Search Results (476)

Operation and maintenance of civil infrastructure facilities such as bridges is the most extended period of the entire lifetime of the structures. This phase provides many opportunities that benefit society. However, such a wide span of operation also exposes bridges to various threats and risks. Therefore, knowledge domains such as Bridge Management System and life-cycle management are crucial ingredients for maintaining the level of performance of bridges and their components. Bridge Management System (BMS), since its emergence in 1975, has been constantly evolving to meet the needs of the industry with advancements in technology through new paradigms. To accelerate the process of creating and managing the data and information about bridge structures, the terms Bridge Information Modeling (BRiM) and Civil Information Modeling have appeared more frequently. Inspired by Building Information Modeling, the incentive is to manage the information better, from the concept until the end-of-life. The amount of created data is extensive and versatile. To address the issue of potential unstructured and heterogeneous information, academic and industrial researchers have been developing classifications, categories, and taxonomies. Given the advancements and growth of Semantic Web technologies, and qualities such as interoperability, machine-readable format, and extensibility, ontology development has become prominent. Current experience and success in creating and adapting ontologies into BIM workflow set examples for other branches in the built environment like civil engineering. Ontologies describing various areas of the bridge domain have been developed. However, proposals of how such information models could be aligned and integrated are seldom seen. This paper presents scoped evaluation of ontologies from bridge operation and maintenance domain. It gives an overview of how well different subjects are compliment entire topic, and it provides recommendations on modeling and evaluating ontologies related to a particular use case. It proposes a methodology that can be used for further development, alignment, and finding ontology gaps in the bridge domain. Full article

This work presents a highly compact and scalable 1.2-kV SiC MOSFET half-bridge building-block module enabled by a die-integrated 3D PCB packaging technology. Compared with conventional DBC-based or TO-247-based SiC half-bridge modules, the proposed design reduces the physical volume and weight by more than 90% while maintaining full compatibility with standard PCB manufacturing processes. The vertically laminated DC+/DC− conductors and symmetric PCB–die–PCB stack establish a tightly confined commutation loop, resulting in a measured power-loop inductance of 2.2 nH and a 3.8 nH gate-loop inductance—representing up to 94% and 89% reduction relative to discrete device implementations. Because the parasitic parameters are intrinsically well-balanced across replicated units and the mutual inductance between adjacent modules remains extremely small, the structure naturally supports current sharing during parallel operation. Thermal and insulation evaluations further confirm the suitability of copper filling via high-Tg laminated PCB substrates for high-power SiC applications, achieving withstand voltages exceeding twice the rated bus voltage. The proposed module is experimentally validated through finite-element parasitic extraction and 950 V double-pulse testing, demonstrating controlled dv/dt behavior and robust switching performance. This work establishes a manufacturable and parallel-friendly packaging approach for high-density SiC power conversion systems. Full article

Cardiovascular disease (CVD), diabetes, and obesity pose major public health challenges in the United Arab Emirates (UAE), contributing substantially to morbidity, mortality, and healthcare expenditure. Despite progress in expanding access and service delivery, Health Quality Management (HQM) practices remain constrained. This study represents one of the first comprehensive, co-productive efforts to evaluate and strengthen HQM for CVD, diabetes and obesity in the UAE. Using a sequential, multi-phase design, it integrates evidence synthesis with the active engagement of interest groups to bridge gaps between research, policy, and practice. Phase 1 involves a scoping review to establish an evidence base on existing HQM practices and system-level challenges. Phase 2 conducts mapping and interviews with health professionals, policymakers, and patients to capture contextual insights. Phase 3 synthesizes findings to identify critical gaps, opportunities, and emerging research questions that can guide future inquiry. Phase 4 convenes consultative and consensus-building workshops to co-produce actionable recommendations and facilitate knowledge translation and exchange among health authorities, academic institutions, and other interest groups. Guided by the Institute of Medicine’s quality domains, the Donabedian model, and WHO quality indicators, this study situates HQM within the UAE’s ongoing shift toward value-based healthcare. The expected outcomes include the identification of key barriers to and facilitators of effective HQM, the formulation of context-specific recommendations to strengthen performance and coordination, production of knowledge translation outputs and the generation of new research priorities, thus contributing to achieving UAE Vision 2031 and global NCD targets. Full article

Drug-induced cardiotoxicity (DICT) severely hampers drug development and threatens patient safety. Together with the growing global burden of cardiovascular disease, there is an urgent need to establish more predictive preclinical models. Recently, human pluripotent stem cell-derived cardiac organoids (hCOs) have emerged as a promising three-dimensional in vitro model, achieving significant progress in simulating the complex structure and function of the human heart. However, existing reviews predominantly focus on technical construction or specific applications, lacking an integrated discussion of pathological model construction and their use under evolving regulatory frameworks. This review distinguishes itself by proposing a novel, holistic framework that bridges “construction technology,” “pathological modeling,” and “application evaluation.” We systematically categorize and summarize three major strategies for building hCO-based pathological models: patient-specific, gene-edited, and microenvironment-modulated approaches. Furthermore, we highlight the unique advantages of hCOs in preclinical drug assessment and detail their cutting-edge applications in early DICT warning, metabolism-related safety evaluation, and personalized drug evaluation. Finally, we address current challenges, including maturation and standardization, and outline future directions involving integration with organ-on-a-chip technology and artificial intelligence. This review aims to provide a theoretical foundation and roadmap toward more reliable and human-relevant drug development paradigms. Full article

The proliferation of binary vulnerabilities in the software supply chain has become a critical security challenge. Existing vulnerability detection approaches—including dynamic analysis, static analysis, and decompilation-assisted analysis—all suffer from limitations such as insufficient coverage, high false-positive and false-negative rates, or poor compatibility. Although decompilation technology can serve as a bridge connecting binary-code and source-code vulnerability detection tools, current schemes suffer from inadequate semantic restoration quality and lack of tool compatibility. To address these issues, this paper proposes LLMVulDecompiler, a binary decompilation model based on fine-tuned large language models designed to generate high-precision decompiled code that integrates directly with source-code static analysis tools. We construct a dedicated training and evaluation dataset that covers multiple compiler optimization levels (e.g., O0–O3) and a diverse set of program functionalities. We adopt a two-stage fine-tuning strategy that involves first building foundational decompilation capabilities, then enhancing vulnerability-specific features. Additionally, we design a low-cost inference pipeline and establish multi-dimensional evaluation criteria, including restoration similarity, compilation success rate, and functional correctness. Experimental results show that the model significantly outperforms baseline models in terms of average edit distance, compilation success rate, and black-box test pass rate on the HumanEval-C benchmark. In tests on 12 real-world CVE (Common Vulnerabilities and Exposures) instances, the approach achieved a detection accuracy of 91.7%, with substantially reduced false-positive and false-negative rates. This study demonstrates the effectiveness of specialized fine-tuning of large language models for binary decompilation and vulnerability detection, offering a new pathway for binary security analysis. Full article

The United Kingdom’s (UK) retrofit revolution is at a crossroads and the efficacy of retrofit interventions is not solely a function of insulation thickness. To truly slash emissions and lift households out of fuel poverty, we must solve the persistent problem of thermal bridging (TB), i.e., the hidden flaws that cause heat to escape, dampness to form, and well-intentioned retrofits to fail. This review moves beyond basic principles to spotlight the emerging tools and transformative strategies to make a difference. We explore the role of advanced modelling techniques, including finite element analysis (FEA), in pinpointing thermal and moisture-related risks, and how emerging materials like vacuum-insulated panels (VIPs) offer high-performance solutions in tight spaces. Crucially, we demonstrate how an integrated fabric-first approach, guided by standards like PAS 2035, is essential to manage moisture, ensure durability, and deliver the comfortable, low-energy homes the UK desperately needs. Therefore, achieving net-zero targets is critically dependent on the systematic upgrade of the building envelope, with the mitigation of TB representing a fundamental prerequisite. The EnerPHit approach applies a rigorous fabric-first methodology to eliminate TB and significantly reduce the building’s overall heat demand. This reduction enables the use of a compact heating system that can be efficiently powered by renewable energy sources, such as solar photovoltaic (PV). Moreover, this review employs a systematic literature synthesis to critically evaluate the integration of TB mitigation within the PAS 2035 framework, identifying key technical interdependencies and research gaps in whole-house retrofit methodology. This article provides a comprehensive review of established FEA modelling methodologies, rather than presenting results from original simulations. Full article

This study presents a sociotechnical framework for mitigating ethnic violent conflicts by integrating information and communication technologies (ICTs) with community-based social capital. Drawing on longitudinal case studies from three conflict-prone regions in Kenya, Mt. Elgon, Muhoroni, and the Turkana–West Pokot borderlands, the research examines how ICT-enabled peace networks, particularly the Early Warning and Early Response System (EWERS), mobilize bonding, bridging, and linking social capital to reduce violence. The study employs a multi-phase qualitative design, combining retrospective analysis, key informant interviews, focus group discussions, action participation, and thematic coding of EWERS data collected between 2009 and 2021. This approach enabled the reconstruction of system evolution, stakeholder dynamics, and community responses across diverse socio-political contexts. Findings demonstrate that embedding ICTs within trusted social structures fosters inter-ethnic collaboration, inclusive decision-making, and trust-building. EWERS facilitated confidential reporting, timely alerts, and coordinated interventions, leading to reductions in livestock theft, improved leadership accountability, emergence of inter-ethnic business networks, and enhanced visibility and response to gender-based violence. The system’s effectiveness was amplified by faith-based legitimacy, local governance integration, and adaptive training strategies. The study argues that ICTs can become effective enablers of peace when sensitively contextualized within local norms, relationships, and community trust. Operationalizing social capital through digital infrastructure strengthens community resilience and supports inclusive, sustainale peacebuilding. These insights offer a scalable model for ICT-integrated violence mitigation in low- and middle-income countries. This is among the first studies to operationalize bonding, bridging, and linking social capital within ICT-enabled peace networks in rural African contexts. By embedding digital infrastructure into trusted community relationships, the framework offers an analytical approach that can inform inclusive violence mitigation strategies across low- and middle-income settings. While the framework demonstrates potential for scalability, its outcomes depend on contextual adaptation and cannot be assumed to replicate uniformly across all environments. Full article

Green buildings increasingly couple electrical, thermal, and hydrogen subsystems, yet these assets are typically monitored and controlled through separate standards and protocols. The resulting heterogeneous information models and communication stacks hinder millisecond-level coordination, plug-and-play integration, and resilient operation. To address this gap, we develop a unified information model and a cross-protocol real-time interaction mechanism based on extensions of IEC 61850. At the modeling level, we introduce new logical nodes and standardized data objects that describe electrical, thermal, and hydrogen devices in a single semantic space, supported by a global unit system and knowledge-graph-based semantic checking. At the communication level, we introduce a semantic gateway with adaptive mapping bridges IEC 61850 and legacy building protocols, while fast event messaging and 5G-enabled edge computing support deterministic low-latency control. The approach is validated on a digital-twin platform that couples an RTDS-based multi-energy system with a 5G test network. Experiments show device plug-and-play within 0.8 s, cross-protocol response-time differences below 50 ms, GOOSE latency under 5 ms, and critical-data success rates above 90% at a bit-error rate of 10⁻³. Under grid-fault scenarios, the proposed framework reduces voltage recovery time by about 60% and frequency deviation by about 70%, leading to more than 80% improvement in a composite resilience index compared with a conventional non-unified architecture. These results indicate that the framework provides a practical basis for interoperable, low-carbon, and resilient energy management in green buildings. Full article

Despite the growing importance of green building projects, limited research has explored the factors influencing intergenerational knowledge transfer (IGKT) in this context. As green building projects are increasingly characterized by high environmental standards, technological complexity, and interdisciplinary collaboration, effective knowledge transfer from older to younger employees becomes crucial for ensuring the success and sustainability of these projects. This study addresses this gap by systematically examining the key factors influencing IGKT in green building projects, applying an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methodology. Firstly, twelve factors were identified across five dimensions: transfer subjects, inter-subject relationships, transfer objects, transfer channels, and transfer context. Based on expert input, a direct influence matrix was constructed, and centrality and cause degrees were calculated to distinguish causal and result factors. Subsequently, the ISM method was employed to classify the key factors hierarchically and develop a multi-level structural model of their interaction paths. Results show that organizational support climate ranked highest in both centrality and influence, while digital transformation capacity emerged as a key driver in green project environments. Surface-level factors (e.g., knowledge absorption and transmission capability) were highly susceptible; intermediate factors (e.g., motivation, knowledge distance) acted as bridges; and deep-level factors (e.g., knowledge complexity and embeddedness), though lower in centrality, posed long-term structural constraints. This study provides valuable insights for enhancing IGKT and fostering effective cross-generational collaboration, which is essential for advancing sustainable practices in the green building sector. Full article

Construction and demolition (C&D) waste remains a critical challenge in India due to accelerated urbanisation and material-intensive construction practices. This study integrates survey-based assessment with machine learning to identify key causes of C&D waste and recommend targeted minimization strategies. Data were collected from 116 professionals representing junior, middle, and senior management, spanning age groups from 20 to 60+ years, and working across building construction, consultancy, project management, roadworks, bridges, and industrial structures. The majority of respondents (57%) had 6–20 years of experience, ensuring representation from both operational and decision-making roles. The Relative Importance Index (RII) method was applied to rank waste causes and minimization techniques based on industry perceptions. To enhance robustness, Random Forest, Gradient Boosting, and Linear Regression models were tested, with Random Forest performing best (R² = 0.62), providing insights into the relative importance of different strategies. Findings show that human skill and quality control are most critical in reducing waste across concrete, mortar, bricks, steel, and tiles, while proper planning is key for excavated soil and quality sourcing for wood. Recommended strategies include workforce training, strict quality checks, improved planning, and prefabrication. The integration of perception-based analysis with machine learning offers a comprehensive framework for minimising C&D waste, supporting cost reduction and sustainability in construction projects. The major limitation of this study is its reliance on self-reported survey data, which may be influenced by subjectivity and regional bias. Additionally, results may not fully generalize beyond the Indian construction context due to the sample size and sectoral skew. The absence of real-time site data and limited access to integrated waste management systems also restrict predictive accuracy of the machine learning models. Nevertheless, combining industry perception with robust data-driven techniques provides a valuable framework for supporting sustainable construction management. Full article

Timely and accurate inspection of concrete bridges is critical to ensuring structural integrity and public safety. Traditional visual inspections conducted by human inspectors are labour-intensive, inconsistent, and often limited in their ability to access all structural components, particularly in hazardous or inaccessible areas. Image-based inspection techniques have emerged as a safer and more efficient alternative, and recent advancements in deep learning have significantly enhanced their diagnostic capabilities. This systematic review critically evaluates 77 studies that applied deep learning approaches to the detection and classification of surface defects in concrete bridges using 2D images. Relevant publications were retrieved from major scientific databases, screened for eligibility, and analyzed in terms of model type, training strategies, and evaluation metrics. The reviewed works encompass a wide spectrum of algorithms—spanning classification, object detection, and image segmentation models—highlighting their architectural features, strengths, and trade-offs in terms of accuracy, computational complexity, and real-time applicability. Key findings reveal that transfer learning, data augmentation, and careful dataset composition are pivotal in improving model performance. Moreover, the review identifies emerging research trajectories, such as integrating deep learning with Building Information Modeling (BIM), leveraging edge computing for real-time monitoring, and developing rich annotated datasets to enhance model generalizability. By mapping the current state of knowledge and outlining future research directions, this study provides a foundational reference for researchers and practitioners aiming to deploy deep learning technologies in bridge inspection and infrastructure monitoring. Full article

This study examines the critical success factors for digital transformation in South Africa’s public services, where systemic inefficiency, corruption, and limited transparency have eroded public trust. Using a PRISMA-guided systematic literature review of 64 studies, this study synthesises evidence on digital governance challenges and opportunities through the lenses of New Public Management and Digital-Era Governance, complemented by value co-creation and a citizen-centred design. The analysis shows that transformation efforts often falter because of infrastructure deficits, bureaucratic resistance, and policy misalignment. Successful initiatives rest on five mutually reinforcing pillars: (1) coherent policy and regulatory frameworks; (2) equitable and reliable digital infrastructure; (3) committed leadership with sustained institutional capacity-building; (4) meaningful citizen engagement via co-design and co-production; and (5) data-enabled accountability and process efficiency. Persistent barriers include disparities in access and digital skills across municipalities, cybersecurity vulnerabilities, and legacy–system incompatibilities that impede end-to-end integration. This study proposes an implementation framework that aligns technical solutions with governance reforms, such as depoliticised administration, performance-based accountability, and localised service customization to enhance operational efficiency and rebuild trust. It concludes that bridging the digital divide and embedding context-sensitive, participatory, and ethically grounded approaches are essential for sustainable digital transformation in South Africa’s unequal socioeconomic landscape. Full article

Elevated stations are essential auxiliary structures within the high-speed rail (HSR) network. The newly constructed integrated elevated station for bridge building possesses a distinctive construction and intricate force transmission pathways, complicating the assessment of the dynamic coupling of train vibrations. Consequently, it is essential to examine the dynamic reaction of trains at such stations. This study utilises numerical simulation and field measurement techniques to examine the dynamic features of the newly constructed integrated elevated station for bridge building. Initially, vibration tests were performed on existing integrated elevated stations for bridge construction to assess their dynamic properties. The collected data were utilised to validate the modelling approach and parameter selection for the numerical model of existing stations, yielding a numerical solution method appropriate for bridge-station integrated stations. Secondly, utilising this technology, a numerical model of the newly integrated elevated station for bridge construction was developed to examine its dynamic features. Moreover, the impact of spatial configuration, train velocity, and operational organisation on the dynamic characteristics was analysed in greater depth. The vibration response level in the waiting hall was assessed. Research results indicate that structural joints alter the transmission path of train vibration energy, thereby significantly affecting the vibration characteristics of the station. The vibration response under double-track operation is notably greater than that under single-track operation. When two trains pass simultaneously at a speed of 200 km/h or higher, or a single train passes at 350 km/h, the maximum Z-vibration level of the waiting hall floor exceeds 75 dB, which goes beyond the specification limit. Full article

This article presented a method for grading and visualising corrosion in steel pedestrian bridges using Building Information Modelling (BIM). Traditional inspection methods are often manual and subjective, which reduces their reliability and repeatability. To enhance the recording and reporting of inspection results, a five-level corrosion severity grading system was developed using matched photographic data from two inspection campaigns conducted in February 2024 and April 2025. The grades were assigned based on visual signs, including surface rust, coating damage, and flaking. A Dynamo script was used to link each grade to the corresponding elements in a Revit model using colour overrides. The proposed approach enables corrosion data to be integrated into the BIM environment in a clear, structured manner. This helps engineers assess the structure’s condition, monitor changes over time, and make informed maintenance decisions. The workflow was demonstrated using case studies from a steel pedestrian bridge in Aveiro, Portugal. The method is adaptable for future digital twin applications and supports the development of BIM-based tools for bridge asset management. The workflow was applied to over 2600 elements, with 75 visually degraded cases identified and classified into five grades, demonstrating the method’s feasibility for systematic corrosion tracking. The proposed workflow was tested on a coastal steel bridge and could be generalised to other bridges with similar environmental conditions. Full article

The agility and scalability of modern industrial control systems critically depend on seamlessly integrating of heterogeneous field devices. However, this integration is fundamentally hindered at the communication level by the diversity of proprietary industrial protocols, which creates data silos and impedes the implementation of advanced control strategies. To overcome this communication barrier, this paper presents GMGbox, a graphical modeling-based protocol adaptation engine. GMGbox encapsulates protocol parsing and data conversion logic into reusable graphical components, effectively bridging the communication gap between diverse industrial devices and control applications. These components are orchestrated by a graphical modeling program engine that enables codeless protocol configuration and supports dynamic loading of protocol dictionary templates to integrate protocol variants, thereby ensuring high extensibility. Experimental results demonstrate that GMGbox can concurrently and reliably parse multiple heterogeneous industrial communication protocols, such as Mitsubishi MELSEC-QNA, Siemens S7-TCP, and Modbus-TCP. Furthermore, it allows engineers to visually adjust protocol algorithms and parameters online, significantly reducing development complexity and iteration time. The proposed engine provides a flexible and efficient data communication backbone for building reconfigurable industrial control systems. Full article