Search Results (906)

This study estimates the number of electric vehicles (EVs) and charging stations in Sakarya Province, Türkiye, for 2030 using advanced artificial intelligence time series methods and statistical approaches. The novelty of the work lies in the application of hyperparameter-optimized LSTM and GRU models alongside Multiple Linear Regression (MLR) to a regional dataset, enabling accurate, data-driven forecasting for regional EV planning. Performance was evaluated using multiple metrics, including R², MAE, MSE, DTW, RMSE, and MAPE, with the GRU model achieving the highest reliability and lowest errors (R² = 0.99, MAE = 0.3, MSE = 2.9, DTW = 123.2, RMSE = 3.1, MAPE = 2.8%) under optimized parameters. The predicted EV counts and charging station numbers from GRU informed a neighborhood-level allocation of charging stations using Google Maps API, considering local population ratios. These results demonstrate the practical applicability of deep learning for regional infrastructure planning and provide a replicable framework for similar studies in other provinces. Full article

While urban green spaces are integral to urban resilience, their long-term dynamics under recurrent flooding have received limited scholarly attention. This study investigates two decades of green space change across 367 counties in the southeastern United States, integrating FEMA disaster records with multi-period land cover data. Employing generalized additive and logistic regression models, the impacts of flood frequency, development intensity, and socioeconomic drivers were assessed. Flood frequency was identified as the primary determinant of urban green space loss. Each additional flood event corresponded to a 0.36% reduction in the five-year green space change rate (p < 0.01), while extreme flood frequency (≥ 10 events) was associated with an 18-fold increase in the odds of long-term degradation. Development intensity exhibited a significant non-linear effect, with loss rates culminating at moderate-to-high intensities. Furthermore, household income functioned as a significant moderator; in extremely flood-prone areas, higher income correlated with enhanced resilience (OR = 0.155, p < 0.05). These findings demonstrate that recurrent floods function as a cumulative pressure. This research highlights the necessity of equitable green infrastructure planning that integrates flood risk with the complex, moderating role of socioeconomic capacity. Full article

Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017–2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from −12.36 to 4.44 mm/year, with an average of −1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran’s I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (V_s³⁰), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo’s decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide. Full article

In recent decades, China has witnessed remarkable growth in housing construction, yet housing-related complaints have not declined significantly, highlighting the gap between housing quality and public expectations. Against this background, this study analyzes 32,277 national surveys to unpack residential satisfaction with green-livable communities in China. Entropy and standard-deviation weighting identified 16 priority indicators; artificial neural networks revealed weak direct influence of basic demographics on satisfaction, highlighting non-linear demand patterns. While 65–75% of respondents are satisfied with most attributes, significant city-level gaps persist—Beijing peaks near 90%, Chongqing falls below 50%. Dissatisfaction converges on three domains: infrastructure (parking, barrier-free access), building performance (leakage, noise, thermal defects) and smart systems (security, energy, health monitoring). Residents’ improvement priorities have shifted from basic shelter to health safety, smart technology, humanistic care and ecological amenities. A “basic-security + quality-upgrade” strategy is proposed: short-term repairs of common defects, medium-term smart-sustainable upgrades and long-term participatory governance. The findings not only enrich the theoretical framework of community satisfaction research but also provide practical guidance for enhancing community quality and meeting residents’ expectations in the context of China’s rapid urbanization and housing development. Full article

The automated inspection of civil infrastructure with Unmanned Aerial Vehicles (UAVs) is hampered by the challenge of accurately segmenting multi-damage in high-resolution imagery. While foundational models like the Segment Anything Model (SAM) offer data-efficient segmentation, their effectiveness is constrained by prompting strategies, especially for geometrically complex defects. This paper presents a comprehensive comparative analysis of deep learning strategies to identify an optimal deep learning pipeline for segmenting cracks, efflorescences, and exposed rebars. It systematically evaluates three distinct end-to-end segmentation frameworks: the native output of a YOLO11 model; the Segment Anything Model (SAM), prompted by bounding boxes; and SAM, guided by a point-prompting mechanism derived from the detector’s probability map. Based on these findings, a final, optimized hybrid pipeline is proposed: for linear cracks, the native segmentation output of the SAHI-trained YOLO model is used, while for efflorescence and exposed rebar, the model’s bounding boxes are used to prompt SAM for a refined segmentation. This class-specific strategy yielded a final mean Average Precision (mAP50) of 0.593, with class-specific Intersection over Union (IoU) scores of 0.495 (cracks), 0.331 (efflorescence), and 0.205 (exposed rebar). The results establish that the future of automated inspection lies in intelligent frameworks that leverage the respective strengths of specialized detectors and powerful foundation models in a context-aware manner. Full article

Accessible tourism is a critical aspect of sustainable development, yet many Southeast Asian destinations lack sufficient infrastructure and services for elderly and disabled travelers. This study develops a Mixed-Integer Linear Programming (MILP) framework to optimize travel itineraries, balancing cost, accessibility, and cultural–environmental priorities. A national accessibility database for Thailand was created, encompassing airports, hospitals, public transport nodes, cultural landmarks, and natural attractions. Compared to baseline conventional itineraries—defined as standard travel routes planned without specific accessibility considerations or optimization techniques—the MILP-optimized routes reduce average travel time by 15–20% and improve accessibility scores by 25%. Sensitivity analyses reveal trade-offs between economic efficiency, inclusivity, and infrastructure capacity, while a schematic accessibility network highlights structural fragmentation among airports, hospitals, and secondary attractions. Scenario analyses show that stricter accessibility thresholds improve inclusivity (index: 0.65 to 0.80) but restrict destination options, whereas high-demand scenarios increase costs and reduce inclusivity. A survey of 30 smart wheelchair users indicates high satisfaction with individualized programs and GPS connectivity. These findings underscore the need for investment in multimodal integration, accessibility upgrades, and a national database to enhance inclusive tourism planning. The framework is transferable to other ASEAN countries, contributing to SDG 3, 8, and 11. Overall, this study should be viewed as a prototype or exploratory contribution, with limitations in real-time applicability, generalizability, and implementation of environmental and ethical aspects. Full article

The widespread adoption of one-time use masks (OUM) has resulted in a substantial new stream of polymer waste, posing a formidable challenge to circular economy and waste management initiatives. Concurrently, the pavement industry continuously seeks innovative modifiers to enhance the durability and service life of asphalt binders. This study presents a novel approach to waste valorization by systematically investigating the potential of shredded OUM as a polymer modifier for asphalt. The research evaluates the impact of various OUM concentrations (up to 10% by weight) on the binder’s chemical, rheological, and performance characteristics. Fourier-transform infrared spectroscopy (FTIR) indicated that the modification is a physical blending process, with the OUM fibers forming a stable reinforcing network within the asphalt matrix, a finding supported by excellent high-temperature storage stability. Rheological assessments revealed a remarkable enhancement in high-temperature performance, with the Zero-Shear Viscosity (ZSV) increasing by nearly 700% (from approximately 450 Pa·s to about 3500 Pa·s) at 10% OUM content, signifying superior rutting resistance. Furthermore, fatigue life, evaluated via the Linear Amplitude Sweep (LAS) test, improved by up to 168% at a 2.5% strain level. However, these benefits were accompanied by a detrimental effect on low-temperature properties, where creep stiffness at −12 °C increased by over 50% and the m-value dropped below the critical 0.30 threshold, indicating a heightened risk of thermal cracking. The study concludes that OUM is a highly effective modifier for improving high-temperature and fatigue performance, with up to 10% content being viable. This research establishes a promising circular economy pathway, transforming a problematic waste stream into a valuable resource for constructing more resilient and sustainable pavement infrastructure. Full article

Background/Objectives: With digital transformation, patient portals and phone consultations are increasingly integrated into hybrid primary care workflows that combine in-person and remote services. This study aimed to assess the acceptability of these tools among patients and identify factors associated with acceptability. Methods: Between April and June 2025, a multicenter cross-sectional survey was conducted in four primary healthcare centers in Slovenia. The sample included 214 people who had used both patient portals and phone consultations within the previous 12 months. Data collected covered socio-demographic and clinical profile, digital communication skills, quality of life, and annual use of digital tools. Acceptability was assessed using the Theoretical Framework of Acceptability (TFA) tool. Univariate and multivariable linear regression analyses were performed to identify factors associated with acceptability. Results: Among the 214 participants (mean age 42.9 ± 14.1 years; 61.2% female), both patient portals and phone consultations were generally acceptable, with similar overall TFA scores (3.9/5). Patient portals were considered as significantly less time-consuming and better for communication, whereas phone consultations were preferred for accessibility and reliability. Multivariable analyses showed that higher digital communication skills and better quality of life predicted greater acceptability for both methods, whereas lower education level and more frequent use were associated with higher acceptability of phone consultations. Conclusions: Acceptability of patient portals and phone consultations varies by education, digital communication skills, and quality of life. This highlights the need for personalized hybrid care solutions. Healthcare providers should offer flexible digital options, invest in digital literacy programs, and develop interoperable eHealth infrastructure to enable safe and sustainable integration of advanced tools such as video consultations. Full article

In the context of the global growth of the collaborative economy, Airbnb has established itself as one of the most influential players in the transformation of the tourist accommodation market, especially in the reconfiguration of urban tourist accommodation. This article examines empirically and critically how this platform operates in Buenos Aires, the most visited city in Argentina and one of the main tourist hubs in South America. Based on a database of 17,249 active listings, the price formation of accommodations is analyzed using a comparative methodological approach between a general linear model (GLM) and a geographically weighted regression (GWR) model. While the GLM allows for capturing general patterns, the GWR reveals significant territorial differences, offering a detailed reading of the spatial behavior of prices in the city. The results show that variables such as the capacity of the accommodation, its type (full house), the host’s condition, the users’ ratings and the proximity to strategic points such as the subway or Plaza de Mayo have a significant influence on prices. In addition, it is shown that the influence of these variables varies by neighborhood, confirming that the pricing logic in Airbnb is deeply territorialized. This study not only provides novel empirical evidence for a Latin American city that has been little explored in the international literature, but also offers useful tools for hosts, urban planners and public decision makers. Its main contribution lies in showing that prices respond not only to accommodation attributes, but also to broader spatial inequalities, opening the debate on the effects of Airbnb on housing access and urban management in cities with strained real estate markets. By shedding light on these territorial asymmetries, the study offers valuable insights for public policy and urban governance and contributes directly to the achievement of Sustainable Cities and Communities (SDG 11), while also supporting Industry, Innovation and Infrastructure (SDG 9) and Reduced Inequalities (SDG 10), by providing practical knowledge that fosters more equitable and sustainable urban development. Full article

Small and medium-sized enterprises (SMEs) constitute the backbone of the EU economy, yet their uneven digital transformation raises challenges for competitiveness and territorial cohesion. This article examines the organizational and spatial aspects of SME digitalization across the European Union using Flash Eurobarometer 486 data and latent class analysis (LCA) combined with Bayesian multilevel multinomial regression. The results reveal four SME digitalization profiles—Digitally Conservative Backbone; Partially Digital and Upgrading; Digitally Advanced and Diversified; and Focused Digital Integrators—reflecting diverse adoption patterns of key technologies such as AI, big data and cloud computing. Digitalization is shaped by organizational factors (firm size, value chain integration, digital barriers) and territorial factors (urbanity, border proximity, national digital infrastructure as measured by the Digital Economy and Society Index, DESI). Contrary to linear modernization assumptions, digital adoption follows geographically embedded trajectories, with sectoral uptake occurring even in low-DESI or non-urban regions. These results challenge core–periphery models and highlight the significance of place-based innovation networks. The study contributes to modernization theory and regional innovation systems by showing that digital inequalities exist not only between countries but also within regions and among adoption profiles, emphasizing the need for nuanced, multi-level digital policy approaches across Europe. Full article

This study investigates how digital infrastructure contributes to smart city performance in emerging economic contexts and whether its impact is shaped by governance models. We estimate the effect of a Digital Technology Index on a composite Smart City Index, employing a generalized least squares (GLS) random-effects model to address heteroskedasticity and serial correlation. The analysis reveals a robust and statistically significant relationship: a one-standard-deviation increase in digital infrastructure corresponds to a 0.7-standard-deviation rise in smart city performance. The relationship is piecewise-linear, stagnating in the early stage before rising sharply after a threshold. To interpret these results, we draw on a qualitative case study of Wang Chan Valley (WCV), a science and innovation hub in Thailand’s Eastern Economic Corridor. WCV exemplifies how early-stage digital investment can amplify smart development outcomes and generate spillover effects across the broader urban region. The case reinforces the hypothesis that digital infrastructure embedded within participatory innovation ecosystems yields greater and more sustainable smart-city gains than technology investment alone. Taken together, the findings contribute to the understanding of how governance mediates the effectiveness of digital infrastructure in driving smart urban transformation within emerging economies. Full article

Urban green spaces (UGSs) are critical to ecological sustainability and human well-being, but equitable access remains a key challenge, particularly in high-density cities. While existing studies have predominantly focused on parks, the role of non-park green spaces (NPGSs) has received limited attention. This study examines the spatial equity of NPGSs—an overlooked but essential component of urban green infrastructure in Inner London—using a typological classification informed by previous research, along with multi-threshold accessibility assessment and spatial justice evaluation. We apply GIS-based buffer analysis, decomposed Gini coefficients, and Moran’s I clustering to quantify distributional disparities. The main findings are as follows: (1) five NPGS types are defined and mapped in Inner London: Natural and Protected, Community and Household, Purpose-Specific, Linear, and Underutilized; (2) significant accessibility inequities exist among NPGS types, with Community and Household demonstrating high equity (Gini coefficient < 0.25), while Underutilized exhibit severe deprivation (Gini coefficient > 0.74); (3) spatial clustering analysis reveals a core–periphery differentiation, characterized by persistent low–low clusters in central boroughs and emerging high–high hot spots in southeastern/northwestern boroughs. This study underscores the critical role of NPGS in complementing park-based greening strategies and provides a transferable framework to assess green equity, thereby contributing to the achievement of the United Nations Sustainable Development Goals (SDGs). Full article

The development of railway infrastructure is considered a key driver of vegetation cover transformation, particularly in ecologically sensitive regions. This study aims to quantify the spatio-temporal impact of the Presnogorkovskaya–Zhanaesil railway corridor in Northern Kazakhstan over the period 1985–2024. Using Landsat imagery and a gradient method of comparative analysis with a control area, an innovative coefficient B was developed to assess changes across various vegetation categories. Multiple linear regression was used to determine the influence of natural factors, including precipitation, temperature, and elevation. The results indicate that while some categories (e.g., dense vegetation or wet areas) show consistent degradation near the railway, the observed patterns are also modulated by environmental gradients. Compared to the control area, buffer zones along the railway exhibit an increased presence of degraded land types (≈309 km² vs. ≈72 km² in the control) and a reduction in productive vegetation cover (over 100 km² loss), especially in recent years. The study concludes that the proposed method allows for a differentiated understanding of anthropogenic and natural drivers of vegetation change, offering a replicable approach for assessing the impact of linear infrastructure in other geographical contexts. Full article

In this paper, the rail mean vertical alignment is estimated through double integration of wheel–rail contact forces measured using dynamometric wheelsets on a dedicated track recording vehicle (TRV). A simplified three degrees of freedom (DOF) linear model of half a train coach has been developed for this purpose. The model’s ability to simulate the average left and right longitudinal level has been tested using vertical contact force recordings from a constant speed track section, as measured by the TRV. The results are compared with available track geometry (TG) data, recorded by the optical system of the same vehicle, used for condition monitoring of the Italian railway infrastructure. Model parameters, such as masses, stiffness, and damping of the suspensive system have been optimized. An error analysis has been conducted on results. A good agreement is found between simulated and recorded vertical alignment at the D1 level, suggesting the feasibility of using contact forces measured with instrumented wheelsets for railway TG condition monitoring. This computationally efficient approach highlights the potential of strain gauges and instrumented wheelsets as alternative or complementary technologies to the widely adopted accelerometers, rate gyros, and optical devices for railway condition monitoring. Given its low computational cost, embedded and real-time TG estimation could be further investigated. Full article

Cloudbursts and other extreme rainfall events are becoming more frequent and intense, making precise forecasts and disaster preparedness more challenging. Despite advances in meteorological monitoring, current models often lack the precision needed for hyperlocal extreme rainfall forecasts. This study addresses the research gap in spatial configuration-aware modeling by proposing a novel framework that combines geometry-based weather station selection with advanced deep learning architectures. The primary goal is to utilize real-time data from well-placed Automatic Weather Stations to enhance the precision and reliability of extreme rainfall predictions. Twelve unique datasets were generated using four different geometric topologies—linear, triangular, quadrilateral, and circular—centered around the target station Chinchwad in Pune, India, a site that has recorded diverse rainfall intensities, including a cloudburst event. Using common performance criteria, six deep learning models were trained and assessed across these topologies. The proposed Bi-GRU model under linear topology achieved the highest predictive accuracy (R² = 0.9548, RMSE = 2.2120), outperforming other configurations. These findings underscore the significance of geometric topology in rainfall prediction and provide practical guidance for refining AWS network design in data-sparse regions. In contrast, the Transformer model showed poor generalization with high MAPE values. These results highlight the critical role of spatial station configuration and model architecture in improving prediction accuracy. The proposed framework enables real-time, location-specific early warning systems capable of issuing alerts 2 h before extreme rainfall events. Timely and reliable predictions support disaster risk reduction, infrastructure resilience, and community preparedness, which are essential for safeguarding lives and property in vulnerable regions. Full article