Search Results (4,175)

Rapid population aging in developing countries has intensified demand for accessible nursing home services, yet spatial disparities in service distribution remain insufficiently examined in secondary cities. This study investigates spatial distribution and multi-dimensional accessibility of nursing homes in Kunming, China, using comprehensive spatial analytical methods to inform sustainable urban development. We analyzed 205 nursing homes with 47,600 beds, evaluating spatial distribution patterns, economic accessibility, and spatial accessibility across different transportation modes. Our analysis reveals a pronounced monocentric pattern with nursing resources concentrated within central urban districts, creating a “primary core-multiple satellite” structure and spatial mismatch between service supply and older adult population needs. A distinct institutional dichotomy exists between publicly and privately operated facilities, establishing a dual-track system with different accessibility implications for social equity. Economic accessibility analysis demonstrates significant barriers in central urban and tourism-oriented districts dominated by higher-priced private facilities, where minimum prices frequently exceed average monthly pension. Spatial accessibility remains inadequate across all transportation modes, with only 24.3% of communities achieving normal or higher accessibility via private car, 21.5% via public bus, and merely 13.9% via walking. These limitations primarily stem from insufficient service capacity (34 beds per 1000 older adults) relative to demographic needs rather than transportation constraints. We recommend three sustainable interventions: implementing demand-based planning mechanisms, establishing progressive pricing policies, and developing older adult-friendly transportation networks. This framework supports sustainable urbanization by promoting spatial equity and efficient resource allocation, providing valuable insights for secondary cities pursuing sustainable development goals. Full article

Concrete bridges, as a vital component of modern transportation infrastructure, have their structural durability directly tied to safety and service life. In recent years, with the aging of bridge structures and increasingly complex environmental conditions, various durability-related deteriorations have become more prominent, significantly affecting structural performance and maintenance costs. This paper presents a systematic analysis of concrete carbonation as a key chemical process and its impact on durability-related pathologies. Particular attention is given to the formation mechanisms and influencing factors of critical deterioration modes such as cracking, reinforcement corrosion, and freeze–thaw damage. A multi-level prevention and mitigation strategy is proposed, encompassing optimized structural material design, strict construction quality control, and effective maintenance and repair techniques. The study concludes that the durability issues of concrete bridge structures exhibit a strong multi-factor coupling effect and proposes a core durability assurance framework. Finally, the paper briefly outlines emerging trends in intelligent monitoring and digital operation and maintenance, offering insights for future durability management of bridges. Full article

This research presents an overview of transportation mode choice, emphasizing key influencing factors and a range of methodological approaches from traditional Random Utility Theory (RUT) models to modern Machine Learning (ML) techniques. A comprehensive review covered 875 papers, which were screened for relevance. The search was conducted on ScienceDirect and Google Scholar between October and November 2024 using the keywords transport and choice model. Search results were reviewed until several consecutive entries no longer contained content relevant to the topic. After the screening and exclusion process, 106 papers remained for analysis. The review reveals that the Multinomial Logit (MNL) model remains the most widely used approach for modeling transportation mode choice, despite a growing interest in ML methods. Cars and buses dominate in passenger transport studies, while trucks, trains, and ships are most common in freight research. Data is typically collected through surveys (for passenger transport) and interviews (for freight), though some studies use secondary sources. Geographically, Asia and Europe are most represented, with regions like South America underrepresented. Travel time and cost are key variables, with increasing attention to the built environment in passenger studies and service reliability in freight studies. Overall, most studies aim to address real-world transport challenges. The review highlights the persistent gap between theoretical advancements and real-world applicability. To support this analysis, it examines the specific research objectives and findings of each study. Full article

Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, whereas V2N primarily focuses on infotainment and information exchange. Achieving reliable V2V transmission alongside high-rate V2N services in resource-constrained, dynamically changing traffic environments poses a significant challenge for resource allocation. To address this, we propose a novel reinforcement learning (RL) framework, termed Graph Attention Network (GAT)-Advantage Actor–Critic (GAT-A2C). In this framework, we construct a graph based on V2V links and their potential interference relationships. Each V2V link is represented as a node, and edges connect nodes that may interfere. The GAT captures key interference patterns among neighboring vehicles while accounting for real-time mobility and channel variations. The features generated by the GAT, combined with individual link characteristics, form the environment state, which is then processed by the RL agent to jointly optimize the resource blocks allocation and the transmission power for both V2V and V2N communications. Simulation results demonstrate that the proposed method substantially improves V2N rates and V2V communication success ratios under various vehicle densities. Furthermore, the approach exhibits strong scalability, making it a promising solution for future large-scale intelligent vehicular networks operating in dynamic traffic scenarios. Full article

Electric Level 4 connected automated vehicles (CAVs) are now allowed to demonstrate their automation capability in shared mobility robotaxi and microtransit services in geofenced areas in several cities around the world. Private and public sector stake-holders need predictions of their adoption without regulatory constraints for personal mobility and use in shared mobility services. In anticipation of the future presence of CAVs in transportation vehicle fleets, governments are planning necessary regulatory and infrastructure changes. Accompanying this need for forecasts is the acknowledgement that CAV adoption decisions must be made under uncertain states of technology and infrastructure readiness. This paper presents a Bayesian predictive modelling framework for electric Level 4 CAV adoption in the 2030–2035 application context. The inputs to the Bayesian model are obtained from effectiveness estimates of CAV applications that are processed with the Monte Carlo method to account for uncertainties in these estimates. Scenarios of CAV adoption in the 2030–2035 period are analyzed using the Bayesian model, including the quantification of the value of new information obtainable from demonstration studies intended to reduce uncertainties in technology and infrastructure readiness. The results show that in the 2030–2035 application context, the CAVs are likely to be adopted, provided that the trajectory of progress in technology and infrastructure readiness continues, and potential adopters are offered opportunities to learn about Level 4 CAV technological capabilities in a real life service environment. The threshold level of the probability of adoption enhances significantly with high-reliability demonstration results that can reduce uncertainties in adoption decisions. The findings of this research can be used by private and public sector interest groups. Full article

Robust, reliable, and secure communications are essential for efficient railway operation and keeping employees and passengers safe. The Future Railway Mobile Communication System (FRMCS) is a global standard aimed at providing innovative, essential, and high-performance communication applications in railway transport. In comparison with the legacy communication system (GSM-R), it provides high data rates, ultra-high reliability, and low latency. The FRMCS architecture will also benefit from cloud computing, following the principles of the cloud-native 5G core network design based on Network Function Virtualization (NFV). In this paper, an approach to the management of virtualized FRMCS applications is presented. First, the key management functionality related to the virtualized FRMCS application is identified based on an analysis of the different use cases. Next, this functionality is synthesized as RESTful services. The communication between application management and the services is designed as Application Programing Interfaces (APIs). The APIs are formally verified by modeling the management states of an FRMCS application instance from different points of view, and it is mathematically proved that the management state models are synchronized in time. The latency introduced by the designed APIs, as a key performance indicator, is evaluated through emulation. Full article

This study explores the potential for reducing CO₂ emissions in China’s express freight sector by promoting a modal shift from air and road transport to high-speed rail (HSR) through the implementation of a carbon tax policy. A bi-level optimization model is employed to analyze the decision-making processes of three key stakeholders: the government, HSR operators, and shippers. The government aims to maximize consumer surplus while reducing CO₂ emissions through a carbon tax policy; HSR operators seek to maximize transportation profit; and shippers select the most efficient transportation mode based on cost and service considerations. A solution algorithm combining particle swarm optimization, the CPLEX solver, and a custom convergence procedure is designed to solve the bi-level programming model and determine the optimal carbon tax rate. The findings from the Beijing–Shanghai corridor case study indicate that a well-designed carbon tax policy, when integrated with robust HSR services, can effectively encourage a modal shift towards HSR. The extent of emission reduction is influenced by both the capacity of HSR infrastructure and the stringency of the carbon tax policy. This research highlights the importance of addressing asymmetries in transportation mode preferences and market demands. The integration of carbon tax policies with HSR services not only mitigates emissions but also promotes greater symmetry and efficiency within the transportation network. Full article

Urban expressway interchanges, though primarily engineered for traffic efficiency, also serve as crucial ecological nodes within urban landscapes. This study evaluates the ecological functions of arborous vegetation across four typical interchange configurations—cloverleaf, single trumpet, double trumpet, and irregular—along the Nanjing Ring Expressway. Using the i-Tree Eco model, we quantified key ecosystem services, including carbon sequestration and storage, air pollutant removal, and stormwater mitigation. Field surveys documented 7985 trees from 45 species, with the 10 most abundant accounting for over two-thirds of total individuals. Results revealed that the trees sequester around 115 tons of carbon annually and store nearly 1850 tons in total, equivalent to an estimated economic benefit of ¥5.8 million. Trees also removed more than 1.5 tons of air pollutants and intercepted nearly 2400 cubic meters of stormwater each year. Species such as Sophora japonica, Phoebe zhennan, and Cinnamomum camphora emerged as key contributors to ecological performance. Among interchange types, double trumpet configurations yielded the highest overall service value, while single trumpet interchanges demonstrated superior efficiency per unit area. These findings highlight the underutilized ecological potential of transport-adjacent green spaces and underscore the importance of species selection and spatial design in maximizing multifunctional benefits. Full article

Mesoscale eddies have a significant influence on primary productivity and upper-ocean variability, particularly in stratified and monsoon-driven basins like the Bay of Bengal (BoB). This study analyzes mesoscale eddies in the BoB from January 2010 to March 2020 using post-processed and gridded daily sea surface height anomaly (SLA) data from the Copernicus Marine Environment Monitoring Service. We used a hybrid detection method combining the Okubo–Weiss parameter and SLA contour analysis to identify 1880 anticyclonic and 1972 cyclonic eddies. Cyclonic eddies were mainly found in the western BoB along the east Indian coast, while anticyclonic eddies were less frequent in this area. Analysis of eddy lifespans revealed that short-lived (1-week) eddies were nearly equally distributed between anticyclonic (48.81%) and cyclonic (51.19%) types. However, for longer-lived eddies, cyclonic eddies became more prevalent, comprising 83.33% of 30-week eddies. A notable, consistent eddy presence was observed east of Sri Lanka, influencing the East India Coastal Current. Most eddies (91%) propagated west/southwestward along the western slope of the Andaman Archipelago, likely influenced by ocean currents and coastal topography, with concentrations in the Andaman Sea and central BoB. These patterns suggest significant interactions between eddies, coastal upwelling zones, and boundary currents, impacting nutrient transport and marine ecosystem productivity. This study contributes valuable insights into the dynamics of ocean circulation and the impacts of eddies, which can inform fisheries management strategies, advance climate resilience measures, expand scientific knowledge, and guide policies related to conservation and sustainable resource utilization. Full article

In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. Full article

Public libraries serve as vital community hubs that foster engagement, empowerment, and education, particularly for vulnerable populations, including refugee children and families. This study examines how Oklahoma’s public libraries contribute to refugee resilience and identifies challenges they face in providing these essential services. Using a qualitative method approach, including 20 semi-structured interviews with library staff, questionnaire surveys, and observations conducted across three Oklahoma library systems (Metropolitan, Pioneer, and Tulsa City-County) the study explored programs, services, and strategies that support refugee adaptation and integration. Findings reveal that libraries excel in three key areas: cognitive services (language literacy, digital access, educational resources), socio-cultural services (community building, cultural exchange), and physiological services (safe spaces, welcoming environments). These services contribute to building human, social, and economic capital, with human capital consistently ranked as most crucial for refugee resilience. However, libraries face significant challenges, with language barriers, program gaps, and outreach limitations being the most prevalent obstacles. Additional barriers include facility constraints, transportation difficulties, resource limitations, and privacy concerns. The study proposes nine comprehensive guidelines for creating sustainable pathways to refugee resilience through enhanced library services, emphasizing proactive community engagement, staff training, multilingual resources, advocacy, strategic partnerships, tailored programming, transportation solutions, cultural competence, and welcoming environments. This study contributes to understanding how public libraries can function as inclusive institutions that support refugee children’s successful integration and development in their new communities. Full article

Urban sprawl and suburbanization are reshaping peri-urban areas, challenging urban planning and community well-being. Our study investigates questions regarding the perception of Cultural Ecosystem Services (CES) and development preferences (DP) related to Green Infrastructure (GI) in Vác, Hungary, including how CES and DP indicators related to GI vary spatially; how they align with municipal DI designations; how they relate to sociodemographic factors; and how they are applicable to urban planning practices. We used PPGIS and structured interviews with 375 residents to collect over 4900 spatial data points in order to analyze how perceived values, development preferences, officially designated GI elements and sociodemographic characteristics, relate to each other. The results show that CES are strongly associated with GI elements, especially along the riverfront and in downtown areas. However, development preferences, especially congestion and safety concerns, were more dispersed, often located in outer residential areas and along transportation routes. Statistical analyses showed significant differences across age, marital status, and co-residence with children, influencing both CES perception and development preferences. Our study highlights the gap between official GI designations and community-valued spaces, emphasizing the importance of participatory planning and the integration of sociodemographic dimensions into planning practices in rapidly transforming suburban environments. Full article

Universities play a critical role in shaping sustainable mobility strategies, especially in urban contexts where the institutional transport system can influence environmental and social outcomes. This study integrates Environmental and Social Life Cycle Assessment (E-LCA and S-LCA) to evaluate the current university transport system from internal combustion engines, diesel, and compressed natural gas (CNG), focusing on the operation and maintenance phases. Also, it compares seven scenarios, including electric, renewable sources, and biodiesel technologies. Environmental impacts were assessed using the ReCiPe 2016 midpoint method, which considers the following impact categories: Global Warming Potential (GWP); Ozone Formation, Human Health (OfHh); Ozone Formation, Terrestrial Ecosystem (OfTe); Terrestrial Acidification (TA); and Fine Particulate Matter Formation (FPmf). The sensitivity analysis explores scenarios to assess the effects of technological transitions and alternative energy sources on the environmental performance. Social impacts are assessed through a Social Performance Index (SPI) and Aggregated Social Performance Index (ASPI), which aggregates indicators such as safety, travel cost, punctuality, accessibility, and inclusive design. Accessibility emerged as the lowest indicator (ranging from 0.61 to 0.67), highlighting opportunities for improvement. Our findings support decision-making processes for integrating sustainable transport strategies into a University Mobility Plan, emphasizing the importance of combining technical performance with social inclusivity. Full article

This study conducts a detailed analysis of cybersecurity threats, including artificial intelligence (AI)-driven cyber-attacks targeting vehicle-to-vehicle (V2V) and electric vehicle (EV) communications within the rapidly evolving field of connected and autonomous vehicles (CAVs). As autonomous and electric vehicles become increasingly integrated into daily life, their susceptibility to cyber threats such as replay, jamming, spoofing, and denial-of-service (DoS) attacks necessitates the development of robust cybersecurity measures. Additionally, EV-specific threats, including battery management system (BMS) exploitation and compromised charging interfaces, introduce distinct vulnerabilities requiring specialized attention. This research proposes a comprehensive and integrated cybersecurity framework that rigorously examines current V2V, vehicle-to-everything (V2X), and EV-specific systems through systematic threat assessments, vulnerability analyses, and the deployment of advanced security controls. Unlike previous state-of-the-art approaches, which primarily focus on isolated threats or specific components such as V2V protocols, the proposed framework provides a holistic cybersecurity strategy addressing the entire communication stack, EV subsystems, and incorporates AI-driven threat detection mechanisms. This comprehensive and integrated approach addresses critical gaps found in the existing literature, making it significantly more adaptable and resilient against evolving cyber-attacks. Our framework aligns with industry standards and regulatory requirements, significantly enhancing the security, safety, and reliability of modern transportation systems. By incorporating specialized cryptographic techniques, secure protocols, and continuous monitoring mechanisms, the proposed approach ensures robust protection against sophisticated cyber threats, thereby safeguarding vehicle operations and user privacy. Full article

Background: Scientific literature confirms the benefits of mother’s own milk (MOM) for both term and preterm infants. The feeding of pathological newborns, in particular the very low birth weight infants (VLBWIs), is dependent on human milk. When MOM is not available, pasteurized donor human milk obtained from a recognized Human Milk Bank (HMB) is the best alternative. Research aims: This survey aims to evaluate the activity of human milk banks (HMBs) in Italy in the years 2023–2024. Methods: Following the previous three surveys performed in 2012, 2016, and 2022, a fourth survey related to 2023 and 2024 was planned in the year 2025. A questionnaire was sent to the 44 HMBs officially operating in Italy, with questions regarding their management and activity, in order to collect national-level data. Results: All 44 Italian HMBs (100%) responded to this survey. The collected data confirm the results of the previous surveys, confirming an optimal adherence to the Italian Ministerial Guidelines. Almost all the HMBs (96%) apply the principles of self-control and the HACCP system, while the home milk collection service still requires improvement. Only 68% of HMBs organize collection and transport of the donated milk from the donor’s home to the bank. In addition, this survey shows the spreading of computerization in the management of the activities of HMBs: 36.4% make use of specific software that could lead to a greater availability of donor human milk for the neonatal centers in the future. The number of donors and the amount of donated milk increased consistently compared to the previous years. Conclusions: In general, this survey shows an improvement in the results obtained in the three previous surveys, with a positive dissemination of the culture of human milk donation in Italy. The impressive response rate to the survey demonstrates the importance of a regular check-up of the activity of HMBs. Full article