Search Results (10,097)

This article presents a novel methodology for evaluating the safety of forklift operations in intralogistics systems using a multi-user simulation model integrated with virtual reality (MUSM-VR). Set against the backdrop of persistent safety challenges in warehouse environments, particularly for inexperienced operators, the study addresses the need for proactive safety assessment tools. The authors develop a simulation framework within the FlexSim 24.2 environment, enhanced by proprietary VR and server integration libraries, enabling interactive, immersive testing of warehouse layouts and operational scenarios. Through literature review and analysis of risk factors, the methodology incorporates human, infrastructural, organizational, and technical dimensions of forklift safety. A case study involving inexperienced participants demonstrates the model’s capability to identify high-risk areas, assess operator behavior, and evaluate the impact of visibility and speed parameters on collision risk. Results highlight the effectiveness of MUSM-VR in pinpointing hazardous intersections and inform design recommendations such as optimal speed limits and layout modifications. The study concludes that MUSM-VR not only facilitates early-stage safety analysis but also supports ergonomic design, operator training, and iterative testing of preventive measures, aligning with Industry 4.0 and 5.0 paradigms. The integration of immersive simulation into design and safety workflows marks a significant advancement in intralogistics system development. Full article

Modular process plants represent a promising strategy to address the increasing need for flexibility and accelerated market deployment in the production of fine and specialty chemicals. However, these modular systems are inherently susceptible to wear and fault development, while condition monitoring methods tailored to such systems remain scarce. This study presents a proof of concept for a targeted fault diagnosis approach of the modular hydraulic systems of such modular process plants and reports on its experimental validation. The methodology comprises two stages: First, model-based symptoms are calculated independently for each module and subsequently utilized within a centralized diagnostic system. This rule-based diagnosis incorporates generalized module interactions, quantified fault degrees, and the plant topology. Importantly, uncertainties arising from measurement equipment, model fidelity, and parameter variability are incorporated and systematically propagated throughout the diagnosis. The validation was conducted on a modular test rig specifically designed to simulate a range of single-fault scenarios across more than 1200 stationary operating points. The results underscore the robustness of the proposed approach: the correct fault was consistently identified, with the estimated fault magnitudes closely aligning with the actual values, exhibiting an average discrepancy of 0.029 for internal leakage of a positive displacement pump. The overall discrepancy for the experimental validation of all fault types was 0.12. Notably, no false alarms were observed, and the displayed uncertainty was considered plausible, though there remains potential for refinement. In summary, this study demonstrates the successful application of model-based symptoms for a rule-based diagnosis, representing a significant advancement toward reliable fault detection in modular hydraulic systems. Full article

The evolution of supply chain management (SCM) into Supply Chain X.0 reflects the integration of advanced technologies and adaptive strategies that redefine operational efficiency, sustainability, and resilience. This systematic literature review examines the impact of implementing Supply Chain X.0, focusing on operational efficiency, economic outcomes, environmental sustainability, and social implications. Following the PRISMA protocol, 83 peer-reviewed articles from 1998 to 2025 were analyzed and sourced from Scopus. Findings reveal that Supply Chain X.0 enhances performance through automation, real-time visibility, predictive analytics, and sustainability initiatives. However, challenges such as high implementation costs, workforce adaptation, data quality, and security persist. This review provides a comprehensive synthesis for understanding these impacts and identifies research gaps and future research directions for smart supply chain development. Furthermore, it offers novelty by synthesizing the entire Supply Chain X.0 evolution (0.0 to 5.0) in one systematic review combining performance and sustainability impacts across all stages with quantified metrics, thus providing a holistic view that bridges historical and modern smart supply chains. It also identifies underexplored research gaps, such as the applicability of X.0 stages in developing economies and the need for standardized eco-metrics. Finally, the review introduces a novel visual framework using VOSviewer to illustrate the interconnectedness of keywords related to the supply chain, performance, sustainability, and AI, offering a tool to guide future integrative research. Full article

This study investigates premature fatigue failure in rocker arm gears of large-mining-height shearers operating at alternating ±45° working angles, where insufficient lubrication generates non-uniform thermal -stress fields. In this study, an integrated multiphysics framework combining transient thermal–fluid–structure coupling simulations with fatigue life prediction is proposed. Transient thermo-mechanical coupling analysis simulated dry friction conditions, capturing temperature and stress fields under varying speeds. Fluid–thermal–solid coupling analysis modeled wet lubrication scenarios, incorporating multiphase flow to track oil distribution, and calculated convective heat transfer coefficients at different immersion depths (25%, 50%, 75%). These coupled simulations provided the critical time-varying temperature and thermal stress distributions acting on the gears (Z6 and Z7). Subsequently, these simulated thermo-mechanical loads were directly imported into ANSYS 2024R1 nCode DesignLife to perform fatigue life prediction. Simulations demonstrate that dry friction induces extreme operating conditions, with Z6 gear temperatures reaching over 800 °C and thermal stresses peaking at 803.86 MPa under 900 rpm, both escalating linearly with rotational speed. Lubrication depth critically regulates heat dissipation, where 50% oil immersion optimizes convective heat transfer at 8880 W/m²·K for Z6 and 11,300 W/m²·K for Z7, while 25% immersion exacerbates thermal gradients. Fatigue life exhibits an inverse relationship with speed but improves significantly with cooling. Z6 sustains a lower lifespan, exemplified by 25+ days at 900 rpm without cooling versus 50+ days for Z7, attributable to higher stress concentrations. Based on the multiphysics analysis results, two physics-informed engineering optimizations are proposed to reduce thermal stress and extend gear fatigue life: a staged cooling system using spiral copper tubes and an intelligent lubrication strategy with gear-pump-driven dynamic oil supply and thermal feedback control. These strategies collectively enhance gear longevity, validated via multiphysics-driven topology optimization. Full article

In horizontal well technology, hydraulic fracturing has been established as an essential technique for enhancing hydrocarbon production. However, the complex architecture of fracture networks challenges conventional monitoring methods. Micro-seismic monitoring, recognized for its superior resolution and sensitivity, enables precise fracture morphology characterization. This study advances diagnostic capabilities through integrated field–laboratory investigations and multi-domain signal processing. Hydraulic fracturing experiments under varied geological conditions generated critical micro-seismic datasets, with quantitative analyses revealing asymmetric propagation patterns (total length 312 ± 15 m, east wing 117 m/west wing 194 m) forming a 13.37 × 10⁴ m³ stimulated reservoir volume. Spatial event distribution exhibited density disparities correlating with geophone offsets (west wing 3.8 events/m vs. east 1.2 events/m at 420–794 m distances). Advanced time–frequency analyses and inversion algorithms differentiated signal characteristics demonstrating logarithmic SNR (Signal-to-Noise Ratio)–magnitude relationships (SNR 0.49–4.82, R² = 0.87), with near-field events (<500 m) showing 68% reduced magnitude variance compared to far-field counterparts. Coupled numerical simulations confirmed stress field interactions where fracture trajectories deviated 5–15° from principal stress directions due to prior-stage stress shadows. Branch fracture networks identified in Stages 4/7/9/10 with orthogonal/oblique intersections (45–65° dip angles) enhanced stimulation reservoir volume (SRV) by 37–42% versus planar fractures. These geometric parameters—including height (20 ± 3 m), width (44 ± 5 m), spacing, and complexity—were quantitatively linked to micro-seismic response patterns. The developed diagnostic framework provides operational guidelines for optimizing fracture geometry control, demonstrating how heterogeneity-driven signal variations inform stimulation strategy adjustments to improve reservoir recovery and economic returns. Full article

Cooperative longitudinal–lateral trajectory optimization is essential for unmanned ground vehicle (UGV) platoons to improve safety, capacity, and efficiency. However, existing approaches often face unstable formation under low penetration rates and rely on fragmented control strategies. This study develops a cooperative longitudinal–lateral trajectory tracking framework tailored for UGV platooning, embedded in a hierarchical control architecture. Dual-stage multi-objective Model Predictive Control (MPC) is proposed, decomposing trajectory planning into pursuit and platooning phases. Each stage employs adaptive weighting to balance platoon efficiency and traffic performance across varying operating conditions. Furthermore, a traffic-aware organizational module is designed to enable the dynamic opening of UGV-dedicated lanes, ensuring that platoon formation remains compatible with overall traffic flow. Simulation results demonstrate that the adaptive weighting strategy reduces the platoon formation time by 41.6% with only a 1.29% reduction in the average traffic speed. In addition, the dynamic lane management mechanism yields longer and more stable UGV platoons under different penetration levels, particularly in high-flow environments. The proposed cooperative framework provides a scalable solution for advancing UGV platoon control and demonstrates the potential of unmanned systems in future intelligent transportation applications. Full article

The prediction of Remaining Useful Life (RUL) constitutes a vital aspect of Prognostics and Health Management (PHM), providing capabilities for the assessment of mechanical component health status and prediction of failure instances. Recent studies on feature extraction, time-series modeling, and multi-task learning have shown remarkable advancements. However, most deep learning (DL) techniques predominantly focus on unimodal data or static feature extraction techniques, resulting in a lack of RUL prediction methods that can effectively capture the individual differences among heterogeneous sensors and failure modes under complex operational conditions. To overcome these limitations, an adaptive RUL prediction framework named ADAPT-RULNet is proposed for mechanical components, integrating the feature extraction capabilities of attention-enhanced deep learning (DL) and the decision-making abilities of deep reinforcement learning (DRL) to achieve end-to-end optimization from raw data to accurate RUL prediction. Initially, Functional Alignment Resampling (FAR) is employed to generate high-quality functional signals; then, attention-enhanced Dynamic Time Warping (DTW) is leveraged to obtain individual degradation stages. Subsequently, an attention-enhanced of hybrid multi-scale RUL prediction network is constructed to extract both local and global features from multi-format data. Furthermore, the network achieves optimal feature representation by adaptively fusing multi-source features through Bayesian methods. Finally, we innovatively introduce a Deep Deterministic Policy Gradient (DDPG) strategy from DRL to adaptively optimize key parameters in the construction of individual degradation stages and achieve a global balance between model complexity and prediction accuracy. The proposed model was evaluated on aircraft engines and railway freight car wheels. The results indicate that it achieves a lower average Root Mean Square Error (RMSE) and higher accuracy in comparison with current approaches. Moreover, the method shows strong potential for improving prediction accuracy and robustness in varied industrial applications. Full article

Cities worldwide are transitioning from demolition–redevelopment-driven expansion to high-quality regeneration centered on stock upgrading, cultural continuity, and long-term operations. Against the backdrop of China’s high-quality urban renewal phase guided by the “anti-massive demolition and construction” policy, this study constructs a calibrated tripartite evolutionary game among government, investors, and residents. By embedding culture–economy parameters—cultural renovation intensity (k), operational profit-sharing ratio between investors and residents (j), cultural identification coefficient (i), and cost-sharing coefficient (w)—we establish a behavioral interaction mechanism of “cultural value conversion–benefit-sharing–cultural identification–cost-sharing.” Simulations based on replicator dynamics demonstrate that sustained tripartite cooperation requires four conditions: cultural intensity surpasses the cost threshold (k ∈ [0.6, 0.7]); the profit-sharing ratio preserves market incentives (j ∈ [0.25, 0.35]); cultural identification reaches a minimum threshold (i ≥ 0.4); and residents’ cost-sharing does not exceed their benefit capacity (w ≤ 0.2). These findings reveal the core tension in China’s high-quality urban renewal stage—namely, the challenge of instituting sustainable operational mechanisms under cultural protection constraints—and globally provide a quantifiable policy toolbox for culture-led urban regeneration. Full article

Background: Class imbalance and limited interpretability remain major barriers to the clinical adoption of machine learning in diabetes prediction. These challenges often result in poor sensitivity to high-risk cases and reduced trust in AI-based decision support. This study addresses these limitations by integrating SMOTE-based resampling with SHAP-driven explainability, aiming to enhance both predictive performance and clinical transparency for real-world deployment. Objective: To develop and validate an interpretable machine learning framework that addresses class imbalance through advanced resampling techniques while providing clinically meaningful explanations for enhanced decision support. This study serves as a methodologically rigorous proof-of-concept, prioritizing analytical integrity over scale. While based on a computationally feasible subset of 1500 records, future work will extend to the full 100,000-patient dataset to evaluate scalability and external validity. We used the publicly available, de-identified Diabetes Prediction Dataset hosted on Kaggle, which is synthetic/derivative and not a clinically curated cohort. Accordingly, this study is framed as a methodological proof-of-concept rather than a clinically generalizable evaluation. Methods: We implemented a robust seven-stage pipeline integrating the Synthetic Minority Oversampling Technique (SMOTE) with SHapley Additive exPlanations (SHAP) to enhance model interpretability and address class imbalance. Five machine learning algorithms—Random Forest, Gradient Boosting, Support Vector Machine (SVM), Logistic Regression, and XGBoost—were comparatively evaluated on a stratified random sample of 1500 patient records drawn from the publicly available Diabetes Prediction Dataset (n = 100,000) hosted on Kaggle. To ensure methodological rigor and prevent data leakage, all preprocessing steps—including SMOTE application—were performed within the training folds of a 5-fold stratified cross-validation framework, preserving the original class distribution in each fold. Model performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, F1-score, and precision. Statistical significance was determined using McNemar’s test, with p-values adjusted via the Bonferroni correction to control for multiple comparisons. Results: The Random Forest-SMOTE model achieved superior performance with 96.91% accuracy (95% CI: 95.4–98.2%), AUC of 0.998, sensitivity of 99.5%, and specificity of 97.3%, significantly outperforming recent benchmarks (p < 0.001). SHAP analysis identified glucose (SHAP value: 2.34) and BMI (SHAP value: 1.87) as primary predictors, demonstrating strong clinical concordance. Feature interaction analysis revealed synergistic effects between glucose and BMI, providing actionable insights for personalized intervention strategies. Conclusions: Despite promising results, further validation of the proposed framework is required prior to any clinical deployment. At this stage, the study should be regarded as a methodological proof-of-concept rather than a clinically generalizable evaluation. Our framework successfully bridges algorithmic performance and clinical applicability. It achieved high cross-validated performance on a publicly available Kaggle dataset, with Random Forest reaching 96.9% accuracy and 0.998 AUC. These results are dataset-specific and should not be interpreted as clinical performance. External, prospective validation in real-world cohorts is required prior to any consideration of clinical deployment, particularly for personalized risk assessment in healthcare systems. Full article

In complex scenarios, such as industrial parks and underground parking lots, efficient and safe autonomous navigation is essential for driverless operation and automatic parking. However, conventional modular navigation methods, especially the A* algorithm, suffer from excessive node traversal and short paths that bring vehicles dangerously close to obstacles. To address these issues, we propose an autonomous navigation approach based on a layered terrain cost map and a nonlinear predictive control model, which ensures real-time performance, safety, and reduced computational cost. The global planner applies a two-stage A* strategy guided by the hierarchical terrain cost map, improving efficiency and obstacle avoidance, while the local planner combines linear interpolation with nonlinear model predictive control to adaptively adjust the vehicle speed under varying terrain conditions. Experiments conducted in simulated and real underground parking scenarios demonstrate that the proposed method significantly improves the computational efficiency and navigation safety, outperforming the traditional A* algorithm and other baseline approaches in overall performance. Full article

With the progressive depletion of coal resources, the recovery of shaft pillars has become an important means of improving resource utilization and reducing waste. Taking the main shaft pillar recovery of the Longxiang Coal Mine at the stage of mine closure as the engineering background, this study systematically investigates ground subsidence prediction and shaft stability control under strip mining with symmetrical extraction. An improved subsidence prediction model was established by integrating the probability integral method with superposition theory, and its validity was verified through numerical simulations and field monitoring data. The results demonstrate that the proposed method can accurately capture the subsidence behavior under complex geological conditions, with prediction errors ranging from 6.4 mm to 399.1 mm. In fully subsided zones, the percentage error was as low as 1.1–3.5%, while larger deviations were observed in areas where subsidence was incomplete, confirming both the reliability and the practical limitations of the method under different conditions. Furthermore, the deformation mechanisms of the shaft during pillar recovery were analyzed. Monitoring results indicated that the maximum subsidence at the east and west sides of the shaft reached 7620.6 mm, accompanied by local cracks exceeding 1500 mm, which caused significant damage to surface structures. To address these risks, a safety control scheme based on an integrated “prediction–monitoring–control” framework is proposed, including shaft wall reinforcement, optimization of mining parameters, and continuous ground subsidence monitoring. By combining real-time monitoring with the superposition of small working face predictions, the scheme enables maximum recovery of shaft pillar coal while ensuring operational safety. This study provides a scientific basis and technical support for shaft pillar recovery in Longxiang Coal Mine and offers valuable theoretical guidance for similar mine closure projects, with significant implications for engineering practice. Full article

Current cavitating water jet technology for mineral liberation predominantly relies on the micro-jet impact generated by bubble collapse. Consequently, conventional nozzle designs often overlook the shear effects on mineral particles within the internal flow path. Moreover, the cavitation cloud evolution mechanisms in nozzles operating on this innovative principle remain insufficiently explored. This study systematically evaluates the cavitation performance of an innovatively designed cavitating jet nozzle with friction-shear effects (CJN-FSE), whose optimized internal structure enhances the interlayer shear and stripping effects crucial for the liberation of layered minerals. Utilizing high-speed imaging, we visualized submerged friction-shear cavitating water jets and systematically investigated the dynamic evolution patterns of cavitation clouds under jet pressures ranging from 15 to 35 MPa. The results demonstrate that the nozzle achieves effective cavitation, with jet pressure exerting a significant influence on the morphology and evolution of the cavitation clouds. As the jet pressure increased from 15 to 35 MPa, the cloud length, width, and average shedding distance increased by 37.05%, 45.79%, and 211.25%, respectively. The mean box-counting dimension of the cloud contour rose from 1.029 to 1.074, while the shedding frequency decreased from 1360 to 640 Hz. Within the 15–25 MPa range, the clouds showed periodic evolution, with each cycle comprising four stages: inception, development, shedding, and collapse. At 30 MPa, mutual interference between adjacent clouds emerged, leading to unsteady shedding behavior. This study thereby reveals the influence of jet pressure on the dynamic evolution patterns and unsteady shedding mechanisms of the clouds. It provides a theoretical and experimental basis for subsequent research into the nozzle’s application in liberating layered minerals and proposes a new design paradigm for cavitation nozzles tailored to the mechanical properties of specific minerals. Full article

Large-scale integration of distributed energy resources (DERs) into distribution networks causes topological-operational situations with multidirectional power flows. One of the main components of distribution networks is the power transformer, which does not have the capabilities for real-time control of distribution network parameters with DERs. The use of solid-state transformers (SSTs) for connecting medium-voltage (MV) and low-voltage (LV) distribution networks of both alternating and direct current has great potential for constructing new distribution networks and enhancing the existing ones. Electricity losses in distribution networks can be reduced through the establishment of MV and LV DC networks. In hybrid AC-DC distribution networks, the SSTs can be especially effective, ensuring compensation for voltage dips, fluctuations, and interruptions; regulation of voltage, current, frequency, and power factor in LV networks; and reduction in the levels of harmonic current and voltage due to the presence of power electronic converters (PECs) and capacitors in the DC link. To control the operating parameters of hybrid distribution networks with solid-state transformers, it is crucial to develop and implement advanced control systems (CSs). The purpose of this review is a comprehensive analysis of the features of the creation of CSs SSTs when they are used in hybrid distribution networks with DERs to identify the most effective principles and methods for managing SSTs of different designs, which will accelerate the development and implementation of CSs. This review focuses on the design principles and control strategies for SSTs, guided by their architecture and intended functionality. The architecture of the solid-state transformer control system is presented with a detailed description of the main stages of control. In addition, the features of the SST CS operating under various topologies and operating conditions of distribution networks are examined. Full article

UAV accident investigation is essential for safeguarding the fast-growing low-altitude airspace. While near-daily incidents are reported, they were rarely analyzed in depth as current inquiries remain expert-dependent and time-consuming. Because most jurisdictions mandate formal reporting only for serious injury or substantial property damage, a large proportion of minor occurrences receive no systematic investigation, resulting in persistent data gaps and hindering proactive risk management. This study explores the potential of using large language models (LLMs) to expedite UAV accident investigations by extracting human-factor insights from unstructured narrative incident reports. Despite their promise, the off-the-shelf LLMs still struggle with domain-specific reasoning in the UAV context. To address this, we developed a human factors analysis and classification system (HFACS)-guided analytical framework, which blends structured prompting with lightweight post-processing. This framework systematically guides the model through a two-stage procedure to infer operators’ unsafe acts, their latent preconditions, and the associated organizational influences and regulatory risk factors. A HFACS-labelled UAV accident corpus comprising 200 abnormal event reports with 3600 coded instances has been compiled to support evaluation. Across seven LLMs and 18 HFACS categories, macro-F1 ranged 0.58–0.76; our best configuration achieved macro-F1 0.76 (precision 0.71, recall 0.82), with representative category accuracies > 93%. Comparative assessments indicate that the prompted LLM can match, and in certain tasks surpass, human experts. The findings highlight the promise of automated human factor analysis for conducting rapid and systematic UAV accident investigations. Full article

With the increasing penetration of renewable energy such as solar and wind power into the grid as well as the addition of modern types of versatile loads such as electric vehicles, the grid system is more prone to system failure and instability. One of the possible solutions to mitigate these conditions and increase the system efficiency is the integration of virtual power plants into the system. Virtual power plants can aggregate distributed energy resources such as renewable energy systems, electric vehicles, flexible loads, and energy storage, thus allowing for better coordination and optimization of these resources. This paper proposes a genetic algorithm-based optimization to coordinate the different elements of the energy management system of a virtual power plant, such as the energy storage system and charging/discharging of electric vehicles. It also deals with the random behavior of the genetic algorithm and its failure to meet certain constraints in the final solution. A novel method is proposed to mitigate these problems that combines a genetic algorithm in the first stage, followed by a gradient-based method in the second stage, consequently reducing the overall electricity bill by 50.2% and the simulation time by almost 95%. The performance is evaluated considering the reference set-points of operation from the obtained solution of the energy storage and electric vehicles by performing tests using a detailed model where power electronics converters and their local controllers are also taken into account. Full article