Search Results (31,419)

Reservoir landslides undergo saturated–unsaturated transitions under hydrological variations. Matric suction significantly influences slip-zone soil strength. Existing studies lack analysis of suction–rate–strength coupling, while Amontons’ model fails for cohesive soils. This study investigated Huangtupo landslide slip-zone soil in the upper reaches of the Yangtze River using pressure plate and saturated salt solution methods to determine the soil–water characteristic curve. Suction-controlled ring shear tests were conducted under three matric suction levels ($\Psi$ = 0, 200, and 700 kPa) across net normal stresses (${\sigma }_{net}$ = 100–800 kPa) and shear rates ($\dot{\gamma }$ = 0.05–200 mm/min). Key findings revealed the following: (1) significant suction–rate coupling effects were shown, with 700 kPa suction yielding 30% higher residual strength than saturated conditions, validating matric suction’s role in enhancing effective stress and particle contact strength; (2) residual cohesion showed strong logarithmic correlation with shear rate, with the fastest growth below 10 mm/min, while the residual friction angle varied minimally (0.68°), contributing little to overall strength; (3) a bivariate model relating residual cohesion to $\dot{\gamma }$ and $\Psi$ was established, overcoming traditional single-factor limitations. The study demonstrates that dual-parameter Coulomb modeling effectively captures multi-field coupling mechanisms in unsaturated slip-zone soils, providing theoretical foundations for landslide deformation prediction and engineering design under dynamic hydrological conditions. Full article

Accurate recognition of personnel behavior in industrial environments is essential for asset protection and workplace safety, yet complex environmental conditions pose a significant challenge to its accuracy. This paper presents a novel, lightweight framework to address these issues. We first enhance a YOLOv8n model with Receptive Field Attention Convolution (RFAConv) and Efficient Multi-scale Attention (EMA) mechanisms, achieving a 6.9% increase in AP50 and a 4.2% increase in AP50:95 over the baseline. Continuous motion trajectories are then generated using the BOT-SORT algorithm and geometrically corrected via perspective transformation to produce a high-fidelity bird’s-eye view. Finally, a set of discriminative trajectory features is classified using a Random Forest model, attaining F1-scores exceeding 82% for all behaviors on our proprietary industrial dataset. The proposed framework provides a robust and efficient solution for real-time personnel behavior recognition in challenging industrial settings. Future work will focus on exploring more advanced algorithms and validating the framework’s performance on edge devices. Full article

In underground mine roadways, enlarged cross-sections have led to escalating surrounding rock stress, resulting in frequent support failures, elevated accident risk, and increased maintenance costs. However, the potential of fiber reinforcement to improve shotcrete under these high-stress conditions remains under-investigated. To address these issues, this study developed a novel fiber-reinforced cement-based composite using field construction-grade washed sand. The effects of binder-to-material ratios, fiber types (polyvinyl alcohol (PVA), polypropylene (PP), and basalt (BF)), and fiber dosages (1%, 2%, and 3%) were systematically investigated under uniaxial tension, uniaxial compression, and variable-angle shear. Based on the experimental results, an optimal mix formulation was determined via orthogonal experimental design to meet mining operational requirements. The findings demonstrate that fiber incorporation significantly enhances mechanical performance. Notably, PP fiber reinforcement increased the tensile strength by up to 675%, while BF fibers improved compressive strength by up to 198.5%, relative to unreinforced shotcrete. This study provides a theoretical foundation for optimizing fiber-reinforced shotcrete mix designs for mining and offers technical insights for field applications. Full article

As the operational speeds of subway trains in China incrementally increase to 160 km/h, the enclosed nature of tunnel environments poses significant challenges by restricting free airflow. This limitation leads to intense airflow disturbances and turbulence phenomena within tunnels, consequently exacerbating aerodynamic noise issues. This study utilizes compressible Large Eddy Simulation (LES) and acoustic finite element methods to construct a computational model of aerodynamic noise for subway trains within tunnels. It employs this model to compare and analyze the near-field noise characteristics of subway trains traveling at 120 km/h on open tracks versus in infinitely long tunnels. The findings indicate that the distribution of sound pressure levels on the surfaces of trains within tunnels is comparatively uniform, overall being 15 dB higher than those on open tracks. The presence of a high blockage ratio in tunnels intensifies the cavity flow between two air conditioning units, making it the region with the highest sound pressure level. The surface sound pressure spectrum within the tunnel shows greater similarity across different segments, with low-frequency sound pressure levels notably enhanced and high-frequency levels attenuating more rapidly compared to open tracks. It is recommended that in tunnels with high blockage ratios, the positioning of subway train air conditioning should not be too high, overly concentrated, submerged, or without the use of sound-absorbing materials. Such adjustments can effectively reduce the sound pressure levels in these areas, thereby enhancing the acoustic performance of the train within the tunnel. Full article

The resource utilization of swine manure represents a critical pathway for advancing sustainable agricultural development. This study, based on survey data from 509 swine farmers in Sichuan Province, employs the Ordered Probit (Oprobit) model and the Conditional Mixed Process (CMP) model to analyze the mechanisms and pathways through which cognition about manure treatment, environmental regulation, and their interaction influence farmers’ behaviors towards manure resource utilization. It further delves into the heterogeneous characteristics of influencing factors. The findings reveal the following: (1) Farmers possess a high level of cognition regarding manure treatment, while environmental regulation is moderately implemented. The principal methods of manure resource utilization focus on recycling to fields and organic fertilizer production, with over 95% of farmers adopting at least one method of resource utilization. (2) Both cognition of manure treatment and environmental regulation significantly promote the behavior of manure resource utilization. There are substitutive or complementary effects between moral cognition and constraint regulation, as well as capability cognition and guidance regulation. (3) Among the farming community, the behavior of large-scale farmers is mainly influenced by moral cognition, whereas non-large-scale farmers are more affected by capability cognition and guidance regulation; middle-aged and young farmers are predominantly influenced by capability cognition, incentives, and guidance regulation, whereas the older generation of farmers is driven more by moral cognition and guidance regulation. Based on these insights, this study proposes targeted strategies for enhancing cognition and regulatory alignment across different groups, aiming to elevate the level of manure resource utilization and promote the green transformation of livestock farming. Full article

For the combination of finite elements and control circuits, the calculation is complex and time-consuming, making direct optimization impractical. In this paper, a new distributed node and magnetic circuit model is proposed to simulate the spatial and temporal variation of the distributed air-gap magnetic density with the current and rotor angle and solve the electromagnetic force wave variation. Compared to other distributed flux-linkage models, the proposed model not only considers the radial magnetic path but also connects adjacent magnetic paths tangentially. The inclusion of this tangential path enhances the mutual interaction between magnetic circuits, leading to a more accurate model. Based on the control circuit model, the electromagnetic force wave changes caused by the harmonic currents under various circuits and operating conditions are calculated, the topology is analyzed and optimized to mitigate critical harmonics, the electromagnetic force wave is reduced, and finally, the model accuracy is verified experimentally. While most distributed flux-linkage models are applied to the optimization of motor performance metrics such as the magnetomotive force (MMF), power, and torque, this paper applies the model to the optimization of the magnetic field strength, the harmonic content, and the corresponding noise, vibration, and harshness (NVH), demonstrating a broader range of applications. This method can be coupled with the control circuit to analyze the changes in electromagnetic force waves and quickly optimize them, improving the accuracy and efficiency of research and development. Full article

This paper innovatively achieves finite-time modified function projection synchronization (MFPS) for different fractional-order chaotic systems. By leveraging the advantages of radial basis function (RBF) neural networks in nonlinear approximation, this paper proposes a novel fractional-order sliding-mode controller. It is designed to address the issues of system model uncertainty and external disturbances. Based on Lyapunov stability theory, it has been demonstrated that the error trajectory can converge to the equilibrium point along the sliding surface within a finite time. Subsequently, the finite-time MFPS of the fractional-order hyperchaotic Chen system and fractional-order chaotic entanglement system are realized under conditions of periodic and noise disturbances, respectively. The effects of the neural network parameters on the performance of the MFPS are then analyzed in depth. Finally, a color image encryption scheme is presented integrating the above MFPS method and exclusive-or operation, and its effectiveness and security are illustrated through numerical simulation and statistical analysis. In the future, we will further explore the application of fractional-order chaotic system MFPS in other fields, providing new theoretical support for interdisciplinary research. Full article

In 2015, the global community set 17 Sustainable Development Goals (SDGs), with the second goal aiming to end hunger by 2030. In sustainable agriculture, seed treatment plays a crucial role and cold plasma (CP) has emerged as a promising, eco-friendly technology for improving seed performance. This review highlights CP as an innovative seed treatment method with significant potential to enhance seed vigor, germination, and crop yield, particularly under stress conditions such as drought, salinity, and biotic challenges. CP works by generating reactive oxygen and nitrogen species (RONS), which modulate key biochemical and physiological responses in seeds. These responses include improvements in water uptake, enhanced germination rates, and better stress tolerance. Moreover, CP exhibits strong antimicrobial properties, making it a chemical-free alternative for seed decontamination. Despite these benefits, the application of CP in large-scale agriculture faces several challenges. Also, this review critically examines the limitations of CP treatment, including the lack of standardized protocols and insufficient field validation. Additionally, it compares CP treatment with conventional chemical and microbial methods, offering insights into its potential advantages and remaining obstacles. This emerging technology holds promise for enhancing crop productivity while minimizing environmental impact, but further research and validation are essential for its broader adoption in sustainable agricultural practices. Full article

The escalating pressure on water resources in agricultural regions has become a catalyst for water conflicts. The adoption of innovative approaches to estimate actual evapotranspiration (ETa) offers potential solutions to mitigate conflicts related to water usage. This research presents the application of a remote sensing-based methodology for estimating actual evapotranspiration (ETa) based on a two-source energy balance model (TSEB) for riparian vegetation in Nebraska, US using the Spatial EvapoTranspiration Modeling Interface (SETMI). Estimated results through SETMI and field data using the eddy covariance system (EC) considering the period 2008–2013 were used to validate the energy balance components and ETa. Modeled energy balance components showed a strong correlation to the ground data from EC, with ET presenting R2 equal to 0.96 and RMSE of 0.73 mm.d⁻¹. In 2012, the lowest adjusted crop coefficient (Kcadj) values were observed across all land covers, with a mean value of 0.49. The years 2013 and 2012, due to the dry conditions, recorded the highest accumulated ETa values (706 mm and 664 mm, respectively). Soybeans and corn exhibited the highest ETa values, recording 699 mm and 773 mm, respectively. Corn and soybeans, together accounting for a substantial portion of the land cover at 15% and 3%, respectively, play a significant role. Given that most fields cultivating these crops are irrigated, both pumped groundwater and surface water directly impact the water source of the Republican River. The SETMI model has generated appropriate estimated daily ETa values, thereby affirming the model’s utility as a tool for assisting water management and decision-makers in riparian zones. Full article

The core objective of Active Vibration and Noise Control (AVNC) is to enhance system performance by generating real-time counter-phase signals of equal amplitude to cancel out vibration and noise interference from mechanical or structural systems. As the demand for low-noise, low-vibration environments grows in fields such as new energy vehicles (NEVs), aerospace, and high-precision manufacturing, traditional AVNC methods—which rely on precise linear models and have poor adaptability to nonlinear and time-varying conditions—struggle to meet the dynamic requirements of complex engineering scenarios. However, with advancements in artificial intelligence (AI) technology, AI-driven Active Vibration and Noise Control (AI-AVNC) technology has garnered significant attention from both industry and academia. Based on a thorough investigation into the state-of-the-art AI-AVNC methods, this survey has made the following contributions: (1) Introducing the theoretical foundations of AVNC and its historical development; (2) Classifying existing AI-AVNC methods from the perspective of control strategies; (3) Analyzing engineering applications of AI-AVNC; (4) Discussing current technical challenges and future development trends of AI-AVNC. Full article

Controlled environment agriculture (CEA) pivots food production from an outdoor field setting to the indoors where growing conditions can be calibrated to fit crop needs. This research investigates vertical farms as a type of CEA. In particular, using the St. Louis area as a case study, it provides data-driven support for optimizing a vertical farm’s business model including its supply chain. The methodology presented here informs agri-preneurs about what crops to grow in a vertical farm, how much to grow given local market demand, and what vertical farm configuration (e.g., Dutch bucket, nutrient film technique, deep water culture) a facility should use. Based on the case study’s base scenario, the simulated vertical farm business would record an economic loss. However, the study did find several paths to improving profitability. First, reducing fixed and variable costs benefits profitability. Proper facility-level production and resource planning helps with managing the fixed costs. Second, increasing market prices may benefit profitability, but it has diminishing returns. As a result, firms can justify making investments that enhance their reputation and market competitiveness, though the advantage these marketing activities provide will decline as prices increase. Third, growing demand or increasing market share does not necessarily improve profitability. Full article

This study presents a robust and extensible hybrid classification framework for accurately detecting diseases in citrus leaves by integrating transfer learning-based deep learning models with classical machine learning techniques. Features were extracted using advanced pretrained architectures—DenseNet201, ResNet50, MobileNetV2, and EfficientNet-B0—and refined via the minimum redundancy maximum relevance (mRMR) method to reduce redundancy while maximizing discriminative power. These features were classified using support vector machines (SVMs), ensemble bagged trees, k-nearest neighbors (kNNs), and neural networks under stratified 10-fold cross-validation. On the lemon dataset, the best configuration (DenseNet201 + SVM) achieved 94.1 ± 4.9% accuracy, 93.2 ± 5.7% F1 score, and a balanced accuracy of 93.4 ± 6.0%, demonstrating strong and stable performance. To assess external generalization, the same pipeline was applied to mango and pomegranate leaves, achieving 100.0 ± 0.0% and 98.7 ± 1.5% accuracy, respectively—confirming the model’s robustness across citrus and non-citrus domains. Beyond accuracy, lightweight models such as EfficientNet-B0 and MobileNetV2 provided significantly higher throughput and lower latency, underscoring their suitability for real-time agricultural applications. These findings highlight the importance of combining deep representations with efficient classical classifiers for precision agriculture, offering both high diagnostic accuracy and practical deployability in field conditions. Full article

Increasing attention is being paid to the use of cover crops as a means of improving soil quality, particularly in relation to soil organic matter (SOM) accumulation and aggregate stability. This study evaluated the effects of soil texture, soil depth, and cover crop type on soil organic carbon (Corg), labile carbon (C_L), and soil structure under field conditions in western Slovakia. A field experiment compared two texturally distinct Phaeozem soils—silty clay loam and sandy loam —and two cover cropping strategies: pea (Pisum sativum L.) monoculture and a four-species mixture of flax (Linum usitatissimum L.), camelina (Camelina sativa L.), white mustard (Sinapis alba L.), and Italian millet (Setaria italica L.). Fine-textured soil accumulated up to 50% more Corg and 1.5 times more C_L than sandy soil, while aggregate stability was up to 90% higher. The surface layer (0–10 cm) contained more SOM, but the deeper layer (10–20 cm) showed greater aggregate stability. Pea cultivation increased total organic carbon, whereas the diverse mixture enhanced labile carbon content and promoted the formation of smaller yet more stable aggregates. Strong correlations between C_L and aggregate stability confirmed the key role of labile organic matter fractions in soil structural stabilisation. Overall, the results demonstrate that the interaction between soil texture and cover crop diversity critically shapes SOM dynamics and soil structure. Combining diverse cover crops with fine-textured soils provides an effective strategy to enhance soil quality, carbon sequestration, and long-term agricultural sustainability. Full article

To ensure the safe operation of the external heat exchanger (EHE) in a circulating fluidized bed (CFB) boiler, it is essential to obtain real-time information on the flow conditions within the bed. This paper establishes a predictive model for the external heat exchanger of the high-temperature reheater in an ultra-supercritical CFB boiler by combining computational fluid dynamics (CFD) with model order reduction and artificial neural networks. The model enables rapid prediction of the solid volume fraction, solid temperature, and gas temperature within the external heat exchanger. The results show that the three predictive models can accurately forecast flow field information under unknown operating conditions. For inlet velocities of 0.225 m/s and 0.325 m/s, the calculation errors are 2.89%, 1.04%, 1.03% and 2.99%, 1.08%, 1.09%, respectively. The predictive models significantly save computational resources, reducing the computation time from 6000 min for the full-order model to approximately 1 s. This lays the foundation for real-time monitoring of the external heat exchanger. Full article

Varroa mites are a major global threat to honeybee colonies. Combining digital twins with scenario-generating models can be an enabler of precision apiculture, allowing for monitoring Varroa spread, generating treatment scenarios under varying conditions, and running remote interventions. This paper presents the conceptual design of this system for large-scale Varroa management in honeybee apiaries, with initial validation conducted through simulations and feasibility analysis. The design followed a design research framework. The proposed system integrates a wireless sensor network for continuous hive sensing, image capture, and remote actuation of treatment. It employs generative time-series models to forecast colony dynamics and a statistical network model to represent inter-colony spread; together, they support spread scenario prediction and what-if evaluations of treatments. The system evolves through continuous updates from field data, improving the accuracy of spread and treatment models over time. As part of our design research, an early feasibility assessment was carried out through the generation of synthetic data for spread model pretraining. In addition, a node-level energy budget for sensing, communication, and in-hive treatment was developed and matched with battery capacity and life calculations. Overall, this work outlines a path toward real-time, data-driven Varroa management across apiary networks, from regional to cross-border scales. Full article