Search Results (61,544)

Inland depression irrigation districts in the arid regions of Xinjiang, owing to the absence of natural drainage conditions, exhibit unique groundwater-salt dynamics and face prominent risks of soil salinization, thus necessitating clarification of their water-salt transport mechanisms to ensure sustainable agricultural development. This study takes the Karamay Agricultural Comprehensive Development Zone as the research subject. The study examines the distribution characteristics of soil salinity, groundwater depth, and Total Dissolved Solids (TDS) of groundwater across diverse soil textures, elucidates the correlative relationships between groundwater dynamics and soil salinity, and forecasts the evolutionary trajectory of groundwater levels within the irrigation district. The findings reveal that groundwater depth in silty soil regions (3.24–3.11 m) substantially exceeds that in silty clay regions (2.43–2.61 m), whereas TDS of groundwater demonstrates marginally elevated concentrations in silty clay areas (19.05–16.78 g L⁻¹) compared to silty soil zones (18.18–16.29 g L⁻¹). Soil salinity exhibits pronounced surface accumulation phenomena and considerable inter-annual seasonal variations: manifesting a “spring-peak, summer-trough” pattern in 2023, which inversely transitioned to a “summer-peak, spring-trough” configuration in 2024, with salinity hotspots predominantly concentrated in silty clay distribution zones. A significant sigmoid functional relationship emerges between soil salinity and groundwater depth (R² = 0.73–0.77), establishing critical depth thresholds of 2.44 m for silty soil and 2.72 m for silty clay, beneath which the risk of secondary salinization escalates dramatically. The XGBoost model demonstrates robust predictive capability for groundwater levels (R² = 0.8545, MAE = 0.4428, RMSE = 0.5174), with feature importance analysis identifying agricultural irrigation as the predominant influencing factor. Model projections indicate that mean groundwater depths across the irrigation district will decline to 2.91 m, 2.76 m, 2.62 m, and 2.36 m over the ensuing 1, 3, 5, and 10 years, respectively. Within a decade, 73.33% of silty soil regions and 92.31% of silty clay regions will experience groundwater levels below critical thresholds, subjecting the irrigation district to severe secondary salinization threats. Consequently, comprehensive mitigation strategies encompassing precision irrigation management and enhanced drainage infrastructure are imperative. Full article

Understanding the dynamic characteristics of droplets in the orientated flow channels of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for their effective heat and water management and bipolar plate design. Therefore, the transient transport dynamics of liquid water within orientated gas flow channels (OGFCs) of PEMFCs are investigated, and a two-phase model based on the volume of fluid (VOF) method is established in the current study. Moreover, the impacts of the size of droplets and the geometrical parameters of baffles on the removal dynamics of liquid water are examined. The results show that baffles effectively promote droplet breakup and accelerate their detachment from the Gas Diffusion Layer (GDL) surface by increasing flow instability and local shear forces. The morphology of water is altered by the high velocity of gaseous flow, which can break up into several smaller droplets and distribute them on the surface of GDL by the gas flow. The shape of the liquid water film changes from a regular cuboid to a big droplet due to the surface tension of the liquid water droplets and the hydrophobicity of the GDL surfaces. Increasing the baffle height can reduce the time needed for the removal of droplets. With the increase in L1* from 0.25 to 0.75, the drainage time decreases slightly; however, for L1* increasing from 0.75 to 1.25, the drainage time remains almost the same. The impacts of different leeward lengths, L2*, on the water coverage ratio and pressure drop are minor. Full article

Objectives: This study aims to evaluate whether contemporary artificial intelligence (AI), including convolutional neural networks (CNNs) for medical imaging and large language models (LLMs) for language processing, could replace physicians in the near future and to identify the principal clinical, technical, and regulatory barriers. Methods: A narrative review is conducted on the scientific literature addressing AI performance and reproducibility in medical imaging, LLM competence in medical knowledge assessment and patient communication, limitations in out-of-distribution generalization, absence of physical examination and sensory inputs, and current regulatory and legal frameworks, particularly within the European Union. Results: AI systems demonstrate high accuracy and reproducibility in narrowly defined tasks, such as image interpretation, lesion measurement, triage, documentation support, and written communication. These capabilities reduce interobserver variability and support workflow efficiency. However, major obstacles to physician replacement persist, including limited generalization beyond training distributions, inability to perform physical examination or procedural tasks, susceptibility of LLMs to hallucinations and overconfidence, unresolved issues of legal liability at higher levels of autonomy, and the continued requirement for clinician oversight. Conclusions: In the foreseeable future, AI will augment rather than replace physicians. The most realistic trajectory involves automation of well-defined tasks under human supervision, while clinical integration, physical examination, procedural performance, ethical judgment, and accountability remain physician-dependent. Future adoption should prioritize robust clinical validation, uncertainty management, escalation pathways to clinicians, and clear regulatory and legal frameworks. Full article

This paper presents a 2D micro-conveyance device based on a 3 × 3 electromagnetic digital actuator array. This device allows the conveyed object to be moved between several discrete positions distributed in the xy-plane through a collaborative actuation of the digital actuators. Each digital actuator includes a mobile permanent magnet placed in a square cavity and can be moved between four discrete positions. An analytical model of the digital actuators was proposed and used to design the conveyance device. Then, a prototype was built using rapid prototyping techniques and was experimentally characterized. The reachable workspace of the conveyance device is 56 mm × 56 mm in the xy-plane, and the proposed architecture enables the workspace to be easily enlarged by adding elementary modules. The distance between two discrete positions is 4 mm, and the positioning repeatability was measured as 5.5 µm. The maximum conveyance velocity and transportable mass were found to be up to 16 mm.s⁻¹ and 15 g, respectively. Full article

In this paper, we analyze the impacts of financial innovation and financial deepening on the economic growth of 14 sub-Saharan African (SSA) countries from 1995 to 2023. The autoregressive distributed lag (ARDL) approach and error correction model (ECM) were used to assess short- and long-run effects. The findings indicate that mobile cellular subscriptions and government spending are the main contributors to national economic growth and that money supply has a positive impact. However, the strong negative effect of capital formation on economic growth is contrary to expectations. Conversely, the findings confirm that gross capital formation has a strong positive effect on gross domestic product (GDP) growth in the long run. Bounds testing reveals varying degrees of cointegration across countries. Long-run relationships were confirmed in Senegal, Côte d’Ivoire, Ethiopia, and Zimbabwe, all of which showed evidence of strong cointegration. These findings support policy recommendations aimed at promoting sustainable economic growth in SSA economies through targeted policies that increase domestic credit in the private sector and attract foreign direct investment (FDI). Full article

To enhance the accuracy of wind power forecasting, this paper proposes a hybrid model that integrates Variational Mode Decomposition (VMD), Improved Dung Beetle Optimization (IDBO) and Support Vector Machine (SVM). First, to reduce the volatility and non-stationarity of wind power data, VMD is applied to decompose the original signal into several intrinsic mode functions (IMFs). Subsequently, the Dung Beetle Optimization (DBO) algorithm is improved using chaotic mapping, a Lévy flight search strategy and adaptive t-distribution. Finally, the penalty coefficient of the SVM is optimized using IDBO, and the VMD-IDBO-SVM model is constructed. This study proposes an improved IDBO algorithm and, for the first time, integrates VMD and IDBO-SVM within the context of wind power forecasting. Experimental results show that the proposed VMD-IDBO-SVM model achieves a MAE of 3.315, an RMSE of 4.130, and an R² of 0.985 on test data from a wind farm, demonstrating a significant improvement compared with the traditional SVM model. It has demonstrated excellent stability and significance in both multi-time-slice validation and statistical testing. Full article

Renewable-driven microgrids require transparent and adaptive coordination mechanisms to manage variability in distributed generation and flexible demand. Conventional pricing schemes and centralized demand-side programs are often insufficient to regulate real-time imbalances, leading to inefficient renewable utilization and limited prosumer participation. This work proposes a blockchain-integrated Stackelberg pricing model that combines real-time price regulation, optimal demand-side management, and peer-to-peer energy exchange within a unified operational framework. The Microgrid Energy Management System (MEMS) acts as the Stackelberg leader, setting hourly prices and demand response incentives, while prosumers and consumers respond through optimal export and load-shifting decisions derived from quadratic cost models. A distributed supply–demand balancing algorithm iteratively updates prices to reach the Stackelberg equilibrium, ensuring system-level feasibility. To enable trust and tamper-proof execution, smart-contract architecture is deployed on the Polygon Proof-of-Stake network, supporting participant registration, day-ahead commitments, real-time measurement logging, demand-response validation, and automated settlement with negligible transaction fees. Experimental evaluation using real-world demand and PV profiles shows improved peak-load reduction, higher renewable utilization, and increased user participation. Results demonstrate that the proposed framework enhances operational reliability while enabling transparent and verifiable microgrid energy transactions. Full article

Federated learning (FL)-based vehicular edge computing networks (VECNs) are emerging as a key enabler of intelligent transportation systems, as their privacy-preserving and distributed architecture can safeguard vehicle data while reducing latency and energy consumption. However, conventional roadside units face processing bottlenecks in dense traffic and at the network edge, motivating the adoption of unmanned aerial vehicle (UAV)-assisted VECNs. To address this challenge, this paper proposes a UAV-assisted VECN framework with FL, aiming to improve model accuracy while minimizing latency and energy consumption during computation and transmission. Specifically, a reputation-based client selection mechanism is introduced to enhance the accuracy and reliability of federated aggregation. Furthermore, to address the channel dynamics induced by high vehicle mobility, we design a robust reinforcement learning-based resource allocation scheme. In particular, an asynchronous parallel deep deterministic policy gradient (APDDPG) algorithm is developed to adaptively allocate computation and communication resources in response to real-time channel states and task demands. To ensure consistency with real vehicular communication environments, field experiments were conducted and the obtained measurements were used as simulation parameters to analyze the proposed algorithm. Compared with state-of-the-art algorithms, the developed APDDPG algorithm achieves 20% faster convergence, 9% lower energy consumption, a FL accuracy of 95.8%, and the most robust standard deviation under varying channel conditions. Full article

Against the backdrop of urban renewal, the transformation and functional enhancement of Existing Urban Industrial Areas (EUIAs) play a crucial role. Focusing on the rational development of underground space in EUIAs, this study explores forecasting methods for the development demand of such underground space, aiming to alleviate the contradiction between the protection of industrial heritage and intensive land use in EUIAs. This paper systematically sorts out the forecasting methods for the scale demand of underground space. Firstly, through a literature review, two major categories of factors influencing underground space demand—driving factors and conditional factors—are summarized, and an indicator system consisting of 23 indicators is constructed. On this basis, the modified Delphi method is employed to screen 7 dominant indicators, including the protection value of industrial heritage, the spatial distribution of industrial heritage, existing underground space, development functions, rail transit, spatial location, and surrounding supporting facilities. Based on the matrix of industrial heritage protection levels and land use nature, the development potential of underground space is evaluated, and a demand level correction model is introduced. Demand intensity is quantified through expert experience-based assignment with reference to typical domestic cases, thereby establishing a demand forecasting model for the underground space scale in EUIAs. Finally, the model is applied to the Saint-Gobain Industrial Area. Through the analysis of its industrial heritage value assessment, land use planning, and location characteristics, the areas with demand for underground space are delineated and their levels are corrected, forecasting a total underground space demand of 224,600–454,600 m². The research results provide a theoretical basis and methodological support for the underground space planning of EUIAs, and offer references for the development practice of similar regions. Full article

Impulsive overtopping generated by dam-break surges is a critical hazard for dikes and flood-protection embankments, especially in reservoirs and mountainous catchments. Unlike classical coastal wave overtopping, which is governed by long, irregular wave trains and usually characterized by mean overtopping discharge over many waves, these dam-break-type events are dominated by one or a few strongly nonlinear bores with highly transient overtopping heights. Accurately predicting the resulting overtopping levels under such impulsive flows is therefore important for flood-risk assessment and emergency planning. Conventional cluster-then-predict approaches, which have been proposed in recent years, often first partition data into subgroups and then train separate models for each cluster. However, these methods often suffer from rigid boundaries and ignore the uncertainty information contained in clustering results. To overcome these limitations, we propose a GMM+MoE framework that integrates Gaussian Mixture Model (GMM) soft clustering with a Mixture-of-Experts (MoE) predictor. GMM provides posterior probabilities of regime membership, which are used by the MoE gating mechanism to adaptively assign expert models. Using SPH-simulated overtopping data with physically interpretable dimensionless parameters, the framework is benchmarked against XGBoost, GMM+XGBoost, MoE, and Random Forest. Results show that GMM+MoE achieves the highest accuracy ($R^2=0.9638$ on the testing dataset) and the most centralized residual distribution, confirming its robustness. Furthermore, SHAP-based feature attribution reveals that relative propagation distance and wave height are the dominant drivers of overtopping, providing physically consistent explanations. This demonstrates that combining soft clustering with adaptive expert allocation not only improves accuracy but also enhances interpretability, offering a practical tool for dike safety assessment and flood-risk management in reservoirs and mountain river valleys. Full article

Background: Mental illness stigma among healthcare professionals can adversely affect patient care and recovery. While attitudes are shifting globally, limited data exist on stigma among non-psychiatrist doctors (NPDs) in India. This study aimed to assess the attitudes of NPDs toward mental illness and psychiatry using the Mental Illness Clinicians’ Attitudes Scale (MICA-4), and to explore associated sociodemographic and clinical factors. Methods: A cross-sectional online survey was conducted across India over six months in 2022, following ethics approval. The survey link was distributed via professional social media platforms using convenience and snowball sampling. Non-psychiatrist doctors with at least an MBBS degree were eligible. The MICA-4 scale assessed stigma across five domains. Descriptive statistics, correlation analyses, and multiple regression analysis were conducted. Results: A total of 102 responses were analysed. The mean MICA-4 score was 48.37, indicating moderately positive attitudes. Domain-wise analysis revealed higher stigma in knowledge/misconception and self-disclosure domains, while attitudes towards ethics and patient care were more favourable. No significant differences were found by gender, specialty, or practice setting. Weekly psychiatric caseload was not associated with reduced stigma. Internal consistency of the scale was low (Cronbach’s α = 0.46), raising concerns about cultural fit. The regression model was statistically significant F (5, 96) = 661.95, p < 0.001, explaining 97.18% of the variance in overall attitudes toward mental illness. Among the five domains, Respect for Psychiatry and Knowledge and Misconceptions emerged as the strongest predictors, highlighting their critical role in shaping positive professional attitudes in the public sector. Conclusions: Stigma toward mental illness persists among NPDs, particularly around misconceptions and help-seeking attitudes. These biases are culturally embedded and may not be significantly influenced by clinical exposure alone. While stigma was generally moderate, persistent misconceptions and self-stigma point to the importance of further developing culturally adapted tools and systemic interventions to promote reflective practice and ethical parity in clinical settings. Full article

To address the failure risks associated with long-term service of subsea Christmas tree-wellhead systems under the complex marine environment of the South China Sea, a multi-factor coupled mechanical analysis method is proposed to evaluate the system’s mechanical characteristics and ensure the safety of deepwater oil and gas production. A dynamic model of lateral vibration under seismic loading is established, considering the combined effects of earthquakes, ocean currents, and seabed soil resistance. Based on the actual operating parameters of a well in the Lingshui area of the South China Sea, a three-dimensional finite element model of the subsea Christmas tree-wellhead assembly was developed in ABAQUS 2023. The combined effects of ocean currents, seismic loading, and corrosion over long-term service were simulated to compute and analyze the distributions of stress, bending moment, and associated failure risk. The results indicate that, under a once-in-a-century current combined with seismic waves of intensity V–VI, the system risk remains controllable. However, when the seismic intensity exceeds level VII, the maximum stress and bending moment reach 324.9 MPa and 6.02 MN·m, respectively, surpassing the allowable limits for an X56-grade surface conductor. Considering corrosion effects over a 25-year service life, the extreme stress values increase by 1–5% while the bending moment increases slightly; corrosion significantly amplifies the system’s failure risk. An analysis of the mudline burial height of the subsea wellhead during long-term service shows that, within a range of 1–7 m, variations in system loading are minimal. Based on the mechanical characteristics analysis, it is recommended that the design of subsea Christmas trees and wellheads incorporate regional seismic history, specify X56-grade surface conductors to mitigate corrosion effects, and install leakage-monitoring devices at critical locations to ensure the long-term service safety of the subsea Christmas tree-wellhead system. Full article

The Jurassic reservoirs in the Fukang Sag of the Junggar Basin exhibit heterogeneous overpressure. As the mechanisms underlying overpressure generation remain poorly constrained, this poses challenges for accurate pre-drilling-pressure prediction and hinders a comprehensive understanding of hydrocarbon accumulation processes. Through integrated analysis of measured pressure, mud weight, and well-logging curves, this study delineates distinct overpressure characteristics in sandstones and identifies the well-logging response to overpressure in mudstones. By coupling the loading-unloading response with the analysis of geological conditions conducive to overpressure, we differentiate the overpressure-generating mechanisms between sandstones and mudstones and assess their implications for deep petroleum exploration. The study reveals significant vertical heterogeneity in pressure regimes, with sandstones exhibiting pressure coefficients ranging from 1.2 to 1.8, locally exceeding 2.1. Strong overpressure preferentially develops in isolated sand bodies linked to deep source kitchens via oil-source faults. The logging response of overpressured mudstones shows high acoustic transit time, high neutron, and low resistivity, deviating from the normal compaction trend, yet demonstrates progressive density increases attributable to chemical compaction processes. Overpressure points with pressure coefficients between 1.2 and 1.4 align with the loading curve dominated by disequilibrium compaction. The overpressure with a pressure coefficient exceeding 1.4 correlates with abrupt unloading responses indicative of fault-transferred overpressure in sandstones. Our results highlight that overpressured fluid migration via faults is a critical process in hydrocarbon migration, with large-magnitude overpressured reservoirs being readily formed near oil-source faults. Multi-overpressure mechanisms create a complex pore-pressure distribution in deep layers, challenging conventional pressure-prediction models. These insights advance predictive models for pore pressure and provide a robust framework for optimizing exploration strategies in the Fukang Sag. Full article

Tree pollen allergies are influenced by regional atmospheric pollen concentrations and flora distribution. Climate change and urban landscaping have altered airborne pollen profiles in Ukraine, potentially affecting sensitization patterns. We examined 7518 patients (57.63% children) sensitized to at least one of 26 molecular components from 19 tree species using ALEX testing (2020–2022). Atmospheric pollen data from Ukrainian aerobiology stations were integrated with clinical data. Regional sensitization was mapped using the Geographic Information System, and Bayesian network modeling determined hierarchical relationships. Sensitization to Cry j 1 (46.01%), Bet v 1 (41.67%), and Fag s 1 (34.38%) dominated across age groups. High Fagales sensitization correlated with elevated atmospheric Betula, Alnus, and Corylus pollen concentrations, confirming environmental exposure-sensitization relationships. Bayesian modeling identified Bet v 1 as the root allergen (89.43% accuracy) driving cascading sensitization to other Fagales and non-Fagales allergens. Unexpectedly high Cry j 1 sensitization despite minimal atmospheric Cryptomeria presence suggests Thuja and Ambrosia cross-reactivity. Fagales sensitization dominated 10 of 17 regions, correlating with forest geography and urban landscaping. This study validates aerobiological monitoring’s clinical relevance. Diagnostic protocols should prioritize Bet v 1 while interpreting Cry j 1 positivity as potential cross-reactivity. Climate-driven shifts in atmospheric pollen patterns require ongoing coordinated aerobiological and clinical surveillance. Full article

The reverse circulation pneumatic down-the-hole (DTH) drilling system employs percussive drilling to achieve high efficiency and strong adaptability across diverse rock formations. However, its cutting removal efficiency remains suboptimal. To enhance reverse circulation performance, a comprehensive understanding of airflow and solid particle dynamics at the borehole bottom is essential. This study investigates rock cutting transportation and distribution under varying drilling parameters and evaluates reverse circulation flow ratio using a Computational Fluid Dynamics (CFD) multiphase flow model, coupled with finite volume analysis of the reverse circulation bit. Simulation results reveal that increasing the input gas flow rate (Q), reducing the equivalent particle diameter (D), and minimizing the borehole enlargement ratio (E) significantly improve cutting removal efficiency, with optimal values identified for each parameter. Additionally, solid volume fraction contours at the borehole bottom indicate that the arrangement of spherical teeth influences the flow field. Optimal values for rock cutting density (ρ), rate of penetration (ROP), and rotational speed (N) were also determined to maximize reverse circulation flow ratio. The Genetic Algorithm–Least Squares Support Vector Machine (GA-LSSVM) method was used to train the response surface data and construct a predictive model, which was then further optimized using Particle Swarm Optimization (PSO) to determine accurate parameter settings. These findings provide operational insights into optimizing drilling parameters to advance efficient drilling performance. Full article