Search Results (1,363)

Lightning-induced breaking accidents of medium-voltage insulated conductors pose a serious threat to the safety of distribution networks, and the key cause lies in the establishment and sustained combustion of the power-frequency follow-current arc after lightning overvoltage breakdown. This paper systematically investigates the formation mechanism and critical conditions of power-frequency follow-current arcs using combined simulation and experimental approaches. Based on the streamer discharge theory, a lightning breakdown model was established and combined with the arc energy balance equation, revealing that the establishment of power-frequency follow-current arcs is essentially determined by the post-breakdown energy competition process. The simulation results show that the required anode electric field strength for lightning breakdown is not less than 3 kV/mm. When the power-frequency voltage reaches 10 kV, Joule heating of the arc continuously exceeds heat dissipation loss, enabling restrike after zero-crossing and sustaining stable burning. Experiments verified this voltage threshold and further revealed that the arc establishment rate exhibits nonlinear growth with increasing power-frequency voltage, exceeding 90% at power-frequency voltages ≥ 10 kV. The study also reveals that increased gap distance reduces the arc establishment rate, while the introduction of insulators can enhance it by approximately 20%. This study clarifies the energy criterion for power-frequency follow-current arc establishment and the influence patterns of key parameters, providing theoretical basis and engineering reference for lightning protection design and arc suppression in medium-voltage insulated lines. Full article

As the prevalence of myopia, or near-sightedness, continues to rise globally, it becomes imperative to determine the mechanisms driving myopia so that appropriate interventions to mitigate it can be developed. Light appears to be critical for normal ocular development, and over the past several decades research has explored the connection between light exposure and myopia development. This review explores the growing field of photobiomodulation, or the use of light to modulate biological processes, to prevent myopia development. To complete this review, relevant texts published from January 1990 to December 2025 were retrieved from the PubMed database using a combination of search terms covering myopia and ocular development, light exposure conditions related to myopia, myopia development in relation to circadian and diurnal regulation, nonvisual opsins and myopia, and light-induced ocular damage. Through this review, we see that photobiomodulation offers a potential intervention to control myopia progression, but the mechanisms behind light’s influence on ocular development remain complex and incompletely understood. This review aims to summarize what is currently known to serve as a basis for future research and to delineate important findings. Full article

The long-term prestress relaxation of soil anchors embedded in saturated clay is a critical issue affecting the safety of geotechnical structures such as slopes and foundation pits. Traditional integer-order constitutive models are often unable to accurately describe the nonlinear and time-dependent relaxation behavior observed in such anchorage systems. Based on fractional calculus theory, this study establishes a constitutive model for the coupled stress relaxation behavior of soil anchors and saturated clay. The Riemann–Liouville fractional derivative and the two-parameter Mittag-Leffler function are introduced to represent the material memory effect and continuous relaxation characteristics. To achieve reliable parameter identification, a hybrid optimization strategy combining the Adaptive Hybrid Differential Evolution (AHDE) algorithm and the Levenberg–Marquardt (L-M) method is proposed. The proposed model and identification approach are validated using field monitoring data from soil anchors in a slope engineering project at the Guangxi Friendship Pass Port. The results show that the proposed model can accurately reproduce the entire stress relaxation process, with a coefficient of determination of R² = 0.9517. Parameter sensitivity analysis further clarifies the influence of key parameters, including the fractional order and viscosity coefficient. The proposed approach provides a systematic theoretical framework and practical reference for the analysis and prediction of long-term prestress relaxation in soil anchorage systems. Full article

Sustainable agriculture in arid and semi-arid regions critically depends on the interactions between soil physical properties, water dynamics, and mechanized field operations. In this context, soil physical attributes, such as texture, bulk density, aggregate stability, and soil water potential, play a crucial role in determining soil–water–machine interactions. Soil attributes such as texture, bulk density, aggregate stability, and soil water potential govern both water movement and retention, as well as traction efficiency, draft energy, and compaction under mechanized traffic. Deviations from the optimal soil moisture range in sandy or calcareous soils increase wheel slip, energy consumption, and soil structural degradation, resulting in uneven infiltration and reduced water-use efficiency. This review synthesizes recent research on these coupled processes, emphasizing how soil mechanics and hydraulics collectively influence irrigation performance and mechanization energy requirements. The novelty of this study lies in presenting an integrated soil–machine–water conceptual framework that captures the continuous interactions and interdependencies among soil physical state, machine behavior, and water movement. By highlighting these dynamic relationships, this review provides a systems-level perspective on energy and water interactions in dryland agroecosystems, offering a foundation for predicting the environmental implications of mechanized operations under arid conditions. Overall, the review demonstrates that sustainable mechanized agriculture in arid regions requires integrated management of soil physical state, machine operation, and irrigation timing, where maintaining soil moisture within an optimal operational range is the key factor for reducing energy losses, preventing soil compaction, and improving water productivity. Full article

Electric vehicle (EV) adoption is generating a rapidly increasing stream of retired lithium-ion batteries for second-life deployment. However, thermal safety concerns continue to limit their reuse. This paper reviews second-life battery (SLB) thermal safety and management and organizes existing work through a mechanism-to-deployment framework linking four domains: degradation mechanisms, cell screening, pack configuration, and monitoring. Evidence indicates that thermal risk depends on the degradation pathway rather than capacity fade. In fact, cells with comparable capacity can exhibit substantially different trigger temperatures depending on whether lithium plating or solid-electrolyte interphase (SEI) growth dominates. Therefore, capacity-based screening is insufficient because cells that satisfy capacity thresholds may still remain thermally unstable. The four domains are tightly coupled: the degradation pathway determines screening requirements; screening outcomes constrain pack design; pack topology influences fault escalation; and together these factors determine what monitoring can reliably detect. This review highlights three gaps and outlines future research directions in the field of SLB thermal safety and management: limited aged-cell thermal characterization by degradation pathway, insufficient diagnostic validation under industrial-throughput conditions, and the incomplete translation of screening outputs into design rules. Full article

This study analyzes the hydrodynamic characteristics of a short magnetorheological squeeze film damper, with emphasis on the fluid microstructure responsible for generating damping forces. The magnetorheological fluid contains non-Brownian spherical particles suspended in a non-magnetic Newtonian fluid. When exposed to a magnetic field, these particles form chain-like structures that restrict fluid motion. In this context, the Mason number characterizes the fluid microstructure and establishes the ratio of viscous to magnetic forces. The mathematical model for solving the flow field, which depends on the continuity and momentum laws, the Bingham rheological model, and boundary conditions at the interfaces, is solved analytically. The Reynolds equation determines the fluid pressure distribution and follows the Sommerfeld boundary condition. Mass imbalance induces chaotic rotor motion, resulting in lateral vibrations. As the journal squeezes the fluid, positive pressure develops, generating damping forces that dissipate vibration energy. The results in this research show that the Mason number significantly affects fluid pressure, which increases as magnetostatic forces exceed viscous forces. This increase in pressure produces damping forces that reduce rotor displacement. Additionally, both radial and tangential forces increase with particle volume fraction, in contrast to classical Newtonian behavior. These findings are relevant to the handling of magnetorheological fluids in vibration control mechanisms. Full article

Nanopositioning systems (NPS) are used in various fields of technology, such as micro- and nanoelectronics, optics, and biotechnology, where demands for higher dynamic performance and sub-nanometer accuracy are continuously increasing. Thus, the determination and compensation of stress-induced negative impacts on the systems gain significance to ensure accurate positioning. Major contributors are temperature gradients. Hence, understanding and predicting temperature changes is crucial for improving such systems. This work focuses on a substructure of an NPS drive system consisting of coil assemblies. This substructure serves as a primary heat source due to the occurrence of ohmic losses, leading to an increase in temperature and therefore significantly influencing the thermal deformation. The aim of this paper is to compose a CFD model with reduced submodels of the coil assembly, which, in comparison to experimental validation data, predicts its temperature development with satisfactory accuracy. By simplification of the system through a number of sub-models, computational effort is significantly lowered. The reduced CFD model not only enables efficient thermal analysis of the coil assembly but also provides a practical approach for broader use in system design and optimization, where fast and reliable thermal predictions are essential. Full article

In cold regions with prolonged subzero temperatures and abundant solar radiation, Trombe walls serve as high-efficiency passive solar building envelopes for improving indoor thermal comfort. This study aims to optimize the thermal performance of Trombe walls via a multimodal data analysis framework and a multiview measurement algorithm. Three distinct Trombe wall configurations were constructed and continuously monitored for 60 consecutive days under typical winter conditions (average temperature: −15 °C; solar radiation intensity: 800–1100 W/m²). Field-measured datasets, including solar radiation intensity, hourly air temperature distribution, and heat exchange efficiency, were systematically analyzed to quantify the impacts of ventilation mode, air gap width, and insulation thickness on thermal performance. The results demonstrate that the hourly peak surface temperature of the optimized Trombe wall reaches 25.7 °C at 13:00, which significantly improves indoor thermal comfort compared with conventional buildings. An air gap width of 6 cm minimizes indoor temperature fluctuations (fluctuation coefficient = 0.08), while a 20 mm insulation layer stabilizes heat loss reduction at 31.1% relative to non-insulated walls. The optimal operational parameter combination (6 cm air gap, 16 °C indoor set temperature) was determined based on the lowest temperature fluctuation and highest thermal efficiency, with experimental results deviating by less than 5% from established analytical models. This study verifies the reliability of the multimodal data analysis framework for Trombe wall performance evaluation, providing practical design guidelines for passive solar building envelopes in cold regions. Full article

Reliable positioning and mapping in roadway tunnels are crucial for vehicle-based monitoring and inspection, especially considering the challenging environmental conditions such as rapidly changing illumination, low-texture environments, and repetitive structural elements. While general LiDAR-inertial odometry (LIO) frameworks and loop-closure detection methods are effective in general scenarios, they often suffer from severe drift or incorrect loop constraints under these specific conditions. These challenges are further exacerbated by the inherent uncertainties associated with low-cost sensors. This paper introduces a narrow field-of-view LiDAR-centric RTK-visual-inertial SLAM system enhanced by three key modules: semantic-assisted loop detection and matching, two-stage RTK quality control, and adaptive factor graph optimization (FGO). In the first module, the proposed semantic loop descriptor (SLD) matching is used to determine the potential loop closure locations and then integrates the corresponding constraint as graph nodes. The quality control module addresses RTK outlier rejection during tunnel entry and exit, employing an event-driven stochastic model to characterize the uncertainty between RTK and the other sensors, effectively suppressing RTK-induced errors. FGO module performs optimization by incorporating LIO, RTK, and loop closure factors, employing a keyframe-based strategy to produce globally optimized poses while continuously updating the map. The proposed Tunnel-SLAM was evaluated against state-of-the-art SLAM algorithms in four extended roadway tunnels, ranging in traveling distance approximately from 5 to 10 km. Experimental results demonstrate that the proposed SLAM achieved a final drift of less than 2 m with loop closure, demonstrating significantly reducing the drift, while other existing SLAM frameworks fail catastrophically or have large drift. Full article

Contemporary agriculture faces the challenge of sustaining crop productivity amid increasing climatic pressures and simplified agronomic practices, such as monoculture. A field experiment conducted from 2022 to 2024 aimed to determine the effects of meteorological conditions and biological amendments on grain yield and yield structure in three maturity groups of continuous maize (Zea mays L.; FAO 200, 230 and 260). The split-plot experiment included applications of the biological amendments Neosol, Bactim Gleba and UGmax. Deteriorating agrometeorological conditions over the years studied led to a progressive decline in mean grain yield, reaching the lowest value in 2024 (5.06 Mg ha⁻¹). The cultivar belonging to the FAO 260 maturity group exhibited the highest yield potential. Application of all biological amendments resulted in a significant increase in grain yield and thousand-grain weight compared with the untreated control. The most effective treatment was UGmax which increased mean grain yield by approximately 14% and thousand-grain weight by 19% compared with the control. Path analysis revealed hierarchical relationships among components of ear structure and grain yield. The primary direct effect on yield increase was the number of kernels per ear, with thousand-grain weight also contributing significantly depending on maturity group. In later-maturing cultivars, kernel number per ear played the dominant role, whereas thousand-grain weight was more influential in earlier-maturing ones. The economic analysis demonstrated that all of the applied biological amendments generated a positive net profit, with the highest additional revenue obtained following the application of UGmax (160 USD·ha⁻¹). These results confirm that biostimulant application affected grain yield formation, and reduced yield losses under stress conditions. Full article

This study addresses the critical need for accurate, continuous monitoring of surface water quality parameters (SWQPs) using remote sensing, overcoming limitations in existing models that often rely on pre-trained networks ill-suited for complex aquatic environments. We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images, acquired between 2023 and 2024. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. The results demonstrated high accuracy, with coefficient of determination (R²) values exceeding 0.97 and low root mean square error (RMSE) across training, validation, and testing subsets. Spatial prediction maps generated at high resolution revealed realistic ecological and hydrological patterns consistent with known regional water quality dynamics in New Brunswick. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site-specific regression models by enabling non-invasive, cost-effective, and continuous monitoring from satellite platforms over a large, heterogeneous province-scale domain. Additionally, model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key spectral bands influencing predictions and provided ecological insights, offering guidance for future sensor design and data reduction strategies. This study addresses a significant research gap by providing a dual-parameter focused, end-to-end deep learning solution optimized for province-scale remote sensing data, facilitating more informed environmental management. This study can support water managers and agencies by providing province-wide DO and TOC maps derived from freely available Sentinel-2 imagery, reducing reliance on sparse field sampling alone and helping to identify areas of low oxygen or high organic carbon. Future work will extend this framework temporally and spatially and explore hybrid CNN architectures incorporating temporal dependencies for improved generalization and accuracy. Full article

In this paper, high-strength high-nitrogen steel Cr₁₈Mn₁₅ was fabricated using centrifugal casting. High-temperature tensile tests were subsequently performed on the centrifugally cast material. Based on the dynamic material model (DMM), power dissipation and instability maps were constructed for the steel. The results revealed that the optimal processing conditions for Cr₁₈Mn₁₅ are within a temperature range of 940 °C to 980 °C and a strain rate range of 0.001 s⁻¹ to 0.01 s⁻¹. Flow instability was observed primarily under high strain rate conditions (1 s⁻¹) at a lower temperature of 900 °C. Four constitutive equation models were established based on the experimental results, and the prediction accuracy was assessed by calculating their average absolute relative errors (AAREs) and correlation coefficients (r). It was found that the Modified-JC constitutive model could simultaneously take care of both accuracy and simulation convergence with an AARE of 17.823 and Pearson’s correlation coefficient (PCC) of 0.968. For the practical application of Cr₁₈Mn₁₅ high-nitrogen steel, a three-layer composite tube forming and a continuous rolling equipment were developed. The rolling and spreading process was simulated using finite elements, and the stress field, strain field, and temperature field in the spreading process were analyzed to determine the following optimum process parameters of the alloy: a temperature of 950 °C, a processing line speed of 1 m/s, and a preheating temperature of 200 °C. Full article

Assessing water quality indices (WQIs) derived from physicochemical measurements accurately and efficiently is essential for effective water resource management. However, conventional monitoring approaches based on single-point measurements and limited spatial coverage face constraints in representing large-scale river environments. To address these limitations, this study integrates high-resolution Google Earth RGB imagery with national water quality monitoring data from South Korea to construct an image-based dataset for WQI estimation. Water quality monitoring records from 1762 sampling sites collected between January 2000 and September 2020 were used to calculate WQI values. The index was computed using seven parameters—temperature, pH, dissolved oxygen, total solids, biochemical oxygen demand, nitrate, and phosphate—following the standard weighting procedure. Corresponding Google Earth RGB imagery acquired within ±1 day of field measurements over the same 2000–2020 period was compiled, resulting in 34108 image–sample pairs. Based on this integrated dataset, a ResNeXt-based convolutional neural network enhanced with convolutional block attention modules was implemented and applied to estimate WQI values from spatial land-use context and river morphology captured in RGB imagery. The proposed model demonstrated superior predictive performance compared to baseline neural network models, achieving a coefficient of determination (R²) of 0.94 and an index of agreement (IOA) of 0.96. Grad-CAM analysis indicates that the model primarily utilizes spatial land-use patterns, riparian context, and river morphology rather than direct visual signals from the water surface itself. These findings suggest that RGB imagery contains spatial information related to observed WQI values. Accordingly, the framework provides a spatially continuous perspective on river conditions that may support large-scale monitoring efforts. Full article

Uranium ore preconcentration is a critical step in achieving environmentally sustainable uranium mining and reducing the operational load of hydrometallurgical processing systems. Conventional radioactive sorting systems predominantly employ a “single-ore-particle intermittent measurement” mode. Under continuous ore flow and high-throughput operating conditions, however, the radiation fields of adjacent ore particles inevitably overlap, which results in gamma-counting interference and blurred ore-segment boundaries, thereby limiting sorting accuracy and system capacity. To address these challenges, this study established a convolutional model that describes the relationship between ore-grade distribution and gamma-response characteristics under continuous ore flow conditions. On this basis, a deconvolution-based method for uranium ore grade calculation was proposed, and an adaptive determination strategy for the characteristic parameter α was introduced to improve grade estimation accuracy and enable reliable identification of ore-segment boundaries. The experimental results showed that, for uranium grades ranging from 0.05% to 0.18% and ore-segment lengths of 16–40 cm, the relative errors between the inverted and true grades of individual segments were all less than 10%. Compared with conventional intermittent measurement and identification schemes, the proposed method achieves stable and accurate grade inversion under conditions of overlapping radiation fields in continuous ore segments. Full article

Ground-level ozone pollution poses significant risks to public health and ecosystems and remains a major environmental challenge worldwide. Accurate forecasting is difficult due to the nonlinear formation mechanisms of ozone and its strong dependence on meteorological conditions. This study proposes a Wind Speed and Direction-Based Dynamic Spatiotemporal Graph Attention Network (WSDST-GAT) for multi-step hourly ground-level ozone prediction. The model integrates a wind-aware dynamic graph to represent anisotropic pollutant transport and a Transformer-based temporal encoder to capture long-range dependencies. Meteorological variables are incorporated to enhance physical interpretability and predictive robustness. A co-kriging module is further employed to reconstruct continuous spatial ozone fields with quantified uncertainty. Using hourly observations from 35 monitoring stations in Beijing, WSDST-GAT achieves a Coefficient of Determination of 0.957, with a Mean Absolute Error of 5.25 μg/m³, and a Root Mean Square Error of 9.58 μg/m³. The prediction intervals demonstrate strong reliability with a Prediction Interval Coverage Probability of 94.01% and a Prediction Interval Normalized Average Width of 0.174. These results indicate that the proposed framework provides an accurate and physically informed solution for ozone forecasting and air quality management. Full article