Search Results (1,102)

Urban areas are responsible for most of Europe’s energy demand and emissions and urgently require building retrofits to meet climate neutrality goals. This study evaluates the energy efficiency potential of three public school buildings in western Macedonia, Greece—a cold-climate region with high heating needs. The buildings, constructed between 1986 and 2003, exhibited poor insulation, outdated electromechanical systems, and inefficient lighting, resulting in high oil consumption and low energy ratings. A robust methodology is applied, combining detailed on-site energy audits, thermophysical diagnostics based on U-value calculations, and a techno-economic assessment utilizing Net Present Value (NPV), Internal Rate of Return (IRR), and SWOT analysis. The study evaluates a series of retrofit measures, including ceiling insulation, high-efficiency lighting replacements, and boiler modernization, against both technical performance criteria and financial viability. Results indicate that ceiling insulation and lighting system upgrades yield positive economic returns, while wall and floor insulation measures remain financially unattractive without external subsidies. The findings are further validated through sensitivity analysis and policy scenario modeling, revealing how targeted investments, especially when supported by public funding schemes, can maximize energy savings and emissions reductions. The study concludes that selective implementation of cost-effective measures, supported by public grants, can achieve energy targets, improve indoor environments, and serve as a replicable model of targeted retrofits across the region, though reliance on external funding and high upfront costs pose challenges. Full article

Promoting social equity is a global issue, and common prosperity is an important goal for human society’s sustainable development. This study is the first to examine climate risks’ impacts on common prosperity from the perspective of corporate employees, providing micro-level evidence for the coordinated development of climate governance and social equity. Employing data from companies listed on the Shanghai and Shenzhen stock exchanges from 2016 to 2023, a fixed-effects model analysis was conducted, and the results showed the following: (1) Climate risks are positively associated with the common prosperity of corporate employees in a significant way, and this effect is mainly achieved through employee guarantees, rather than employee remuneration or employment. (2) Climate risk will increase corporate financing constraints, but it will also force companies to improve their ESG performance. (3) The mechanism tests show that climate risks indirectly promote improvements in employee rights and interests by forcing companies to improve the quality of internal controls and audits. (4) The results of the moderating effect analysis show that corporate size and performance have a positive moderating effect on the relationship between climate risk and the common prosperity of corporate employees. This finding may indicate the transmission path of “climate pressure—governance upgrade—social equity” and suggest that climate governance may be transformed into social value through institutional changes in enterprises. This study breaks through the limitations of traditional research on the financial perspective of the economic consequences of climate risks, incorporates employee welfare into the climate governance assessment framework for the first time, expands the micro research dimension of common prosperity, provides a new paradigm for cross-research on ESG and social equity, and offers recommendations and references for different stakeholders. Full article

The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products. Full article

Ensuring data integrity and adaptability in aircraft health monitoring (AHM) is vital for safety-critical aviation systems. Traditional digital twin (DT) and federated learning (FL) frameworks, while effective in enabling distributed, privacy-preserving fault detection, lack mechanisms to remove the influence of corrupted or adversarial data once these have been integrated into global models. This paper proposes a novel FL–DT–FU framework that combines digital twin-based subsystem modeling, federated learning for collaborative training, and federated unlearning (FU) to support the post hoc correction of compromised model contributions. The architecture enables real-time monitoring through local DTs, secure model aggregation via FL, and targeted rollback using gradient subtraction, re-aggregation, or constrained retraining. A comprehensive simulation environment is developed to assess the impact of sensor drift, label noise, and adversarial updates across a federated fleet of aircraft. The experimental results demonstrate that FU methods restore up to 95% of model accuracy degraded by data corruption, significantly reducing false negative rates in early fault detection. The proposed system further supports auditability through cryptographic logging, aligning with aviation regulatory standards. This study establishes federated unlearning as a critical enabler for resilient, correctable, and trustworthy AI in next-generation AHM systems. Full article

This study extends our prior blockchain-based traceability framework, WEave, for application to a furniture supply chain scenario, while using the original multi-tier apparel supply chain as an anchoring use case. We integrate circular economy principles such as product reuse, recycling traceability, and full lifecycle transparency to bolster sustainability and resilience in supply chains by enabling data-driven accountability and tracking for closed-loop resource flows. The enhanced approach can track post-consumer returns, use of recycled materials, and second-life goods, all represented using a closed-loop supply chain topology. We describe the extended network architecture and smart contract logic needed to capture circular lifecycle events, while proposing new metrics for evaluating lifecycle traceability and reuse auditability. To validate the extended framework, we outline simulation experiments that incorporate circular flows and cross-industry scenarios. Results from these simulations indicate improved transparency on recycled content, audit trails for returned products, and acceptable performance overhead when scaling to different product domains. Finally, we offer conclusions and recommendations for implementing WEave functionality into real-world settings consistent with the goals of digital, resilient, and sustainable supply chains. Full article

Background: The emergency department (ED) often represents the entry point to care for patients that require urgent medical attention or have no alternative for medical treatment. This has implications on scope of practice and how quality of care is measured. A diverse array of methodologies has been developed to evaluate the quality of clinical care and broadly includes quality improvement (QI), quality assurance (QA), observational research (OR) and clinical quality registries (CQRs). Considering the overlap between QI, QA, OR and CQRs, we conceptualized a modular framework for TECOR to effectively and efficiently streamline clinical quality evaluations. Streamlining is both appropriate and justified as it reduces redundancy, enhances clarity and optimizes resource utilization, thereby allowing clinicians to focus on delivering high-quality patient care without being overwhelmed by excessive data and procedural complexities. The objective of this study is to describe the process for designing a modular framework for ED CQRs using TECOR as a case study. Methods: We performed a scoping audit of all quality projects performed in our ED over a 1-year period (1 January 2021 to 31 December 2021) as well as data mapping and categorical formulation of key themes from the TECOR dataset with clinical data sources. Both these processes then informed the design of TECOR. Results: For the audit of quality projects, we identified 29 projects. The quality evaluation methodologies for these projects included 12 QI projects, 5 CQRs and 12 OR projects. Data mapping identified that clinical information was fragmented across 11 distinct data sources. Through thematic analysis during data mapping, we identified three extraction techniques: self-extractable, manual entry and on request. Conclusions: The modular framework for TECOR aims to enable an efficient streamlined approach that caters to all aspects of clinical quality evaluation to enable higher throughput of clinician-led quality evaluations and improvements. TECOR is also an essential component in the development of a learning health system to drive evidence-based practice and the subject of future research. Full article

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

An accurate assessment of sidewalk conditions is critical for ensuring compliance with the Americans with Disabilities Act (ADA), particularly to safeguard mobility for wheelchair users. This paper presents a novel 3D reconstruction framework based on neural radiance field (NeRF), which utilize a monocular video input from consumer-grade cameras to generate high-fidelity 3D models of sidewalk environments. The framework enables automatic extraction of ADA-relevant geometric features, including the running slope, the cross slope, and vertical displacements, facilitating an efficient and scalable compliance assessment process. A comparative study is conducted across three surveying methods—manual measurements, LiDAR scanning, and the proposed NeRF-based approach—evaluated on four sidewalks and one curb ramp. Each method was assessed based on accuracy, cost, time, level of automation, and scalability. The NeRF-based approach achieved high agreement with LiDAR-derived ground truth, delivering an F1 score of 96.52%, a precision of 96.74%, and a recall of 96.34% for ADA compliance classification. These results underscore the potential of NeRF to serve as a cost-effective, automated alternative to traditional and LiDAR-based methods, with sufficient precision for widespread deployment in municipal sidewalk audits. Full article

Background/Objectives: The increased accessibility of information has resulted in a rise in patients trying to self-diagnose and opting for self-medication, either as a primary treatment or as a supplement to medical care. Our objective was to evaluate the reliability, comprehensibility, and readability of the responses provided by ChatGPT 4.0 when queried about the most prevalent orthopaedic problems, thus ascertaining the occurrence of misguidance and the necessity for an audit of the disseminated information. Methods: ChatGPT 4.0 was presented with 26 open-ended questions. The responses were evaluated by two observers using a Likert scale in the categories of diagnosis, recommendation, and referral. The scores from the responses were subjected to subgroup analysis according to the area of interest (AoI) and anatomical region. The readability and comprehensibility of the chatbot’s responses were analyzed using the Flesch–Kincaid Reading Ease Score (FRES) and Flesch–Kincaid Grade Level (FKGL). Results: The majority of the responses were rated as either ‘adequate’ or ‘excellent’. However, in the diagnosis category, a significant difference was found in the evaluation made according to the AoI (p = 0.007), which is attributed to trauma-related questions. No significant difference was identified in any other category. The mean FKGL score was 7.8 ± 1.267, and the mean FRES was 52.68 ± 8.6. The average estimated reading level required to understand the text was considered as “high school”. Conclusions: ChatGPT 4.0 facilitates the self-diagnosis and self-treatment tendencies of patients with musculoskeletal disorders. However, it is imperative for patients to have a robust understanding of the limitations of chatbot-generated advice, particularly in trauma-related conditions. Full article

Highway infrastructure faces growing exposure to natural hazards, necessitating more proactive and data-driven risk mitigation strategies. This study explores the integration of Disaster Risk Reduction Audits (DRRAs) into the lifecycle of highway infrastructure projects as a structured method for enhancing disaster resilience and operational safety. Using case analyses and scenario-based labor estimation models across design and construction phases, this research quantifies the resource requirements and effectiveness of DRRA application. The results show a statistically significant reduction in disaster occurrence rates in projects where a DRRA was implemented, despite slightly higher labor inputs. These findings highlight the value of adopting phased DRRA implementation as a national standard, with flexibility across different project types and scales. This study concludes that institutionalizing DRRAs, particularly when supported by digital platforms and decision-support tools, can serve as a critical component in transforming traditional infrastructure management into a more resilient and adaptive system. Full article

Background/Objectives: Electronic medical record systems play a crucial role in the operation of modern healthcare institutions, enabling the foundational data necessary for advancements in personalized medicine. Despite their importance, the software supporting these systems frequently experiences data availability and integrity issues, particularly concerning patients’ personal information. This study aims to present a decentralized architecture that integrates both clinical and personal patient data, with a provenance mechanism to enable data tracing and auditing, ultimately supporting more precise and personalized healthcare decisions. Methods: A system implementation based on the solution was developed, and a feasibility study was conducted with synthetic medical records data. Results: The system was able to correctly receive data of 190 instances of the entities designed, which included different types of medical records, and generate 573 provenance entries that captured in detail the context of the associated medical information. Conclusions: For the first cycle of the research, the system developed served to validate the main features of the solution, and through that, it was possible to infer the feasibility of a decentralized EHR and PHR health system with formal provenance data tracking. Such a system lays a robust foundation for secure and reliable data management, which is essential for the effective implementation and future development of personalized medicine initiatives. Full article

Prefabricated buildings, known for their energy efficiency, environmental benefits, and industrial advantages, play a crucial role in urban renewal. Previous studies on the carbon emissions of prefabricated buildings mainly concentrate on the assessment and auditing of carbon emissions at the materialization and construction phase. Few of them have analyzed the carbon emissions at the operational phase or the influence mechanisms of prefabricated buildings on carbon emissions in urban renewal. Thus, this paper explored the factors and mechanisms that influence carbon emissions in prefabricated buildings in China’s urban renewal. Firstly, the factors that influence the carbon emissions of prefabricated buildings in China’s urban renewal were identified through meta-analysis. Secondly, the theoretical model was developed to illustrate the influence paths of prefabricated buildings on the carbon emissions of urban renewal. Finally, the structural equation model (SEM) was used to test the hypotheses in the theoretical model using data collected from questionnaires. The results show that the carbon emission reduction potential of prefabricated buildings is influenced by four aspects, namely, socioeconomic factors, policy regulations, building operation, and materialization. Policy regulations have the greatest impact on the carbon emissions of prefabricated buildings. They not only directly affect the carbon emissions of urban renewal but also influence carbon emissions indirectly through the social economy aspect. The direct impact of social economy on the carbon emissions of prefabricated buildings is insignificant, while it can indirectly affect the carbon emission reduction in prefabricated buildings by influencing building operations and the materialization stage. The findings could help provide strategies for prefabrication and enhance the reduction potential of urban renewal. Full article

Provable data possession (PDP) is a technique that enables the verification of data integrity in cloud storage without the need to download the data. PDP schemes are generally categorized into public and private verification. Public verification allows third parties to assess the integrity of outsourced data, offering good openness and flexibility, but it may lead to privacy leakage and security risks. In contrast, private verification restricts the auditing capability to the data owner, providing better privacy protection but often resulting in higher verification costs and operational complexity due to limited local resources. Moreover, most existing PDP schemes are based on classical number-theoretic assumptions, making them vulnerable to quantum attacks. To address these challenges, this paper proposes an identity-based PDP with a designated verifier over lattices, utilizing a specially leveled identity-based fully homomorphic signature (IB-FHS) scheme. We provide a formal security proof of the proposed scheme under the small-integer solution (SIS) and learning with errors (LWE) within the random oracle model. Theoretical analysis confirms that the scheme achieves security guarantees while maintaining practical feasibility. Furthermore, simulation-based experiments show that for a 1 MB file and lattice dimension of n = 128, the computation times for core algorithms such as TagGen, GenProof, and CheckProof are approximately 20.76 s, 13.75 s, and 3.33 s, respectively. Compared to existing lattice-based PDP schemes, the proposed scheme introduces additional overhead due to the designated verifier mechanism; however, it achieves a well-balanced optimization among functionality, security, and efficiency. Full article

Loan default prediction is a critical task for financial institutions, directly influencing risk management, loan approval decisions, and profitability. This study evaluates the effectiveness of machine learning models, specifically XGBoost, Gradient Boosting, Random Forest, and LightGBM, in predicting loan defaults. The research investigates the following question: How effective are machine learning models in predicting loan defaults compared to traditional approaches? A structured machine learning pipeline is developed, including data preprocessing, feature engineering, class imbalance handling (SMOTE and class weighting), model training, hyperparameter tuning, and evaluation. Models are assessed using accuracy, F1-score, ROC AUC, precision–recall curves, and confusion matrices. The results show that Gradient Boosting achieves the highest overall classification performance (accuracy = 0.8887, F1-score = 0.8084, recall = 0.8021), making it the most effective model for identifying defaulters. XGBoost exhibits superior discriminatory power with the highest ROC AUC (0.9714). A cost-sensitive threshold-tuning procedure is embedded to align predictions with regulatory loss weights to support audit requirements. Full article

The increase in garbage generated in modern societies demands the implementation of a more sustainable model as well as new methods for efficient waste management. This article describes the development and implementation of a prototype of a smart bin that automatically sorts waste using a multi-agent system and blockchain integration. The proposed system has sensors that identify the type of waste (organic, plastic, paper, etc.) and uses collaborative intelligent agents to make instant sorting decisions. Blockchain has been implemented as a technology for the immutable and transparent control of waste registration, favoring traceability during the classification process, providing sustainability to the process, and making the audit of data in smart urban environments transparent. For the computer vision algorithm, three versions of YOLO (YOLOv8, YOLOv11, and YOLOv12) were used and evaluated with respect to their performance in automatic detection and classification of waste. The YOLOv12 version was selected due to its overall performance, which is superior to others with mAP@50 values of 86.2%, an overall accuracy of 84.6%, and an average F1 score of 80.1%. Latency was kept below 9 ms per image with YOLOv12, ensuring smooth and lag-free processing, even for utilitarian embedded systems. This allows for efficient deployment in near-real-time applications where speed and immediate response are crucial. These results confirm the viability of the system in both accuracy and computational efficiency. This work provides an innovative solution in the field of ambient intelligence, characterized by low equipment cost and high scalability, laying the foundations for the development of smart waste management infrastructures in sustainable cities. Full article