Search Results (5,535)

This paper presents a comparative modeling and analysis of an induction furnace for melting aluminum (Al) and copper (Cu), focusing on their electromagnetic behavior and heating performance. The study employs ANSYS Maxwell software version 16.0 with the finite element method (FEM) to simulate eddy current generation, Joule heating, and current density distribution in the metallic workpieces. The effects of coil geometry, input current, and operating frequency (50–100 kHz) on heating efficiency and skin depth are investigated. Estimated heating times based on ohmic losses are provided, revealing significant differences between aluminum and copper due to their distinct electrical and thermal properties. The results demonstrate that higher frequencies concentrate heating near the surface, reducing skin depth, while copper exhibits more uniform heating than aluminum. These findings offer practical insights for optimizing induction furnace design and operation for different non-ferrous metals. Full article

Post-earthquake bridge crack detection is a critical step in assessing structural safety. Traditional manual detection of bridge cracks is time-consuming, labor-intensive, and poses significant risks. This paper focuses on the automatic identification of structural cracks by analyzing their morphology, orientation, and distribution characteristics, and preliminarily distinguishes them from non-structural damages such as surface stains and coating peeling. Therefore, this paper proposes a bridge crack recognition algorithm based on image processing. First, the input crack image undergoes preprocessing to obtain a binary image, reducing measurement errors caused by environmental factors or uneven illumination, using an improved Retinex algorithm to enhance image brightness. Second, an improved wavelet transform method is employed to remove large-area noise. Then, connected component analysis is used to filter out point-like and patch-like noise, resulting in a complete and clear crack skeleton. Finally, the crack length, width, and other characteristic values are obtained using an image pixel coordinate calculation method, achieving non-contact, non-destructive measurement of concrete surface crack characteristics. The algorithm is based on two-dimensional image processing and does not directly measure crack depth, but the extracted parameters such as length, width, and area ratio provide important surface-based evidence for rapid post-earthquake bridge structural safety assessment. Multiple experimental results show that the proposed algorithm has a maximum width measurement relative error of less than 2.3%, a length measurement relative error within 8%, and an average peak signal-to-noise ratio (PSNR) of the denoised image increased to 74.73 dB. This algorithm provides an effective automated detection tool for rapid post-earthquake bridge safety assessment. Full article

In side-impact collisions, the occupant in the non-impacted far-side position faces a high risk of death and serious injury. However, current research on injury to far-side occupants remains limited. This study utilized 40 real-world side collision cases to extract dynamic boundary condition parameters of the impacted vehicle through kinematic reconstruction. These parameters were input into a simplified finite element (FE) vehicle model equipped with a human body FE model in the far-side position. Simulation calculations were performed to obtain head and chest injury parameters for the far-side occupant and assess their injury risk. Finally, the study focused on analyzing the effect of vehicle motion boundary conditions on far-side occupant’s injury risk. The assessment based on the head injury criterion HIC₁₅ shows a low head injury risk for the far-side occupant. However, using the BrIC metric, which accounts for head rotational motion, reveals a significant risk of severe traumatic brain injury in some cases. Regarding chest injury, analysis based on the effective plastic strain of ribs indicated a low risk of rib fractures. However, results from the chest viscosity criterion (VC) and internal organ strain analysis suggested a high risk of soft tissue injury in the chest. This computational investigation, leveraging biofidelic human models, underscores that the human body’s response to complex, multi-directional impacts is not fully captured by traditional metrics. This study concludes that addressing the protection of the far-side occupant is essential in side-impact safety design, with particular emphasis on the unique injury risks posed by vehicle rotational motion, potentially inspiring biomimetic safety systems that better adapt to these complex loading conditions. Full article

Achieving sustainable rice production is essential for food security, rural livelihoods, and the resilience of rice-based food systems that supply raw materials to the food processing industry. Improving technical efficiency (TE) at the farm level is a key pathway to reduce resource use and environmental pressures per unit of output while ensuring a stable supply of high-quality rice for downstream processing and value-added products. Drawing on micro-survey data collected in 2021–2022 from 455 rice farmers selected through a multi-stage sampling strategy in Jiangsu Province, China, this study investigates how agricultural productive services (APSs) affect stage-specific technical efficiency along the production process and discusses the implications for sustainable rice production and the rice-based food industry. We apply a stochastic frontier production function to estimate overall and stage-specific TE and examine the effects of different APS combinations for land preparation, sowing, fertilization, pest control, and harvesting. The results show that overall participation in APSs significantly improves rice farmers’ TE. Stage-specific analysis reveals that APSs in land preparation, sowing, and harvesting are associated with higher TE, supporting more sustainable use of machinery and labor, while APSs for fertilization and pesticide application do not consistently improve TE and may reflect potential overuse of chemical inputs. Multi-stage service combinations that include both production and pest-control operations can further enhance TE. These findings suggest that well-designed APSs can contribute to sustainable intensification and low-carbon transformation of rice production, thereby strengthening the sustainability of rice-based food systems. Policy interventions should guide APS providers and farmers toward integrated, precision-oriented, and environmentally friendly service packages that support both farm-level efficiency and the sustainability goals of the broader food industry. Full article

Accurate prediction of peak particle velocity (PPV) in open-pit mine blasting is critical for ensuring operational safety and effective vibration control. This study proposes a hybrid modeling approach that integrates the Centered Collision Optimization (CCO) algorithm with Extreme Gradient Boosting (XGBoost), enhanced by SHAP-based sensitivity analysis to improve model transparency and mechanistic interpretability. A comprehensive dataset was constructed based on 193 field-measured blasting records collected from the Panzhihua Iron Mine in China, incorporating nine key input parameters. Model performance was rigorously evaluated using four widely recognized metrics: coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and variance accounted for (VAF). The results demonstrate that the CCO–XGBoost model achieves superior predictive performance, with R² = 0.967, RMSE = 0.110, MAE = 0.067, and VAF = 96.35%, outperforming conventional approaches. SHAP-based sensitivity analysis reveals that blast-to-monitor distance (R) is the dominant negative predictor of PPV, contributing 43% to the total influence, with its vibration attenuation effect intensifying significantly when R exceeds 54 m. Charge per hole (q) and total charge per delay (Q) are identified as the primary positive influencing factors, accounting for 24% and 20% of the total contribution, respectively: the positive promoting effect of q on PPV strengthens markedly when q exceeds 17 kg, while Q exerts a continuous positive increasing influence on PPV when it exceeds 253 kg. Compared to existing hybrid models, the CCO–XGBoost uniquely avoids local optima and ensures higher global stability. This study fills the gap by providing quantifiable engineering thresholds for practical vibration control, making the model directly applicable to on-site blasting optimization. Full article

This study systematically investigates the structural optimization and control strategies of a plasma power supply-based rotating electrical machine. Firstly, stress simulation analysis was conducted on both conventional and optimized motor structures using ANSYS 2025 R1 software. The results demonstrate the maximum stress at the motor bearings decreased from 1.295 MPa to 0.865 MPa after optimization, representing a 33.2% reduction. Secondly, dynamic balance simulation performed with Adams 2024 software revealed that the centroid offset range of the optimized motor was reduced from ±0.05 mm to ±0.0175 mm, achieving a 65% improvement. Furthermore, a motor driver board supporting SVPWM and FOC algorithm was designed and implemented, featuring wide voltage input, multiple output channels, and comprehensive protection functions. Experimental verification confirmed that the developed control system could generate ideal three-phase saddle wave and sinusoidal current waveforms, ensuring smooth motor operation. The system demonstrated excellent dyne pen test results on plasma-sprayed acrylic plates, effectively validating the feasibility of both structural optimization and control strategies. The research outcomes provide theoretical foundations and technical support for high-performance motor design in demanding applications such as plasma spraying. Full article

Dry gas reservoirs play a pivotal transitional role in meeting the net-zero target worldwide. Accurate modelling and simulation of this energy source require fast and reliable prediction of the gas compressibility factor (Z-factor). The experimental measurements of Z-factor are the most reliable source; however, they are expensive and time-consuming. This makes developing accurate predictive models essential. Traditional methods, such as empirical correlations and Equations of States (EoSs), often lack accuracy and computational efficiency. This study aims to address these limitations by leveraging the predictive power of machine learning (ML) techniques. Hence in this study three ML models of Artificial Neural Network (ANN), Group Method of Data Handling (GMDH), and Genetic Programming (GP) were developed. These models were trained on a comprehensive dataset comprising 1079 samples where pseudo-reduced pressure (Ppr) and pseudo-reduced temperature (Tpr) served as input and experimentally measured Z-factors as output. The performance of the developed ML models was benchmarked against two cubic EoSs of Peng–Robinson (PR) and van der Waals (vdW), and two semi-empirical correlations of Dranchuk-Abou-Kassem (DAK) and Hall and Yarborough (HY), and recent developed ML based models, using statistical metrics of Mean Squared Error (MSE), coefficient of determination (R²), and Average Absolute Relative Deviation Percentage (AARD%). The proposed ANN model reduces average prediction error by approximately 70% relative to the PR equation of state and by over 35% compared with the DAK correlation, while maintaining robust performance across the full Ppr and Tpr of dry gas systems. Additionally paired t-tests and Wilcoxon signed-rank tests performed on the ML results confirmed that the ANN model achieved statistically significant improvements over the other models. Moreover, two physical equations using the white-box models of GMDH and GP were proposed as a function of Ppr and Tpr for prediction of the dry gas Z-factor. The sensitivity analysis of the data shows that the Ppr has the highest positive effect of 88% on Z-factor while Tpr has a moderate effect of 12%. This study presents the first unified, statistically validated comparison of ANN, GMDH, and GP models for accurate and interpretable Z-factor prediction. The developed models can be used as an alternative tool to bridge the limitation of cubic EoSs and limited accuracy and applicability of empirical models. Full article

Trade competitiveness can coexist with structurally fragile value chains. When chain feasibility fractures from trade competitiveness, competitiveness without coherence becomes sustainability’s opposite. This paper proposes revisiting the concept of sustainability in agri-food systems, through the lens of structural coherence, understood as the alignment between trade competitiveness, export-destination diversification, and value chain capacity. The research goal is to design and operationalize a diagnostic instrument for structural coherence testing through the triangulation of constant market share analysis (CMSA), the Herfindahl–Hirschman Index (HHI), and physical structural input–output analysis (I-OA). CMSA measures two elements: demand- and competitiveness-driven export dynamics. Export patterns are further explored to verify if there are any destination-market concentration risks (HHI). I-OA closes the loop by linking trade outcomes to internal value chain capacity and efficiency. With clear upstream–downstream segmentation, the sunflower oilseed value chain of the European Union (EU) represents an empirically fertile ground, relevant in the context of the geopolitical disruptions of Black Sea trade corridors and double-cropping dynamics with food-fuel and land-use trade-offs. Focusing on Bulgaria, France, Hungary, Romania, and Spain, which collectively account for more than 85% of EU sunflower seed production, this paper benchmarks post-2013 Common Agricultural Policy (CAP) programming effects, utilized as a proxy for a period of stability, against the post-2020 window, marked by a sequence of crises. Diagnosis is facilitated through findings triangulation, enabling deriving CAP-relevant policy recommendations, aligned with country-specific binding constraints. Results show heterogeneous structurally incoherent profiles: Bulgaria suffers from growth-induced stress, France’s chain efficiency is eroded, the Hungarian chain lacks competitiveness, Romania is raw-export dependent with value-added leakage, and Spain is structurally constrained by physical limits. Policy recommendations target reorienting market-driven low value-added trade behaviors toward structurally sustainable value chain trajectories. Full article

This research studied the role of the fisheries sector, specifically pond-based grouper aquaculture, in coastal Lamongan, Indonesia, which is crucial for coastal food security and economy. Despite relatively high productivity, technical efficiency was not optimal because of its limited livelihood assets, which include human, natural, social, financial, and physical capital. The gap in ownership of these assets has resulted in technical efficiency variations across farmers and has affected both their livelihoods and environmental sustainability. Previous research has mostly focused on capture fisheries or non-grouper species, leaving a critical gap regarding the linkage between livelihood assets and technical efficiency in pond-based grouper aquaculture. This research measured livelihood asset levels, technical efficiency, and the effect of assets on efficiency, using quantitative data from 83 respondents representing the total 105 grouper farming households in coastal Lamongan. Livelihood assets were assessed through scoring and index analysis, technical efficiency was estimated using Stochastic Frontier Analysis (SFA), and the determinants of inefficiency were examined through Tobit regression with robust standard errors. The results found that the average livelihood asset index was 0.47 (moderate), with financial capital being the weakest component. Technical efficiency averaged 0.83, indicating efficient use of inputs while still allowing room for improvement. Natural capital (land area and water resources) and financial capital (income and savings) significantly affected technical inefficiency, whereas human, social, and physical capital did not. These findings emphasize the importance of strengthening the financial capital and the management of natural resources optimally to promote the efficiency and sustainability of grouper aquaculture in coastal Lamongan, Indonesia. Full article

This paper addresses the high-precision transportation control of a large space flexible truss using four space robots, with a focus on dynamic modeling and control strategy design. The system’s dynamic model is derived based on Kane’s method, which facilitates efficient modeling of the complicated rigid–flexible dynamics. Considering the truss’s flexible vibration as a key disturbance source, a nonlinear disturbance observer (NDO) is designed to achieve effective disturbance estimation. Then, to ensure high-precision trajectory tracking of such a complicated dynamics system, an integral sliding mode control (ISMC) strategy is developed based on NDO. Furthermore, leveraging the system’s actuator redundancy, the actuator inputs are weighted and allocated by accounting for individual actuator performance, which enhances the operational reliability. The effectiveness of the proposed control strategy is verified through theoretical analysis and numerical simulations. Full article

China is the world’s largest pear producer, yet its production remains constrained by structural inefficiencies and regional disparities. Clarifying the spatiotemporal evolution of pear production land and its driving mechanisms is essential for improving efficiency and supporting sustainable agricultural development. Using provincial panel data from 29 Chinese regions during 2001–2020, this study analyzes changes in pear yield, planting area, and yield per unit area by integrating the Production Concentration Index, Exploratory Spatial Data Analysis, and Comparative Advantage Analysis. A Spatial Durbin Model is applied to quantify both direct and spatial spillover effects of natural conditions, opportunity costs, infrastructure, technology, market demand, and policy. The results indicate a shift in pear production from area-driven expansion to efficiency-oriented growth, alongside a gradual westward relocation and declining spatial dependence. While core producing regions remain dominant, several western regions have enhanced their comparative advantages. Labor-related factors are crucial: expanding non-agricultural employment opportunities constrain pear production (−0.482), but agricultural mechanization indirectly increases rural labor hiring costs (0.089), whereas agricultural mechanization (0.144) and moderate increases in labor costs (0.126) contribute positively to regional production efficiency. Improved transportation infrastructure, irrigation, fertilizer input, market demand, and policy further promote pear production, with evident spatial spillover effects. These research findings provide empirical support for optimizing regional pear production layouts and formulating applicable policies. Full article

Conventional assessments of fault rupture propagation in overlying soil layers often rely on static or quasi-static analysis, neglecting the dynamic nature of fault displacement and inertial effects. This study develops a comprehensive simulation method for the entire process from rupture initiation to propagation under dynamic fault displacement. The method integrates a nonlinear elastic constitutive model based on the Hardin backbone curve with a non-uniform input technique for seismic waves on both sides of the fault using viscoelastic artificial boundaries. To demonstrate the distinct capabilities of this dynamic method, we conduct a comparative study on normal and reverse faulting driven by fault displacement time histories of identical magnitude but opposite sense. The simulations reveal that: (1) the fault displacement required for rupture initiation and propagation remains consistent between dynamic and quasi-static analyses; (2) crucially, the proposed method captures the transient dynamic response of fault rupture in the overlying soil. The study confirms that the proposed dynamic simulation framework is essential for resolving transient peak responses, oscillatory behavior, and deformation features associated with different faulting mechanisms, providing a more realistic tool for seismic risk assessment compared to conventional static approaches. Full article

Clouds critically influence Earth’s radiation balance and climate, making accurate cloud detection essential for improving climate models. This study develops the TSAR model to improve the cloud detection accuracy of the FY-4A CLM product by incorporating physical features. The input features include FY-4A brightness temperature (BT) data from channels 8–14, geometric parameters (satellite zenith angle (SAZ), satellite azimuth angle (SAA), solar zenith angle (SOZ), solar azimuth angle (SOA), and latitude), and four ERA5 meteorological factors (2 m air temperature (T2m), skin temperature (SKT), air temperature profiles (ATP), and relative humidity profiles (RH)). Using the CALIPSO cloud detection product as labels, the model outputs cloud/clear-sky classification results. Additionally, four machine learning (ML) algorithms—RF, LightGBM, XGBoost, and MLP—achieved overall accuracies of 91.5%, 92.2%, 92.5%, and 92.8%, respectively, considerably outperforming the FY-4A L2 CLM product (83.1%). The results demonstrate that incorporating physical factors significantly improves cloud detection performance regardless of the algorithm employed. Incorporating meteorological factors notably improved nighttime and water–cloud detection, narrowing day–night accuracy gaps. Shapley additive explanation (SHAP) analysis indicated feature contributions of 15.8%, 50.8%, and 33.3% from geometric, BT, and meteorological variables, respectively, with stronger meteorological effects at mid- to high-latitudes. These findings demonstrate that integrating meteorological factors significantly improves FY-4A cloud detection accuracy and consistency, highlighting the MLP-TSAR model’s effectiveness for reliable all-day operational applications. Full article

In this article, an adaptive neural network (ANN) controller based on feature augmentation (FA) is designed for quadrotors. The proposed controller consists of two components: a position sub-controller and an attitude sub-controller. We use the ANN to estimate unknown internal and external disturbance terms within quadrotors. To improve the learning accuracy of the ANN, we design an FA structure, which enables networks to more effectively learn the characteristics in the data. To increase the learning rate of the ANN, a state predictor (SP) is proposed to anticipate the state errors, which subsequently updates the learning rate of the ANN. Based on stability analysis, we prove that the closed-loop system is input-to-state stable (ISS). Finally, the effectiveness of our proposed control algorithm is demonstrated by comparing it with related control algorithms on both the MATLAB R2020a/Simulink simulation platform and a quadrotor experimental platform. Full article

This study employed a Gleeble-3800TM thermal simulator to conduct thermal cycle experiments on the coarse-grained heat-affected zone (CGHAZ) of Nb-Ti-La microalloyed steel under welding heat inputs of 50, 80, 100, and 120 kJ/cm. A systematic analysis was carried out to investigate the influence of heat input on the microstructure and impact toughness of the CGHAZ. The results indicate that the microstructure of the CGHAZ across different heat inputs consists of acicular ferrite (AF), granular bainite ferrite (GBF), polygonal ferrite (PF), as well as hard phases such as M/A constituents and degenerated pearlite (DP). With increasing heat input, the content of GBF decreases monotonically, while the content of PF increases monotonically, and the amount of hard phases rises continuously. In contrast, the content of AF initially increases and then decreases, reaching its peak at 100 kJ/cm. The microstructural changes induced by higher heat input lead to increased inhomogeneity in the local microstrain, thereby causing a monotonic reduction in crack initiation energy. Regarding crack propagation energy, the optimal performance is achieved at 100 kJ/cm due to the formation of a high proportion of AF, which heterogeneously nucleates on La-rich inclusions. This structure provides a high density of high-angle grain boundaries that effectively hinder crack propagation. Consequently, under the combined influence of crack initiation and propagation behaviors, the CGHAZ exhibits the best impact toughness at a heat input of 100 kJ/cm. Full article