Search Results (335)

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Hydrogen produced from renewable sources has the potential to tackle various energy challenges, from allowing cost-effective transportation of renewable energy from production to consumption regions to decarbonizing intensive energy consumption industries. Due to its application versatility and non-greenhouse gaseous emissions characteristics, it is expected that hydrogen will play an important role in the decarbonization strategies set out for 2050. Currently, there are some barriers and challenges that need to be addressed to fully take advantage of the opportunities associated with hydrogen. The present work aims to characterize the state of the art of different hydrogen production, storage, transport, and distribution technologies, which compose the hydrogen value chain. Based on the information collected it was possible to conclude the following: (i) Electrolysis is the frontrunner to produce green hydrogen at a large scale (efficiency up to 80%) since some of the production technologies under this category have already achieved a commercially available state; (ii) in the storage phase, various technologies may be suitable based on specific conditions and purposes. Technologies of the physical-based type are the ones mostly used in real applications; (iii) transportation and distribution options should be viewed as complementary rather than competitive, as the most suitable option varies based on transportation distance and hydrogen quantity; and (iv) a single value chain configuration cannot be universally applied. Therefore, each case requires a comprehensive analysis of the entire value chain. Methodologies, like life cycle assessment, should be utilized to support the decision-making process. Full article

This paper presents a study on open energy data in Spain and its contribution to sustainability, analyzing its content and its reuse potential. Since energy plays an important role in the sustainability and economic development of a country or region, energy strategies must be managed through public policies that promote the development of this sector. In this sense, open data is relevant for decision-making in the energy sector, especially in areas such as energy consumption and renewable energy policies. Our research aims to analyze the work of Spain’s autonomous communities in the field of energy information by conducting a population analysis of all datasets tagged in the energy category. After compiling the information and eliminating irrelevant datasets (those that are mislabeled, obsolete, or have a scope less than the level of the autonomous community), it can be seen that the supply is very scarce and that this category is one of the least populated among all existing categories. The typological analysis indicates that information on consumption is the one offering the most datasets, followed, at a short distance, by heterogeneous and difficult-to-classify information and by the set related to energy certificates or audits (the most recurrent, as it is offered only once by the autonomous communities). One of the main findings of the research is the heterogeneity of the initiatives and the significant differences in scores on an indicator created for this purpose. The ranking has taken into account both the existence of information and the quality of reuse, with Catalonia, the Basque Country, and Cantabria being the leaders (with Castilla y León, the performance reaches 60%, so the three remaining communities do not reach 40%). The research concludes with recommendations based on the gaps detected: more data should be published that can drive economic development and environmental sustainability, reduce heterogeneity, and facilitate the use of these data for greater applicability, which will increase the chances that open energy data can contribute more to sustainability. Full article

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies. Full article

The increasing complexity of maritime traffic imposes growing demands on the safety and rationality of ship-collision-avoidance decisions. While most existing research focuses on simple encounter scenarios, autonomous collision-avoidance strategies that comply with the International Regulations for Preventing Collisions at Sea (COLREGs) in complex multi-ship environments remain insufficiently investigated. To address this gap, this study proposes a novel collision-avoidance framework that integrates a quantitative COLREGs analysis with a distributed stochastic search mechanism. The framework consists of three core components: encounter identification, safety assessment, and stage classification. A cost function is employed to balance safety, COLREGs compliance, and navigational efficiency, incorporating a distance-based weighting factor to modulate the influence of each target vessel. The use of a distributed stochastic search algorithm enables decentralized decision-making through localized information sharing and probabilistic updates. Extensive simulations conducted across a variety of scenarios demonstrate that the proposed method can rapidly generate effective collision-avoidance strategies that fully comply with COLREGs. Comprehensive evaluations in terms of safety, navigational efficiency, COLREGs adherence, and real-time computational performance further validate the method’s strong adaptability and its promising potential for practical application in complex multi-ship environments. Full article

Under the dual pressures of global climate warming and rapid urbanization, the Yangtze River Basin, as the world’s largest urban agglomeration, is facing intensifying thermal environmental stress. Although river ecosystems demonstrate significant thermal regulation functions, their spatial thresholds of cooling effects and multiscale driving mechanisms have remained to be systematically elucidated. This study retrieved land surface temperature (LST) using the split window algorithm and quantitatively analyzed the changes in the river cold island effect and its driving mechanisms in the Yangtze River Basin by combining multi-ring buffer analysis and the optimal parameter-based geographical detector model. The results showed that (1) forest land is the main land use type in the Yangtze River Basin, with built-up land having the largest area increase. Affected by natural, socioeconomic, and meteorological factors, the summer temperatures displayed a spatial pattern of “higher in the east than the west, warmer in the south than the north”. (2) There are significant differences in the cooling magnitude among different land types. Forest land has the maximum daytime cooling distance (589 m), while construction land has the strongest cooling magnitude (1.72 °C). The cooling effect magnitude is most pronounced in upstream areas of the basin, reaching 0.96 °C. At the urban agglomeration scale, the Chengdu–Chongqing urban agglomeration shows the greatest temperature reduction of 0.90 °C. (3) Elevation consistently demonstrates the highest explanatory power for LST spatial variability. Interaction analysis shows that the interaction between socioeconomic factors and elevation is generally the strongest. This study provides important spatial decision support for formulating basin-scale ecological thermal regulation strategies based on refined spatial layout optimization, hierarchical management and control, and a “natural–societal” dual-dimensional synergistic regulation system. Full article

Among marine organisms, baleen whale species like the humpback whale (Megaptera novaeangliae) are a case for which individual-based models are necessary to study population changes because individual trait variabilities predominate over average demographic rates to govern population dynamics. These models require quantification of individual organisms’ dynamics with respect to local conditions, which implies optimal strategy frameworks cannot be used. Instead, to quantify how individuals perform according to the environmental conditions they encounter, we formulated a model linking individual mechanical characteristics of swimming and diving with their aerobic metabolism and behavior. The model simulates the dynamics of swimming and diving for the reported range of whale sizes (1000 to 50,000 kg). Additional processes simulate foraging events including rapid accelerations and water engulfment, which modifies whale shape, weight and drag. Simulations show how the energy cost of swimming at equilibrium increases geometrically with velocity and linearly with mass. The duration and distance covered under apnea vary monotonically with mass but not with velocity; hence, there is a positive mass-dependent optimal velocity that maximizes the distance and duration of apnea. The dive limit was explored with a combination of the physiological state, mechanical force produced and distance to return to surface. This combination is imposed as an inequality constraint on the whale individual. The inequality constraint, transformed as a multi-layer perceptron, which continuously processes information about oxygen, depth and relative velocity, provides the whale individual with autonomous decision-making about whether or not to continue the dive. The results also highlight where missing metabolic information is needed to simulate the dynamics of a population of autonomous individuals at the scale of the Global Ocean. Full article

Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total cost consisting of construction and transportation costs as the objective function. Then, to solve the nonlinear model, a novel artificial eagle optimization algorithm (AEOA) is proposed by simulating the collective migration behaviors of artificial eagles when facing a severe living environment. Three main strategies are designed to help the algorithm effectively explore the decision space: the situational awareness and analysis stage, the free exploration stage, and the flight formation integration stage. In the first stage, artificial eagles are endowed with intelligent thinking, thus generating new positions closer to the optimum by perceiving the current situation and updating their positions. In the free exploration stage, artificial eagles update their positions by drawing on the current optimal position, ensuring more suitable habitats can be found. Meanwhile, inspired by the consciousness of teamwork, a formation flying method based on distance information is introduced in the last stage to improve stability and success rate. Test results from the CEC2022 suite indicate that the AEOA can obtain better solutions for 11 functions out of all 12 functions compared with 8 other popular algorithms. Faster convergence speed and stronger stability of the AEOA are also proved by quantitative analysis. Finally, the trade hub location and allocation method is proposed by combining the optimization model and the AEOA. By solving two typical simulated cases, this method can select suitable hubs with lower construction costs and achieve reasonable allocation between hubs and the rest of the towns to reduce transportation costs. Thus, it is used to solve the trade hub location and allocation problem of Henan province in China to help the government make sound decisions. Full article

Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections. Full article

The city of Uvira, located in the eastern Democratic Republic of Congo (DRC), is increasingly experiencing flood events with devastating impacts on human life, infrastructure, and livelihoods. This study evaluates flood susceptibility in Uvira using Geographic Information Systems (GISs), and an Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Making approach. It integrates eight factors contributing to flood occurrence: distance from water bodies, elevation, slope, rainfall intensity, drainage density, soil type, topographic wetness index, and land use/land cover. The results indicate that proximity to water bodies, drainage density and slope are the most influential factors driving flood susceptibility in Uvira. Approximately 87.3% of the city’s land area is classified as having high to very high flood susceptibility, with the most affected zones concentrated along major rivers and the shoreline of Lake Tanganyika. The reliability of the AHP-derived weights is validated by a consistency ratio of 0.008, which falls below the acceptable threshold of 0.1. This research provides valuable insights to support urban planning and inform flood management strategies. Full article

In clinical practice, left ventricle hypertrophy (LVH) continues to pose a considerable challenge, highlighting the need for more reliable diagnostic approaches. This study aims to propose an automated framework for the quantification of LVH extent and the classification of myocardial segments according to hypertrophy severity using a deep learning-based algorithm. The proposed method was validated on 133 subjects, including both healthy individuals and patients with LVH. The process starts with automatic LV segmentation using U-Net and the segmentation of the left ventricle cavity based on the American Heart Association (AHA) standards, followed by the division of each segment into three equal sub-segments. Then, an automated quantification of regional wall thickness (RWT) was performed. Finally, a convolutional neural network (CNN) was developed to classify each myocardial sub-segment according to hypertrophy severity. The proposed approach demonstrates strong performance in contour segmentation, achieving a Dice Similarity Coefficient (DSC) of 98.47% and a Hausdorff Distance (HD) of 6.345 ± 3.5 mm. For thickness quantification, it reaches a minimal mean absolute error (MAE) of 1.01 ± 1.16. Regarding segment classification, it achieves competitive performance metrics compared to state-of-the-art methods with an accuracy of 98.19%, a precision of 98.27%, a recall of 99.13%, and an F1-score of 98.7%. The obtained results confirm the high performance of the proposed method and highlight its clinical utility in accurately assessing and classifying cardiac hypertrophy. This approach provides valuable insights that can guide clinical decision-making and improve patient management strategies. Full article

In view of the complex operating environments of wind farms and the characteristics of multi-branch mixed collector lines, in order to improve the accuracy of single-phase grounding fault location, the convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (attention) were combined to construct a single-phase grounding fault location strategy for the CNN–BiLSTM–attention hybrid model. Using a zero-sequence current as the fault information identification method, through the deep fusion of the CNN–BiLSTM–attention hybrid model, the single-phase grounding faults in the collector lines of the wind farm can be located. The simulation modeling was carried out using the MATLAB R2022b software, and the effectiveness of the hybrid model in the single-phase grounding fault location of multi-branch mixed collector lines was studied and verified. The research results show that, compared with the random forest algorithm, decision tree algorithm, CNN, and LSTM neural network, the proposed method significantly improved the location accuracy and is more suitable for the fault distance measurement requirements of collector lines in the complex environments of wind farms. The research conclusions provide technical support and a reference for the actual operation and maintenance of wind farms. Full article

This study presents a comprehensive multi-objective optimization framework for optimal placement and sizing of distributed generation (DG) units in electric bus (E-bus) transit systems integrated with a high-power flash charging infrastructure. An enhanced Multi-Objective Grey Wolf Optimizer (MOGWO), utilizing Euclidean distance-based Pareto ranking, is developed to minimize power loss, voltage deviation, and voltage violations. The framework incorporates realistic E-bus operation characteristics, including a 31-stop, 62 km route, 600 kW pantograph flash chargers, and dynamic load profiles over a 90 min simulation period. Statistical evaluation on IEEE 33-bus and 69-bus distribution networks demonstrates that MOGWO consistently outperforms MOPSO and NSGA-II across all DG deployment scenarios. In the three-DG configuration, MOGWO achieved minimum power losses of 0.0279 MW and 0.0179 MW, and voltage deviations of 0.1313 and 0.1362 in the 33-bus and 69-bus systems, respectively, while eliminating voltage violations. The proposed method also demonstrated superior solution quality with low variance and faster convergence, requiring under 7 h of computation on average. A five-method compromise solution strategy, including TOPSIS and Lp-metric, enabled transparent and robust decision-making. The findings confirm the proposed framework’s effectiveness and scalability for enhancing distribution system performance under the demands of electric transit electrification and smart grid integration. Full article

This paper investigates sustainable decision systems within green E-business models by analyzing how different O2O (online-to-offline) fulfillment structures affect emission-reduction efforts and pricing strategies in a two-tier supply chain consisting of a manufacturer and a new retailer. Three practical sales formats—package self-pickup, nearby delivery, and hybrid—are modeled using Stackelberg game frameworks that incorporate key factors such as inconvenience cost, logistics cost, processing fees, and emission-reduction coefficients. Results show that the manufacturer’s emission-reduction decisions and both parties’ pricing strategies are highly sensitive to cost conditions and consumer preferences. Specifically, higher inconvenience and abatement costs consistently reduce profitability and emission efforts; the hybrid model exhibits threshold-dependent advantages over single-mode strategies in terms of carbon efficiency and economic returns; and consumer green preference and distance sensitivity jointly shape optimal channel configurations. Robustness analysis confirms the model’s stability under varying parameter conditions. These insights provide theoretical and practical guidance for firms seeking to develop adaptive, low-carbon fulfillment strategies that align with sustainability goals and market demands. Full article