Search Results (1,326)

From the perspective of interactions between energy firm behavior and government intervention strategies, this study develops a contagion control model for energy firm debt default risk utilizing cellular automata and complex network theory. This research investigates the spatio-temporal evolution of risk transmission and evaluates the efficacy of various mitigation protocols through computational simulation. The research results indicate that: (1) An escalation in both the transmission likelihood and the rate of immunity decay significantly amplifies the propagation strength of debt default risks. Conversely, the stability of the energy firm network is bolstered as the probabilities of immunity and recovery increase. (2) The contagion intensity for debt default risk is positively correlated with market noise, the risk appetite of energy firms, and their corporate influence. It is negatively correlated with risk awareness, creditworthiness, regulatory intensity, and policy subsidies. Furthermore, it exhibits an inverted U-shaped relationship with investor sentiment. (3) Within the interconnected network of energy firms, risk contagion can be effectively mitigated not only by enhancing risk perception and credit standing but also by guiding risk preference and managing firm influence. Furthermore, the integration and adjustment of government intervention strategies, such as regulatory intensity and policy subsidies, can more efficiently accelerate the eradication of debt default risk among energy firms. Full article

Particulate emerging contaminants (PECs) pose increasing ecological risks due to their widespread occurrence and complex environmental behaviors, yet their heterogeneous toxic mechanisms remain poorly understood, especially under environmentally relevant conditions and concentration gradients. Here, Chlorella vulgaris was used as a model organism to systematically compare the effects of polystyrene nanoparticles (PSNPs), silver nanoparticles (AgNPs), and titanium dioxide nanoparticles (TiO₂NPs) across environmentally relevant and elevated concentrations (100 μg/L and 10 mg/L). Distinct toxicity pathways were identified among PEC types. PSNPs primarily induced chronic interference via particle–cell interactions, heteroaggregation, sedimentation-driven shading, and extracellular polymeric substance (EPS) regulation, rather than ROS-dominated toxicity. In contrast, AgNPs exhibited transformation-driven toxicity, undergoing intracellular speciation into Ag₂S, AgCl, and Ag⁺, which triggered oxidative stress, membrane damage, and lipid peroxidation. TiO₂NPs showed relatively high bioavailability and persistent oxidative stress effects. These results demonstrate that PEC toxicity evolves with particle type and concentration. Importantly, oxidative stress alone is insufficient to capture PEC ecotoxicity, which also involves the long-term impacts on algal behavior, sedimentation dynamics, and energy metabolism. This study provides mechanistic insights into PEC-induced algal toxicity and supports the source-oriented management of particulate pollutants in aquatic environments, particularly in hotspot scenarios such as wastewater discharge and sediment resuspension. Full article

Tri-reforming of methane (TRM) has emerged as a promising pathway for low-carbon syngas production by integrating steam reforming, dry reforming, and partial oxidation within a single process. This coupling enables simultaneous CH₄ utilization and CO₂ valorization while enabling internal heat generation and flexible adjustment of the H₂/CO ratio for downstream synthesis. However, TRM performance cannot be adequately evaluated using conversion or energy efficiency alone, because the process involves complex interactions among competing reaction pathways, transport phenomena, catalyst stability, and thermodynamic irreversibility. This review provides a multiscale critical assessment of TRM from both first-law energy and second-law exergy perspectives, linking reaction-network fundamentals to reactor-level behavior and system-level performance. The literature evidence shows that although high temperatures and near-autothermal operation can enhance CH₄ conversion and reduce external heat demand, these conditions may simultaneously intensify deep oxidation, hotspot formation, carbon-forming tendencies, and exergy destruction. While equilibrium analyses help define feasible operating windows, they are insufficient without kinetic modeling and reactor-scale studies that capture spatial non-uniformities and pathway competition. Across reported TRM systems, exergy destruction is consistently concentrated within the reformer, identifying the reacting core as the dominant thermodynamic bottleneck. Accordingly, the key challenge in TRM is not simply to maximize conversion but to preserve chemical work potential while maintaining syngas quality and operational stability. Viewed from this perspective, TRM is better understood as an irreversibility-aware multiscale design problem in which optimal performance depends on the integrated optimization of catalyst functionality, reactor architecture, heat management, and system-level operation. Full article

The growing penetration of renewable energy has intensified building load fluctuations, substantially increasing balancing costs. Electric vehicles (EVs) in building clusters often have considerable idle parking time beyond essential charging needs, enabling them to provide significant flexibility while meeting scheduled demands. This EV flexibility can balance intra-day load deviations and enable arbitrage in day-ahead electricity markets. However, conventional model-based approaches are fundamentally limited by their dependence on forecasting accuracy under high uncertainty from renewable generation and EV behavior. To address this, we propose a novel bi-level online optimization framework. The upper level employs a Lyapunov optimization-based algorithm that operates without predictions, making real-time decisions on total EV charging power to balance supply-demand mismatches. The lower level introduces novel flexibility metrics for individual EVs—encompassing temporal, volumetric, and cross-day dimensions—and optimizes power allocation by minimizing flexibility loss. Furthermore, we model EV flexibility as virtual queues and rigorously derive mathematical bounds on their limits, providing theoretical support for managing flexibility reserves. Rigorous analysis validates the framework’s feasibility, and comprehensive simulations demonstrate its superiority over benchmark algorithms, achieving significant cost reductions under various uncertainty scenarios. Full article

Objectives: To examine disordered eating behaviors, orthorexic tendencies, binge-eating episodes, attitudes toward exercise, perceived hormone-related symptoms and body image perception among competitive bodybuilders across different levels of competitive experience. Methods: In this cross-sectional study, 60 competitive bodybuilders (29 men, 31 women) completed an anonymous online questionnaire. The survey evaluated demographic characteristics, coaching and training management, phase-specific symptoms (such as libido, sleep, eating behaviors, and menstrual alterations), orthorexic tendencies, exercise addiction, and body-image perception. Results: Both sexes reported reduced libido, increased hunger, and sleep disturbances, along with frequent weight monitoring and common binge-eating episodes. Moreover, females frequently reported menstrual irregularities. ORTO-15 scores indicated a potential risk of orthorexia nervosa, while EAI-3 scores suggested a risk of exercise addiction in novice females and advanced males, with differences in mood regulation and guilt across sex and experience. Males showed higher perceived and ideal muscle mass, whereas females reported higher perceived body fat and a preference for leaner physiques. Conclusions: Competitive bodybuilders of both sexes exhibit post-competition binge eating, mood- and appearance-driven exercise behaviors, and pronounced body-image concerns. Screening, education on energy availability, structured post-competition support, and health-focused coaching are recommended to prevent the progression from sport-specific practices to clinical pathology. Full article

Proton exchange membrane fuel cell–battery hybrid power systems provide an effective solution to overcome the limited endurance of battery-powered multirotor unmanned aerial vehicles. However, the highly transient power demands of quadcopter platforms, combined with balance-of-plant losses and operational constraints, create significant challenges for reliable energy management. This study proposes a degradation-aware stress-mitigation model predictive control-based energy management framework to maximize mission endurance under realistic conditions. A control-oriented, physics-consistent model is developed using manufacturer polarization data from a 500 W Aerostak proton exchange membrane fuel cell. The model captures polarization behavior, balance-of-plant loads, battery dynamics, and direct current-bus power balance. The model predictive control strategy optimally allocates power by maintaining direct current-bus stability, regulating battery state-of-charge within safe limits, and constraining fuel cell power ramp rates to mitigate degradation. High-fidelity simulations are conducted under stochastic wind disturbances and mission-dependent load profiles, including takeoff, climb, cruise, and maneuvering phases. The results show continuous power delivery without unmet load demand. The hybrid system achieves a flight endurance of 220–224 min, consuming a total of 89.99 g of hydrogen at an average rate of 0.398–0.412 g/min, indicating a notable reduction under the considered operating conditions. Additionally, long-term analysis indicates that over 97% of initial endurance is preserved after 100 cycles, demonstrating robustness against fuel cell aging. An analytical real-time feasibility assessment further indicates that the control-oriented formulation is compatible with the computational resources of typical unmanned aerial vehicle-class onboard processors, while the integration of adaptive and robust predictive control techniques is identified as a direction for future work. Full article

Northern Colombian Creole-Antillean avocado constitutes a promising agroindustrial resource because of its lipid-rich composition and regional availability. Despite this potential, the industrial exploitation of this biomass remains limited, particularly regarding the technical assessment of large-scale oil production systems. In this study, an avocado oil production process was evaluated through computer-aided simulation combined with the Water–Energy–Product (WEP) methodology to assess operational behavior, resource utilization, and process efficiency from an integrated technical perspective. The evaluated system achieved an overall production yield of 9.43%, mainly affected by the elevated raw material requirements associated with oil generation. Nevertheless, the extraction stage exhibited favorable technical performance, reaching an oil recovery efficiency of 81.42%. Concerning water management, the process required 26.85 m³/t of freshwater and generated wastewater equivalent to 96.05% of the total water consumed, revealing important limitations related to water integration and recirculation within the process configuration. From an energy perspective, the system presented a specific energy intensity of 19,929 MJ/t, with natural gas representing the predominant energy source throughout the operation. Overall, the obtained results demonstrate that the proposed process is technically viable for avocado oil production while also identifying critical opportunities for improving resource utilization, decreasing water demand, and enhancing the operational sustainability of the system. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Energy efficiency and positive users’ experiences are key to sustainable healthcare. However, limited research has been conducted to examine the relationship between them. A framework-based systematic literature review using the PRISMA methodology was conducted to identify key links in existing literature between the two dimensions in hospitals. A total of 138 publications including articles, reviews, and conference papers were retrieved from the Scopus database on 23 March 2026. Selected studies were critically appraised for methodological quality and potential bias prior to the analysis and narrative synthesis using the TCCM framework. This review advances the field by reconceptualizing indoor environmental quality as the mediating link between energy efficiency and user experience in hospitals, offering an integrated multi-dimensional framework linking both dimensions. Key linking constructs were identified as spatial, environmental, psychological, behavioral, and organizational. Future pathways must integrate qualitative users’ experiences with quantitative energy efficiency data to enrich the understanding of hospital sustainability. Considering stakeholder priorities, conducting comprehensive evaluations of indoor environmental quality, and adopting holistic and interdisciplinary methodologies and simulations in hospital designs and retrofits are essential to address gaps in healthcare sustainability. Architectural measures must prioritize natural light, acoustic comfort, air quality, and access to views. The findings provide practical guidance for hospital designers, managers, and policymakers on strategies that promote healthcare sustainability through energy efficiency and positive user experiences. Full article

Electric vehicle (EV) charging behaviors exhibit significant heterogeneity in terms of price sensitivity, time-of-day preference, and weekend charging habits, creating challenges for charging demand prediction and service management. To address this issue, this paper proposes a three-variable charging response framework that jointly considers electricity price, time-of-day preference, and weekend preference. Using real charging-order data from a public charging platform, four behavioral parameters, namely baseline charging demand (Q₀), price sensitivity (α), time preference (β), and weekend preference (γ), are estimated through nonlinear least squares (NLS). Based on the extracted parameter vectors, K-means clustering is employed to identify five representative user groups: Commuting-Dominant, elastic energy-saving, Weekend-Switching, Night-Preferential, and discount-sensitive users. The results reveal substantial behavioral heterogeneity among users. To validate the proposed framework, both parameter interpretability analysis and benchmark comparisons are conducted. Compared with the best baseline model, the proposed method reduces the test RMSE from 11.5 kWh to 8.3 kWh (27.8%), decreases the test MAPE from 25.3% to 18.7% (26.1%), and improves the test R² from 0.70 to 0.80. The proposed framework provides an interpretable approach for EV charging behavior modeling and user segmentation, offering practical support for differentiated pricing, charging demand management, and intelligent charging service operation. Full article

With the increasing penetration of electric vehicles (EVs), multi-virtual power plant (multi-VPP) systems face growing challenges in coordinating heterogeneous flexible resources, managing stochastic EV charging and discharging behaviors, and maintaining distribution network security. This paper develops an integrated collaborative scheduling strategy for multi-VPPs with EV cluster participation. In the proposed framework, EV clusters, energy storage systems, and distributed generation units are coordinated under distribution-network operational constraints. The regulation capability of EV clusters is characterized by considering state of charge (SOC) dynamics, charging/discharging power limits, arrival and departure times, vehicle availability, and user travel requirements and is further embedded into the scheduling decision space of each VPP. To coordinate operational economy and nodal voltage security, a voltage-security-aware optimization objective is formulated and transformed into a Markov game. A multi-agent deep reinforcement learning (MADRL) method is then adopted to learn coordinated scheduling policies among multiple VPP agents. Case studies show that the proposed method achieves stable convergence after approximately 3500 training episodes, with a normalized reward exceeding 0.92, and outperforms TD3, DDPG, and PPO in terms of convergence speed and training stability. The scheduling results further indicate that the proposed strategy effectively coordinates EV clusters and energy storage systems, maintains nodal voltages within safe limits, and improves the operational performance of multi-VPP systems. These results demonstrate the applicability of the proposed framework for secure and economic collaborative scheduling in distribution networks. Full article

Against the backdrop of frequent adjustments and iterations in global climate policies, the issue of policy uncertainty surrounding corporate energy structure upgrades has become increasingly prominent. A key concern for achieving global green sustainable development is how to efficiently advance corporate low-carbon transition. In view of this, we construct the energy structure low-carbon transition at the enterprise level, and explore the influence and mechanism of climate policy uncertainty on the energy structure low-carbon transition of enterprises from the perspective of enterprise willingness and ability. The research findings indicate: (1) Corporate energy structure low-carbon transition is substantially impeded by climate policy uncertainty, and this conclusion is upheld by a battery of robustness and endogeneity analyses. (2) Climate policy uncertainty inhibits corporate energy structure low-carbon transition by reducing management’s long-term behavior, lowering green technology innovation levels, and weakening effective investment. (3) According to heterogeneity analysis, non-state-owned businesses, areas with lax environmental regulations, and businesses with poor climate risk awareness are more affected by the inhibiting impact caused by climate policy uncertainty. In addition to offering theoretical underpinnings and helpful advice for governments looking to create stable climate policies and enhance climate governance systems, this paper gives fresh perspectives on the fundamental reasoning behind corporate energy structure decarbonization. Full article

Documentary videos on green energy consumption are widely distributed via platforms such as YouTube, yet the verbal framing strategies embedded in their subtitle transcripts remain systematically understudied. This study applies the Analysis of Topic Model Networks (ATMN)—an unsupervised machine learning approach combining LDA topic modeling, semantic network analysis, and hierarchical clustering—to subtitle transcripts extracted from 60 YouTube green energy consumption documentaries. Three distinct framing communities are identified: (1) the Technological Supply Frame, which foregrounds zero-carbon resources, renewable generation, smart grid systems, and AI-enabled energy management as the technical foundation of decarbonization; (2) the Socioeconomic Transition Frame, the most thematically expansive, which positions the energy transition simultaneously as an economic opportunity, a behavioral imperative, and a systemic industrial transformation spanning green investment, end-use substitution, industrial decarbonization, and green mobility; and (3) the Ecological Governance Frame, which integrates ecological co-benefits with international climate commitments to construct the transition as a globally mandated planetary responsibility. Together, these frames reveal a richer and more multi-dimensional verbal framing landscape than previously documented in the green energy communication literature, extending beyond techno-optimism or environmentalism to encompass financial, governance, and behavioral dimensions within a single integrated corpus. The identified framing strategies offer actionable guidance for policymakers, energy enterprises, and media producers seeking to accelerate green energy consumption transition through targeted, evidence-based video communication. Full article

Electric vehicle (EV) charging networks face major operational challenges, including demand uncertainty, peak-load congestion, and anomalous charging behavior, particularly in multi-station environments. This study proposes an anomaly-aware Q-learning framework for real-time scheduling in multi-station EV charging systems by integrating short-term load forecasting, anomaly detection, and intelligent scheduling within a unified operational pipeline. The framework combines Prophet, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models for short-term demand forecasting, while Convolutional Neural Networks (CNN), Autoencoders, and Isolation Forests are employed for anomaly detection. Forecasting and anomaly information are incorporated into a Q-learning scheduler to support adaptive charger allocation and congestion management. Evaluation using a four-year, real-world dataset comprising more than 2000 EV charging sessions demonstrates improved scheduling performance, achieving reductions in peak load and waiting time while improving energy delivery consistency. The framework further demonstrates low scheduling latency, supporting suitability for real-time deployment in OCPP-compliant smart charging infrastructures. Full article

Background/Objectives: Insomnia is a prevalent sleep disorder with substantial public health implications, yet epidemiological data from Serbia remain limited. This study aimed to assess the prevalence of clinically significant insomnia symptoms in the adult population of Serbia and to examine associated sociodemographic, comorbidity, psychosocial, and lifestyle factors. Materials and methods: A cross-sectional study was conducted from September 2023 to September 2025, including 2577 adults aged 18–89 years across Serbia. Insomnia symptom severity was measured using the Insomnia Severity Index (ISI), with scores ≥ 15 indicating clinically significant insomnia symptoms. Sociodemographic, comorbidity, psychosocial, and lifestyle factors were assessed via self-reported questionnaires. Multivariable logistic regression with LASSO variable selection was used to identify factors independently associated with clinically significant insomnia symptoms. Results: The prevalence of clinically significant insomnia symptoms (ISI ≥ 15) was 10.9%. Independent factors associated with clinically significant insomnia symptoms included being single (OR = 1.54) or divorced (OR = 1.75), lower educational attainment (OR = 0.71 per level increase), being retired (OR = 1.83) or a student (OR = 1.66), dermatological comorbidities (OR = 2.99), use of anxiolytic medications (OR = 2.44), exposure to stressful life events (OR = 1.88), engagement in late-night activities (OR = 1.37), consumption of coffee/tea (OR = 2.22), energy drink consumption (OR = 1.52), and late-night eating habits (OR = 1.27). Conclusions: Clinically significant insomnia symptoms among adults in Serbia are influenced by a complex interplay of sociodemographic, comorbidity, psychosocial, and lifestyle factors. These findings underscore the need for integrated approaches that address both medical and modifiable behavioral determinants in the prevention and management of insomnia symptoms. Full article