Search Results (720)

To address the complex trade-offs among charging efficiency, battery lifespan, energy efficiency, and safety in high-power electric vehicle (EV) fast charging, this paper presents an intelligent control framework based on contrastive learning and manifold-constrained multi-objective optimization. A multi-physics coupled electro-thermal-chemical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, incorporating both continuous and discrete decision variables—such as charging power and cooling modes—into a unified optimization framework. An environment-adaptive optimization strategy is also developed. To enhance learning efficiency and policy safety, a contrastive learning–enhanced policy gradient (CLPG) algorithm is proposed to distinguish between high-quality and unsafe charging trajectories. A manifold-aware action generation network (MAN) is further introduced to enforce dynamic safety constraints under varying environmental and battery conditions. Simulation results demonstrate that the proposed framework reduces charging time to 18.3 min—47.7% faster than the conventional CC–CV method—while achieving 96.2% energy efficiency, 99.7% capacity retention, and zero safety violations. The framework also exhibits strong adaptability across wide temperature (−20 °C to 45 °C) and aging (SOH down to 70%) conditions, with real-time inference speed (6.76 ms) satisfying deployment requirements. This study provides a safe, efficient, and adaptive solution for intelligent high-power EV fast-charging. Full article

This paper studies joint data collection and wireless power transfer in a UAV-assisted IoT network. A rotary-wing UAV follows a fly–hover–communicate cycle. At each hover, it simultaneously receives uplink data in full-duplex mode while delivering radio-frequency energy to nearby devices. Using a realistic propulsion-power model and a nonlinear energy-harvesting model, we formulate trajectory and hover control as a multi-objective optimization problem that maximizes the aggregate data rate and total harvested energy while minimizing the UAV’s energy consumption over the mission. To enable flexible trade-offs among these objectives under time-varying conditions, we propose a dynamic, state-adaptive weighting mechanism that generates environment-conditioned weights online, which is integrated into an enhanced deep deterministic policy gradient (DDPG) framework. The resulting dynamic-weight MODDPG (DW-MODDPG) policy adaptively adjusts the UAV’s trajectory and hover strategy in response to real-time variations in data demand and energy status. Simulation results demonstrate that DW-MODDPG achieves superior overall performance and a more favorable balance among the three objectives. Compared with the fixed-weight baseline, our algorithm increases total harvested energy by up to 13.8% and the sum data rate by up to 5.4% while maintaining comparable or even lower UAV energy consumption. Full article

This study is intended to conduct a comprehensive review of federalism. This study starts from the institutional aspect and analyzes how federalism, as a compound structure, divides power between the central and local governments. Then, this study mentions that federalism also has its normative connotations, which are traceable to the theological concept of a covenant. We also elaborate on how the success of the United States’ federalism strengthened its institutional aspect while overshadowing the older covenant tradition. Next, this study presents a typological framework of federalism, introducing concepts such as coming-together federalism and holding-together federalism; dual federalism and cooperative federalism; decentralization and non-centralization; and asymmetrical federalism, non-territorial autonomy, and consociationalism, presidential and parliamentary federalism, as well as democratic federalism and authoritarian federalism/facade federalism. Next, this study compares monist federalism with multinational federalism. Then, this study examines the specific applications of federalism in fiscal, environmental, health-care, and social-welfare policies. By reviewing the history, theoretical origins, institutional development, and contemporary manifestations of federalism, this study provides a roadmap for scholars in the field of federal studies. Finally, this study also puts forward several testable hypotheses, aiming to provide operational research agendas for future studies. Full article

Electric vehicle (EV) adoption is a critical step toward achieving sustainable transportation and reducing carbon emissions, especially in regions like Saudi Arabia that are undergoing rapid urban development and energy diversification. However, the widespread adoption of EVs is hindered by a variety of interrelated economic, infrastructural, and policy-related factors. This study aims to systematically identify and structure these influencing factors using Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. Based on a thorough literature review and expert consultation, 17 key factors affecting EV adoption in Saudi Arabia were identified. The ISM results reveal that purchase price, long-term savings, resale value, urban planning, and accessibility are among the most influential drivers of adoption. The MICMAC analysis complements these insights by categorizing the variables based on their driving and dependence power. The developed hierarchical model provides insights into the complex interdependencies among these factors and offers a strategic framework to support policymakers and stakeholders in accelerating EV uptake. The study contributes to a deeper understanding of the dynamics influencing EV adoption in emerging markets. Full article

Wind power is integral to the transformation of energy systems towards sustainability. However, the increasing number of wind turbines approaching the end of their service life presents significant challenges in terms of waste management and environmental sustainability. Rotor blades, typically composed of thermoset polymer composites reinforced with glass or carbon fibres, are particularly problematic due to their low recyclability and complex material structure. The aim of this article is to provide a system-level review of current end-of-life strategies for wind turbine components, with particular emphasis on blade recycling and decision-oriented comparison, and its integration into circular economy frameworks. The paper explores three main pathways: operational life extension through predictive maintenance and design optimisation; upcycling and second-life applications; and advanced recycling techniques, including mechanical, thermal, and chemical methods, and reports qualitative/quantitative indicators together with an indicative Technology Readiness Level (TRL). Recent innovations, such as solvolysis, microwave-assisted pyrolysis, and supercritical fluid treatment, offer promising recovery rates but face technological and economic as well as environmental compliance limitations. In parallel, the review considers deployment maturity and economics, including an indicative mapping of cost and deployment status to support decision-making. Simultaneously, reuse applications in the construction and infrastructure sectors—such as concrete additives or repurposed structural elements—demonstrate viable low-energy alternatives to full material recovery, although regulatory barriers remain. The study also highlights the importance of systemic approaches, including Extended Producer Responsibility (EPR), Digital Product Passports and EU-aligned policy/finance instruments, and cross-sectoral collaboration. These instruments are essential for enhancing material traceability and fostering industrial symbiosis. In conclusion, there is no universal solution for wind turbine blade recycling. Effective integration of circular principles will require tailored strategies, interdisciplinary research, and bankable policy support. Addressing these challenges is crucial for minimising the environmental footprint of the wind energy sector. Full article

Cultural heritage is increasingly mobilized as a tool of international engagement, yet the diplomatic uses of heritage remain conceptually underdeveloped and analytically fragmented. This paper introduces the Heritage Diplomacy Spectrum, a multidimensional framework that maps how states and affiliated actors use heritage—both tangible and intangible—to pursue strategic, symbolic, and normative goals in cross-border contexts. Drawing on critical heritage studies, international relations, and memory politics, this study identifies six analytical dimensions (e.g., proactive vs. reactive, cultural vs. historical, strategic vs. moral) and develops seven ideal types of heritage diplomacy, ranging from soft power projection to post-dependency and corrective diplomacy. These ideal types, constructed in the Weberian tradition, serve as heuristic tools to illuminate the varied motivations and diplomatic postures underlying heritage-based engagement. A central matrix is presented to illustrate how each type aligns with different strategic logics and affective registers. This study argues that heritage diplomacy constitutes a distinct modality of heritage governance—one that transcends soft power narratives and encompasses conflict, reconciliation, symbolic redress, and identity assertion. The framework contributes both to theory-building and policy analysis, offering a diagnostic lens through which the ethical, political, and communicative dimensions of heritage diplomacy can be more systematically understood. Full article

This study presents a holistic framework for adaptive thermal energy storage (A-TES) in solar-assisted systems. This framework aims to support a reliable industrial energy supply, particularly during periods of limited sunlight, while also facilitating industrial decarbonization. In previous studies, the focus was not on addressing the framework of the entire problem, but rather on specific parts of it. Therefore, the innovation in this study lies in bringing these aspects together within a unified framework through a data-driven approach that combines the analysis of efficiency, technology, environmental impact, sectoral applications, operational challenges, and policy into a comprehensive system. Sensible thermal energy storage with an adaptive approach can be utilized in numerous industries, particularly concentrated solar power plants, to optimize power dispatch, enhance energy efficiency, and reduce gas emissions. Simulation results indicate that stable regulations and flexible incentives have led to a 60% increase in solar installations, highlighting their significance in investment expansion within the renewable energy sector. Integrated measures among sectors have increased energy availability by 50% in rural regions, illustrating the need for partnerships in renewable energy projects. The full implementation of novel advanced energy management systems (AEMSs) in industrial heat processes has resulted in a 20% decrease in energy consumption and a 15% improvement in efficiency. Making the switch to open-source software has reduced software expenditure by 50% and increased productivity by 20%, demonstrating the strategic advantages of open-source solutions. The findings provide a foundation for future research by offering a framework to analyze a specific real-world industrial case. Full article

Microgrids (MGs) have emerged as pivotal players in the energy transition by enabling the efficient integration of distributed energy resources and the provision of ancillary services to the power system. Despite their technical capabilities, MGs still face economic and regulatory barriers that hinder their widespread deployment in electricity markets. This paper presents a structured methodological framework to assess the economic viability of MGs delivering services such as peak shaving, loss compensation, and voltage support, among others. The proposed approach considers three distinct scenarios: (1) MGs supplying energy to local loads, (2) hybrid MGs combining local supply with ancillary services, and (3) MGs exclusively dedicated to ancillary services. The framework incorporates adjusted levelized cost of electricity (LCOE), levelized avoided cost of electricity (LACE), and net value metrics, while accounting for tax incentives and market price signals. A case study based in Colombia (Cali and Camarones) validates the framework through simulations conducted in HOMER Pro V3.18.4 and MATLAB Online. The results indicate that remuneration schemes based on availability and service utilization significantly enhance the viability of MGs. The proposed methodology is applicable to emerging regulatory environments and offers guidance for designing public policies that promote the active participation of MGs in supporting grid operations. Full article

This paper introduces a power-driven systems engineering methodology for the early-phase design of highly miniaturized satellites: PlanarSats. We derive an analytical framework linking power requirements, contingency policies, solar-cell performance, and subsystem integration to determine the absolute minimum satellite size. Through idealized and detailed case studies, we explore the trade-offs inherent in subsystem selection and integration constraints. Sensitivity analysis identifies critical factors affecting minimum area and operational envelopes. Our framework provides a clear tool for balancing functionality, reliability, and physical limits in next-generation ultra-small satellite missions. Full article

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article

This study presents a stochastic Markov-based modeling framework for occupant behavior and residential lighting demand in Luxembourg. Integrating demographic data, time-use surveys, Markov chains, and dual-layer optimization, the model enhances the accuracy of non-HVAC energy demand simulations. The Harmonized European Time Use Surveys (HETUS) provide a detailed activity-based energy modeling approach, while Bayesian and constraint-based optimization improve data calibration and reduce modeling uncertainties. A Luxembourg-specific stochastic load profile generator links occupant activities to energy loads, incorporating occupancy patterns and daylight illuminance calculations. This study quantifies lighting demand variations across household types, validating results against empirical TUS data with a low mean squared error (MSE) and a minor deviation of +3.42% from EU residential lighting demand standards. Findings show that activity-aware dimming can reduce lighting demand by 30%, while price-based dimming achieves a 21.60% reduction in power demand. The proposed approach provides data-driven insights for energy-efficient residential lighting management, supporting sustainable energy policies and household-level optimization. Full article

Sudan’s rural regions face acute challenges in energy access, exacerbated by ongoing conflict that has destroyed major power infrastructure and crippled conventional electricity generation. This study investigates the technical and economic feasibility of photovoltaic (PV) solar systems as a sustainable alternative for powering off-grid rural communities. Using MATLAB simulations (Version 24b), Global Solar Atlas data, and HOMER software (Version 4.11) for hybrid system optimization, a case study of a village in Shariq al-Nil, Khartoum, demonstrates the viability of solar energy to meet residential, medical, and agricultural needs. Beyond technical analysis, this paper highlights the transformative role of solar energy in post-conflict reconstruction, with potential applications in powering irrigation systems and supporting agricultural livelihoods. It also emphasizes the importance of integrating community-centered policy frameworks to ensure equitable access, long-term adoption, and sustainable development outcomes. The findings advocate for policies that support renewable energy investment as a cornerstone of rebuilding efforts in Sudan and similar contexts affected by conflict and infrastructure collapse. Full article

Urban regeneration has increasingly become a global strategy for promoting sustainable urban development, with the renewal of deteriorating residential communities serving as a key dimension of this process. Within the framework of a people-centered development paradigm, growing attention has been directed toward the necessity of securing residents’ satisfaction in community renewal initiatives. This study employs advanced textual analysis of resident submissions collected from government–citizen interaction platforms to investigate the determinants of satisfaction with renewal projects. Leveraging the semantic comprehension capabilities of large language models (LLMs), we identify both salient keywords and sentiment orientations embedded in residents’ narratives. Guided by the theoretical framework of resident satisfaction, the extracted keywords are organized into seven thematic domains: basic infrastructure improvement, quality-enhancement renovation, solicitation of residents’ preferences, residents’ decision-making power, policy transparency, construction governance, and community-level communication. Regression modeling is subsequently applied to assess the relative influence of these thematic domains on residents’ satisfaction. The findings suggest that insufficient integration of residents’ preferences at the preliminary stages of participation constitutes a principal source of dissatisfaction during the implementation of renewal projects. Furthermore, the study compares Latent Dirichlet Allocation (LDA) topic modeling with LLMs-based topic clustering, revealing the latter’s superior capacity to capture thematic structures in complex, long-form textual data. These results underscore the potential of LLMs to enhance the analytical rigor of research on urban regeneration and citizen participation. Full article

The fashion industry faces a critical sustainability crisis, contributing up to 10% of global carbon emissions and generating 92 million tons of textile waste annually. The study highlights the complex interplay of material flows, business models, power structures, and cultural mindsets, presenting a multi-scaled framework for advancing cleaner production and circularity in one of the world’s most resource-intensive sectors. This study proposes a transformative model for circular bioeconomy in fashion, integrating systems-change theory, degrowth economics, and emotional durability. Through case studies, including Patagonia, Eileen Fisher, and EU policy frameworks, the paper demonstrates how circular strategies can reduce waste, extend product lifecycles, and promote ethical labor practices. Notably, brands implementing take-back programs and recycled materials have diverted over 1.5 million garments from landfills and achieved up to 70% recycled content. The study critically addresses challenges such as technological solutionism, systemic greenwashing, and waste colonialism, concluding that incremental changes are insufficient. A paradigm shift in business models, consumer culture, and policy is essential for a regenerative and just fashion future. Full article

The construction industry in Pakistan faces persistent challenges due to uncertainties such as behavioral intention, risk identification, and stakeholder perception, which often lead to significant losses in construction activities and human resources. This study aims to quantitatively evaluate these critical factors within the theoretical framework of Building Information Modeling (BIM) and the Technology Acceptance Model (TAM). Specifically, key constructs—Behavioral Intention (BI), Hazard Identification (HI), and Stakeholder Perception (SP)—are analyzed to assess their influence on construction safety management practices. A structured questionnaire was distributed electronically to construction professionals across various ongoing projects in Pakistan. The questionnaire items were based on a five-point Likert scale, and reliability was confirmed with high Cronbach’s alpha values for BI (0.82), HI (0.92), and SP (0.91). To evaluate the relationships between constructs, descriptive statistics and multiple regression analysis were employed. The regression results showed strong model fit for BI and HI (R² = 0.945), and near-perfect fit for SP (R² = 0.998), demonstrating robust predictive power. Significant correlations were found among independent variables such as Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Use (ATU), and others. This study further identifies Trust (TR) and Organizational Culture (OC) as critical predictors of stakeholder perception in the BIM context. A conceptual framework was developed incorporating statistical parameters (e.g., p-values, R², t-stats) to categorize the effectiveness of BIM and TAM theoretical integration for safety risk management. This approach is novel in its use of TAM-based constructs to evaluate BIM-related safety outcomes in the Pakistani construction sector—a context where such empirical evidence is limited. The findings provide predictive insights into how behavioral, perceptual, and organizational variables influence construction safety performance, offering practical implications for BIM adoption and safety policy design. Full article