integration algorithm is proposed by combining Archard’s theory with ABAQUS finite element simulation and ALE adaptive meshing technology, enabling real-time iterative updates of wear morphology and contact stress. This approach constructs an exponential model correlating cumulative wear depth with docking cycles (R² = 0.997). Prototype experiments demonstrate a mean absolute percentage error (MAPE) of 14.6% between simulated and measured wear depths, confirming model validity. With a critical wear threshold of 0.8 mm, the predicted service life reaches 45,270 cycles, meeting 50-year operational requirements (safety margin: 50.9%). This research provides theoretical frameworks and engineering guidelines for wear-resistant design, material selection, and life evaluation in high-reliability automatic docking systems. Full article

The removal of antibiotics from aqueous media along with their recovery is still an open research topic, due to their practical and economical importance. Adsorption allows these two objectives to be achieved, provided that the adsorbent used is chemically and mechanically stable and has a low preparation cost. In this study, PET (polyethylene terephthalate) fibers, obtained by mechanically processing PET waste, were used for the adsorption of rifampicin (RIF) and rifaximin (RIX) antibiotics from aqueous media. The experimental adsorption capacity of PET fibers for the two antibiotics (RIF and RIX) was determined at different pH values (2.0–6.5), adsorbent dose (0.4–20.0 g/L), contact time (5–1440 min), initial antibiotic concentration (4.0–67.0 mg/L), and temperature (10, 22, and 50 °C); the experimental values of these parameters were analyzed using a neuro-evolutive technique (ANE) combining sequential deep learning (DL) models with a differential evolution algorithm. The obtained optimal ANN-DL algorithm was then used to obtain the optimal models for the adsorption of RIF and RIX on PET fibers, which should adequately describe the adsorption dynamics for both antibiotics. The adsorption processes are spontaneous and endothermic (ΔG < 0, ΔH > 0) and are described by the Langmuir model (R² > 0.97) and the pseudo-second order kinetic model (R² > 0.99). The retention of RIF and RIX on the surface of PET fibers occurs through physicochemical interactions, and the FTIR spectra and microscopic images support this hypothesis. The presence of inorganic anions in the aqueous solution leads to an increase in the adsorption capacities of RIF (max. 7.6 mg/g) and RIX (max. 3.6 mg/g) on PET fibers, which is mainly due to the ordering of water molecules in the solution. The experimental results presented in this study allowed for the development of the adsorption mechanism of RIF and RIX on PET fibers, highlighting the potential practical applications of these adsorption processes. Full article

Avian influenza viruses (AIVs) pose significant risks to occupational populations engaged in poultry farming, livestock handling, and live poultry market operations due to frequent exposure to infected animals and contaminated environments. This review synthesizes evidence on AIV exposure patterns and risk factors through a comprehensive analysis of viral characteristics, host dynamics, environmental influences, and human behaviors. The main routes of transmission include direct animal contact, respiratory contact during slaughter/milking, and environmental contamination (aerosols, raw milk, shared equipment). Risks increase as the virus adapts between species, survives longer in cold/wet conditions, and spreads through wild bird migration (long-distance transmission) and live bird trade (local transmission). Recommended control measures include integrated animal–human–environment surveillance, stringent biosecurity measures, vaccination, and education. These findings underscore the urgent need for global ‘One Health’ collaboration to assess risk and implement preventive measures against potentially pandemic strains of influenza A viruses, especially in light of undetected mild/asymptomatic cases and incomplete knowledge of viral evolution. Full article

Riverbed dynamics are natural processes that are strongly driven by human and climatic factors. In the last two centuries, the anthropogenic influence and impact of climate change on European rivers has resulted in significant degradation of riverbeds. This research paper aims to determine the historical evolution (1856–2021) and future trends of the Ialomița riverbed (Romania) under the influence of anthropogenic impact and climate change. The case study is a reach of 66 km between the confluences with the Ialomicioara and Pâscov rivers. The localisation in a contact zone between the Curvature Subcarpathians and the Târgoviște Plain, the active recent tectonic uplift of the area, and the intense anthropogenic intervention gives to this river reach favourable conditions for pronounced riverbed dynamics over time. To achieve the aim of the study, we developed a complex methodology which involves the use of Geographical Information System (GIS) techniques, hierarchical cluster analysis (HCA), the Mann–Kendall test (MK), and R programming. The results indicate that the evolution of the Ialomița River aligns with the general trends observed across Europe and within Romania, characterised by a reduction in riverbed geomorphological complexity and a general transition from a braided, multi-thread into a sinuous, single-thread fluvial style. The main processes consist of channel narrowing and incision alternating with intense meandering. However, specific temporal and spatial evolution patterns were identified, mainly influenced by the increasingly anthropogenic local influences and confirmed climate changes in the study area since the second half of the 20th century. Future evolutionary trends suggest that, in the absence of river restoration interventions, the Ialomița riverbed is expected to continue degrading on a short-term horizon, following both climatic and anthropogenic signals. The findings of this study may contribute to a better understanding of recent river behaviours and serve as a valuable tool for the management of the Ialomița River. Full article

Water within coal reservoirs exerts dual effects on methane adsorption–desorption by competing for adsorption sites and reducing permeability. The bound water effect, caused by coal wettability, significantly constrains coalbed methane (CBM) production, rendering investigations into coal wettability crucial for efficient CBM development. Compared with other geological formations, coals are characterized by a highly developed microporous structure, making the CO₂ sequestration mechanism in coal seams closely linked to the microscale interactions among gas, water, and coal matrixes. However, the intrinsic mechanisms remain poorly understood. In this study, molecular dynamics simulations are employed to investigate the wettability behaviors of CO₂, CH₄, and water on different coal matrix surfaces under varying temperature and pressure conditions, for coal macromolecules representative of four coal ranks. The study reveals the evolution of water wettability in response to CO₂ and CH₄ injection, identifies wettability differences among coal ranks, and analyzes the microscopic mechanisms governing wettability. The results show the following: (1) The contact angle increases with gas pressure, and the variation in wettability is more pronounced in CO₂ environments than in CH₄. As pressure increases, the number of hydrogen bonds decreases, while the peak gas density of CH₄ and CO₂ increases, leading to larger contact angles. (2) Simulations under different temperatures for the four coal ranks indicate that temperature has minimal influence on low-rank Hegu coal, whereas for higher-rank coals, gas adsorption on the coal surface increases, resulting in reduced wettability. Interfacial tension analysis further suggests that higher temperatures reduce water surface tension, cause dispersion of water molecules, and consequently improve wettability. Understanding the wettability variations among different coal ranks under variable pressure–temperature conditions provides a fundamental model and theoretical basis for investigating deep coal seam gas–water interactions and CO₂ geological sequestration mechanisms. These findings have significant implications for the advancement of CO₂-ECBM technology. Full article