Search Results (3,221)

Background: Congenital hearing loss affects 1–3 per 1000 newborns and requires early detection to prevent developmental delays. Although Thailand implements universal screening, fragmented data systems limit effectiveness. To address this, Chiangrai Prachanukroh Hospital introduced a dedicated newborn hearing screening (NHS) database in 2023 to improve tracking, coordination, and monitoring in a resource-limited setting. Objectives: To evaluate the advantages and limitations of NHS database integration on screening coverage, referral rates, follow-up completion, and diagnostic timeliness. Methods: A retrospective analytic study was conducted over 24 months, comparing outcomes before (July 2022–June 2023) and after (July 2023–June 2024) database implementation. Key indicators included screening coverage, follow-up attendance, diagnostic ABR completion, and workflow efficiency, with the study period also encompassing the implementation of the database and adaptations to the screening algorithm. Data were analyzed using the chi-square test and fisher’s exact tests, supplemented by qualitative observations of system performance. Results: Among 8290 newborns, screening coverage before one month increased from 83.47% to 96.64% (p < 0.001), while referral rates decreased from 18.44% to 6.47% (p < 0.001). Diagnostic ABR completion improved from 7.41% to 52.63% within three months (p < 0.001) and from 59.26% to 84.21% within six months (p = 0.06). The database improved workflow coordination, but challenges persisted, including incomplete data, limited interoperability, caregiver-related follow-up barriers, and low hearing-aid uptake. Conclusions: Integration of the NHS database, as well as protocol changes, improved screening coverage, referral accuracy, and diagnostic timeliness, but follow-up and early intervention barriers persisted. Continued progress will require stronger interoperability, improved family engagement, and digital infrastructure investment, with tele-audiology and decision-support tools helping expand access and efficiency. Full article

Wireless Sensor Networks (WSNs) are increasingly being used in mission-critical infrastructures. In such applications, they are evaluated on the risk of cyber intrusions that can target the already constrained resources. Traditionally, Intrusion Detection Systems (IDS) in WSNs have been based on machine learning techniques; however, these models fail to capture the nonlinear, temporal, and topological dependencies across the network nodes. As a result, they often suffer degradation in detection accuracy and exhibit poor adaptability against evolving threats. To overcome these limitations, this study introduces a hybrid deep learning-based IDS that integrates multi-scale convolutional feature extraction, dual-stage attention fusion, and graph convolutional reasoning. Moreover, bidirectional long short-term memory components are embedded into the unified framework. Through this combination, the proposed architecture effectively captures the hierarchical spatial–temporal correlations in the traffic patterns, thereby enabling precise discrimination between normal and attack behaviors across several intrusion classes. The model has been evaluated on a publicly available benchmarking dataset, and it has been found to attain higher classification capability in multiclass scenarios. Furthermore, the model outperforms conventional IDS-focused approaches. In addition, the proposed design aims to retain suitable computational efficiency, making it appropriate for edge and distributed deployments. Consequently, this makes it an effective solution for next-generation WSN cybersecurity. Overall, the findings emphasize that combining topology-aware learning with multi-branch attention mechanisms offers a balanced trade-off between interpretability, accuracy, and deployment efficiency for resource-constrained WSN environments. Full article

Urban resilience has emerged as a crucial objective for achieving sustainable urban development. However, its practical integration into planning remains limited. This study evaluates the extent to which resilience thinking is integrated into Chengdu’s urban planning system by combining policy-theoretical analysis with empirical evidence. Drawing on a framework of nine resilience attributes, we conduct content analysis of Chengdu’s three types of statutory plan documents (Socioeconomic Development Plan, Urban and Rural Plan, and Land Use Plan) and a questionnaire survey of 70 expert planners. The results reveal that resilience is reflected implicitly in the plans through engineering-oriented attributes such as robustness, efficiency, and connectivity. In contrast, social and ecological attributes like inclusion, redundancy, and innovation are largely absent. Planners demonstrate moderate awareness of resilience, yet associate it predominantly with rapid response and infrastructure robustness rather than long-term adaptation or community capacity-building. These findings indicate the dominant top-down, growth-centric planning logic that constrains the adoption of broader socio-ecological resilience concepts. This paper concludes with policy recommendations for institutionalizing resilience in Chinese urban planning through legal mandates; multi-sectoral coordination; and participatory, adaptive planning frameworks. Full article

Integrated energy cyber-physical systems (IECPS) face escalating cyber-attack threats due to their deep cyber-physical coupling, while traditional resilience models relying solely on fixed resources exhibit rigidity and limited adaptability. This review investigates IECPS attack mechanisms through the lens of the confidentiality, integrity, and availability framework, revealing their cross-layer propagation characteristics. We explicitly distinguish between fixed and mobile resources. Fixed resources include energy sources, transmission and distribution network facilities, coupling and conversion devices, fixed energy storage systems, and communication and control infrastructure. Mobile resources are grouped into five categories: mobile electricity resources, mobile gas resources, mobile heat resources, mobile hydrogen resources, and mobile communication resources. Fixed resources provide geographically anchored capacity and structural redundancy, and they offer static operational flexibility. Mobile resources, in contrast, provide spatially reconfigurable and rapidly deployable support for sensing, temporary multi-energy supply, and emergency communications. Building on this distinction, this review proposes a full-cycle resilience enhancement framework that encompasses pre-event prevention, in-progress response, and post-event recovery, with a particular focus on collaborative mechanisms between fixed and mobile resources. Furthermore, this review examines the foundational theories and key supporting technologies for such coordination, including fixed-mobile resource scheduling, intelligent perception and data fusion, communication security, and collaborative scheduling optimization. Key technical gaps and challenges in fixed-mobile resource collaboration are identified. Ultimately, this review aims to provide theoretical insights and practical guidance for developing resilient, adaptive, and secure integrated energy systems in the face of evolving cyber-physical threats. Full article

With the acceleration of global climate change and urbanization, urban flooding disasters have become increasingly frequent, posing significant threats to urban safety and sustainable development. Enhancing Urban Flood Resilience (UFR) has become a central issue in urban risk management and spatial planning. This study aims to scientifically assess UFR by employing the core concepts of resistance, recovery, and adaptation from urban resilience theory. A set of 20 indicators for assessing UFR is selected from four aspects: infrastructure, social economy, technological monitoring, and the ecological environment. Addressing the limitations of traditional evaluation methods, which struggle to effectively handle data gaps and ambiguous boundaries, and fail to balance subjective and objective weights, this study introduces the unascertained measure theory and adopts a combined weighting method to construct a UFR evaluation model. Using 2023 statistical data from Jiangxi Province, a comprehensive evaluation of flood resilience was conducted across 11 prefecture-level cities within the province. The analysis indicates that, among level-2 indicators, infrastructure holds the highest weight at 43.7%. Regarding resilience dimensions, resistance dominates with a weight of 54.6%. Furthermore, significant spatial disparities exist in flood resilience levels across Jiangxi Province: high resilience cities are distributed in central and northern Jiangxi, moderately high resilience cities account for the largest proportion. Only one city, Pingxiang, exhibits moderate resilience. Full article

Short-term photovoltaic (PV) power forecasting is pivotal for grid stability and high renewable-energy integration, yet existing hybrid deep-learning models face three unresolved challenges: they fail to balance accuracy, computational efficiency, and interpretability; cannot mitigate iTransformer’s inherent weakness in local feature capture (critical for transient events like minute-level cloud shading); and rely on linear concatenation that mismatches the nonlinear correlations between global multivariate trends and local fluctuations in PV sequences. To address these gaps, this study proposes a novel lightweight hybrid framework—DSC-Chebyshev KAN-iTransformer—for 15-min short-term PV power forecasting. The core novelty lies in the synergistic integration of Depthwise Separable Convolution (DSC) for low-redundancy local temporal pattern extraction, Chebyshev Kolmogorov–Arnold Network (Chebyshev KAN) for adaptive nonlinear fusion and global nonlinear modeling, and iTransformer for efficient capture of cross-variable global dependencies. This design not only compensates for iTransformer’s local feature deficiency but also resolves the linear fusion mismatch issue of traditional hybrid models. Experimental results on real-world PV datasets demonstrate that the proposed model achieves an $R^2$ of 0.996, with root mean square error (RMSE) and mean absolute error (MAE) reduced by 19.6–62.1% compared to state-of-the-art baselines (including iTransformer, BiLSTM, and DSC-CBAM-BiLSTM), while maintaining lightweight characteristics (2.04M parameters, 3.90 GFLOPs) for urban edge deployment. Moreover, Chebyshev polynomial weight visualization enables quantitative interpretation of variable contributions (e.g., solar irradiance dominates via low-order polynomials), enhancing model transparency for engineering applications. This research provides a lightweight, accurate, and interpretable forecasting solution, offering policymakers a data-driven tool to optimize urban PV-infrastructure integration and improve grid resilience amid the global energy transition. Full article

The resilience concept, born for ecological systems, has been successfully adapted to other domains. Considering the case of transport networks, the evaluation of resilience is crucial to ensure their functionality. The objective of this paper is to provide a set of indicators that can be used to quantify the resilience of a transport network with particular attention to the access to emergency health services. The methodology adopted allows for obtaining some centrality measures and some metrics on network efficiency, to identify the most critical nodes and arcs in the network, and to determine how the removal of nodes/arcs affects efficiency. In this study, the resilience indicators of the road network of Messina, a city located in the northeastern tip of Sicily (Italy), are analyzed. The analysis focuses on three main emergency hospital facilities and, by examining the connectivity of the road network, the accessibility to these critical centers in different scenarios will be assessed. A relevant aspect of the proposed methodology is the exclusive use of open data in road network definition, which makes the procedure easily transferable to other areas. The results of the proposed application provide useful indications for improving road infrastructure and urban planning, as well as replicating the methodology in other geographical areas. Full article

Considering the significant role that emergency infrastructure projects (EIPs) play globally in responding to emergency events, public sentiment towards EIPs has become an increasingly important factor to consider. However, limited studies have analysed the public’s sentiment specifically towards EIPs in emergency and urgent circumstances. This study analyses public sentiment characteristics by collecting objective big data from popular posts and comments related to EIPs on Sina Weibo. Sentiment information was extracted using text mining methods, and sentiment was measured using a long short-term memory (LSTM) model. Findings indicate that (1) Positive sentiment predominates in the data. (2) Public sentiment of temporary EIPs remains relatively stable, while long-term adaptive EIPs earn more pronounced sentiment fluctuation. (3) There are regional differences in public sentiment; Hebei, Shandong and Shanghai exhibit slightly lower stability with positive sentiment being slightly lower than or equal to neutral sentiment. The findings contribute to the literature by focusing innovatively on the public perspective of EIPs under urgent circumstances by exploring public sentiment characteristics and evolution and are of particular significance for related government departments and project managers in decision-making and construction management. Full article

This paper presents a novel integration of System-Theoretic Process Analysis (STPA) and System Dynamics (SD) for hazard and resilience analysis in safety-critical infrastructure systems. The methodology is applied iteratively to assess the safety and continuity of a hospital’s oxygen supply system, a key element of critical health infrastructure, addressing both technical and managerial factors. STPA identifies unsafe interactions between system components, which are systematically translated into a system dynamics simulation model. This dynamic perspective allows for the exploration of how hazards evolve over time and how control strategies influence overall system resilience. Unlike previous conceptual approaches, this study applies the integrated framework to a real-world incident of oxygen supply failure. The model structure is derived from STPA artifacts and validated using expert input and incident data. Simulation experiments uncovered emergent risk patterns, such as alarm delays, staff stress, and insufficient training, that are not evident through STPA alone. These insights support targeted interventions, including enhanced drill frequency and resource allocation, to strengthen infrastructure resilience. By embedding dynamic simulation within the STPA framework, this research moves beyond static hazard identification to enable scenario-based testing and conditional estimation of system response to support risk-informed decision-making. The resulting methodology is traceable, repeatable, and adaptable, offering a practical and generalizable tool for systemic risk analysis in critical infrastructures. Full article

Identifying area functions around urban rail transit (URT) stations is crucial for optimizing urban planning and infrastructure allocation. Traditional methods relying on static land-use data fail to capture dynamic human–environment interactions, while emerging mobility datasets suffer from spatial granularity limitations. This study bridges this gap by integrating spatiotemporal patterns of dockless bike sharing (DBS) with Point of Interest (POI) configurations to characterize station functions. Taking Beijing as a case study, we develop a cluster analysis framework that synthesizes DBS density fluctuations, parking distribution shifts between day/night periods, and POI features. Cluster results reveal functionally distinct station groups with statistically significant differences in both DBS usage patterns and POI distributions. Critically, high-density urban cores exhibit concentrated bicycle usage aligned with mixed POI agglomerations, while suburban zones demonstrate commuter-oriented fluctuations with evening residential surges. This alignment between DBS-derived activity signatures and POI-based land-use features provides actionable insights: planners can optimize bicycle parking in residential clusters, calibrate last-mile connections in employment cores, and adapt infrastructure to localized functional transitions—ultimately enhancing URT-integrated sustainable development. Full article

The reassessment of structural capacity is critical to maintain the safety, serviceability, and sustainability of ageing civil engineering infrastructure. Structural Health Monitoring (SHM) allows in situ measurements to be incorporated into structural models, updating the performance and reliability estimation based on available information. Digital Twins can be used to capture the behaviour of the system in the real world as live data and make use of rich sensorial data flow from the structural system. However, the growing complexity of multi-domain models, as well as decision-making and stakeholder interactions, makes it necessary to implement a structured modelling framework. This paper proposes a Model-Based Systems Engineering (MBSE) framework that incorporates an MBSE layer to coordinate model dependencies, correlate important parameters, and enforce traceability between measurement data, probabilistic assessment, and decision-making. Illustrated with a prototype application to an idealised case study of a bridge, this paper describes how using MBSE as a scalable, adaptive, and comprehensive framework can help enable data-driven structural reassessment. The work illustrates that MBSE can be used in civil engineering processes across multi-disciplinary departments to benefit the system lifecycle over time and identifies areas of further research required before the approach can be adopted for large-scale, real-world infrastructure. Full article

This article examines Daniel Suarez’s techno-thrillers Daemon (2006) and Freedom™ (2010) as works of speculative fiction that critically engage with themes of posthuman identity, algorithmic governance, and ecological agency. Rather than portraying artificial intelligence as a dystopian threat, the novels imagine the Daemon, which is a self-replicating system launched upon its creator’s death, as an infrastructural force that reorganizes global systems of power, labor, and survival. Through a posthumanist reading, drawing on thinkers such as Donna Haraway, Karen Barad, Rosi Braidotti, and N. Katherine Hayles, this article interprets the Daemon not as malevolent code, but as an ecological actor embedded in material networks, capable of fostering adaptive forms of life and governance. By reading Suarez’s fiction through the lens of posthuman ecocriticism and infrastructural media theory, the article offers a model for understanding freedom, not as a static right, but as a relational capacity earned through participation in sympoietic systems. It argues that speculative fiction can function as a cartographic tool, mapping not only future technologies but future ontologies. Full article

In the context of accelerating global urbanization and sustainable development challenges, impervious surfaces, as a key component of urban land cover, are significantly associated with regional economic development. This study takes Harbin, a typical cold region city, as a research object and constructs a three-level analytical framework of “land surface classification-economic simulation-mechanism analysis.” By innovatively integrating multi-source remote sensing, demographic, and economic data, the research addresses gaps in understanding urban sustainability in cold environments. An enhanced XGBoost algorithm was employed to achieve high-precision classification of ten land surface materials, resulting in a high overall accuracy. Furthermore, a gridded GDP spatialization model developed using high-resolution population data demonstrated superior performance compared to traditional methods. Machine learning-assisted analysis revealed that asphalt and metal surfaces are the most significant impervious materials driving economic output, reflecting the respective influences of transportation infrastructure and industrial agglomeration. Spatial pattern analysis indicates that Harbin’s impervious surfaces exhibit a lower fractal dimension and a distinct grid-like morphology compared to the typical subtropical city of Guangzhou, underscoring urban form adaptations to cold climatic constraints. The strong spatial coupling between gradients of GDP intensity and the attenuation of impervious surface density is quantitatively confirmed. This study provides a quantitative basis and a transferable technical framework for optimizing land use intensity and infrastructure planning in cold cities, thereby offering a scientific foundation for sustainable, intensive land utilization in climate-vulnerable urban systems. Full article

The SCS-CN (Soil Conservation Service Curve Number) model is important for flood forecasting as it provides a relatively simple and widely used methodology for estimating the amount of surface runoff from a rainfall event, which is a crucial input in predicting flood volumes and peaks in ungauged or data-scarce watersheds. Thus, the authors developed a hydrological model based on the SCS-CN curve methodology and GIS (Geographic Information Systems) to estimate the flood hydrograph in the upper parts of the Biała Lądecka River basin in Poland. The numerical model was calibrated based on the data available from the Polish Institute of Meteorology and Water Management (IMGW). The output of the model demonstrates the effect in the flood hydrograph at the town of Lądek-Zdrój. Additionally, hydraulic routing calculations were included to analyze the possible causes of the dam failure of the Stronie Śląskie reservoir in the year 2024. The main purpose of this study is to corroborate the influence of climate change on flood events and their consequences, as well as to assist in forecasting future catastrophic hydrological events and thus earlier adaptation and reinforce the infrastructure in our territories against future flooding. Full article

This paper presents an innovative concept for the adaptive transformation of decommissioned coal mine shafts into advanced reduced-gravity research facilities, addressing both post-mining land management and continuous advancements in microgravity research. The proposed solution leverages existing underground infrastructure to create an exceptionally long drop tower, approximately 900 m, surpassing the operational capabilities of all current global facilities. The facility employs electromagnetic propulsion and braking systems compatible with maglev technology, enabling extended microgravity durations and the precise simulation of multiple planetary gravity environments. Comprehensive numerical simulations, taking into account realistic mining shaft geometries, aerodynamic resistance, and mechanical vibration isolation, demonstrate that the system achieves free-fall periods of at least 10 s, which will be longer in the case of a capsule drop for research in reduced-gravity conditions (controlled deceleration of the capsule during the drop). The six-point suspension system effectively isolates experimental payloads from vibrations generated during descent. Beyond technological innovation, the facility exemplifies multidimensional sustainability by integrating scientific advancement with regional economic revitalization, employment generation for mining communities, industrial heritage preservation, and alignment with European Green Deal objectives. This globally unique research center would provide unprecedented opportunities for materials science, space biology, and industrial experimentation, while demonstrating innovative repurposing of post-mining assets. Full article