Search Results (5,488)

Underground coal mines are widely distributed across China, and underground mining is highly concealed. The rapid and accurate identification of the spatial distribution of coal mining subsidence over large areas is of significant importance for the reuse of land resources in mining areas and the detection of illegal mining activities. The traditional method of monitoring subsidence basins has limitations in terms of monitoring range and timeliness. The development of synthetic aperture radar (InSAR) technology has provided a valuable tool for monitoring mining subsidence areas. However, this method faces challenges in quickly and effectively monitoring subsidence basins using wide-swath SAR images. With the rapid development of deep learning and computer vision technologies, leveraging advanced deep learning models in combination with InSAR technology has become a crucial research direction to enhance the monitoring efficiency of surface subsidence in mining areas. Therefore, this paper proposes a new method for the rapid identification of mining subsidence basins in mining areas, which integrates Deformable Detection Transformer (Deformable DETR) and InSAR technology. First, the real deformation sample set of the mining area, obtained through interference processing, is combined with simulated deformation samples generated using the dynamic probability integral method, elastic transformation, and various noise synthesis techniques to construct a mixed InSAR sample set. This mixed sample set is then used to train the Deformable DETR model and compared with common deep learning methods. The experimental results show that the monitoring accuracy is significantly improved, with the model achieving a Precision of 0.926, Recall of 0.886, F1-score of 0.905, and mean Intersection over Union (mIoU) of 0.828. The detection model was applied to monitor the dynamically updated mining subsidence in the Huainan mining area from 2023 to 2024, detecting 402 subsidence basins. Further training demonstrates that the model exhibits strong robustness. Therefore, this method reduces the construction cost of the target detection training set and holds significant application potential for monitoring geological disasters in large-scale mining areas. Full article

Farmers’ low-carbon transition has become a critical issue for achieving sustainable agricultural development. Fundamentally, this transition is driven by multi-actor collaboration and is subject to stochastic disturbances. However, the collaborative governance mechanisms that facilitate farmers’ low-carbon transformation remain insufficiently understood, particularly under the influence of random factors. To address this gap, we construct a four-party game model involving farmers, government, enterprises, and financial institutions by employing a stochastic evolutionary game approach that incorporates random disturbance factors to capture real-world uncertainty. Numerical simulations are conducted to examine how different policy tools and external environments shape the system’s evolutionary path. The results show the following: (1) In the early transition stage, external uncertainties cause notable fluctuations in strategy evolution, during which the government, farmers, and enterprises gradually form a collaborative mechanism, while financial institutions remain reluctant to participate due to risk and policy uncertainty. (2) Government subsidies, profit returns, and risk-sharing mechanisms exhibit a substitutive relationship, and an appropriate mix of these tools can effectively enhance the willingness of farmers and enterprises to adopt low-carbon practices. (3) Excessive government incentives may crowd out the role of green credit from financial institutions. (4) The profit-sharing ratio among farmers exerts the strongest motivational effect in the early stage, while higher levels of risk-sharing and reputation benefits are more effective in stabilizing the system structure and enhancing transition resilience. This study reveals the dynamic mechanisms of multi-actor interaction in agricultural low-carbon transition and provides theoretical and policy insights for differentiated government strategies and collaborative emission reduction. Full article

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

In the context of intelligent manufacturing, the integrated scheduling problem of dual rail-guided vehicles (RGVs) and multiple parallel processing equipment in flexible manufacturing systems has gained increasing importance. This problem exhibits spatiotemporal coupling and dynamic constraint characteristics, making traditional optimization methods ineffective at finding optimal solutions. At the problem formulation level, the dual resource scheduling task is modeled as a mixed-integer optimization problem. An intelligent scheduling framework based on action mask-constrained Proximal Policy Optimization (PPO) deep reinforcement learning is proposed to achieve integrated decision-making for production equipment allocation and RGV path planning. The approach models the scheduling problem as a Markov Decision Process, designing a high-dimensional state space, along with a multi-discrete action space that integrates machine selection and RGV motion control. The framework employs a shared feature extraction layer and dual-head Actor-Critic network architecture, combined with parallel experience collection and synchronous parameter update mechanisms. In computational experiments across different scales, the proposed method achieves an average makespan reduction of 15–20% compared with numerical methods, while exhibiting excellent robustness under uncertain conditions including processing time fluctuations. Full article

Pruning is a regular and essential urban tree maintenance practice aimed at sustaining overall health, ecosystem services, and public safety. However, knowledge of post-pruning recovery dynamics remains limited, which in turn hinders accurate assessments of growth and ecological functions. To address this, we examined recovery dynamics of Acacia auriculiformis, a common urban species. Tree height and crown radius were recorded monthly for 12 months after pruning. Trees were classified into two size groups based on diameter at breast height (DBH, trunk diameter measured at 1.3 m above ground): medium (DBH < 45 cm) and large (DBH ≥ 45 cm). A generalized linear mixed model (GLMM), appropriate for repeated measures and non-normal data, was fitted using a Tweedie distribution and a log-link function to model the recovery pattern. Results showed continuous growth over time, with medium-sized trees presenting significantly higher crown radius growth than large trees (p = 0.006), while height growth did not differ (p = 0.788). The best model for height included time (AIC = −846.4), whereas crown recovery was best modelled by time and size class (AIC = −1586.6). These findings demonstrate that, in this study, medium-sized A. auriculiformis generally recover faster, especially in crown expansion. This exploratory study suggests that tree size may influence post-pruning recovery and can provide a reference for subsequent differentiated management studies. The morphological modeling further provides preliminary quantitative evidence for annual recovery dynamics in urban A. auriculiformis. Full article

Stratocumulus clouds are cloud systems composed of stratiform clouds with embedded convective clouds, possessing strong catalytic potential and serving as key target cloud systems for weather modification operations. In this study, the parameterization of ice nucleation for silver iodide (AgI) particles was applied to the Thompson microphysics scheme in the WRF model. Numerical experiments were designed for a stratocumulus cloud that occurred over the Hulunbuir region, northeastern China, on 31 May 2021, to investigate how the structure and evolution of cloud macro- and microphysical properties and precipitation formation respond to glaciogenic seeding. The simulation results indicate that AgI nucleation increased ice concentrations at 4–5 km altitude, enhancing ice crystal formation through condensation–freezing and deposition nucleation and the growth of ice particles through auto-conversion and riming, leading to increased precipitation. The results also show that owing to the non-uniform distribution of supercooled water within this stratocumulus cloud system, the consumption of AgI and the enhanced ice nucleation release latent heat more strongly in regions with higher supercooled water content. This leads to more pronounced isolated updrafts, altering the structure of shear lines and subsequently influencing regional precipitation distribution after silver iodide seeding concludes. These findings reveal that seeding influences both the microphysical and dynamic structures within clouds and highlight the non-uniform seeding effects within cloud systems. This study contributes to a deeper understanding of the effects of artificial seeding on stratocumulus clouds in high-latitude regions and holds significant reference value for artificial weather modification efforts in mixed-phase stratiform clouds. Full article

Background/Objectives: Computational studies using drift diffusion models on go/no-go escape tasks consistently show that individuals with suicidal ideation (SI) preferentially engage in active escape from negative emotional states. This study extends these findings by examining how individuals with SI update beliefs about action–outcome contingencies and uncertainty when trying to escape an aversive state. Methods: Undergraduate students with (n = 58) and without (n = 62) a lifetime history of SI made active (go) or passive (no-go) choices in response to stimuli to escape or avoid an unpleasant state in a laboratory-based negative reinforcement task. A Hierarchical Gaussian Filter (HGF) was used to estimate trial-by-trial trajectories of contingency and volatility beliefs, along with their uncertainties, prediction errors (precision-weighted), and dynamic learning rates, as well as fixed parameters at the person level. Bayesian mixed-effects models were used to examine the relationship between trial number, SI history, trial type, and all two-way interactions on HGF parameters. Results: We did not find an effect of SI history, trial type, or their interactions on perceived volatility of reward contingencies. At the trial level, however, participants with a history of SI developed progressively stronger contingency beliefs while simultaneously perceiving the environment as increasingly stable compared to those without SI experiences. Despite this rigidity, they maintained higher uncertainty during escape trials. Participants with an SI history had higher dynamic learning rates during escape trials compared to those without SI experiences. Conclusions: Individuals with an SI history showed a combination of cognitive inflexibility and hyper-reactivity to prediction errors in escape-related contexts. This combination may help explain difficulties in adapting to changing environments and in regulating responses to stress, both of which are relevant for suicide risk. Full article

The dynamic fracture behavior of rocks subjected to impact loading is a fundamental issue within the field of rock dynamics. This study aims to construct microstructure models of heterogeneous minerals representative of various typical rocks and establish a coupled SHPB impact simulation system with FLAC-PFC to examine the mechanisms of fracture, energy dissipation law, and the characteristics of acoustic emission (AE) responses in rocks acted upon by impact loads. The main results obtained reveal the following: (i) The fracture mechanisms of various lithologies under impact loading exhibit common characteristics, predominantly behaving as composite failure mechanisms. The observed distribution characteristics are mixed and interwoven with shear-tension-implosion failures, with a tendency to aggregate from the boundaries towards the interior of samples. (ii) The AE fracture strength of various lithologies predominantly ranges from −8.25 to −5.25, with peak frequencies observed between −7 to −6. The sequence of AE-based B-values, ranked from highest to lowest, is as follows: red sandstone > green sandstone > slate > granite > blue sandstone > basalt. (iii) The T-k distribution for various lithologies follows CLVD (+)-first. (iv) A significant correlation exists between the energy-time density and the B-value. Rocks exhibiting high energy dissipation capacity are characterized primarily by small-amplitude AE events and small-scale fractures, whereas those with low energy dissipation capacity are mostly marked by large-amplitude AE events and large-scale fractures. These research findings provide a fairly solid theoretical basis for understanding the fracture mechanisms and energy dissipation behaviors of rocks subjected to impact loading. Full article

In this study, the evolved gas analysis of polypropylene (PP), mixed wood biomass (WB), cardboard (CB), and their blends was investigated using a coupled thermo-gravimetric analysis–Fourier transform infrared spectroscopy (TG–FTIR) approach. The data obtained were used to semi-quantify the yield of volatile products from the individual feedstocks and their blends. Using N₂/O₂ (80/20) as the gasifying agent, the TG–FTIR setup was operated from ambient temperature to 850 °C at heating rates of 20 and 40 °C/min. The results indicated that the C–H stretching functional group exhibited higher yields in blends with greater PP mass percentages. In the CB/WB blends, C–H stretching recorded the lowest yield, ranging from 5 to 10 a.u. Conversely, blends containing an average PP mass of 16% showed C–H yields between 20 and 25 a.u. The levels of C–H were observed to increase proportionally with the PP mass fraction in the sample. Furthermore, the evolution of gases from carbonyl functional groups was the highest in the three-component blend with equal mass percentages, with C=O yields reaching 20–25 a.u. at 20 °C/min and 35–40 a.u. at 40 °C/min. The production of carbon monoxide (CO) was also highest in the three-component blend with equal mass percentages, yielding 9–10 a.u. Among the two-component blends, the PP/CB 50/50% blend exhibited the highest CO levels, ranging from 8 to 9 a.u. Overall, higher heating rates resulted in comparatively greater yields across all functional groups, particularly for C–H volatiles. These findings underscore the significance of blend composition and thermal ramping in optimising gasification performance. The results contribute to a deeper understanding of co-gasification dynamics and support the development of targeted feedstock strategies for efficient thermochemical conversion and improved control over volatile emissions. Full article

Global climate change has led to frequent extreme weather events such as high temperatures and droughts, severely threatening the heat and water balance during the growing season of summer maize. To adapt to these changes, adjusting planting dates to optimize crop development has become a key agronomic measure for mitigating climate stress and ensuring yield. Against this backdrop, precise monitoring of leaf area index (LAI) is crucial for evaluating the effectiveness of planting date regulation and achieving precision management. To reveal the impact of planting date variations on summer maize LAI inversion and address the limitations of single data sources in comprehensively reflecting complex environmental conditions affecting crop growth, this study examined summer maize at different planting dates across the North China Plain. Through stepwise regression analysis (SRA), multiple vegetation indices (VIs) and 0–2nd order fractional order derivatives (FODs), spectral parameters were dynamically screened. These were then integrated with effective accumulated temperature (EAT) to optimize model inputs. Partial Least Squares Regression (PLSR), Random Forest (RF), Support Vector Regression (SVR), and Adaptive Boosting Regression (AdaBoot) algorithms were employed to construct LAI inversion models for summer maize across different planting dates and mixed planting dates. Results indicate that, compared to empirical VIs and “tri-band” parameters, randomly selected dual-band combination VIs exhibit the strongest correlation with summer maize LAI. Key bands identified through SRA screening concentrated in the 0.7–1.2 order range, primarily distributed across the red edge and near-infrared bands. Multi-feature models incorporating EAT significantly improved retrieval accuracy compared to single-feature models. Optimal models and feature combinations varied across planting dates. Overall, the VIs + EAT combination exhibited the highest stability across all models. Ensemble learning algorithms RF and AdaBoost performed exceptionally well, achieving average R² values of 0.93 and 0.92, respectively. The model accuracy for the 20-day delayed planting (S4) decreased significantly, with an average R² of 0.62, while the average R² for other planting dates exceeded 0.90. This indicates that the altered environmental conditions during the later growth stages of LAI due to delayed planting hindered LAI estimation. This study provides an effective method for estimating summer maize LAI across different planting dates under climate change, offering scientific basis for optimizing adaptive cultivation strategies for maize in the North China Plain. Full article

High-viscosity aramid copolymer solutions are widely used in fiber manufacturing and advanced composite applications, but their elevated viscosity poses significant challenges for mixing and agitation processes. This study employs computational fluid dynamics (CFD) simulations to enhance the mixing performance of such systems. Flow behavior around the impeller was analyzed within a cylindrical stirred tank while varying the number of baffles (0, 2, 4, and 6) and comparing two different impeller designs (A and B). Simulation results showed that installing a sufficient number of baffles—particularly four—effectively suppressed swirling flows commonly observed in high-viscosity fluids, thereby significantly improving mixing efficiency. Additionally, impeller geometry played a critical role in performance: the axial-flow impeller promoted faster homogenization and broader circulation compared with the radial-flow design. Through this CFD-based analysis, this study elucidates the key mechanisms governing mixing in high-viscosity fluids and provides practical design and operational guidelines for industrial stirred tank systems. These findings complement existing empirical guidelines focused on low-viscosity fluids and contribute to improving the efficiency and reliability of high-viscosity polymer processing. Full article

Modeling complex non-physical systems is essential for understanding the interdependent dynamics of human-centered adaptive environments. This study extends the Goal Tree–Success Tree and Master Logic Diagram (GTST-MLD) framework to represent and analyze these systems beyond their traditional engineering applications. A mixed-methods approach, combining a systematic literature review, expert interviews, and survey-based validation, was employed to test the framework using the teaching–learning process in Higher Education (HE) as an illustrative case study. The results show how function-centered modeling within the GTST-MLD structure decomposes the complexity of the system and reveals pedagogical bottlenecks, providing a structured basis for designing adaptive strategies. Rather than measuring learning gains directly, the model offers a structured representation of the conceptual and methodological pathways that influence learner engagement, conceptual integration, and adaptability. Within this bounded context, this study demonstrates a reproducible GTST-MLD modeling procedure for non-physical systems, an auditable dependency structure, based on explicitly defined nodes and edges, and a coherent alignment between Threshold Concepts (TCs), Learning Outcomes (LOs), and methodological strategies. Together, these contributions offer a basis for diagnosing and optimizing complex non-physical systems and form a foundation for future empirical evaluation. Full article

This study presents a mixed-integer nonlinear programming (MINLP) framework to optimize the allocation of electric vehicle charging stations (EVCSs) in existing indoor parking facilities. The model minimizes total life-cycle cost by jointly determining charger types and placements while accounting for spatial feasibility and investment constraints. A hybrid search method that combines complete enumeration with dynamic programming is applied to identify the least-cost configuration within geometric and electrical limitations. The results show that configurations combining dual- and quad-port chargers outperform single-port layouts by reducing redundant electrical and installation costs. The analysis confirms that integrating life-cycle costing with spatial feasibility yields a practical decision-support tool for property owners seeking to expand charging capacity within existing facilities. Overall, the framework demonstrates that cost-efficient retrofitting of EV charging infrastructure can be achieved without additional land development, supporting broader sustainability objectives and promoting low-carbon mobility. Future research will extend the model to multiple facility layouts and incorporate sensitivity and uncertainty analyses to evaluate robustness under varying geometric and economic conditions. The findings of this paper provide a practical foundation for future planning studies and demonstrate how cost-optimized retrofit strategies can support the scalable expansion of EV charging infrastructure in existing facilities. Full article

We use deep Physics-Informed Neural Networks (PINNs) to simulate stratified forced convection in plane Couette flow. This process is critical for atmospheric boundary layers (ABLs) and oceanic thermoclines under global warming. The buoyancy-augmented energy equation is solved under two boundary conditions: Isolated-Flux (single-wall heating) and Flux–Flux (symmetric dual-wall heating). Stratification is parameterized by the Richardson number $(R_i\in [-1,1\left]\right),$ representing $\pm 2 °\mathrm{C}$ thermal perturbations. We employ a decoupled model (linear velocity profile) valid for low-Re, shear-dominated flow. Consequently, this approach does not capture the full coupled dynamics where buoyancy modifies the velocity field, limiting the results to the laminar regime. Novel contribution: This is the first deep PINN to robustly converge in stiff, buoyancy-coupled flows ($\mid R_i\mid \le 1$) using residual connections, adaptive collocation, and curriculum learning—overcoming standard PINN divergence (errors $>28\%$). The model is validated against analytical ($R_i=0$) and RK4 numerical ($R_i\ne 0$) solutions, achieving $L_2$ errors $\le 0.009\%$ and $L_{\mathrm{\infty }}$ errors $\le 0.023\%$. Results show that stable stratification $(R_i>0)$ suppresses convective transport, significantly reduces local Nusselt number ($Nu$) by up to $100\%$ (driving $Nu$ towards zero at both boundaries), and induces sign reversals and gradient inversions in thermally developing regions. Conversely, destabilizing buoyancy $(R_i<0)$ enhances vertical mixing, resulting in an asymmetric response: $Nu$ increases markedly (by up to $140\%$) at the lower wall but decreases at the upper wall compared to neutral forced convection. At $5–10\times$ lower computational cost than DNS or RK4, this mesh-free PINN framework offers a scalable and energy-efficient tool for subgrid-scale parameterization in general circulation models (GCMs), supporting SDG 13 (Climate Action). Full article

To address the efficiency and safety challenges arising from the mixed operation of autonomous and human-driven container trucks during the automation transformation of traditional container terminals, this study designed a simulation-based optimization framework for mixed vehicle scheduling. A spatio-temporal graph dynamic scheduling model was constructed, incorporating node capacity, arc capacity, and path constraints, to establish a multi-objective optimization model aimed at minimizing the maximum completion time of internal trucks and the average waiting time of external trucks. An improved NSGA-II algorithm was employed to generate task assignment solutions, which were evaluated using discrete-event simulation, integrating a dynamic programming-based yard block selection strategy for external trucks and a congestion-aware path planning algorithm. Experimental results demonstrate that the dynamic priority strategy effectively adapts to different traffic flow scenarios: under low external truck flow, the autonomous internal truck priority strategy reduces task completion time by 18% to 25%, while under high flow, the external truck priority strategy significantly decreases the average waiting time. The optimal configuration ratio between internal and external trucks was identified as approximately 1:2. This research provides a theoretical basis and decision support for enhancing terminal operational efficiency and automation transformation. Full article