Search Results (4,356)

Wide thermal cracks are a common form of pavement distress affecting primary state and county highways, urban residential streets, and parking lots across the Southwest climatic regions. These cracks are primarily caused by thermal fatigue, driven by diurnal temperature variations despite the lack of extremely cold events. This research aims to identify and analyze the local factors contributing to the initiation and propagation of thermal fatigue cracks. Field cores are collected from 12 sites exhibiting wide thermal cracks in the Phoenix metropolitan area in Arizona to evaluate their volumetric properties and the degree of binder aging. Advanced finite element (FE) models were developed to examine the influence of pavement structures and local climatic conditions on the development of tensile stresses due to thermal fatigue. The FE analysis indicated a high magnitude of thermal stresses due to cyclic temperature variations in Arizona compared to colder regions in the United States. Based on the forensic investigation and analysis performed, the initiation of wide cracks was shown to be primarily due to repeated localized damage from frequent thermal fatigue events on severely aged pavements. This damage is exacerbated by low air voids in mineral aggregate, an insufficient effective binder volume. and excessive binder aging, which compromise the structural integrity of the pavement. Full article

Background and Objectives: The COVID-19 pandemic had a significant impact on physical activity among various populations. Due to a lack of country-representative studies on the prevailing trends in leisure-time physical activity since the COVID-19 pandemic, the aim of this study was to assess the temporal, social, and demographic inequalities in the prevalence of engagement in exercise and sports among working-age citizens of Lithuania from 2021 to 2024. Materials and Methods: This study included four samples of working-age citizens (1600 per year, 6400 in total). Four surveys were conducted, and the distribution of respondents among the groups was compared. Results: In general, the prevalence of engagement in exercise and sports did not change over a four-year period (48.8%, p = 0.256). The prevalence of regular exercise and sports increased, while engagement in irregular exercise and sports decreased (p = 0.014). Binary logistic regression analysis showed that younger age, male sex, being single, having no children under 18 years of age, selecting foods for health strengthening, positive self-assessment of nutrition and health status, use of dietary supplements, attention to purchasing healthy products, and university education attainment were associated with engagement in exercise and sports (regular or irregular) (p < 0.05). Analysis focused specifically on regular exercise and sports revealed associations with a longer time since the onset of the COVID-19 pandemic, younger age, urban residence, selection of foods for health strengthening, positive assessment of nutrition and health status, and university education attainment (p < 0.05), while no significant associations were observed with sex, marital status, presence of children under 18 years of age, use of dietary supplements, or attention to purchasing healthy products (p > 0.05). Conclusions: The overall prevalence of physical activity engagement among working-aged Lithuanian citizens did not change from 2021 to 2024, engagement in regular and irregular exercise and sports has changed. Engagement in regular and irregular exercise and sports is associated with different social profiles. Full article

Terrestrial ecosystems in Hunan Province have undergone marked yet spatially heterogeneous vegetation changes under concurrent climate change and intensifying human activities. The aim of this study is to resolve how vegetation responses vary among land-use types by quantifying kernel Normalized Difference Vegetation Index (kNDVI) dynamics during 2000–2023 using precipitation, temperature, and solar radiation, coupled with trend analysis and a partial-derivative-based attribution. Mean kNDVI increased overall at 0.0016 yr⁻¹; vegetation improved over 76.30% of the area, whereas 5.72% of the area experienced degradation. Built-up land exhibited the largest degraded fraction (35.04%). Human activities and temperature emerged as the dominant drivers of kNDVI change, contributing 62.25% and 27.92%, respectively, while precipitation (3.08%) and solar radiation (6.77%) played comparatively minor roles. Spatially, human activities primarily controlled vegetation dynamics in plains and urban clusters (~78% of the area), whereas temperature constrained vegetation in high-elevation mountain ranges. Analysis along the human footprint (HFP) gradient reveals that driver composition remains steady in resilient ecosystems (farmland and forest), despite increasing anthropogenic pressure, whereas fragile ecosystems (grassland and bareland) exhibited pronounced volatility and heightened sensitivity to environmental constraints. These findings provide a quantitative basis for developing sustainable ecological security strategies, incorporating region-specific measures such as adaptive afforestation, sustainable agricultural management, and strict ecological protection, to enhance ecosystem resilience by prioritizing the climate resilience of mountain forests and the stability of fragile grassland systems. Full article

The dynamic development of electromobility and tightening emissions regulations are making electric light commercial vehicles an increasingly important element of modern urban transport. The purpose of this article is to analyze and compare selected models of electric light commercial vehicles available on the market in terms of four key operational parameters: range, charging time, payload, and energy consumption. These parameters directly impact the efficiency of vehicle operation in real-world conditions, especially in last-mile transport. The study employed a multi-criteria decision method (MCDM), which evaluated 10 alternatives and objectively assigned criterion weights using the CRITIC method, which takes into account data variability and correlations between criteria. The article presents the interdependencies between these factors, emphasizing the need to find a compromise between maximum range and usable payload, as well as the impact of charging time on vehicle operational availability. The analysis aims to identify design and technological solutions that contribute most to improving the efficiency of electric light commercial vehicles in urban and suburban applications. Full article

This study compares the streamflow simulation performance of a semi-distributed hydrological model, DWAT (Dynamic Water Resources Assessment Tool), and two conceptual models, PRMS and TANK, across three watersheds in the Republic of Korea representing mountainous (Okdong-gyo), mixed-use (Wonbu-gyo), and urbanized (Daegok-gyo) conditions. All models were calibrated and validated using identical hydroclimatic datasets and evaluation periods to ensure a fair comparison. Model performance was evaluated using nine statistical metrics (R², NSE, LogNSE, KGE, RMSE, MAE, RE, VE, and RSR), supplemented by low-flow analysis based on a Q90 threshold and non-parametric statistical tests. DWAT exhibited the most stable and highest overall performance across all watersheds, with particularly strong results in the urbanized Daegok-gyo basin (NSE = 0.85, R² = 0.88). The TANK model performed best in the mixed-use Wonbu-gyo basin (NSE = 0.82, R² = 0.83), whereas PRMS showed a systematic tendency to underestimate streamflow, especially under high-flow and low-flow conditions. Statistical comparisons using Friedman and post hoc Dunn tests confirmed that performance differences among models were statistically significant (p < 0.001). Overall, the results demonstrate that hydrological model performance strongly depends on watershed characteristics and provide a quantitative and statistically supported basis for selecting appropriate runoff simulation models according to basin type. Full article

Beamforming is highly significant for the physical layer of wireless communication systems, for multi-antenna systems such as multiple input multiple output (MIMO) and massive MIMO, since it improves spectral efficiency and reduces interference. Traditional linear beamforming methods such as zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming provide closed-form solutions. Yet, their performance drops when they face non-ideal conditions such as imperfect channel state information (CSI), dynamic propagation environment, or high-dimensional system configurations, primarily due to static assumptions and computational limitations. These limitations have led to the rise of deep learning-based beamforming, where data-driven models derive beamforming solutions directly from CSI. By leveraging the representational capabilities of cutting-edge deep learning architectures, along with the increasing availability of data and computational resources, deep learning presents an adaptive and potentially scalable alternative to traditional methodologies. In this work, we unify and systematically compare our two unsupervised learning architectures for uplink receive beamforming: a simple neural network beamforming (NNBF) model, composed of convolutional and fully connected layers, and a transformer-based NNBF model that integrates grouped convolutions for feature extraction and transformer blocks to capture long-range channel dependencies. They are evaluated in a common multi-user single input multiple output (MU-SIMO) system model to maximize sum-rate across single-antenna user equipments (UEs) under 3GPP-compliant channel models, namely TDL-A and UMa. Furthermore, we present a FLOPs-based asymptotic computational complexity analysis for the NNBF architectures alongside baseline methods, namely ZFBF and MMSE beamforming, explicitly characterizing inference-time scaling behavior. Experiments for the simple NNBF are performed under simplified assumptions such as stationary UEs and perfect CSI across varying antenna configurations in the TDL-A channel. On the other hand, transformer-based NNBF is evaluated in more realistic conditions, including urban macro environments with imperfect CSI, diverse UE mobilities, coding rates, and modulation schemes. Results show that the transformer-based NNBF achieves superior performance under realistic conditions at the cost of increased computational complexity, while the simple NNBF presents comparable or better performance than baseline methods with significantly lower complexity under simplified assumptions. Full article

Hyperspectral image (HSI) classification is pivotal for remote sensing applications, including environmental monitoring, precision agriculture, and urban land-use analysis. However, its accuracy is often limited by scarce labeled data, class imbalance, and domain discrepancies between standard RGB and HSI imagery. Although recent deep learning approaches, such as 3D convolutional neural networks (3D-CNNs), transformers, and generative adversarial networks (GANs), show promise, they struggle with spectral fidelity, computational efficiency, and cross-domain adaptation in label-scarce scenarios. To address these challenges, we propose the Transformer–Graph Convolutional Network–Diffusion with Hybrid Domain Adaptation (TGDHTL) framework. This framework integrates domain-adaptive alignment of RGB and HSI data, efficient synthetic data generation, and multi-scale spectral–spatial modeling. Specifically, a lightweight transformer, guided by Maximum Mean Discrepancy (MMD) loss, aligns feature distributions across domains. A class-conditional diffusion model generates high-quality samples for underrepresented classes in only 15 inference steps, reducing labeled data needs by approximately 25% and computational costs by up to 80% compared to traditional 1000-step diffusion models. Additionally, a Multi-Scale Stripe Attention (MSSA) mechanism, combined with a Graph Convolutional Network (GCN), enhances pixel-level spatial coherence. Evaluated on six benchmark datasets including HJ-1A and WHU-OHS, TGDHTL consistently achieves high overall accuracy (e.g., 97.89% on University of Pavia) with just 11.9 GFLOPs, surpassing state-of-the-art methods. This framework provides a scalable, data-efficient solution for HSI classification under domain shifts and resource constraints. Full article

Enhancing blue infrastructure is a critical pathway to strengthening urban water resilience and improving living environments. However, divergent perceptions and demands among multiple stakeholders may lead to misalignment between governance priorities and implementation pathways, thereby limiting governance effectiveness. Recognizing and addressing these differences has become essential for enhancing the performance of blue infrastructure governance and public satisfaction. Taking Shaanxi Province as a case study, this research systematically identifies core issues and disparities in public demands regarding water governance of blue infrastructure by analyzing governmental documents and public demands. The study aims to support a shift in governance strategy from a “provision-driven” to a “demand-driven” approach. A “topic identification–demand extraction–problem diagnosis” framework is adopted: first, the LDA model is used to analyze government platform texts and derive a macro-level thematic framework; subsequently, the BERTopic model is applied to mine public comments and identify micro-level demands; finally, the Jaccard similarity algorithm is employed to compare the two sets of topics, revealing the gap between policy provisions and public demands. The findings indicate the following: first, government agendas are highly concentrated on macro-level strategies (the topic “Integrated Water Ecosystem Management and Strategic Planning” accounts for 72.91% of weighting), whereas public appeals focus on specific, micro-level daily concerns such as infrastructure quality, drinking water safety, and drainage blockages; second, the Jaccard semantic correlation between the two is generally low (ranging from 6.05% to 14.62%), confirming a significant “topic-term overlap”; third, spatial analysis further reveals a geographical mismatch, particularly in core urban areas, which exhibit a “system-lag” type of misalignment characterized by high public demand but insufficient governmental attention. The research aims to clarify governance discrepancies, providing a basis for optimizing policy priorities and enabling targeted governance, while also offering insights for establishing a sustainable water resource management system. Full article

Developing vehicle classification remains a fundamental challenge for intelligent traffic management in the Indian urban environment, where traffic exhibits high heterogeneity, density and unpredictability. In the Indian subcontinent, vehicle movement is erratic, congestion is high, and vehicle types vary significantly. Conventional global benchmarks often fail to capture these complexities, highlighting the need for a region-specific dataset. To address this gap, the present study introduced the EAHVSD dataset, a novel real-world image collection comprising 10,864 vehicle images from four distinct classes, acquired from roadside surveillance cameras at multiple viewpoints and under varying conditions. This dataset is designed to support the development of an automatic traffic counter and classifier (ATCC) system. A comprehensive evaluation of eleven state-of-the-art deep learning models, namely VGG16, VGG19, MobileNetV2, Xception, AlexNet, ResNet50, ResNet152, DenseNet121, DenseNet201, InceptionV3, and NASNetMobile, was carried out. Among these, the highest accuracy result has been achieved by VGG-16, MobileNetV2, InceptionV3, DenseNet-121, and DenseNet-201. We developed a stacking-based meta-ensemble framework to leverage the complementary strengths of its components and overcome their individual limitations. In this approach, a meta-learner classifier integrates the predictions of the best-performing models, thereby improving robustness, scalability, and real-world adaptability. The proposed ensemble model achieved an overall classification accuracy of 96.04%, a Cohen’s Kappa of 0.93, and an AUC of 0.99, consistently outperforming the individual models and existing baselines. A comparative analysis with prior studies further validates the efficacy and reliability of the stacking-based meta-ensemble method. These findings position the proposed frameworks as a robust and scalable solution for efficient vehicle classification under practical surveillance constraints, with potential applications in intelligent transportation systems and traffic management. Full article

Arid and semi-arid regions of Mexico, such as the Bajío of Guanajuato, face a huge challenge in water resource management. The municipality of León, located in the State of Guanajuato, persistently lacks access to water resources despite having high coverage in urban areas by the León Water Utility System (SAPAL, the abbreviation in Spanish of “Sistema de Agua Potable y Alcantarillado de León”), particularly in peri-urban and rural areas. In this context, this study compares water distribution network expansion with rainwater harvesting (RWH) systems in four rural communities of León. A cost–benefit analysis (CBA) with a 20-year horizon and a 10% social discount rate (SDR) was applied. Results indicate that network expansion is financially unfeasible, whereas RWH emerges as a technically and economically viable alternative, providing household savings and strengthening community resilience. Full article

Road transport systems are central to sustainable mobility and the energy transition because they account for a large share of final energy use and remain heavily dependent on fossil fuels. With more than 90% of transport energy still supplied by petroleum-based fuels, improving energy efficiency and reducing emissions in road networks has become a strategic priority. This review compares Australia, Hong Kong, and the United Kingdom to examine how road-design standards and emerging digital technologies can improve energy performance across planning, design, operations, and maintenance. Using Australia’s Austroads Guide to Road Design, Hong Kong’s Transport Planning and Design Manual (TPDM), and the UK’s Design Manual for Roads and Bridges (DMRB) as core reference frameworks, we apply a rubric-based document analysis that codes provisions by mechanism type (direct, indirect, or emergent), life-cycle stage, and energy relevance. The findings show that energy-relevant outcomes are embedded through different pathways: TPDM most strongly supports urban operational efficiency via coordinated/adaptive signal control and public-transport prioritization; DMRB emphasizes strategic-network flow stability and whole-life carbon governance through managed motorway operations and life-cycle assessment requirements; and Austroads provides context-sensitive, performance-based guidance that supports smoother operations and active travel, with implementation varying by jurisdiction. Building on these results, the paper proposes an AI-enabled benchmarking overlay that links manual provisions to comparable energy and carbon indicators to support cross-jurisdictional learning, investment prioritization, and future manual revisions toward safer, more efficient, and low-carbon road transport systems. Full article

Decarbonizing industry, improving urban sustainability, and expanding clean energy use are key global priorities. This study presents a techno-economic analysis (TEA) and life-cycle assessment (LCA) of green hydrogen (GH₂) production via water electrolysis for low-carbon applications in the Central Asian region, with Uzbekistan considered as a representative case study. Solar PV and wind power are used as renewable electricity sources for a 44 MW electrolyzer. The assessment also incorporates recent advances in alkaline water electrolyzers (AWE) enhanced with metal–organic framework (MOF) materials, reflecting improvements in efficiency and hydrogen output. The LCA, performed using SimaPro, evaluates the global warming potential (GWP) across the full hydrogen production chain. Results show that the MOF-enhanced AWE system achieves a lower levelized cost of hydrogen (LCOH) at 5.18 $/kg H₂, compared with 5.90 $/kg H₂ for conventional AWE, with electricity procurement remaining the dominant cost driver. Environmentally, green hydrogen pathways reduce GWP by 80–83% relative to steam methane reforming (SMR), with AWE–MOF delivering the lowest footprint at 1.97 kg CO₂/kg H₂. In transport applications, fuel cell vehicles powered by hydrogen derived from AWE–MOF emit 89% less CO₂ per 100 km than diesel vehicles and 83% less than using SMR-based hydrogen, demonstrating the substantial climate benefits of advanced electrolysis. Overall, the findings confirm that MOF-integrated AWE offers a strong balance of economic viability and environmental performance. The study highlights green hydrogen’s strategic role in the Central Asian region, represented by Uzbekistan’s energy transition, and provides evidence-based insights for guiding low-carbon hydrogen deployment. Full article

This paper is dedicated to analysing sustainability and digitalisation in the transport systems of the European Union (EU) and Ukraine, with a particular focus on three representative subsectors: freight rail, urban public transport and last-mile postal logistics. It explores how technological innovation, operational efficiency and environmental responsibility interact within these sectors under distinct institutional and economic conditions: mature, market-based systems in the EU and resilience-driven systems in wartime Ukraine. This study applies a comparative, descriptive–analytical methodology using secondary data drawn from corporate sustainability reports, official statistics and sectoral databases for 2022. Quantitative KPls were complemented with a qualitative assessment of digitalisation maturity to ensure cross-country comparability. Through a comparative analysis of KPIs, such as freight volumes, emissions intensity, revenue efficiency and digital maturity, this study identifies structural and policy gaps that hinder progress toward sustainable mobility. This study develops a multi-dimensional framework combining operational, financial, environmental and digital indicators. In this paper, digital integration refers to the degree to which transport operators embed digital tools such as tracking, data management and automation into their core processes, while environmental efficiency denotes the ability to deliver transport services with minimal resource consumption and carbon emissions per operational unit. Institutional resilience is understood here as the capacity of transport organisations and governing institutions to maintain functionality, adapt and recover under crisis or systemic stress, which is particularly relevant for Ukraine’s wartime context. The findings demonstrate that while EU operators lead in transparency, digital integration and environmental performance, Ukrainian actors exhibit rapid adaptive innovation and significant potential for technological leapfrogging during reconstruction. This paper concludes that the EU must overcome regulatory inertia and infrastructure fatigue, while Ukraine should institutionalise resilience and transparency. Full article

The rapid growth of urban areas has increased the need for more efficient methods of pavement inspection and maintenance. However, conventional techniques remain slow, labor-intensive, and limited in spatial coverage, and their performance is strongly affected by traffic, weather conditions, and operational constraints. In response to these challenges, it is essential to synthesize the technological advances that improve inspection efficiency, coverage, and data quality compared to traditional approaches. Herein, we present a systematic review of the state of the art on the use of unmanned aerial vehicles (UAVs) for monitoring and assessing pavement deterioration, highlighting as a key contribution the comparative integration of sensors (photogrammetry, LiDAR, and thermography) with recent automatic damage-detection algorithms. A structured review methodology was applied, including the search, selection, and critical analysis of specialized studies on UAV-based pavement evaluation. The results indicate that UAV photogrammetry can achieve sub-centimeter accuracy (<1 cm) in 3D reconstructions, LiDAR systems can improve deformation detection by up to 35%, and AI-based algorithms can increase crack-identification accuracy by 10% to 25% compared with manual methods. Finally, the synthesis shows that multi-sensor integration and digital twins offer strong potential to enhance predictive maintenance and support the transition towards smarter and more sustainable urban infrastructure management strategies. Full article

Conventional research on digital business development offers a limited view, overwhelmingly concerned with the isolated effects of individual variables while overlooking their synergistic relationships. This study challenges this reductive perspective by applying fuzzy set Qualitative Comparative Analysis (fsQCA) to Chinese city-level data. We specifically investigate how elements from the socio-technical framework interact synergistically to shape the urban digital business ecosystem. The results demonstrate that no single factor is sufficient as a determinant. Instead, we observe equifinality, meaning multiple distinct configurations can lead to equally high performance. Furthermore, the causal configurations for failure are not mirror images of those for success but instead exhibit a distinctive pattern. The influence of government size exemplifies this asymmetry. For policymakers, the implication is that effective strategies for urban digital business must be holistic and context sensitive, moving beyond universal prescriptions. Full article