Search Results (469)

Modern organizations increasingly operate under a structural condition shaping their environment: resource scarcity. Firms experience supply disruptions, volatility, and growing uncertainty when access to energy, raw materials, water, and other critical inputs becomes limited. Sustainability-oriented approaches encourage responsible resource use, but they do not fully explain how organizations achieve stability and adapt under persistent resource constraints. In this context, organizational resilience—the ability of firms to absorb shocks, adapt, and ultimately transform in response to sustained pressures—has emerged as an important complementary perspective. This paper develops an integrated conceptual framework explaining how circular management practices contribute to organizational resilience in resource-constrained environments. Drawing on the circular economy, organizational resilience, and dynamic capabilities literature, the framework establishes links between circular practices and resilience outcomes mediated by organizational enablers. To complement the conceptual development, an exploratory expert-based evaluation was conducted as a form of exploratory content validation focused on face validity, conceptual coherence, and perceived relevance of the proposed framework. The results indicate strong expert agreement regarding the coherence and applicability of the model. The study integrates circular economy and resilience theories into a unified framework and provides a conceptual foundation for future empirical research on circular management and organizational resilience. Full article

The rapid integration of electric vehicles (EVs) and decentralized renewable energy sources is transforming urban power systems, while simultaneously increasing the complexity of real-time coordination across charging infrastructure, distributed energy resources, and grid-support devices. This systematic review synthesizes recent research on multi-agent reinforcement learning (MARL) for EV–grid coordination, with emphasis on four emerging dimensions: forecasting-informed control, safety-constrained learning, explainability and interpretability, and trustworthy decentralized coordination. A systematic literature search was conducted in IEEE Xplore, Scopus, Web of Science, ScienceDirect, MDPI, and arXiv, covering primarily the period 2016–2025, with selected early-2026 studies retained where relevant, with selected earlier foundational studies retained for context. The review was conducted and reported in accordance with the PRISMA 2020 framework. A total of 412 records were identified through database searching; after duplicate removal and screening, 58 studies were included in the final qualitative synthesis. The reviewed literature shows that MARL is increasingly being applied to EV charging coordination, demand-side management, community energy systems, transactive energy, and ancillary grid services. The evidence further indicates that forecasting integration improves anticipatory control, safety-aware formulations enhance operational reliability, and explainability-oriented designs help address transparency and trust barriers in safety-critical grid environments. However, the literature remains limited by heterogeneous benchmarks, inconsistent evaluation metrics, and a lack of real-world deployment evidence. This review provides a structured synthesis of current methodologies, identifies critical research gaps, and outlines future directions for the development of safe, interpretable, and deployment-ready MARL frameworks for urban energy systems. Full article

Digital transformation represents a strategic factor for enhancing organizational performance in the energy sector; however, its impact on operational productivity in Latin American public enterprises remains understudied. The purpose of this study was to analyze the relationship between Digital Transformation 4.0 and Operational Productivity at the Esmeraldas Refinery of EP PETROECUADOR, Ecuador’s most significant public oil-refining facility. A quantitative, non-experimental, cross-sectional design was employed, with a structured survey administered to 200 employees and analyzed through descriptive statistics, Pearson correlation, and multiple linear regression. The results confirmed a positive and statistically significant relationship between Digital Transformation 4.0 and Operational Productivity. Among the dimensions analyzed, Process Digitalization emerged as the strongest predictor of operational performance, followed by Digital Infrastructure, which recorded a favorable assessment among respondents, and overall Digital Transformation, which reflected a moderate level of strategic implementation within the organization. Digital Talent, while positively correlated with productivity, did not yield an independent significant effect within the joint regression model. These findings provide empirical evidence of the value of technological adoption in public industrial contexts and suggest that future research should incorporate mediating variables such as organizational culture, change management, and sustainability-oriented digital strategies to further explore this relationship. Full article

Geothermal silica has emerged as a promising and underutilised precursor for silicon-based lithium-ion battery anodes. Geothermal silica can be recovered from brines, scales, and solid residues generated during geothermal energy production, creating an opportunity to valorise existing waste streams while mitigating silica-scaling problems. This review examines the formation, availability, and material characteristics of geothermal silica, with particular emphasis on its high silica content, commonly reported in the range of ~50–98 wt% in solid geothermal residues, as well as its generally amorphous nature and porous structure. It then evaluates the main processing steps required to convert geothermal silica into battery-relevant silicon, including extraction, purification, and silica-to-silicon reduction, with particular focus on magnesiothermic reduction. Among the available routes, methods that provide improved impurity control while preserving porous or amorphous precursor structures appear most relevant for achieving favourable electrochemical performance. Recent comparative findings indicate that geothermal silica can, in some cases, be competitive with biomass-derived silica sources in terms of purity, composition, and morphology, although these advantages are not universal and depend on source-specific chemistry, impurity profile, and processing conditions. Reported electrochemical studies further show that geothermal-silica-derived silicon and silica-based composites can deliver electrochemically relevant capacities, in some cases exceeding the theoretical capacity of graphite (~372 mAh g⁻¹), although performance varies significantly across studies. In addition, specific surface areas of ~50–150 m² g⁻¹ reported for some geothermal silica materials may support further silicon processing and influence electrochemical behaviour. Overall, geothermal silica represents a technically relevant and sustainability-oriented pathway toward silicon-based anode materials; however, further work is needed on source consistency, impurity management, structural control, long-term cycling stability, and scalable production. Full article

Ras El-Bar, a premier historic coastal resort on Egypt’s Nile Delta, has experienced a marked increase in drowning incidents in recent years, despite the presence of extensive coastal protection structures. While these measures, particularly detached breakwaters (DBWs), groins, and port jetties, were originally implemented to mitigate shoreline erosion, their influence on nearshore hydrodynamics and swimmer safety remains insufficiently understood. In this context, the present study integrates high-resolution bathymetric data, remote sensing observations, and coupled numerical modeling (CMS-Wave and CMS-Flow) to examine how these interventions have altered wave–current interactions. The results indicate that the modified coastal setting produces distinct flow regimes, ranging from weak offshore currents (<0.1 m/s) to moderate rip currents (≈0.25 m/s) within DBW shadow zones, and locally intensified flows exceeding 0.7 m/s in shallow nearshore areas. These conditions facilitate the development of vortices and persistent rip currents, particularly within inter-DBW embayments. A simulation-based swimming risk map was developed by integrating water depth and simulated current characteristics, classifying the coastline into safe, moderate-risk, and high-risk zones. High-risk zones, concentrated within inter-DBW embayments at depths exceeding 2 m, show broad spatial agreement with available drowning and rescue incident records, subject to the limitations of the informal dataset, while the shallow accretional shadow zones landward of the DBWs exhibit comparatively lower hydrodynamic energy and safer conditions. Overall, the study demonstrates that coastal protection structures, although effective in controlling erosion, may unintentionally increase human risk when safety considerations are not incorporated into their design and management. Accordingly, a set of integrated, sustainability-oriented measures is proposed, including enhanced real-time monitoring, regulated beach access, adaptive sand nourishment, and targeted public awareness, with the aim of achieving a more balanced and resilient approach to coastal zone management. Full article

Sustainable forklift technologies have become essential in modern industrial logistics due to increasing environmental regulations, rising energy costs, and heightened occupational safety requirements. Given the complexity and variety of sustainable forklift options, selecting the most appropriate one has become a critical multi-criteria decision-making (MCDM) problem for companies. This study aims to determine the most appropriate sustainable forklifts by considering multiple qualitative and quantitative criteria that play a critical role in the forklift selection process of companies. To this end, meetings are conducted with managers possessing expertise in sustainability and logistics at companies operating in Turkey. Based on these insights, ten forklift alternatives and six evaluation criteria are identified. This is the first time, in this study, sustainability criteria such as sustainability in occupational health and safety, sustainability in agility, sustainability in ergonomics, durability and material sustainability, sustainability in load lifting capacity and sustainability in price are incorporated into the evaluation. To the best of our knowledge, no study in existing literature has specifically focused on sustainable forklift selection, incorporating the comprehensive sustainability-oriented criteria considered in this study. The Analytic Hierarchy Process (AHP) is employed to determine the weight of each criterion. Subsequently, forklift alternatives are ranked using the Multi-objective Optimization by Ratio Analysis (MOORA) ratio approach, the Additive Ratio Assessment (ARAS), and the Elimination and Choice Translating Reality (ELECTRE) methods. Moreover, weights derived based on different subjective and objective weighting schemes, specifically FUCOM, BWM, and Entropy, as well as the resulting ranking outcomes are comparatively examined to assess the impact of varying weighting structures on the robustness and consistency of the final decision results. The proposed methodology is applied within manufacturing and logistics companies in Turkey to assess its practical effectiveness. As a result of this study, the most appropriate sustainable forklifts for the companies are identified. Furthermore, the outcomes of the applied methods yield consistent/similar results. The results emphasize that managers should place greater importance on the criteria of sustainability in occupational health and safety—identified as the most critical factor—followed by durability and material sustainability, and sustainability in load lifting capacity when selecting forklifts. Sensitivity analyses indicate that the method yields consistent and effective results. Moreover, it demonstrates the robustness and accuracy of the forklift evaluations. In this context, this study serves as a guided reference for companies in the selection of sustainable forklifts. Full article

This paper presents a data-driven surrogate model for predicting cabin airflow and its integration into system-level electric vehicle simulations for energy management analysis. The model employs a graph-based neural network with a mirror-symmetric predictor–corrector architecture and is trained on a dataset generated using computational fluid dynamics (CFD) covering a defined range of inlet velocities and temperatures. The surrogate appropriately reconstructs temperature fields and captures the dominant airflow structures at significantly lower computational cost than CFD. Quantitative evaluation shows high accuracy in passenger-relevant regions, while localized discrepancies remain confined mainly to shear-layer zones. The model enables near-real-time inference and is coupled with a system-level modeling framework for control-oriented simulations that are impractical with CFD. The study is tailored to a specific geometry and operating range, showing that targeted training strategies and physics-based extensions improve robustness, particularly under limited data conditions. Full article

Structural health monitoring (SHM) plays an increasingly important role in managing aging, safety-critical infrastructure under growing environmental and operational demands. In recent years, fiber-optic sensors (FOSs) have attracted significant attention for SHM applications due to their immunity to electromagnetic interference, durability in harsh environments, multiplexing capability, and suitability for both localized and fully distributed measurements. In parallel, advances in machine learning (ML) have enabled new approaches for extracting actionable insights from large, high-dimensional sensing datasets. This paper presents a systematic review of FOS-based SHM systems integrated with ML across civil, transportation, energy, marine, and aerospace infrastructures. Following PRISMA 2020 guidelines, peer-reviewed studies were identified and synthesized to examine sensing principles, deployment configurations, data characteristics, and learning-based analytical strategies. Fiber optic technologies are categorized into point-based, quasi-distributed, and fully distributed systems, and their capabilities for capturing strain, temperature, and spatiotemporal structural responses are critically evaluated. ML approaches are examined from a task-oriented perspective, including damage detection, localization, severity assessment, environmental compensation, and prognosis, with emphasis on the alignment between sensing configurations and appropriate learning paradigms. Key challenges remain, particularly regarding large data volumes, environmental variability, limited labeled damage datasets, model generalization, and system-level integration. Emerging directions such as physics-informed and hybrid learning, transfer learning, uncertainty-aware modeling, and integration with digital twins are discussed as pathways toward more robust and scalable SHM systems. By jointly addressing sensing physics and data-driven intelligence, this review provides a structured reference and practical roadmap for advancing intelligent FOS-based SHM in next-generation infrastructure. Full article

In recent years, the rapid expansion of electric vehicle (EV) charging infrastructure and the increasing penetration of renewable energy sources require highly efficient and dynamically robust power electronic interfaces. In photovoltaic (PV)-assisted EV charging stations and DC microgrids, bidirectional DC-DC converters (BDCs) are essential for managing power flow between PV arrays, battery energy storage systems, and the DC bus supplying EV chargers. This paper presents a novel voltage and current control design for a BDC operating in a PV-powered DC microgrid oriented to EV charging applications. Following a detailed mathematical model of the converter, a digital current controller and a predictive voltage regulator were developed using Model-Based Predictive Control (MBPC). The proposed cascade control structure enables accurate DC bus voltage regulation and seamless bidirectional power flow under dynamic load variations representative of EV charging and discharging scenarios. The control scheme was evaluated in MATLAB/SIMULINK^® and experimentally validated through Field-Programmable Gate Array (FPGA)-based test benches using an OPAL-RT real-time (RT) simulator, integrating the RT-LAB and RT-eFPGAsim environments. The predictive controller achieved precise regulation in both buck and boost modes, reaching efficiencies of 97.07% and 98.57%, respectively. The results demonstrate that integrating MBPC with RT validation provides high performance, fast dynamic response, and computational efficiency, making the proposed approach suitable for renewable-integrated EV charging stations and next-generation DC microgrid-based mobility systems. Full article

Building-Integrated Photovoltaics (BIPV) can contribute to decarbonisation, but its large-scale deployment requires accurate energy yield predictions that justify these systems during the decision-making process to ensure cost-effectiveness. In urban contexts, boundary conditions involve modelling strategies that can reliably represent the effect of shading from nearby elements. However, specific tools for proper modelling BIPV are not generally available and the workflow frequently requires the combination of different tools. Nowadays there is still no clear nor unique strategy for modelling BIPV, and expert groups are currently working on benchmarking analyses. This work compares energy yield estimations from two PV simulation software tools, System Advisor Model and PVsyst to seven years of experimental data (2017–2023) from five BIPV façade arrays distributed across three orientations (east, south and west). The main focus was twofold. Firstly, to analyse their management of shadows by following two different shading approaches: their built-in 3D modelling tools and a Digital Surface Model (DSM). Secondly, to evaluate the capability of these tools to simulate the performance of real BIPV systems. Results manifest that conventional and accessible PV software can be suitable for BIPV modelling as long as care is taken to properly assess the effect of shading, especially from urban tree canopies. The novel DSM strategy proposed is proven effective and can be a valid alternative in certain cases when the availability of in situ data is limited. Full article

This study presents a comprehensive and systematic integrative review of Neighborhood-Level Energy Hubs (NLEHs) as pivotal enablers of sustainable and resilient urban energy systems. In response to accelerating climate pressures, rapid urbanization, and the decentralization of energy production, NLEHs are conceptualized as multi-carrier platforms that enable coordinated energy generation, storage, conversion, and exchange at the neighborhood scale. Utilizing a PRISMA-informed methodology to synthesize 125 core studies, the review systematically evaluates recent advances across five interconnected dimensions: conceptual foundations, system typologies, energy flow architectures, urban integration, and optimization paradigms. Unlike conventional reviews, this study explicitly bridges the critical gap between techno-economic optimization and socio-environmental priorities. A key novelty is the proposed mathematical integration of energy justice and Social Life Cycle Assessment (S-LCA) directly into optimization algorithms (e.g., MILP and MPC) as dynamic constraints and penalty terms. Particular emphasis is placed on participatory governance models, lifecycle sustainability metrics, and digitalization tools such as AI-driven energy management systems and urban digital twins. The analysis further reveals critical research gaps, highlighting a stark geographic dichotomy between high-tech, market-driven NLEHs in the Global North and resilience-oriented hybrid microgrids in the Global South, alongside the lack of adaptive regulatory frameworks. By proposing a unified Cyber–Physical–Social perspective, this study provides actionable insights for planners, policymakers, and researchers to support the development of scalable, inclusive, and context-sensitive NLEH implementations. Ultimately, the paper contributes to redefining neighborhood-scale energy systems as not only efficient and low-carbon infrastructures, but also as socially equitable, globally scalable, and institutionally adaptive components of future smart cities. Full article

Agrivoltaics (AV) has emerged as an integrated land-use innovation capable of simultaneously addressing food, energy, and water challenges, yet its systemic implications for farming system sustainability remain insufficiently synthesized. This review adopts a farming system dynamics perspective to examine how AV systems reorganize biophysical, ecological, and socio-economic interactions across agroecosystems. Drawing upon agroecological principles, pathways of sustainable intensification and ecological intensification, and resource-loop strategies in circular economy, we identify the key elements and cause-and-effect relationships that shape AV system performance. Evidence indicates that the co-location of photovoltaics (PV) structures and crop cultivation generates new system properties, altered light distribution, moderated microclimates, redistributed soil moisture, and diversified production functions that influence productivity, resource-use efficiency, ecological services, and farm resilience. Using causal loop analysis, we conceptualize four central feedback dynamics: (i) PV–crop trade-offs and spatial-sharing relationships; (ii) microclimate modifications and crop physiological responses; (iii) ecological performance and landscape-level interactions; and (iv) circularity loops connecting resource conservation, renewable-energy substitution, soil processes, and material flows. This feedback collectively determines eco-efficiency outcomes, including enhanced land-equivalent productivity, improved water-use efficiency, strengthened regulating services, and reductions in external energy dependence. At the farming-system scale, AV diversifies income streams and stabilizes yields under climatic variability, whereas at the landscape scale, it fosters multifunctionality by supporting regenerative resource flows and ecological resilience. Building on these insights, we propose an integrated framework that links agroecological elements with dynamic feedback structures to guide context-specific AV design, management, and governance. This system-oriented synthesis provides a foundation for future research and policy efforts aimed at optimizing AV as a circular, resilient, and sustainable farming system innovation. Full article

The increasing emphasis on sustainable and decentralized energy has elevated microgrids as a central element of modern power systems. By integrating renewable energy sources, advanced energy storage technologies, and intelligent control strategies, microgrids enhance efficiency, stability, and flexibility and play a vital role in creating resilient and adaptable energy networks. This review provides a comprehensive analysis of Energy Management Systems (EMSs) in microgrids, distinguishing between planning-oriented tools for techno-economic evaluation and control-oriented platforms for real-time operation and optimization. Hierarchical control architectures spanning primary, secondary, and tertiary levels are examined, highlighting their roles in frequency and voltage regulation, load sharing, and economic dispatch. Optimization techniques for EMSs are analyzed across deterministic, stochastic, metaheuristic, and artificial intelligence/machine learning methods, addressing objectives, constraints, uncertainties, and multi-timeframe decision-making. AI-based methods, including supervised learning, deep learning, and reinforcement learning, are highlighted for their ability to enhance predictive control, system autonomy, and operational efficiency, despite their computational demands. Future trends emphasize AI-based predictive control, deep learning for energy forecasting, multi-microgrid coordination, hybrid energy storage management, and cybersecurity enhancements. Overall, an intelligent EMS, combined with innovative technologies, is critical for developing resilient, scalable, and sustainable microgrid solutions that meet the evolving demands of modern energy systems. Full article

Background/Objectives: To evaluate the short-term effects of structured physical activity (PA), alone or combined with dietary counselling, in early-stage chronic kidney disease (CKD) patients managed in primary healthcare (PHC). Methods: This non-randomised controlled study was conducted in Croatia from 1 September to 30 November 2025. Ninety adults aged 40–75 years with early-stage CKD were allocated to three groups: structured PA, combined PA and dietary counselling, or control. Interventions included kinesiologist-led PA and, in the combined group, dietitian-led Mediterranean/plant-based counselling. Outcomes included estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (ACR), cardiometabolic risk factors, behavioural measures, quality of life, and sleep quality. Statistical significance was set at p < 0.01. Results: Seventy-eight participants completed follow-up. Changes in eGFR did not differ between groups (p = 0.310). Mean ± standard deviation changes in ACR were −1.10 ± 6.37, −0.86 ± 2.88, and +1.18 ± 3.13 in the PA, combined, and control groups, respectively (p = 0.017, not meeting the prespecified significance threshold). Significant between-group differences were observed for selected patient-reported and PA outcomes, including emotional well-being, energy/fatigue, role limitations due to emotional problems, sedentary time, and total PA (all p ≤ 0.006). Conclusions: Structured PA, with or without dietary counselling, improved PA behaviour and selected patient-reported outcomes in early-stage CKD managed in PHC but did not demonstrate significant short-term effects on kidney-related outcomes. These findings support the feasibility of integrating lifestyle-oriented interventions into PHC as part of integrated CKD care, while larger, longer-term studies are needed. Full article

Transportation asset management (TAM) has traditionally centered on technical performance and economic efficiency. In recent years, however, there has been increasing recognition of the environmental and social impacts of maintenance and rehabilitation (M&R) activities. This paper presents a systematic review of how Environmental, Social, and Governance (ESG) metrics are being incorporated into TAM. Using PRISMA 2020, four major databases were searched, identifying 75 studies since 2010. Environmental metrics were the most developed, especially those measuring emissions, energy use, and material consumption. Social metrics appeared less frequently and are typically used descriptively, including indicators of income inequality, user costs, and equity-focused metrics such as the Benefit Distribution Ratio and Social Return on Investment. Governance was the least explored pillar and is generally addressed through fiscal transparency, risk management, or institutional practices rather than explicit measurable indicators. Overall, the review shows growing interest in integrating ESG into TAM, but the adoption of social and governance metrics remains limited. In particular, governance indicators are rarely operationalized as measurable variables within TAM decision-making, highlighting a critical gap in the literature. This study synthesizes ESG-related indicators used in TAM and provides a structured foundation for future research and more comprehensive sustainability-oriented decision frameworks. Full article