Search Results (17,375)

Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management. Full article

Effective and sustainable planning for future smart transportation systems is hindered by outdated traffic management models that fail to capture real-world dynamics, leading to congestion and significant environmental impact. To address this, advanced machine learning models are required to provide high-fidelity insights into urban mobility. In this work, we propose an adaptive machine learning approach to traffic pattern recognition that synergizes the HDBSCAN and k-means clustering algorithms. By employing a data-driven weighted voting mechanism, our solution provides a robust analytical foundation for sustainable planning, integrating structural analysis with precise cluster refinement. The crafted model was validated using a high-fidelity simulation of the Khmelnytskyi, Ukraine, transport network, where it demonstrated a superior ability to identify distinct traffic modes, achieving a V-measure of 0.79–0.82 and improving cluster compactness by 10–14% over standalone algorithms. It also attained a scenario identification accuracy of 92.8–95.0% with a temporal coherence of 0.94. These findings confirm that our adaptive approach is a foundational technology for intelligent transport systems, enabling the planning and deployment of more responsive, efficient, and sustainable urban mobility solutions. Full article

The transition of Türkiye’s energy market toward net-zero emissions by 2053 requires modeling approaches capable of capturing complex interactions and long-term uncertainties. In this study, a long-term agent-based modeling (ABM) framework was developed, integrating econometric demand forecasting with a seasonal autoregressive integrated moving average (SARIMA) model and machine learning (ML)-based day-ahead market (DAM) price prediction. Of the ML models tested, CatBoost achieved the highest accuracy, outperforming XGBoost and Random Forest, and supported investment analysis through net present value (NPV) calculations. The framework represents major market actors—including generation units, investors, and the market operator—while also incorporating the impact of Türkiye’s first nuclear power plant (NPP) under construction and the potential introduction of a carbon emissions trading scheme (ETS). All model components were validated against historical data, confirming robust forecasting and market replication performance. Hourly simulations were conducted until 2053 under alternative policy and demand scenarios. The results show that renewable generation expands steadily, led by onshore wind and solar photovoltaic (PV), while nuclear capacity, ETS implementation, and demand assumptions significantly reshape prices, generation mix, and carbon emissions. The nuclear plant lowers market prices, whereas an ETS substantially raises them, with both policies contributing to emission reductions. These scenario results were connected to actionable policy recommendations, outlining how renewable expansion, ETS design, nuclear development, and energy efficiency measures can jointly support Türkiye’s 2053 net-zero target. The proposed framework provides an ex-ante decision-support framework for policymakers, investors, and market participants, with future extensions that can include other energy markets, storage integration, and enriched scenario design. Full article

With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems. Full article

To address the challenges of inefficient path planning, discontinuous trajectories, and inadequate safety margins in autonomous spraying vehicles for greenhouse environments, this paper proposes a hierarchical motion control architecture. At the global path planning level, the heuristic function of the A* algorithm was redesigned to integrate channel width constraints, thereby optimizing node expansion efficiency. A continuous reference path is subsequently generated using a third-order Bézier curve. For local path planning, a state-space sampling method was adopted, incorporating a multi-objective cost function that accounts for collision distance, curvature change rate, and path deviation, enabling the real-time computation of optimal obstacle-avoidance trajectories. At the control level, an adaptive look-ahead distance pure pursuit algorithm was designed for trajectory tracking. The proposed framework was validated through a Simulink-ROS co-simulation environment and deployed on a Huawei MDC300F computing platform for real-world vehicle tests under various operating conditions. Experimental results demonstrated that compared with the baseline methods, the proposed approach improved the planning efficiency by 38.7%, reduced node expansion by 16.93%, shortened the average path length by 6.3%, and decreased the path curvature variation by 65.3%. The algorithm effectively supports dynamic obstacle avoidance, multi-vehicle coordination, and following behaviors in diverse scenarios, offering a robust solution for automation in facility agriculture. Full article

Contemporary research on electric vehicle (EV) consumption is predominantly focused on the vehicle’s powertrain and battery technology. However, the analyses indicate that the actual state of the various electrical subsystems in the vehicle can have a significant impact on the overall consumption figures. The primary objective of this article is to demonstrate the capabilities of our vehicle simulation model, which was developed with a particular focus on the electrical subsystems of vehicles, when employed in a 3D digital representation of a real environment. The central scientific contribution of this work is the systematic quantification of subsystem-level energy usage in real-world scenario simulation. This provides a novel framework for the evaluation of EV energy distribution, thereby informing future strategies and models. Full article

This study develops a combustible material pyrolysis model capable of numerically predicting and analyzing ultra-fast fire scenarios. The model was subsequently applied to investigate fire behavior in industrial complex facilities. Based on a propolis model and a User-Defined Function (UDF), the proposed approach simulated the mass loss of specimens due to pyrolysis and combustion, and the results were compared with experimental data. A strong correlation confirmed the reliability of the model. Using this validated framework, flame propagation patterns under various fire scenarios were analyzed, providing a quantitative characterization of the thermal behavior and propagation mechanisms of ultra-fast fires in industrial complexes. Full article

Plea bargaining underlies the majority of criminal convictions in the United States, yet concerns remain about its potentially coercive effects, particularly when sentencing differentials between plea offers and potential trial outcomes are large. This experiment examined practicing attorneys’ plea-related recommendations in a 2 (Defendant guilt status: guilty or innocent) × 3 (Potential trial sentence: low, moderate, or high) between-subjects design. Using an interactive computer simulation designed to convey legal scenarios engagingly, we measured attorneys’ plea recommendations, willingness to recommend the plea (WTRP), and maximum acceptable plea sentences. The results reflected Prospect Theory’s utility function, with plea acceptance recommendations increasing as potential trial sentences increased, provided the plea sentence remained within an acceptable range. Attorneys also accepted longer maximum plea sentences as trial penalties became more severe. An interaction between defendant guilt status and potential trial sentence showed that attorneys wanted shorter maximum plea sentences for innocent defendants, though this effect was moderated by trial sentence severity. These findings contribute to our understanding of how attorneys evaluate plea offers and illustrate how large sentencing differentials can shape their recommendations in ways that may affect the fairness of the plea-bargaining process. Full article

The industrial motor systems account for 45% of global electricity consumption. A life cycle model is established to quantify the potential environmental benefits of typical adjustable permanent magnet drives (APMDs, 1250 kW) versus variable frequency drives (VFDs) in China. The model covers mining of metals, manufacturing, operation, and recycling phases of APMDs, incorporating empirical data from China’s 3232 coal-fired units. Four scenarios are set up: business-as-usual, moderate, aggressive, and full-retrofit. Key findings demonstrate that APMDs reduce operational energy consumption by 94.5% compared to VFDs through significantly declining frequency conversion losses and cooling requirements. The life cycle carbon emissions of APMDs (29.7 tonnes CO₂_eq) represent merely 5% of VFDs emissions (570 tonnes CO₂_eq), achieving a 95% reduction. Within APMDs’ footprint, recycling contributes a 45% emission offset (−13.3 tonnes CO₂-eq), while operational efficiency drives the majority of the reduction. Sensitivity analysis identifies electricity emission factors, NdFeB production emissions, and metal recycling rates as primary sensitivity drivers (sensitivity index ST = 0.80). Scenario simulations confirm that the aggressive retrofit strategy (covering high- and moderate-potential units) could reduce annual GHG emissions of 3.12 million tonnes CO₂_eq., with corresponding 89% decreases in particulate matter (PM). This research demonstrates that APMDs are an effective pathway for the low-carbon transition in coal power systems. Their large-scale implementation can potentially necessitate breakthroughs in tiered retrofit policies, thereby providing crucial technological support for industrial carbon neutrality. Full article

This study presents a scenario analysis of Kazakhstan’s electricity market using the PyPSA-KZ model, with a focus on the integration of renewable energy sources (RES). As Kazakhstan transitions towards a low-carbon economy, this study evaluates the technical and economic implications of increasing RES penetration under various scenarios, ranging from 10% to 60% RES shares, with projections targeted for the year 2030. The study simulates system behavior across scenarios and analyzes key indicators, including total system cost, electricity tariff, generation mix, thermal ramping, and CO₂ emissions. Results indicate that up to 30% RES integration is feasible without significant structural changes, delivering reduced system costs and emissions. However, scenarios beyond 30% reveal growing flexibility challenges, necessitating investment in grid modernization, energy storage, and flexible backup capacity. The model outcomes are benchmarked against the International Energy Agency’s 2030 carbon neutrality scenarios and show strong alignment, particularly at 45% RES share. Comparative insights are also drawn from international experiences in Denmark and China. This research demonstrates that the PyPSA-KZ model is a powerful tool for planning Kazakhstan’s energy transition and offers data-driven recommendations to support national energy security and climate goals. Full article

Pedestrian trajectory prediction is crucial for autonomous systems navigating human-populated environments. However, existing methods face fundamental challenges including spurious correlations induced by confounding social environments, passive uncertainty modeling that limits prediction diversity, and bias coupling during feature interaction that contaminates trajectory representations. To address these issues, we propose a novel Causal Intervention and Counterfactual Reasoning (CICR) framework that shifts trajectory prediction from associative learning to a causal inference paradigm. Our approach features a hierarchical architecture having three core components: a Multisource Encoder that extracts comprehensive spatio-temporal and social context features; a Causal Intervention Fusion Module that eliminates confounding bias through the front-door criterion and cross-attention mechanisms; and a Counterfactual Reasoning Decoder that proactively generates diverse future trajectories by simulating hypothetical scenarios. Extensive experiments on the ETH/UCY, SDD, and AVD datasets demonstrate superior performance, achieving an average ADE/FDE of 0.17/0.24 on ETH/UCY and 7.13/10.29 on SDD, with particular advantages in long-term prediction and cross-domain generalization. Full article

Rapid biofuel expansion has significantly reshaped agricultural land use in the United States, raising concerns about the conversion and long-term sustainability of marginal croplands. Understanding how policy incentives influence these land-use changes remains a key challenge in sustainable land management. This study aims to quantify the effects of the Renewable Fuel Standard on cropland expansion and subsequent abandonment in the U.S. Midwest using a probabilistic and spatially explicit framework. The analysis integrates geospatial datasets from USDA, USGS, gridMET, and the U.S. Energy Information Administration, combining indicators of soil productivity, slope, precipitation, temperature, and market accessibility. Bayesian logistic regression models were developed to estimate pre-policy baseline probabilities of corn cultivation and to generate counterfactual scenarios—hypothetical conditions representing land-use patterns in the absence of policy incentives. Results show that over one-quarter of marginal land cultivated in 2016 would likely not have been planted without biopower policy-related incentives, indicating that policy-driven expansion extended into less suitable areas. A second-stage analysis identified regions where such lands were later abandoned, revealing the role of climatic and economic constraints in shaping long-term sustainability. These findings demonstrate the effectiveness of integrating probabilistic modelling with high-resolution spatial data to evaluate causal policy effects and quantify counterfactual impacts—that is, the measurable differences between observed and simulated land-use outcomes. Full article

When internal faults occur in a microgrid, the switching between grid-connected and islanded modes can lead to extended tripping times for traditional inverse-time overcurrent (ITOC) protection and failure in coordination between protection levels. To address these issues, this paper proposes an improved inverse-time overcurrent protection scheme based on a composite parameter protection factor. This scheme utilizes the phase relationship between the positive-sequence voltage fault component at the bus and the positive-sequence current fault component in the feeder after a fault occurrence, combined with the severity of bus voltage sags, to construct a composite parameter protection factor. This factor incorporates a phase-difference acceleration factor and a voltage-sag acceleration factor, aiming to shorten the operation time of the inverse-time overcurrent protection. Furthermore, leveraging the proportional relationship between the composite parameter protection factor and the fault location, the coordination between different protection levels is optimized. Simulations were conducted using PSCAD/EMTDC. The simulation results verify the effectiveness of the proposed improved scheme under various fault scenarios. Full article

Wireless Sensor Networks (WSNs) are vital to a wide range of applications, spanning from environmental monitoring to safety-critical systems. Ensuring dependable operation in these networks critically depends on selecting an optimal node scheduling algorithm; however, this remains a major challenge since no single approach performs best under all conditions. To address this issue, this paper proposes an AI-driven framework that evaluates scenario-specific functional requirements—such as coverage, connectivity, and network lifetime—to identify the optimal node scheduling algorithm from a pool that includes Hidden Markov Models (HMMs), BAT, Bird Flocking, Self-Organizing Maps (SOFMs), and Long Short-Term Memory (LSTM) networks. The framework was evaluated using a neural network trained on simulated data and tested across five real-world scenarios: healthcare monitoring, military operations, industrial IoT, forest fire detection, and disaster recovery. The results clearly demonstrate the effectiveness of the proposed framework in identifying the most suitable algorithm for each scenario. Notably, the LSTM algorithm frequently achieved near-optimal performance, excelling in critical objectives such as network lifetime, connectivity, and coverage. The framework also revealed the complementary strengths of other algorithms—HMM proved superior for maintaining connectivity, while Bird Flocking excelled in extending network lifetime. Consequently, this work validates that a scenario-aware selection strategy is essential for maximizing WSN dependability, as it leverages the unique advantages of diverse algorithms. Full article

The numerical reservoir simulation is a valuable tool to enhance heavy oil recovery by assessing different production strategies (like SAGD and CSS) and operational scenarios. While numerous studies have developed complex models, a systematic review identifying the most critical parameters for achieving accurate production forecasts is lacking. In this work, diverse studies have been reviewed regarding the numerical models of steam injection technologies by examining various parameters (reservoir properties and operating conditions) employed and their impact on the results obtained. Additionally, the effect of using kinetic models in simulations, as well as the modeling of solvent and catalyst injection, is discussed. The outcomes highlight that oil recovery for steam injection methods requires effective steam chamber management and an understanding of geomechanical changes due to the significant role of thermal convection on energy transfer and oil displacement. Increasing steam injection pressures can enhance energy efficiency and reduce emissions, but controlling the gases generated during the reaction poses difficulties. The gas formation within the reservoir in simulations is crucial to prevent overestimating oil production and improving precision. This can be achieved using simple kinetic models, but it is essential to incorporate gas–water solubilities to mimic actual gas emissions and avoid gas buildup. Crucially, our synthesis of the literature demonstrates that incorporating gas–water solubilities and kinetic models for H₂S production can improve the prediction accuracy of gas trends by up to 20% compared to oversimplified models. Enhanced recovery methods (adding solvent and catalyst injection) provide advantages compared with conventional steam injection methods. However, suitable interaction models between oil components and solid particles are needed to improve steam displacement, decrease water production, and enhance recovery in certain circumstances. The use of complex reaction schemes in numerical modeling remarkably enhances the prediction of experimental reservoir data. Full article