Search Results (4,820)

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes. Full article

Background: Geographic Information Systems (GIS) have emerged as a critical tool in healthcare research, facilitating the assessment of healthcare accessibility through spatial analysis and data visualisation. This scoping review synthesises literature published between 2020 and 2024, a period marked by the COVID-19 pandemic and rapid methodological innovation, providing a timely overview of how GIS has been applied to evaluate healthcare access across European countries. Methods: The review underscores the role of GIS methodologies in identifying geographic disparities, optimising resource distribution, and informing policy decisions. Results: Key findings highlight significant urban-rural differences in healthcare access, shaped by factors such as transportation infrastructure, population density, and healthcare facility distribution. Additionally, GIS has proven valuable in examining the link between healthcare accessibility and utilisation, with better access generally correlating with higher service use. Conclusions: Despite its potential, challenges including data availability, methodological variability, and uneven adoption across regions limit its broader implementation. The review emphasises the need for integrating advanced technologies to foster more equitable healthcare access throughout Europe. Full article

Fire hazard presents a critical challenge to the structural reliability of underground modular infrastructure. This study examines the fire resistance performance of prefabricated monolithic utility tunnels featuring longitudinal threaded connections. A series of fire exposure tests was conducted on assembled utility tunnel specimens using different bolt materials and thermal conditions, enabling evaluation of fire behavior, deformation behavior, and residual capacity. The observed thermal properties revealed significant temperature gradients across tunnel sections, with the peak internal–external differential reaching 536.8 °C. Post-fire mechanical degradation was evident in reduced stiffness and ductility, and the residual load-bearing capacity declined by up to 12.28% compared to unexposed specimens. Specimens using high-strength threaded bolts demonstrated superior performance compared to stainless steel bolt specimens, exhibiting a 4.67% higher residual capacity and 13.87% less residual deformation. A sequential thermal–mechanical finite element model was developed and calibrated based on experimental results, offering a reliable simulation framework for investigating fire-induced damage and residual strength in modular utility tunnel systems. These findings provide a quantitative basis for fire safety assessment. Full article

This study investigates the mechanical behavior and fatigue performance of orthotropic steel bridge decks, with a focus on rib-to-deck welded connections and the impact of geometric symmetry on stress distribution. Two full-scale models with full-penetration butt welds were tested under static compression loads, yielding failure forces of 27 kN (experimental) and 26 kN (analytical), with only a 3% difference. Finite element simulations using ANSYS 16.1 validated these results and enabled parametric studies. Rib plate thicknesses ranging from 5 mm to 9 mm were analyzed to assess their influence on stress distribution and deformation. The geometric ratio h′/tr, which reflects the symmetry of the trapezoidal rib web, was found to be a critical factor in stress behavior. At h′/tr = 38 (tr = 7 mm), compressive and tensile stresses are balanced, demonstrating a symmetric stress field; at h′/tr = 33 (tr = 8 mm), and fatigue performance at the RDW root drops by 47%. Increasing h′/tr improves fatigue life by increasing the number of load cycles to failure. Stress contours revealed that compressive stress concentrates in the rib plate above the weld toes, while tensile stress localizes at the RDW root. The study highlights how symmetric geometric configurations contribute to balanced stress fields and improved fatigue resistance. Multiple linear regression analysis (SPSS-25) produced predictive equations linking stress values to applied load and geometry, offering a reliable tool for estimating stress without full-scale simulations. These findings underscore the importance of optimizing h′/tr and leveraging structural symmetry to enhance resilience and fatigue resistance in welded joints. This research provides practical guidance for improving the design of orthotropic steel bridge decks and contributes to safer, longer-lasting infrastructure. Full article

The green economy has become an economic necessity and a cultural discourse due to the rapid global movement towards sustainability. This paper discusses the representation of green economy in Qatar and Malaysia, two countries with different political and cultural background but similar ambitions to attain sustainable development on social media. Through the application of qualitative techniques, namely thematic analysis and critical discourse analysis, the re-search analyzed Twitter, Facebook, Instagram, and LinkedIn posts discussing sustainability, renewable energy, and green innovation by using hashtags and stories on the topic. The results indicate that four major themes exist in both settings, and they are sustainability as national pride and identity, corporate–government branding of green efforts, grassroot and citizen involvement, and conflicts around contradictions and skepticism. Green economy in Qatar is constructed as a symbol of prestige and international presence, which is directly connected to the Qatar National Vision 2030, and popularized at the state and corporate levels. Big projects, financial solutions like green bonds, and sustainable infrastructure are mentioned in narratives and criticism is afforded little space. The environmental sustainability is part of cultural representation and collective accountability, grassroots mobilization, youth activism, and defiance of official and corporate language in Malaysia. A dynamic and critical digital discourse is often criticized by the citizens when they face perceived greenwashing. The research adds to the theoretical knowledge of understanding of framing theory that civic space plays a role in the development of sustainability discourses and the importance of critical discourse analysis in studying power relations in environmental discourse. In practice, the study recommends that Qatar should engage its citizens in more than just symbolic branding; Malaysia should enhance transparency and consistency of its policies to curb the skepticism of its people. In general, the paper highlights the fact that social media is not simply a medium of communication but rather a controversial field on which the definitions of sustainability are actively discussed. Full article

Distributed acoustic sensing (DAS) systems enable real-time monitoring of physical events across extended areas using optical fiber that detects vibrations through changes in backscattered light patterns. In perimeter security applications, these systems must accurately distinguish between legitimate activities and potential security threats by analyzing complex spatio-temporal data patterns. However, the high dimensionality and noise content of raw DAS data presents significant challenges for effective feature extraction and event classification, particularly when computational efficiency is required for real-time deployment. Traditional approaches or current machine learning methods often struggle with the balance between information preservation and computational complexity. This study addresses the critical need for efficient and accurate feature extraction methods that can identify informative signal components while maintaining real-time processing capabilities in DAS-based security systems. Here we show that wavelet packet decomposition (WPD) combined with a cascaded machine learning approach achieves 98% classification accuracy while reducing computational load through intelligent channel selection and preliminary filtering. Our modified peak signal-to-noise ratio metric successfully identifies the most informative frequency bands, which we validate through comprehensive neural network experiments across all possible WPD channels. The integration of principal component analysis with logistic regression as a preprocessing filter eliminates a substantial portion of non-target events while maintaining high recall level, significantly improving upon methods that processed all available data. These findings establish WPD as a powerful preprocessing technique for distributed sensing applications, with immediate applications in critical infrastructure protection. The demonstrated gains in computational efficiency and accuracy improvements suggest broad applicability to other pattern recognition challenges in large-scale sensor networks, seismic monitoring, and structural health monitoring systems, where real-time processing of high-dimensional acoustic data is essential. Full article

The Internet of Medical Things (IoMT) with edge computing provides opportunities for the rapid growth and development of a smart healthcare system (SHM). It consists of wearable sensors, physical objects, and electronic devices that collect health data, perform local processing, and later forward it to a cloud platform for further analysis. Most existing approaches focus on diagnosing health conditions and reporting them to medical experts for personalized treatment. However, they overlook the need to provide dynamic approaches to address the unpredictable nature of the healthcare system, which relies on public infrastructure that all connected devices can access. Furthermore, the rapid processing of health data on constrained devices often leads to uneven load distribution and affects the system’s responsiveness in critical circumstances. Our research study proposes a model based on AI-driven and edge computing technologies to provide a lightweight and innovative healthcare system. It enhances the learning capabilities of the system and efficiently detects network anomalies in a distributed IoMT network, without incurring additional overhead on a bounded system. The proposed model is verified and tested through simulations using synthetic data, and the obtained results prove its efficacy in terms of energy consumption by 53%, latency by 46%, packet loss rate by 52%, network throughput by 56%, and overhead by 48% than related solutions. Full article

The widespread adoption of electric vehicles (EVs) is contingent on high-power fast-charging infrastructure that can also provide grid stabilization services through bidirectional power flow. While the constituent power stages of such off-board chargers are well-known, a critical research gap exists in their system-level integration, where sub-optimal dynamic interaction between independently controlled stages often leads to DC-link instability and poor transient performance. This paper presents a rigorous, system-level study to address this gap by developing and optimizing a unified control framework for a high-power bidirectional EV fast-charging system. The system integrates a three-phase active front-end rectifier with an LCL filter and a four-phase interleaved bidirectional DC/DC converter. The methodology involves a holistic dynamic modeling of the coupled system, the design of a hierarchical control strategy augmented with a battery current feedforward scheme, and the system-wide optimization of all Proportional–Integral (PI) controller gains using the Artificial Bee Colony (ABC) algorithm. Comprehensive simulation results demonstrate that the proposed optimized control framework achieves a critically damped response, significantly outperforming a conventionally tuned baseline. Specifically, it reduces the DC-link voltage settling time during charging-to-discharging transitions by 74% (from 920 ms to 238 ms) and eliminates voltage undershoot, while maintaining excellent steady-state performance with grid current total harmonic distortion below 1.2%. The study concludes that system-wide metaheuristic optimization, rather than isolated component-level design, is key to unlocking the robust, high-performance operation required for next-generation EV fast-charging infrastructure, providing a validated blueprint for future industrial development. Full article

Traffic safety is a critical issue in highway operation management, where accurate identification of accident hotspots enables proactive risk prevention and facility optimization. Traditional methods relying on historical statistics often fail to capture macro-level environmental patterns and micro-level dynamic variations. To address this challenge, we propose a Dual-Branch Feature Adaptive Gated Fusion Network (DFAGF-Net) that integrates satellite remote sensing imagery with traffic flow time-series data. The framework consists of three components: the Global Contextual Aggregation Network (GCA-Net) for capturing macro spatial layouts from remote sensing imagery, a Sequential Gated Recurrent Unit Attention Network (Seq-GRUAttNet) for modeling dynamic traffic flow with temporal attention, and a Hybrid Feature Adaptive Module (HFA-Module) for adaptive cross-modal feature fusion. Experimental results demonstrate that the DFAGF-Net achieves superior performance in accident hotspot recognition. Specifically, GCA-Net achieves an accuracy of 84.59% on satellite imagery, while Seq-GRUAttNet achieves an accuracy of 82.51% on traffic flow data. With the incorporation of the HFA-Module, the overall performance is further improved, reaching an accuracy of 90.21% and an F1-score of 0.92, which is significantly better than traditional concatenation or additive fusion methods. Ablation studies confirm the effectiveness of each component, while comparisons with state-of-the-art models demonstrate superior classification accuracy and generalization. Furthermore, model interpretability analysis reveals that curved highway alignments, roadside greenery, and varying traffic conditions across time are major contributors to accident hotspot formation. By accurately locating high-risk segments, DFAGF-Net provides valuable decision support for proactive traffic safety management and targeted infrastructure optimization. Full article

Speech and language offer a rich, non-invasive window into brain health. Advances in machine learning (ML) have enabled increasingly accurate detection of neurodevelopmental and neurodegenerative disorders through these modalities. This paper envisions the future of ML in the early detection of neurodevelopmental disorders like autism spectrum disorder and attention-deficit/hyperactivity disorder, and neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease, through speech and language biomarkers. We explore the current landscape of ML techniques, including deep learning and multimodal approaches, and review their applications across various conditions, highlighting both successes and inherent limitations. Our core contribution lies in outlining future trends across several critical dimensions. These include the enhancement of data availability and quality, the evolution of models, the development of multilingual and cross-cultural models, the establishment of regulatory and clinical translation frameworks, and the creation of hybrid systems enabling human–artificial intelligence (AI) collaboration. Finally, we conclude with a vision for future directions, emphasizing the potential integration of ML-driven speech diagnostics into public health infrastructure, the development of patient-specific explainable AI, and its synergistic combination with genomics and brain imaging for holistic brain health assessment. Overcoming substantial hurdles in validation, generalization, and clinical adoption, the field is poised to shift toward ubiquitous, accessible, and highly personalized tools for early diagnosis. Full article

Shield tunneling beneath existing railways remains a critical challenge in urban infrastructure development, as it risks destabilizing overlying soil structures and compromising railway safety. This study presents an integrated methodology combining physical model tests and three-dimensional numerical simulation, validated by their mutual agreement, to capture the settlement and deformation induced by twin shield tunneling beneath an operational railway under the complex geological conditions of the Qingdao Metro. A parametric study was subsequently conducted to systematically evaluate the influence of critical construction parameters, including grouting pressure, grout stiffness, and chamber pressure, on railhead settlement. Additionally, a comparative analysis assessed the effectiveness of settlement control measures, including D-type beam reinforcement, deep-hole grouting reinforcement, and their combined application. Results show that railhead deformation primarily manifests as settlement, with cumulative effects from sequential tunneling of the left and right lines. Proximity to fault zones intensifies crown subsidence, while tunneling induces significant soil stress relaxation, particularly in geologically weaker strata. Within optimal ranges, increased grouting pressure, chamber pressure, and grout stiffness effectively reduce railhead settlement; however, their efficacy diminishes beyond specific thresholds. The combined D-type beam and deep-hole grouting reinforcement scheme proved most effective in controlling settlement, ensuring railway operational safety and construction stability. These findings provide essential theoretical and practical guidance for optimizing shield tunneling strategies in complex urban environments, enhancing the safety and reliability of critical railway infrastructure. Full article

Large-format additive manufacturing (LFAM) is an enabling manufacturing technology capable of producing large parts with highly complex geometries for a wide variety of applications, including automotive, infrastructure/construction, and aerospace mold and tooling. In the past decade, the LFAM industry has seen widespread use of bio-based, glass, and/or carbon fiber reinforced thermoplastic composites which, when printed, serve as a lower-cost alternative to metallic parts. One of the highest-volume materials utilized by the industry is carbon fiber (CF)-filled polycarbonate (PC), which in out-of-autoclave applications can achieve comparable mechanical performance to metal at a significantly lower cost. Previous work has shown that if this material is recovered at various points throughout the manufacturing process for both the lab and pilot scale, it can be mechanically recycled with minimal impacts on the functional performance and printability of the material while significantly reducing the feedstock costs. End-of-life (EOL) CF-PC components were processed through industrial shredding, melt compounding, and LFAM equipment, followed by evaluation of the second-life material properties. Experimental assessments included quantitative analysis of fiber length attrition, polymer molecular weight degradation using gel permeation chromatography (GPC), density changes via pycnometry, thermal performance using dynamic mechanical analysis (DMA), and mechanical performance (tensile properties) in both the X- and Z-directions. Results demonstrated a 24.6% reduction in average fiber length compared to virgin prints, accompanied by a 21% decrease in X-direction tensile strength and a 39% reduction in tensile modulus. Despite these reductions, Z-direction tensile modulus improved by 4%, density increased by 6.8%, and heat deflection temperature (HDT) under high stress retained over 97% of its original value. These findings underscore the potential for integrating mechanically recycled CF-PC into industrial LFAM applications while highlighting the need for technological innovations to mitigate fiber degradation and enhance material performance for broader adoption. This critical step toward circular material practices in LFAM offers a pathway to reducing feedstock costs and environmental impact while maintaining functional performance in industrial applications. Full article

Damaged road markings are widespread, and timely detection and repair of severely damaged areas is critical to the maintenance of transport infrastructure. This study proposes a method for detecting the degree of marking damage based on the top view perspective. The method improves the minimum outer rectangle detection algorithm through pavement data enhancement and multi-scale feature fusion detection head, and establishes mathematical models of different types of markings and their minimum outer rectangles to achieve accurate detection of the degree of marking damage. The experimental results show that the improved minimum bounding rectangle detection method achieves an mAP of 97.4%, which is 4.5% higher than that of the baseline model, and the minimum error in the detection of the degree of marking damage reaches 0.54%. The experimental data verified the simplicity and efficiency of the proposed method, providing important technical support for realizing large-scale road repair and maintenance in the future. Full article

Childhood immunization is one of the most effective public health measures, yet inequities remain in marginalized populations across Asia, where parental adherence is essential to sustaining the Expanded Program on Immunization. This narrative review examines determinants of adherence among under-five children in disadvantaged communities. Following PRISMA guidelines, searches of PubMed, Scopus, and Google Scholar identified studies published between 2015 and 2025, with earlier key works included as relevant. Twenty-one studies from South, Southeast, and East Asia were analyzed. Five domains were associated with adherence: socioeconomic and access factors, where maternal education, household income, and possession of immunization cards were positive predictors, while remote residence was a barrier; trust, cultural beliefs, and social norms, with misinformation and vaccine controversies reducing uptake, and provider trust and supportive norms improving it; migration and mobility, as migrant, stateless, and left-behind children had lower coverage due to weak registration and disrupted caregiving; household and caregiver dynamics, where decision-making by family or community members shaped uptake, while large family size and maternal employment limited adherence; and health system capacity, with inadequate infrastructure and follow-up hindering coverage and integration with maternal–child health services facilitating it. Addressing these intersecting barriers through equity-focused strategies is critical to achieving universal immunization coverage. Full article

In response to global sustainability challenges, this study investigates how Digital New-Quality Productive Forces (DNQPF), which integrate digitalization with green innovation, contribute to Sustainable Industrial Structural Upgrading (SISU) in China. Using panel data from 30 provinces spanning 2011–2023, a multidimensional DNQPF index was constructed, and a comprehensive econometric framework was applied, including two-way fixed effects, mediation and moderation analyses, Hansen threshold models, and Spatial Durbin models. The results indicate that DNQPF significantly enhances SISU (β = 0.291, p < 0.01), with household consumption upgrading serving as the key mediating channel. Regional heterogeneity is evident: Eastern provinces show strong effects (β = 0.295, p < 0.01) and central provinces exhibit catch-up potential (β = 0.467, p < 0.10), while the Western and Northeastern regions display insignificant effects due to digital infrastructure disparities. The threshold effects reveal diminishing returns beyond a DNQPF level of 0.239 (coefficient decline from 0.518 to 0.323, p < 0.01), a marketization level of 6.181, and an innovation level of 9.520. Spatial analysis further confirms positive spillovers (direct effects = 0.282–0.320; indirect effects = 0.260–1.317; p < 0.05). These findings enrich endogenous growth theory by integrating digital and green development into emerging economies and underscore DNQPF’s role in advancing SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). Coordinated digital–green strategies, institutional reforms, and inclusive infrastructure are therefore critical for achieving sustainable industrial transformation in China and beyond. Full article