Search Results (7,908)

With the rapid growth of e-commerce, intra-city express delivery has expanded rapidly, leading to various social issues, such as traffic congestion and air pollution. To address these problems, we focus on designing a multimodal intra-city express system in which parcels are collected from clients via local tours operated by a fleet of identical trucks, temporarily stored in satellite hubs, and then sent to the center hub via underground railway for further sorting and distribution. The problem involves capacitated hub location, client-to-hub allocation, and vehicle routing. Several practical constraints are considered in the routing aspect, including vehicle capacity, time windows, and maximum path length. With these practical considerations, we first formulate a star hub location-routing problem with time windows (SHLRPTW). Second, we use a branch-and-price-and-Benders-cut (BPBC) algorithm to solve it, which combines the Benders decomposition framework and branch-and-price-and-cut (BPC) framework. The BPBC algorithm is tailored, and several acceleration techniques are applied. Third, numerical experiments show that the proposed BPBC algorithm solves more instances and achieves smaller optimality gaps (0.75%) than CPLEX (19.55%) and the pure BPC algorithm (0.83%). The computational times are also critically reduced, with average speed-ups of 74.01 and 5.97, respectively. Furthermore, sensitivity analysis indicates that the BPBC algorithm performs much better than the BPC algorithm when the unit backbone transportation cost is high. Finally, case studies show the usefulness of the proposed model and algorithm. Full article

Supercritical CO₂ pipeline transportation is a critical component of the carbon capture, utilization and storage (CCUS) industry chain, where long distance operation introduces inherent risks of accidental leakage. During the leakage process of supercritical CO₂ pipelines, throttling pressure reduction and the Joule–Thomson effect generate distinct negative pressure wave characteristics. The magnitude of the leakage directly impacts localization effectiveness, particularly under small leakage conditions where negative pressure wave signals are less pronounced, so the leakage is difficult to effectively detect. To solve this problem, the mutual correlation function model for pipeline leakage was developed by using the mutual correlation analysis method, and it was verified by the dense-phase CO₂ leakage data from Trondheim University of Technology. Based on the TGNET software, the actual pipeline model of the Yanchang oilfield is established, and the captured leakage signal is imported into MATLAB for differential pressure conversion, using the verified cross-correlation function model of the differential pressure signal to calculate the time difference between the arrival of the negative pressure wave at the two ends of the pipeline. Finally, the actual leakage location was determined. The simulation results indicate that the leakage detection method based on mutual correlation analysis of negative pressure wave signals exhibits varying localization performance under different leakage rates. By enhancing negative pressure wave characteristics and utilizing mutual correlation analysis, this method effectively addresses the challenges of indistinct negative pressure wave features and difficult localization during small leakage conditions. When leakage exceeds 5%, the relative error is controlled within ±5.40%, meeting the preliminary localization requirements for rapid identification and regional determination in engineering applications. Through the application of actual engineering cases, it is shown that this method has high accuracy in pipeline leakage detection. These findings provide theoretical and methodological support for supercritical CO₂ pipeline leakage detection in the CCUS projects currently under construction. Full article

Market and government are the main bodies in solving the problem of income inequality, especially as both undergo electronic informatization. This study explores the effect of e-commerce and e-government on regional income inequality, along with its impact mechanisms and spatial characteristics. The results show a significant “sword effect” impact: e-commerce exacerbates income inequality, while e-government suppresses it. This conclusion remains valid after endogeneity and robustness tests. Mechanistically, e-commerce widens the gap by promoting industrial agglomeration and worsening resource misallocation, while e-government narrows it by enhancing fiscal transparency and alleviating resource misallocation. Spatially, all three variables exhibit spatial correlation and β-convergence; e-commerce and income inequality show α-divergence, while e-government shows α-convergence. E-commerce presents a negative spatial spillover of “aggravating local inequality but suppressing adjacent regional inequality,” while e-government’s inhibitory effect is limited to local cities. Their impacts show significant heterogeneity across regional gradients and geographical locations, providing a basis for differentiated policy implications. Full article

The combined effects of urbanization and climate warming subject cold coastal cities to summer heatwaves and winter extreme cold, yet most studies emphasize built-environment modifications for summer overheating and lack evaluation methods and planning-oriented strategies to balance seasonal trade-offs. Using Dalian as a case study, we develop a seasonal net-benefit model that quantitatively characterizes and reconciles seasonally differentiated built-environment effects on land surface temperature (LST) and interprets urban heterogeneity within the Local Climate Zone (LCZ) framework. Summer LST is mainly governed by static factors such as greenspace configuration and topography, whereas winter LST is more sensitive to development intensity and locational factors, including building density and the Normalized Difference Built-up Index (NDBI). Coastal areas and mountainous green corridors are net-benefit zones performing well in both seasons, while dense industrial and compact low-rise areas account for ~80% of pronounced net-penalty zones. Compact mid- and high-rise neighborhoods show more favorable structural climatic conditions but with substantial retrofit potential (Retrofit Seasonal Net-Benefit Index (R-SNBI) markedly lower than Structural Seasonal Net-Benefit Index (S-SNBI) by ~3). Large low-rise problems mainly stem from an unfavorable structure rather than insufficient greenness, whereas industrial land has greater improvement potential via blue–green spaces. The framework supports refined climate adaptation, sustainability-oriented planning, and identifying urban renewal priority areas in cold-climate cities. Full article

The problem of urban surface runoff (USR) treatment is associated with the presence of high concentrations of specific pollutants. One of these pollutants is petroleum product (PP), whose concentration depends on the season and the location of the formation of snow masses, meltwater, and rainwater. For USR treatment, it is possible to use very environmentally friendly and inexpensive technologies. The article discusses natural sorbents based on wood materials, which effectively remove dissolved petroleum products from water. Pine sawdust and shredded branches of maple, birch, and poplar are used as raw materials, which are waste products from the city’s woodworking enterprise and utilities. These materials were pre-microwave (MW) treated to improve their sorption properties. As a result of the experiment, it turned out that modified pine sawdust and crushed maple pinwheels proved to be the most effective sorbents. The maximum sorption capacity values were 0.689 mg/g and 0.952 mg/g for pine and maple sorbents, respectively. This article proposes schemes for filtering devices that can be used in practice in an urban environment. Full article

Background: This study evaluates an additive manufacturing (AM) network designed to balance economic performance, lead time, and environmental impact within the healthcare logistics and supply chain. Methods: An integrated framework is proposed that identifies optimal AM facility locations using spatial K-means clustering and optimizes delivery routes through a multi-objective vehicle routing problem with time windows (MOVRPTW). This framework was applied to a case study in Phra Nakhon Si Ayutthaya, Thailand, utilizing hospital geocoordinates, demand profiles, and CO₂ emission factors to evaluate centralized versus decentralized network configurations. Results: Findings demonstrate that hub structures derived from K-means clustering achieve the highest economic efficiency, reducing the AM part cost per unit to 698.51 Baht. In contrast, a fully centralized network resulted in a significantly higher unit cost of 4759.79 Baht, while clustering based on hospital types yielded a unit cost of 959.34 Baht. Quantitative results indicate that the multi-objective approach provides a superior trade-off, achieving lead time requirements while maintaining operational costs and emissions. Conclusions: The results indicate that the proposed framework, particularly through spatial clustering, offers a practical decision-support tool for designing AM networks that achieve a balance between operational efficiency and sustainability objectives in healthcare logistics. Full article

Professionals working in biological conservation seek to understand, manage, and restore populations of native organisms using many techniques. A common approach for this discipline is using long-term data collections to inform decision making. However, several quantitative issues complicate statistical analysis of monitoring datasets and can reduce the utility of results for conservation decision making. Integrating results from multiple analyses applied to the same dataset (i.e., approaching the same biological problem using different techniques) is one way to address concerns related to field data that violate statistical assumptions. This process allows data analysts, researchers, and managers to assemble insights based on the weight of evidence. Here we tested whether three different statistical techniques [(1) multiple logistic regression on original data, (2) multiple logistic regression on standardized data (i.e., mean of 0 and standard deviation of 1), and (3) random forest analysis] identified a similar hierarchy for selecting natural and anthropogenic habitat regressors. Our examination of how environmental variables affected Plains Minnow (Hybognathus placitus), a state-threatened fish, is relevant to other taxa and locations. We gained useful information from redundancies (i.e., agreements across analyses). New directions also emerged by addressing ambiguities (i.e., disagreements among results across analyses). When multiple analyses were integrated into one ecological story, a clearer interpretation emerged. Viewing different statistical tests as facilitators that provide mutual advantages can advance the understanding and application of statistical analyses applied to non-experimental field datasets. Full article

This paper presents an optimization-based method to support distribution system operators (DSOs) in planning large-scale photovoltaic (PV) integration at the medium-voltage (MV) level. The PV hosting capacity (PV-HC) problem is formulated as a mixed-integer quadratically constrained program (MIQCP) without linearizing approximations to determine PV sizes and locations while enforcing operating limits and planning constraints, including candidate PV locations, per-unit PV capacity limits, active power exchange with the upstream grid, and PV power factor. Our method defines two HC solution classes: (i) sparse solutions, which allocate the PV capacity to a limited subset of candidate nodes, and (ii) non-sparse solutions, which are derived from locational hosting capacity (LHC) computations at all candidate nodes, and are then aggregated into conservative zonal HC values. The approach is implemented in a Hosting Capacity–Distribution Planning Tool (HC-DPT) composed of a Python–AMPL optimization environment and a Python–OpenDSS probabilistic evaluation environment. The worst-case operating conditions are obtained from probabilistic models of demand and solar irradiance, and Monte Carlo simulations quantify the performance under uncertainty over a representative daily window. To support integrated assessment, the index $G_{\exp }$ is introduced to jointly evaluate exported energy and changes in local distribution losses, enabling a system-level interpretation beyond loss variations alone. A strategy was also proposed to derive worst-case scenarios from zonal HC solutions to bound performance metrics across multiple PV integration schemes. Results from a real MV case study show that PV location policies, export constraints, and zonal HC definitions drive differences in losses, exported energy, and solution quality while maintaining computation times compatible with DSO planning workflows. Full article

Background: With the growing emphasis on full-process disease management, efficient post-discharge care has become increasingly critical. Although prior studies have examined follow-up services, resource allocation, and facility location in primary healthcare, model-based optimization of collaborative frameworks between comprehensive hospitals and primary care systems remains limited. Methods: We study a cooperative community hospital selection problem involving contractual cooperation, patient engagement, and follow-up resource allocation. A multi-objective mixed-integer programming model is developed to maximize patient accessibility and minimize total hospital costs, and an NSGA-II-based heuristic is proposed for solution generation. A real-world case study using data from a comprehensive hospital in Chengdu, China, is conducted. Results: The proposed approach produces a Pareto set that quantifies the accessibility–cost trade-off and reveals a knee region with diminishing returns: moderate expansion of cooperating providers substantially improves accessibility, whereas further expansion yields limited additional gains while increasing hospital cost. Sensitivity analyses indicate that cost-related parameters and follow-up frequencies are key drivers of the trade-off. Conclusions: The proposed optimization framework serves as an implementable decision aid for designing hospital–primary care collaboration for post-discharge follow-up: it supports partner selection and capacity planning and indicates levers to improve performance. Full article

Urban air pollution is a major public health concern, especially in densely populated cities. This problem also includes food safety issues in outdoor retail environments, where fresh products may be exposed to airborne pollutants. This study examines the presence of polycyclic aromatic hydrocarbons (PAHs) on fruits sold at indoor and outdoor locations across Budapest and several Hungarian cities. Results showed higher PAH concentrations on fruit sold outdoors, with benzo[a]pyrene (BAP) exceeding 2 µg/kg in 62% of outdoor samples and in 22% of indoor ones. Washing with water reduced contamination by 40–50% on average, with some samples showing over 65% reduction for BAP. Differences across fruit types were limited overall, though statistically significant for BAP in certain cases, highlighting compound-specific variability. Correlation analysis revealed weak but interpretable associations between PAH levels and ambient air quality indicators, with a moderate correlation for fine particulate matter ≤ 2.5 µm (PM_2.5) (r = 0.4355) and a weaker one for the calculated Air Quality Index (AQI) (r = 0.2148). These findings suggest that while urban microenvironments influence contamination, the general air quality indices may not predict surface PAH burden reliably. The study highlights the role of public wells in enabling citizen-level mitigation through rinsing and calls for integrated urban health strategies considering food exposure alongside infrastructural access. Full article

Domain incremental learning for semantic segmentation has gained lots of attention due to its importance for many fields including urban planning and autonomous driving. The catastrophic forgetting problem caused by domain shift has been alleviated by structure expansion of the model or data rehearsal. However, these methods ignore similar contextual knowledge between the new and the old data domain and assume that new knowledge and old knowledge are completely mutually exclusive, which cause the model to be trained in a suboptimal direction. Motivated by the prompt learning, we proposed a new domain incremental learning framework named RS-VDP. The key innovation of RS-VDP is to utilize a visual domain prompt to change the optimization direction from input data space and feature space. First, we designed a domain prompt based on a dynamic location module, which applied a visual domain prompt according to a local entropy map to update the distribution of the input images. Second, in order to filter the feature vectors with high confidence, a representation feature alignment based on an entropy map module is proposed. This module ensures the accuracy and stability of the feature vectors involved in the regularization loss, alleviating the problem of semantic drift. Finally, we introduced a new evaluation metric to measure the overall performance of the incremental learning models, solving the problem that the traditional evaluation metric is affected by the single-task accuracy. Comprehensive experiments demonstrated the effectiveness of the proposed method by significantly reducing the degree of catastrophic forgetting. Full article

More than 50 years ago John D. Calhoun conducted a series of experiments devoted to studying the social behavior of mice. The longest of them lasted more than four years and led to the creation of the concept of the so-called “behavioral sink”. The population of mice, which had all possible resources at their disposal but were located in a limited room, disintegrated after an initial phase of strong development. In our paper we are going to reproduce the effects of this experiment. The crucial problem in every simulation approach is to determine the set of the most important parameters which influence the global as well as local effects of the simulated process. In the studied case we have the problem that a lot of important information is missing. The author of the original work focused rather on social mechanisms, often omitting key numerical data related to the course of the experiment. In this paper we try to propose a certain set of parameters. By using them we can reproduce Calhoun’s results qualitatively and, with some deviation, quantitatively. Full article

Accurate reconstruction of train collision accidents is essential for understanding impact conditions, assessing crashworthiness, and supporting safety improvements. This study proposes a surrogate-based optimization framework for reconstructing structural damage in train collisions from post-accident observations. The pre-impact kinematic state, expressed by a six-dimensional vector of relative offsets, rotations, and impact velocity, is formulated as an inverse problem in which a Sum of Squared Relative Deviations (SSRD) between measured and simulated residual deformations serves as the objective function. A reduced two-vehicle finite element (FE) model is developed to capture the dominant impact dynamics, an Optimal Latin Hypercube Design is used to sample the parameter space, and a Kriging surrogate model is constructed to approximate the response. A simulated annealing algorithm is applied to search for the global minimum. The framework is demonstrated on a real high-speed rear-end collision of electric multiple units. The Kriging model achieves a coefficient of determination of about 0.85, and the optimized kinematic state yields FE-predicted residual deformations that agree with field measurements at key locations to within about 5%. The results show that the method can efficiently reconstruct physically plausible collision scenarios and provide insight into parameter sensitivity and identifiability for railway safety analysis. Full article

Multimodal multiobjective optimization problems (MMOPs) are widely encountered in real-world applications. While numerous evolutionary algorithms have been developed to locate equivalent Pareto-optimal solutions, existing Multimodal Multiobjective Evolutionary Algorithms (MMOEAs) often struggle to handle large-scale decision variables and sparse Pareto sets due to the curse of dimensionality and unknown sparsity. To address these challenges, this paper proposes a novel approach named MASR-MMEA, which stands for Large-scale Sparse Multimodal Multiobjective Optimization via Multi-stage Search and Reinforcement Learning (RL)-assisted Environmental Selection. Specifically, to enhance search efficiency, a multi-stage framework is established incorporating three key innovations. First, a dual-strategy genetic operator based on improved hybrid encoding is designed, employing sparse-sensing dynamic redistribution for binary vectors and a sparse fuzzy decision framework for real vectors. Second, an affinity-based elite strategy utilizing Mahalanobis distance is introduced to pair real vectors with compatible binary vectors, increasing the probability of generating superior offspring. Finally, an adaptive sparse environmental selection strategy assisted by Multilayer Perceptron (MLP) reinforcement learning is developed. By utilizing the MLP-generated Guiding Vector (GDV) to direct the evolutionary search toward efficient regions and employing an iteration-based adaptive mechanism to regulate genetic operators, this strategy accelerates convergence. Furthermore, it dynamically quantifies population-level sparsity and adjusts selection pressure through a modified crowding distance mechanism to filter structural redundancy, thereby effectively balancing convergence and multimodal diversity. Comparative studies against six state-of-the-art methods demonstrate that MASR-MMEA significantly outperforms existing approaches in terms of both solution quality and convergence speed on large-scale sparse MMOPs. Full article

Monocular depth estimation is one of the key tasks in autonomous driving, which derives depth information of the scene from a single image. And it is a fundamental component for vehicle decision-making and perception. However, approaches currently face challenges such as visual artifacts, scale ambiguity and occlusion handling. These limitations lead to suboptimal performance in complex environments, reducing model efficiency and generalization and hindering their broader use in autonomous driving and other applications. To solve these challenges, this paper introduces a Neural Radiance Field (NeRF)-based monocular depth estimation method for autonomous driving. It introduces a Gaussian probability-based ray sampling strategy to effectively solve the problem of massive sampling points in large complex scenes and reduce computational costs. To improve generalization, a lightweight spherical network incorporating a fine-grained adaptive channel attention mechanism is designed to capture detailed pixel-level features. These features are subsequently mapped to 3D spatial sampling locations, resulting in diverse and expressive point representations for improving the generalizability of the NeRF model. Our approach exhibits remarkable performance on the KITTI benchmark, surpassing traditional methods in depth estimation tasks. This work contributes significant technical advancements for practical monocular depth estimation in autonomous driving applications. Full article