Search Results (837)

Automated guided vehicles (AGVs) are key transportation resources in automated container terminals, where operational scheduling and charging decisions exhibit strong spatiotemporal coupling characteristics. When AGVs are assigned to transport “reefer” containers, interruptions in external power supply during transit may lead to temperature fluctuations, posing potential risks to cargo quality and transportation safety. To address this issue, this paper proposes a two-stage coordinated optimization framework for AGV operations and charging, considering reefer container temperature constraints. Specifically, an AGV transportation scheduling model is first developed to characterize quay-crane operations, yard allocation, AGV travel processes, battery dynamics, and reefer container transit-time limitations associated with temperature maintenance requirements. Subsequently, a port microgrid scheduling model integrating charging stations, photovoltaic generation, wind power, and energy storage systems is established to coordinate AGV charging strategies with energy system operations. Based on these models, a two-stage optimization framework is constructed, in which AGV task assignment and yard allocation are optimized in the first stage to improve operational efficiency, while energy scheduling is optimized in the second stage to minimize system operating costs under the operational decisions obtained in the first stage. Numerical results demonstrate that the proposed method effectively reduces the transportation time of reefer containers, alleviates temperature-related transportation risks, enhances the coordination between logistics operations and energy management, and improves terminal operational efficiency while ensuring the safety and quality of reefer container transportation. The proposed framework provides an effective solution for the integrated optimization of logistics and energy systems in automated container terminals. Full article

Blockchain technology has introduced a rapidly expanding range of consensus mechanisms, each designed to satisfy different operational requirements related to security, scalability, decentralization, transaction throughput, and energy efficiency. Selecting an appropriate consensus mechanism has consequently become a complex multi-criteria decision problem, particularly for developers and organizations without extensive expertise in distributed systems and blockchain architectures. Existing tools primarily address protocol benchmarking and static documentation, leaving the decision-support dimension largely unaddressed. This paper presents a web-based expert system designed to support the selection of blockchain consensus mechanisms according to specific user-defined operational requirements. The proposed solution was implemented using the MERN technology stack, consisting of MongoDB, Express.js, React, and Node.js, enabling a modular and scalable architecture suitable for future expansion and maintenance. The recommendation process is based on a two-phase filtering and scoring algorithm. In the first phase, mechanisms incompatible with mandatory user-defined constraints, including network type and key resource type, are systematically eliminated. In the second phase, the remaining mechanisms are ranked using attribute matching across criteria encompassing energy efficiency, scalability, security, decentralization, and transaction speed. The system returns the three most suitable consensus mechanisms for the given operational scenario together with their key characteristics. In addition to recommendation functionality, the application supports user authentication, recommendation history management, and administrative maintenance of the consensus mechanism database, which currently contains 38 distinct blockchain consensus protocols. Experimental evaluation through twelve representative usage scenarios demonstrated that the system consistently produces contextually relevant recommendations aligned with user-specified requirements. A comparative analysis with existing tools confirms that the proposed system occupies a distinct decision-support role currently absent from the available tooling landscape. A comparative analysis against four representative existing tools indicates that the proposed system combines a set of decision-support capabilities not jointly offered by any one of them. The presented approach contributes a transparent and extensible decision-support framework intended to simplify architectural planning and management of blockchain-based distributed systems. Full article

AI technologies are becoming operational components of urban infrastructure systems, including transport networks, structural health monitoring platforms, water utilities, energy systems, and public facilities. These systems support prediction, diagnosis, control, maintenance planning, and asset-management decisions across long service periods. However, much of the existing literature still evaluates infrastructure AI at the model-design or deployment-performance stage, with limited attention to post-deployment validity, operational degradation, update control, and end-of-life management. This survey examines AI applications in urban infrastructure from a lifecycle-management perspective, covering deployment, runtime monitoring, maintenance and adaptation, governance, and retirement. The review applies a PRISMA-guided search and screening protocol to classify retained studies by lifecycle phase, infrastructure domain, deployment evidence, monitoring strategy, adaptation mechanism, governance control, and benchmark support. The cross-domain analysis indicates that deployment-stage accuracy alone is not sufficient for long-term reliability assessment, because sensor wear, environmental variation, asset aging, data drift, maintenance intervention, topology change, and operating-regime shifts can alter model behavior after deployment. The findings further show that current research provides limited support for linking model outputs to maintenance actions, validating model updates under operational constraints, documenting governance evidence, estimating lifecycle cost, defining retirement criteria, and building shared lifecycle benchmarks. The survey concludes that urban infrastructure AI should be managed as a long-term socio-technical asset, with continuous validation, model-health monitoring, controlled adaptation, audit-ready governance, and retirement planning integrated into infrastructure operations. Full article

The accelerating transition toward renewable electricity generation has elevated grid-scale electrochemical energy storage from an ancillary grid service to a foundational infrastructure requirement. This review provides a comprehensive account of rechargeable battery technologies for stationary grid applications, spanning advanced lithium-ion systems, sodium-ion and post-lithium multivalent chemistries, vanadium and organic flow batteries, solid-state architectures, and high-energy-density future systems such as lithium-sulfur and metal-air cells. The techno-economic context of grid-scale storage is systematically examined, including performance metrics, market drivers, and regulatory frameworks. Each battery chemistry is analyzed with respect to electrochemical mechanism, cycle life, energy density, safety profile, material availability, and commercial readiness. Non-electrochemical storage technologies are discussed as system-level alternatives. Battery safety engineering, thermal management system design, thermal runaway mechanisms and prevention, and failure containment strategies are examined in depth, followed by analysis of critical material supply-chain vulnerabilities, life-cycle assessment, and recycling pathways. The expanding role of artificial intelligence, machine learning, and digital twin frameworks in optimizing performance and enabling predictive maintenance is reviewed. Key challenges, including material bottlenecks, manufacturing scalability, long-duration storage gaps, and the absence of harmonized performance standards, are identified, and the review concludes with a techno-economic roadmap toward cost-competitive, resilient, and low-carbon grid storage. Full article

Background: Malnutrition and protein-energy wasting (PEW) are prevalent in maintenance hemodialysis (HD) patients and pose significant challenges to clinical care. Although adequate dietary protein intake is essential for maintaining nutritional status, phosphorus control often complicates nutritional management. In addition, HD is associated with dialysis-related amino acid (AA) loss, which may further aggravate nutritional vulnerability. This study examined the associations of dialysis adequacy with nutritional target attainment, biochemical parameters, and intradialytic AA reduction in maintenance HD patients. Methods: This cross-sectional study included 45 stable maintenance HD patients. Dietary intake was assessed using a 2-day pre-dialysis dietary record, and protein and energy intake attainment were calculated relative to recommended targets. Pre-HD and post-HD plasma AA concentrations were measured to determine intradialytic AA reduction. Correlation analyses, group comparisons, and multivariable regression analyses were performed. Results: Serum albumin was not significantly associated with protein intake, suggesting that it may not adequately reflect current dietary protein adequacy when used as an isolated marker. Patients with adequate protein intake attainment had lower serum phosphorus levels; however, after adjustment, dialysis adequacy assessed by spKt/V, rather than protein intake attainment itself, remained independently associated with serum phosphorus levels. Dialysis adequacy was significantly associated with both protein and energy intake attainment. HD was associated with significant reductions in most plasma AAs. Patients with adequate protein intake attainment showed lower intradialytic reductions in total amino acids, essential amino acids, branched-chain amino acids, and aromatic amino acids. Conclusions: Dialysis adequacy was associated with nutritional target attainment, phosphorus control, and intradialytic plasma AA reduction in maintenance HD patients. Adequate protein intake attainment was associated with lower category-specific AA reduction, whereas serum albumin alone did not reflect current dietary protein adequacy. These findings should be interpreted as associations and confirmed in larger prospective studies. Full article

Building Information Modeling-based (BIM) Digital Twins (DT) are increasingly adopted to support sustainable building management; however, the growing body of secondary literature—systematic reviews, surveys, and bibliometric studies—remains methodologically fragmented and lacks a consolidated tertiary synthesis. This fragmentation prevents researchers and practitioners from identifying consistent guidelines, validated frameworks, and comparable software ecosystems across the field. To address this gap, this study conducts a tertiary review of 57 secondary studies published between 2018 and 2025, following Kitchenham and Charters’ evidence-based guidelines and the PICOC framework. Data extraction and comparative analysis were conducted across seven criteria, including methodological approaches, proposed frameworks, enabling technologies, software tools, and reported limitations. The results reveal that bibliometric analyses and systematic literature reviews dominate the field, with few yielding structured frameworks and taxonomies; nearly half of the reviewed studies do not assess specific software platforms. Artificial intelligence and the internet of things are among the most widely studied enabling technologies, primarily associated with energy management, predictive maintenance, and structural monitoring, yet their integration with BIM-based DT platforms is inconsistently documented. The findings expose persistent interoperability constraints, insufficient data governance structures, limited maturity models, and a lack of large-scale empirical validation—gaps that this tertiary synthesis maps and prioritizes to guide future research toward reproducible, scalable, and sustainability-oriented DT implementations in the architecture, engineering, and construction sector. Full article

Smart public lighting systems are increasingly evolving from energy-efficiency technologies into distributed urban data infrastructures. This study examines an IoT-based smart public lighting pilot implemented on a university campus in southern Quito, Ecuador, with the objective of quantifying its informational capacity, storage requirements, and scalability potential. The analysis was based on 11 smart luminaires and 49 days of real operational data. Measurements were recorded every 10 min, considering 10 variables per record, including measured consumption, reference consumption, active power, accumulated energy, operating hours, operational status, events, faults or alarms, energy savings, and avoided CO₂ emissions. The results show that each luminaire can generate 1440 data values per day. Under the same acquisition configuration, a network of 10,000 luminaires would generate approximately 5.256 billion data values per year, while a network of 100,000 luminaires would generate approximately 52.56 billion data values per year, requiring an estimated 1051.2 GB/year of storage. These values are interpreted as scalability scenarios rather than definitive city-wide predictions, given the limited scale and observation period of the pilot. As a methodological contribution, the study proposes the Smart Lighting Informational Capacity Index (SLICI) to estimate the daily data intensity generated by smart lighting networks. The findings demonstrate that the expansion of smart public lighting requires not only efficient luminaires, but also robust digital architectures for data storage, processing, interoperability, cybersecurity, and analytics. The study positions smart public lighting as a strategic platform for urban energy management, predictive maintenance, and data-driven sustainable planning, while highlighting the need for future validation over longer periods and across different urban typologies. Full article

Public transport corporations in Tamil Nadu face increasing operational and financial pressure due to rising diesel fuel prices, maintenance costs, and operational inefficiencies associated with the conventional bus systems. In many districts, diesel-powered public transport services also suffer from irregular vehicle dispatch and poor timetable adherence, leading to unreliable passenger service. Meanwhile, the rapid penetration of electric two-wheelers and four-wheelers indicates a broader transition towards electrified mobility. Extending electrification to public transport requires prudently designed operational planning, as electric buses operate under battery capacity constraints and charging coordination constraints. In such systems, strict adherence to the scheduling of trips and efficient energy management becomes critical for maintaining service reliability. To address these challenges, this study proposes a Tri-Level Hybrid Electric Bus Scheduling (TLH-EBS) framework integrating Particle Swarm Optimization for global search, Rule-Based Scoring Large Neighborhood Search for adaptive schedule improvement, and Mixed Integer Linear Programming for exact repair optimization. The framework simultaneously optimizes fleet size, depot charging infrastructure allocation, and daily bus assignment under timetable constraints. The proposed model has been applied in three interconnected corridors in Madurai District, which are Thirumangalam, Arapalayam, and Mattuthavani, covering 810 scheduled daily timetabled trips between 05:00 AM and 12:30 AM. Computational results show that the hybrid framework has achieved a 2.8% reduction in annual scheduling cost compared to the best conventional optimization method. Furthermore, compared to equivalent diesel-based operations, the optimized electric system has demonstrated approximately 32.3% annual cost savings, confirming the economic viability of integrated fleet–charger scheduling for district-level electric bus deployment. Full article

The rapid digitalization and decarbonization of electrical power systems have brought increased operational complexity and new occupational risk dynamics. This transition renders traditional compliance-based safety models inadequate for managing the emerging complexities of cyber–physical and socio-technical systems. This paper develops a conceptual socio-technical safety architecture for occupational risk management in electrical power systems, grounded in the concepts of systems innovation and socio-technical modeling. A structured narrative review of international standards, accident investigations, and emerging technologies is conducted to reinterpret hazards as interacting subsystems within a dynamic, adaptive framework. The proposed framework synthesizes technical safety controls, human reliability factors, and artificial intelligence-driven predictive maintenance within a single architecture, supported by dynamic feedback loops. The model addresses nonlinear risk propagation across smart grid applications, hydrogen systems, and battery energy storage systems. By transitioning from a reactive to a proactive, adaptive approach to safety governance, the architecture enhances the resilience of electrical power systems, reduces the potential for cascading failures, and aligns occupational safety with infrastructure modernization strategies for electrical power systems. The framework provides a conceptual basis for integrating technology innovation with occupational risk management across complex energy infrastructures undergoing digital transformation. Full article

The reliable inspection of power transmission and distribution infrastructure is essential for ensuring energy security, operational continuity, and asset reliability. Conventional inspection procedures are labor-intensive, costly, and often expose maintenance teams to hazardous environments. In this context, Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence have emerged as an effective solution for large-scale infrastructure monitoring. This paper presents a field-validated framework for automated inspection of power transmission and distribution assets using autonomous UAV image acquisition and deep learning analysis. The proposed approach enables multiclass detection of electrical components and anomalies in high-resolution aerial imagery, without requiring computationally intensive 3D reconstruction. The framework integrates autonomous data collection, object detection, and dedicated condition assessment models into a scalable inspection workflow. The system was validated across six transmission and distribution lines located in five Brazilian states, covering 2925 support structures and a wide range of environmental and operational conditions. Experimental results achieved an overall mAP₅₀ of 0.9572 across seven target classes, with individual scores ranging from 0.8945 for corrosion detection to 0.9935 for ceramic disc insulators. Complementary classification models achieved accuracies of 0.97 for insulator contamination assessment, 0.92 for pin attachment configuration, and 0.98 for ceramic pin integrity evaluation. The results demonstrate the feasibility of deploying Artificial Intelligence (AI)-assisted UAV inspections in real utility scenarios, providing a scalable alternative for preventive maintenance, asset management, and condition-based monitoring of electrical infrastructure. Full article

Vertical greenery is increasingly applied in modern cities for environmental improvement and landscape enhancement. Given the insufficient coverage of recent developments in research and practice by prior reviews, this paper conducts a systematic review based on literature from Web of Science and global patent databases following PRISMA guidelines, with CiteSpace used for bibliometric analysis. This study summarizes the theoretical achievements of vertical greenery in ecological environment, building energy efficiency and technical materials. It also analyzes practical innovations via patent mining—a new supplement compared with traditional reviews. The environmental impacts of both outdoor and indoor vertical greenery are elaborated on: outdoor systems improve urban microclimate, noise control and air quality; indoor systems enhance indoor comfort, air purification and people’s mental status. Current innovations are categorized into structure and equipment, intelligent management, and social–cultural values. The outcomes of this work offer practical guidance for the design, construction and maintenance of vertical greenery in real projects. This paper also identifies future research priorities for the long-term development of vertical greenery. Full article

This study proposes a BIM-integrated Eco-Digital framework for enhancing predictive maintenance and sustainability assessment in educational buildings, supporting their transition towards Digital Twin readiness. Existing facilities often rely on reactive maintenance practices, fragmented data systems, and limited integration of sustainability indicators, which hinder efficient lifecycle management and environmental performance. To address these challenges, the research develops a data-driven methodology that combines condition assessment (CA), Markov-based deterioration prediction (DP), and sustainability metrics within an interoperable BIM–facility management (FM) environment. The framework is validated through a real educational building case study, where a hierarchical asset structure and relative weighting system are established to prioritize maintenance actions based on both functional importance and condition state. The Markov model is employed to predict component deterioration under uncertainty, enabling proactive maintenance planning without reliance on real-time IoT data. Sustainability is incorporated through energy consumption analysis and lifecycle performance indicators, linking maintenance decisions to environmental impacts. Results demonstrate improved maintenance prioritization, enhanced predictive capability, and better integration of sustainability considerations within facility management workflows. The proposed framework mainly contributes to providing a practical, scalable approach for transforming conventional buildings into data-driven, sustainable assets, offering a viable pathway toward Digital Twin-enabled environments, particularly in contexts with limited digital infrastructure. Full article

This study was developed within the framework of the GOLL (Green Olympic Living Lab and Environment Change) project, promoted by the Municipality of Coimbra, Portugal. The project uses the Mário Mexia Multisport Pavilion (MMMP) and the Olympic Swimming Pools Complex (OSPC) as living lab case studies for sustainability-oriented sports infrastructure management. The study combines a review of best practices in sustainable sports facilities with an applied case study focusing on infrastructure characterisation and the identification of intervention requirements (InRs). The review addresses the environmental, economic, and social dimensions of sustainable sports facilities, including energy and water efficiency, digital technologies, renewable energy integration, waste management, mobility, certification systems, and user inclusion. The adopted methodology integrates a literature review, technical inspections, and the analysis of building systems and resource consumption. The findings highlight the significant potential for improving operational performance, resource efficiency, and overall sustainability by adopting more integrated maintenance and management approaches. However, practical implementation remains dependent on overcoming challenges related to costs, data integration, and stakeholder engagement. The paper also discusses the potential adoption of integrated maintenance approaches, including the potential adoption of tailored digital management solutions and certification schemes, which may support more structured and proactive management. Within the GOLL living lab environment, this contributes to more informed technical, operational, and policy decision-making for the sustainable rehabilitation and management of sports facilities. Full article

Apolipoprotein A-IV (ApoA-IV) is a glycoprotein secreted by the small intestine to regulate lipid metabolism and satiety. Its role in insulin-independent glucose homeostasis remains largely unknown. In this study, we demonstrate that intestinal ApoA-IV overexpression significantly attenuates diet-induced obesity and hyperglycemia following severe β-cell loss. Over a 20-week high-fat diet challenge, ApoA-IV transgenic (ApoA-IV-Tg) mice maintained significantly lower adiposity than wild-type controls, driven by elevated energy expenditure and fatty acid oxidation rather than reduced caloric intake. Beyond weight maintenance, ApoA-IV maintained excellent systemic glycemic control and enhanced peripheral insulin sensitivity. Most notably, ApoA-IV significantly attenuated hyperglycemia following streptozotocin (STZ)-induced β-cell ablation, maintaining glucose stability despite severe insulin deficiency. Mechanistically, this protection results from a blunted glucagon response and the subsequent suppression of the hepatic pCREB-G6Pase gluconeogenic signaling pathway. In vitro evidence confirms that ApoA-IV directly inhibits pancreatic α-cell glucagon secretion through an LDL receptor-related protein 1 (LRP1)-dependent pathway, reinforced by the precise co-localization of LRP1 and glucagon in pancreatic islets. Furthermore, ApoA-IV-Tg mice were protected from the STZ-induced corticosterone surge and systemic lipolysis. Collectively, these findings establish the ApoA-IV–LRP1 signaling axis as a potent metabolic switch, providing a promising insulin-independent strategy for managing obesity and diabetes. Full article

Visceral organ mass is a major determinant of maintenance energy requirements in cattle, suggesting that equations to predict visceral organ mass could increase the accuracy of estimates of energy requirements. The objective of this meta-analysis was to quantify the relationships of visceral organ mass with the chemical composition of the diet, as well as animal and management characteristics. A database of 170 treatment means from 38 studies was assembled from published literature. Mixed-effects models with animal, management, and diet characteristics as fixed effects and study as a random effect were selected based on the lowest corrected Akaike information criterion (AICc) and evaluated via leave-one-trial-out cross-validation. Out of 16 organs, 15 had concordance correlation coefficient (CCC) values over 0.900, and cross-validated coefficient of determination (R²) values ranged from 0.728 to 0.967 across organs. Dry-matter intake, days on feed, and fiber-related diet characteristics (roughage level, neutral detergent fiber, and physically effective neutral detergent fiber) were the most consistently retained predictors, with crude protein and metabolizable energy concentrations being retained less frequently. These equations provide a quantitative basis for more accurate estimation of visceral organ mass in cattle. Full article