Search Results (578)

To mitigate the mismatch between fluctuating renewable generation and load demand in highway service area multi-microgrid systems, this paper develops a day-ahead capacity optimization model based on the coordinated operation of fixed and mobile energy storage. A ring-structured multi-microgrid architecture is established, incorporating a “one-to-many” interaction mode of mobile storage stations. A coordinated control strategy is then proposed to enable flexible power dispatch and resource sharing among microgrids. The objective function minimizes both investment and operating costs of energy storage on a day-ahead timescale, and the model is solved using an optimization approach. Case study results demonstrate that introducing mobile energy storage significantly reduces the required capacity of local fixed storage, enhances energy interconnection among microgrids, and improves overall storage utilization and system economy. Full article

Rapid development and scale expansion of cities are the core characteristics of the urbanization process, which effectively promote the formation of agglomeration economies, infrastructure sharing, and social mobility improvement. However, it also brings various negative effects such as unequal public services, traffic congestion, and environmental pollution. The principle of isobenefit urbanism proposes that walking accessibility of various service facilities is an important indicator for measuring whether a city is livable, fair, and sustainable. This study specifically examines the impacts of environmental factors on the implementation of isobenefit urbanism in the central urban area of Beijing, a megacity. By obtaining open-source data and performing ArcGIS (10.8.1) analysis, using 183 blocks in Beijing’s core area, we normalized Strava pedestrian heat by road area and regressed it on 12 built environment indicators. The final model (R = 0.650, R² = 0.422, and adjusted R² = 0.381) identifies five significant predictors: block area (β = 0.215, p = 0.001) and average building height (β = 0.299, p = 0.012) are positively associated with walking heat, while building density (β = −0.235, p = 0.003), intersection density (β = −0.321, p < 0.001), and average distance to bus stop (β = −0.196, p = 0.003) are negatively associated. Land use mix and facility supply show positive but nonsignificant effects after controls. These estimates provide actionable levers for isobenefit urbanism in megacity cores. Full article

Across East Asia, rapid population aging and regional decline threaten the sustainability of rural and island communities. Yeonpyeong Island provides a critical context for examining how spatial infrastructure shapes older residents’ daily challenges. The aim of this study is to identify how older adults evaluate their housing and community environments and to determine whether these perceptions signal a transition toward more integrated and community-based care settings. Using a primary quantitative survey of 102 older residents, supplemented by contextual input from a local representative, the study analyzes how health decline, mobility constraints, and housing obsolescence interact with aspirations for service-integrated and socially connected living. Composite scores for perceived home modification needs remained consistently in the mid-to-upper range (approximately 3.5–4.0 on a 5-point scale). Acceptance of alternative, cohousing-type community housing also remained above the midpoint (approximately 3.5–4.1), reflecting an unusually high level of openness in a setting traditionally characterized by low receptivity to residential change and limited local housing alternatives. Safety risks, poor accessibility, and inadequate facilities function as push factors, while preferences for shared programs, proximity-based reassurance, and integrated hubs operate as pull factors, together signaling readiness for more supportive communal living. By integrating Push–Pull Theory with Environmental Press and Life-Space perspectives, the study contributes theoretically by extending these frameworks to the community scale and empirically by providing resident-level evidence from an under-researched island context. The findings highlight how older adults act as evaluators of their environments, articulating practical signals for spatial restructuring and integrated care planning. Full article

Recent policy reports and state briefs continue to highlight the trend of out-migration from California. This outflow has been pronounced over the last three years, revealing a substantial net loss (i.e., net migration) of approximately 740,000 residents. However, there has been comparatively less emphasis on new residents moving to California. Over the past decade, California has attracted substantial in-migration from both domestic and international sources with annual inflows often exceeding 300,000 individuals. As such, studying in-migration is noteworthy as it shapes economic, political, and social landscapes. In-migration can alter the demographic profiles of regions, thereby impacting community dynamics, cultural diversity, and the provision of social services. Using pooled data from the American Community Survey (ACS) from 2021 to 2023 and employing a two-way fixed effects regression framework, I study how temporal changes in racial and ethnic composition, age structure, educational attainment, and economic indicators influence in-migration rates per 1000 residents at the public use microdata level (PUMA). The analysis reveals that higher proportions of Asian and Hispanic populations, as well as an increased share of college-educated residents, are positively associated with in-migration. Notably, higher supplemental poverty rates are also associated with greater in-migration, a counterintuitive finding that may reflect mobility toward affordable housing markets. These findings emphasize the importance of recognizing demographic and intra-regional variability, which can aid policymakers and planners in assessing and delivering public services. Full article

Background: Maintaining physical activity (PA) is vital for older people, particularly those with frailty and mobility limitations. Wearable activity trackers and digital feedback tools show promise for encouraging PA, but their feasibility and acceptability in aged care remain underexplored. This study evaluated the feasibility and acceptability of using wearable and mobile devices for step tracking and examined the usability of three interfaces (Fitbit, mobile app, and website) for reviewing PA progress in aged care. Methods: This is a user experience and feasibility study that does not involve objective physical activity quantification or device performance analysis. It is a mixed-methods feasibility study conducted with 14 participants aged ≥65 years from residential and community aged care services in metropolitan and regional New South Wales, Australia. Participants used a Fitbit Inspire 3 linked to a study website and a mobile phone step-counting app to monitor their steps across the three interfaces for four weeks. Feasibility was evaluated through enrolment and retention, and acceptability through a facilitator-led survey. Quantitative items on usability, comfort, motivation and device preference were summarised descriptively; open-ended responses were analysed thematically to identify user experiences, benefits, and barriers. Results: Step tracking was feasible, with 82% enrolment and 93% retention. Participants preferred the Fitbit over the mobile phone or website due to its ease of use, visibility and more enjoyable experience. Step tracking increased awareness of PA and supported confidence to move more. Participants valued reminders, rewards and opportunities for social sharing. Reported barriers included illness, usability challenges and occasional technical issues. Conclusions: Wearable step trackers show promise for supporting PA among older people receiving aged care. Despite the small sample and short follow-up, strong acceptability signals suggest that simple digital tools could enhance the reach and sustainability of mobility-promoting interventions into aged care systems. Future studies should examine long-term adherence, usability across diverse mobility and cognitive needs, and conditions for successful scale-up. Full article

The rapid growth of dockless electric scooter (e-scooter) sharing services has transformed short-distance urban mobility, offering convenience and sustainability benefits while amplifying challenges related to demand imbalance, fleet rebalancing, and spatial inequity. Accurate spatiotemporal demand prediction is therefore essential for optimizing resource allocation and supporting data-driven policy interventions. This study proposes a hybrid deep learning framework that integrates a Graph Convolutional Network (GCN) with a Gated Recurrent Unit (GRU) and community detection to enhance short-term prediction of e-scooter pick-up and drop-off demands. The Louvain algorithm is employed to partition urban areas into mobility-based communities, enabling the model to capture functional connectivity rather than relying solely on geographic proximity. Using real-world e-scooter trip data from Calgary, Canada, the model’s performance is evaluated against established baselines, including a Masked Fully Convolutional Network (MFCN) and conventional GRU architectures. Results show that the proposed approach achieves up to 11.8% improvement in mean absolute error (MAE) compared with the MFCN baseline and more robust generalization across temporal horizons. The findings demonstrate that integrating community structures into graph-based learning effectively captures complex urban dynamics, providing practical insights for sustainable micromobility operation and service deployment. Full article

Accurate user positioning in 6G networks is essential for next-generation mobile services. However, classical approaches such as time-difference-of-arrival (TDoA) remain vulnerable to dense multipath and NLoS conditions commonly found in indoor and industrial environments. This paper proposes an AI-driven RF fingerprinting framework that leverages uplink channel state information (CSI) to achieve robust and privacy-preserving 2D localization. A lightweight convolutional neural network (CNN) extracts location-specific spectral–spatial fingerprints from CSI tensors, while a federated learning (FL) scheme enables distributed training across multiple gNBs without sharing raw channel data. The proposed integration of CSI tensor processing with FL and structured pruning is introduced as a novel solution for practical 6G edge positioning. To further reduce latency and communication costs, a structured pruning mechanism compresses the model by 40–60%, lowering the memory footprint with negligible accuracy loss. A performance evaluation in 3GPP-compliant indoor factory scenarios indicates a median positioning error below 1 m for over 90% of cases, significantly outperforming TDoA. Moreover, the compressed FL model reduces the FL communication load by ~38% and accelerates local training, establishing an efficient, secure, and deployment-ready positioning solution for 6G networks. Full article

Urban mobility in Lisbon faces persistent constraints driven not only by congestion, parking scarcity, and emissions but also by deeper structural issues such as fragmented governance and limited cross-peripheral public transport connectivity. These shortcomings hinder integrated mobility planning and motivate the exploration of Shared Autonomous Vehicles (SAVs) as a complementary urban transport solution. Existing SAV frameworks rarely integrate governance coordination, data interoperability, and contextual adaptation for medium-sized European cities. This study addresses this gap by designing and validating a reference model for the deployment of SAVs in Lisbon using a design–science approach combining a literature review, enterprise architecture modelling, and stakeholder validation. The proposed model contributes the following: (i) a governance coordination framework for multi-actor urban mobility ecosystems; (ii) an integrated digital and application architecture supporting multimodal services and user trust mechanisms; and (iii) a technology layer enabling V2X communication and interoperable mobility data flows. The model is demonstrated through Lisbon-specific scenarios aligned with local sustainable mobility strategies. Scenario interpretation is informed by literature-based performance benchmarks—including travel-time reductions of 13–42%, energy-use reductions of 12%, and GHG reductions of 5.6%—which are used as reference indicators rather than simulation outputs. The resulting framework bridges strategic policy and implementable system architecture, supporting the transition towards integrated, sustainable, and autonomous mobility in medium-sized European cities. Full article

As a financial center of Asia, Hong Kong has been the leading edge of fintech innovation, with the a leading ranking of the Global Innovation Index, which only ranked the fifth among all the payment methods in 2023 whereas mainland China achieved 90% acceptance in 2018. Since Hong Kong is part of China and shares similar origins and cultures, we found the need to study consumer behaviors in both of the two regions. We use comparison study methodology to find out the reasons of the difference in the usage. This research aims to investigate the factors influencing the acceptance of mobile payment services in Hong Kong and its difference in mainland China. In this research, we use the Partial Least Square Structural Equation Modeling methodology which discovers several significant factors influencing the actual use of mobile payment systems in Hong Kong and mainland China and tries to explain this. The findings will contribute to a better understanding of user behaviors and preferences, assisting stakeholders to address the challenges, develop effective strategies to increase the acceptance and use of mobile payment services, and promote payment convenience in Hong Kong. Full article

Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Florence and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still-limited number of studies on free-floating and electric-scooter-sharing systems, the objective of this work is to quantify the performance of electric bikes and e-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Trips were reconstructed starting from GPS data of origin and destination of the trip with a shortest path criteria that considers the availability of bike lanes. Results show that e-bikes are from 22 to 26% faster on average with respect to muscular bikes, extending trip range in Bologna but not in Florence. Electric modes attract more users than traditional bikes, e-bikes have from 40 to 128% higher daily turnover in Bologna and Florence and e-scooters from 33 to 62% higher in Florence with respect to traditional bikes. Overall, turnover is fairly low, with less than two trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode are investigated and compared. Finally, spatial analysis was conducted to observe demand asymmetries in the two case studies. The results aim to support planners and operators in designing and managing more efficient and user-oriented services. Full article

This paper investigates the spatiotemporal patterns and accessibility implications of shared e-scooter use in Tel Aviv, drawing on a complete year (2024) of trip-level data from all licensed providers. Shared micromobility services are often promoted as tools for reducing car dependency and improving urban accessibility, yet their actual usage patterns and equity outcomes remain underexplored, especially outside North America and Western Europe. This study aims to address this gap by integrating over 9 million reconstructed scooter trips with public transport accessibility data, local weather records, and institutional calendar effects. Multivariate regression was applied to quantify temporal and environmental determinants of demand, seasonal-trend decomposition to reveal cyclical usage patterns, and spatial analysis to assess whether scooters extend or reinforce existing mobility hierarchies. Findings indicate that scooter use in Tel Aviv is highly structured, peaking during afternoon hours, dropping during holidays and rain, and reflecting the weekly rhythms of the workweek in Tel Aviv. However, spatial patterns show a strong concentration of usage within already well-connected central areas, with limited activity in low-accessibility zones. These results suggest that shared e-scooters are not currently fulfilling their potential as first- or last-mile connectors; instead, they primarily serve as short-range, intra-core alternatives to walking. Full article

Delays caused by motor vehicle traffic, accidents, and environmental pollution present considerable challenges to sustainable urban mobility. To address these issues, transportation system users are encouraged to adopt active transportation methods, micromobility options, and public transit. Electric scooters have become a notably popular micromobility choice, especially following the emergence of vehicle-sharing companies in 2018, a trend that gained further momentum during the COVID-19 pandemic. This study explored the demographic characteristics, attitudes, and behaviors of e-scooter users in Istanbul through an online survey conducted from 1 September 2023 to 1 May 2024. A total of 462 e-scooter users participated, providing valuable insights into their preferred modes of transportation across 24 different scenarios specifically designed for this research. The responses were analyzed using various machine learning techniques, including Artificial Neural Networks, Decision Trees, Random Forest, and Gradient Boosting methods. Among the models developed, the Decision Tree model exhibited the highest overall performance, demonstrating strong accuracy and predictive capabilities across all classifications. Notably, all models significantly surpassed the accuracy of discrete choice models reported in existing literature, underscoring the effectiveness of machine learning approaches in modeling transportation mode choices. The models created in this study can serve various purposes for researchers, central and local authorities, as well as e-scooter service providers, supporting their strategic and operational decision-making processes. Future research could explore different machine learning methodologies to create a model that more accurately reflects individual preferences across diverse urban environments. These models can assist in developing sustainable mobility policies and reducing the environmental footprint of urban transportation systems. Full article

Free-floating shared e-scooters (FFSE) have been promoted as a sustainable urban mobility solution, yet their true energy and environmental impact remain contested. This study conducts an attributional life cycle assessment (aLCA) across 32 cities in Europe and North America to evaluate the fossil energy consumption and greenhouse gas (GHG) emissions of FFSE systems. By integrating real-world operational data—including vehicle lifespan, daily turnover rates, and city-specific modal substitution patterns—we quantify the direct and net environmental impacts under varying rebalancing and charging scenarios. Results indicate that FFSE systems do not inherently provide net energy and environmental benefits. Instead, achieving net reductions in greenhouse gas emissions and fossil energy consumption depends on systems operating beyond specific thresholds of service life and total travel distance. These thresholds vary dramatically across cities, influenced by modal substitution patterns and local operational efficiency (i.e., rebalancing intensity, daily turnover rates, and trip distance). Cities with high car displacement and efficient operations achieve net GHG and energy savings at lower life cycle mileages, whereas systems that replace walking or public transit often have negative impacts. Additionally, the distribution of energy and environmental impacts across the life cycle shifts fundamentally with vehicle longevity. When the travel distance exceeds 4000–5000 km, it transitions from being manufacturing-dominated to operation-dominated, with rebalancing and electricity use becoming the primary contributors. The research provides evidence-based guidance for policymakers and operators seeking to maximize the contribution of shared micromobility systems to energy conservation and emission reduction. Full article

This study investigates the role of modern small-scale cities in addressing public health challenges through the lens of spatial justice, using the city of Tartu, Estonia, as a case study. Tartu has been recognized for its progressive public health initiatives, including the Tartu Health Care College, Mental Health Centre, Smoke-Free Tartu campaign, Health Trail network, Healthy School Program, and an expanding smart bike-sharing system. By employing Geographic Information Systems (GIS), we map and analyze the spatial distribution and accessibility of health-promoting infrastructure, such as healthcare facilities, green and blue spaces, health trails, and mobility services, across the urban landscape. A population-weighted accessibility assessment indicates that, although Tartu’s central districts (e.g., Kesklinn (HRI: 0.972)) are well-served, peripheral and densely populated districts such as Annelinn (HRI: 0.351) and Ropka (HRI: 0.377) exhibit notable deficits in health-related infrastructure. However, access to green infrastructure and mobility services is more evenly distributed citywide, reflecting a relatively equitable provision of non-clinical health assets. These findings highlight both the strengths and spatial gaps in Tartu’s health-oriented urban design, emphasizing the need for targeted investment in underserved areas. The study contributes to emerging studies on health-justice planning in small-scale urban contexts and demonstrates how spatial analytics can be guided to advance distributional justice in the provision of public health infrastructure. Ultimately, this research indicates the essential role of spatial analysis in guiding inclusive and data-informed health planning in urban environments. Full article

Stroke is the second-largest cause of death and disability worldwide, and many patients require intensive care for airway compromise, hemodynamic instability, cerebral edema, or systemic complications. This review summarizes key aspects of ICU management in both acute ischemic stroke (AIS) and hemorrhagic stroke (HS). Priorities are airway protection, oxygenation, individualized blood pressure targets, and strict control of temperature and glucose. Neurological monitoring and prompt management of intracranial pressure (ICP), together with timely surgical interventions (hemicraniectomy or hematoma evacuation), are central to acute care. Seizures are treated promptly, while routine prophylaxis is not recommended. Prevention of aspiration pneumonia, venous thromboembolism, infections, and other intensive care unit (ICU) complications is essential, along with early nutrition, mobilization, and rehabilitation. Prognosis and decisions about intensity of care require shared discussions with families and involvement of palliative services, when appropriate. Many practices remain based on observational data or extrapolation from other populations, underlining the need for stroke-specific clinical trials. Outcomes are consistently better when patients are managed in specialized stroke or neurocritical care units with a multidisciplinary treatment approach Full article