Search Results (3,930)

Demand-side flexible resources in building energy systems hold significant potential for enhancing grid reliability and operational efficiency. However, their effective coordination remains challenging due to the complexity of modeling and aggregating heterogeneous loads. To address this, this paper proposes a feasible region aggregation and coordination method for load aggregators based on a GNMTL-LSTM-Zonotope framework. A Gradient Normalized Multi-Task Learning Long Short-Term Memory (GNMTL-LSTM) model is developed to forecast the power trajectories of diverse flexible resources, including air-conditioning systems, energy storage units, and diesel generators. Using these predictions and associated uncertainty bounds, dynamic feasible regions for individual resources are constructed with Zonotope structures. To enable scalable aggregation, a Minkowski sum-based method is applied to merge the feasible regions of multiple resources efficiently. Additionally, a directionally weighted Zonotope refinement strategy is introduced, leveraging time-varying flexibility revenues from energy and reserve markets to enhance approximation accuracy during high-value periods. Case studies based on real-world office building data from Shandong Province validate the effectiveness, modeling precision, and economic responsiveness of the proposed method. The results demonstrate that the framework enables fine-grained coordination of flexible loads and enhances their adaptability to market signals. This study is the first to integrate GNMTL-LSTM forecasting with market-oriented Zonotope modeling for heterogeneous demand-side resources, enabling simultaneous improvements in dynamic accuracy, computational scalability, and economic responsiveness. Full article

Longevity and healthy aging result from the complex interaction of genetic, epigenetic, microbial, behavioral, and environmental factors. The central nervous system—particularly the cerebral cortex—and the autonomic nervous system (ANS) play key roles in integrating external and internal signals, shaping energy metabolism, immune tone, and emotional regulation. This narrative review examines how the brain–ANS axis interacts with epigenetic regulation, telomere dynamics, the gut microbiome, and the exposome to influence biological aging and resilience. Relevant literature published between 2010 and 2025 was selected through comprehensive database searches (PubMed, Scopus, Google Scholar), with a focus on studies addressing the multisystemic determinants of aging. Emphasis is placed on lifestyle-related exposures, such as diet, physical activity, psychosocial support, and environmental quality, that modulate systemic physiology through neurovisceral pathways. Drawing on empirical findings from classical Blue Zones and recent observational research in the Cilento region of southern Italy, this review highlights how context-specific factors—such as clean air, mineral-rich water, Mediterranean dietary patterns, and strong social cohesion—may foster bioelectric, metabolic, and neuroimmune homeostasis. By integrating data from neuroscience, systems biology, and environmental epidemiology, the review proposes a comprehensive model for understanding healthy longevity and supports the development of personalized, context-sensitive strategies in geroscience and preventive medicine. Full article

Heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems account for a significant share of global energy demand, prompting intensive research into advanced thermal enhancement techniques. Among these, nanofluids—colloidal suspensions of nanoparticles in base fluids—have shown promise in boosting heat transfer performance. This review provides a structured and critical evaluation of nanofluid applications in HVAC&R systems, synthesizing research published from 2015 to 2025. A total of 200 peer-reviewed articles were selected from an initial pool of over 900 through a systematic filtering process. The selected literature was thematically categorized into experimental, numerical, hybrid, and AI/ML-based studies, with further classification by fluid type, performance metrics, and system-level relevance. Unlike prior reviews focused narrowly on thermophysical properties or individual components, this work integrates recent advances in artificial intelligence and hybrid modeling to assess both localized and systemic enhancements. Notably, nanofluids have demonstrated up to a 45% improvement in heat transfer coefficients and up to a 51% increase in the coefficient of performance (COP). However, the review reveals persistent gaps, including limited full-system validation, underexplored real-world integration, and minimal use of AI for holistic optimization. By identifying these knowledge gaps and research imbalances, this review proposes a forward-looking, data-driven roadmap to guide future research and facilitate the scalable adoption of nanofluid-enhanced HVAC&R technologies. Full article

In response to contemporary challenges such as the COVID-19 pandemic, climate change, armed conflicts, and economic instability, healthcare systems worldwide are increasingly confronted with multifaceted and overlapping crises—collectively referred to as polycrisis. These interconnected threats amplify one another, placing unprecedented strain on healthcare infrastructure, governance, and equity. The COVID-19 pandemic alone led to an estimated 16.3 million missed hospitalizations in 2020 and 14.7 million in 2021, revealing systemic vulnerabilities and deepening social inequalities. Armed conflicts, such as in Syria and Gaza, have devastated healthcare access. In Gaza, by mid-2024, 85% of the population had been forcibly displaced, with only 17 of 36 hospitals partially functioning and over 885 healthcare workers killed. Climate change further exacerbates health burdens, with over 86% of urban residents globally exposed to harmful air pollution, contributing to 1.8 million deaths annually. This study introduces a novel perspective by applying social epidemiology to the analysis of polycrisis. While the existing literature often emphasizes political or economic dimensions, our approach highlights how overlapping crises affect population health, social vulnerability, and systemic resilience. By integrating sociodemographic and environmental data, social epidemiology supports crisis-resilient care models, targeted interventions, and equitable health policies. We argue for a stronger mandate to invest in data infrastructure, enhance surveillance, and embed social determinants into health system responses. Full article

Buildings are significant contributors to global energy consumption and greenhouse gas emissions, with air conditioning systems representing a large share of this demand. Multi-building cooperative energy management is a promising solution for improving energy efficiency, but traditional control methods often struggle with dynamic environments and complex interactions. This study proposes an enhanced Soft Actor–Critic (SAC) algorithm, termed ORAR-SAC, to address these challenges in building cluster energy management. The ORAR-SAC integrates an Ordered Reward-based Experience Replay mechanism to prioritize high-value samples, improving data utilization and accelerating policy convergence. Additionally, an adaptive temperature parameter regularization strategy is implemented to balance exploration and exploitation dynamically, enhancing training stability and policy robustness. Using the CityLearn simulation platform, the proposed method is evaluated on a cluster of three commercial buildings in Beijing under time-of-use electricity pricing. Results demonstrate that ORAR-SAC outperforms conventional rule-based and standard SAC strategies, achieving reductions of up to 11% in electricity costs, 7% in peak demand, and 3.5% in carbon emissions while smoothing load profiles and improving grid compatibility. These findings highlight the potential of ORAR-SAC to support intelligent, low-carbon building energy systems and advance sustainable urban energy management. Full article

In the context of advancing sustainable energy solutions, this paper provides a detailed modelling study of the process integration of biomass gasification to produce syngas and subsequent methanation for methane production. The process is assumed to take place in a circulating fluidised bed and three adiabatic fixed-bed reactors. To address the low H₂/CO ratio of syngas produced from biomass gasification using air, three pre-methanation scenarios were evaluated: water gas shift reaction (scenario 1), H₂ addition through Power-to-Gas (scenario 2), and splitting syngas into pure H₂ and CO and then recombining them in a 3:1 ratio (scenario 3). The findings reveal that each scenario presents a unique balance of efficiency, decarbonisation potential, and technological integration. Scenario 2 achieves the highest overall efficiency at 62%, highlighting the importance of integrating renewable electricity into the methane industry. Scenario 1, which incorporates WGS and CO₂ capture, offers an environmentally friendly solution with an overall efficiency of 59%. In contrast, Scenario 3, involving H₂/CO separation and recombination, achieves only 44.4% efficiency due to energy losses during separation, despite its operational simplicity. Methane yields were highest in Scenario 1, while Scenario 2 offers the most significant potential for integration with decarbonised power systems. The model was validated using published data and feedstock characteristics from experimental work and industrial projects. The results showed good agreement and supported the accuracy of the simulation in reflecting realistic biomass processing for methane production. Full article

The air source heat pump coupled with energy storage system is a key technology for flexibly utilizing clean energy. The capacity configuration parameters and control strategies of this coupled system are two important aspects that significantly affect its performance. In order to explore the methods of setting configuration parameters and provide reasonable operation strategies, a simulation model of the coupled system under a time-of-use electricity pricing strategy is established and verified with measured data. Through multi-objective optimization of the system, configuration schemes considering economy, energy saving, and flexibility are given. Subsequently, based on the load prediction model, an optimal control strategy is proposed with the objective function of minimizing the operating cost. The optimization amplitude of the schemes considering the three indicators reached 11.09%, 13.37%, and 29.03%, respectively. Under the proposed control strategies, the typical daily electricity consumption decreased by 14.65% to 24.06%, and the operating electricity cost is saved by approximately 17.32%. By reasonably designing the parameters of the coupled system, its economic, energy-saving performance, and flexibility can be improved by more than 11% compared to a system designed using traditional methods. By adopting the control strategy based on hourly load prediction, the operating cost can be reduced significantly. Full article

Aircraft encounter complex ground and air scenarios during service, necessitating a comprehensive analysis of extensive global load cases during the design phase to ensure structural reliability and safety. While high-fidelity finite element analysis enables precise assessment of load case criticality, its prohibitive human and computational costs constrain aircraft iterative development. To overcome this challenge, this study proposes a Global Load Case Analysis (GLCA) system for identifying critical load cases across structural sections. The method is driven by aerodynamic load data and structural response data from coarse-grid models. First, it achieves a quantitative ranking of global load case criticality, providing engineers with a standardized severity metric. Second, based on defined criticality relationships, it identifies coverage, coupling, and differentiation patterns among load cases to establish criticality hierarchies. Finally, a novel 1DCNN architecture with specialized training strategies learns the GLCA system’s behavioral patterns, enabling accurate prediction of criticality for newly added load cases without computationally intensive reanalysis. The results demonstrate strong agreement between GLCA and high-fidelity model analyses: quantitative ranking achieves 95.98% average accuracy with complete identification of critical load cases. Predictions for new load cases yield coefficients of determination (R²) > 0.98 and 97.91% average criticality classification accuracy. Furthermore, GLCA operates 335 times more efficiently than high-fidelity finite element analysis. This approach effectively substitutes high-fidelity modeling during load development, reducing human effort and shortening aircraft design iteration cycles. Full article

Understanding the coupling mechanism and coordinated development between digital transformation and operational efficiency in logistics enterprises is vital for optimizing resource allocation and promoting high-quality, sustainable growth in the logistics industry. This study analyzes panel data from 52 listed logistics enterprises in China from 2014 to 2023. It constructs evaluation index systems for digital transformation and operational efficiency and applies an integrated methodology comprising the super-efficiency SBM model, coupling coordination degree model, and random forest regression model to evaluate efficiency, assess coupling dynamics, and forecast future trends. The main findings are as follows: (1) Overall operational efficiency has shown a pattern of fluctuating growth, increasing from 0.520 to 0.585. Road transport consistently outperformed other sectors, water transport maintained steady growth, and air transport exhibited significant volatility, particularly during the COVID-19 pandemic. (2) The coupling coordination degree remains in the initial coordination stage (0.642–0.677), with road transport achieving intermediate-level coordination (0.718) by 2021. Water transport showed gradual but stable improvement, and air transport remained unstable due to external shocks. (3) Road transport leads in overall industry performance, while water transport exhibits stable progress, and air transport is hindered by international supply chain disruptions and technological adoption challenges. (4) Projections for 2024–2026 suggest an average annual growth rate of 0.31% in coupling coordination across all subsectors, although inter-sectoral synergistic mechanisms require further enhancement. Based on these findings, this study proposes targeted recommendations: increasing comprehensive investments in digital technologies across the entire supply chain, cultivating interdisciplinary talent, optimizing risk management frameworks, and refining policy support. These measures aim to strengthen the integration of digital transformation and operational efficiency, contributing to the sustainable development of the logistics industry. Full article

Icing represents a significant hazard to the flight safety of unmanned aerial vehicles (UAVs), particularly affecting critical aerodynamic surfaces such as air intakes, wings, and empennages. While conventional adhesive electrothermal de-icing systems are straightforward to operate, they present safety concerns, including a 15–25% increase in system weight, elevated anti-/de-icing power consumption, and the risk of interlayer interface delamination. To address the objectives of reducing weight and power consumption, this study introduces an innovative electrothermal–structural–durability co-design strategy. This approach successfully led to the development of a glass fiber-reinforced polymer (GFRP) component that integrates anti-icing functionality with structural load-bearing capacity, achieved through an embedded hot-pressing process. A stress-damage cohesive zone model was utilized to accurately quantify the threshold of mechanical performance degradation under electrothermal cycling conditions, elucidating the evolution of interfacial stress and the mechanism underlying interlayer failure. Experimental data indicate that this novel component significantly enhances heating performance compared to traditional designs. Specifically, the heating rate increased by approximately 202%, electrothermal efficiency improved by about 13.8% at −30 °C, and interlayer shear strength was enhanced by approximately 30.5%. This research offers essential technical support for the structural optimization, strength assessment, and service life prediction of UAV anti-icing and de-icing systems in the aerospace field. Full article

Accurate and timely land monitoring is crucial for addressing global environmental, economic, and societal challenges, including climate change, sustainable development, and disaster mitigation. While single-source remote sensing data offers significant capabilities, inherent limitations such as cloud cover interference (optical), speckle noise (radar), or limited spectral information (LiDAR) often hinder comprehensive and robust characterization of land surfaces. Recent advancements in synergistic harmonization technology for land monitoring, along with enhanced signal processing techniques and the integration of machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize the comprehensive applications of synergistic harmonization technology for geosciences, with a particular focus on recent advancements. Most of the existing review papers focus on the application of a single technology in a specific area, highlighting the need for a comprehensive review that integrates synergistic harmonization technology. This review provides a comprehensive review of advancements in land monitoring achieved through the synergistic harmonization of optical, radar, and LiDAR satellite technologies. It details the unique strengths and weaknesses of each sensor type, highlighting how their integration overcomes individual limitations by leveraging complementary information. This review analyzes current data harmonization and preprocessing techniques, various data fusion levels, and the transformative role of machine learning and deep learning algorithms, including emerging foundation models. Key applications across diverse domains such as land cover/land use mapping, change detection, forest monitoring, urban monitoring, agricultural monitoring, and natural hazard assessment are discussed, demonstrating enhanced accuracy and scope. Finally, this review identifies persistent challenges such as technical complexities in data integration, issues with data availability and accessibility, validation hurdles, and the need for standardization. It proposes future research directions focusing on advanced AI, novel fusion techniques, improved data infrastructure, integrated “space–air–ground” systems, and interdisciplinary collaboration to realize the full potential of multi-sensor satellite data for robust and timely land surface monitoring. Supported by deep learning, this synergy will improve our ability to monitor land surface conditions more accurately and reliably. Full article

The livestock sector significantly contributes to atmospheric emissions of various pollutants, such as ammonia (NH₃) and particulate matter of diameter under 2.5 µm (PM2.5) from activity and barn management. The objective of this study was to evaluate the reliability of low-cost sensors integrated with an IoT system for monitoring PM2.5 concentrations in a dairy barn. To this end, data acquired by a PM2.5 measurement device has been validated by using a high-precision one. Results demonstrated that the performances of low-cost sensors were highly correlated with temperature and humidity parameters recorded in its own IoT platform. Therefore, a parameter-based adjustment methodology is proposed. As a result of the statistical assessments conducted on this data, it has been demonstrated that the analysed sensor, when corrected using the proposed correction model, is an effective device for the purpose of monitoring the mean daily levels of PM2.5 within the barn. Although the model was developed and validated by using data collected from a dairy barn, the proposed methodology can be applied to these sensors in similar environments. Implementing reliable and affordable monitoring systems for key pollutants is crucial to enable effective mitigation strategies. Due to their low cost, ease of transport, and straightforward installation, these sensors can be used in multiple locations within a barn or moved between different barns for flexible and widespread air quality monitoring applications in livestock barns. Full article

This paper presents a calibrated and dynamically responsive simulation framework for hybrid energy systems that integrate Proton Exchange Membrane Fuel Cells (PEMFCs) and batteries, targeting applications in light commercial vehicles (LCVs). The aim is to support the design and assessment of energy management strategies (EMS) under realistic operating conditions. A publicly available PEMFC model is used as the starting point. To improve its representativeness, calibration is performed using experimental polarization curve data, enhancing the accuracy of the stack voltage model, and the air compressor model—critical for maintaining stable fuel cell operation—is adjusted to reflect measured transient responses, ensuring realistic system behavior under varying load demands. Quantitatively, the calibration results are strong: the R² values of both the fuel cell polarization curve and the overall system efficiency are around 0.99, indicating excellent agreement with experimental data. The calibrated model is embedded within a complete hybrid vehicle powertrain simulation, incorporating longitudinal dynamics and control strategies for power distribution between the battery and fuel cells. Simulations conducted under WLTP driving cycles confirm the model’s ability to replicate key behaviors of PEMFC-battery hybrid systems, particularly with respect to dynamic energy flow and system response. In conclusion, this work provides a reliable and high-fidelity simulation environment based on empirical calibration of key subsystems, which is well suited for the development and evaluation of advanced EMS algorithms. Full article

The utilization of ammonia as a fuel for gas turbines involves practical challenges due to its low reactivity, narrow flammability limits, and slow laminar flame propagation. One of the potential solutions to enhance the combustion reactivity of ammonia is co-firing with syngas. This paper presents an experimental and numerical investigation of the laminar burning velocity (LBV) of ammonia/syngas/air mixtures under elevated pressures (up to 10 bar) and temperatures (up to 473 K). Experiments were conducted in a constant-volume combustion chamber with a total volume of 11 L equipped with a dual-electrode capacitive discharge ignition system. A systematic sensitivity analysis was conducted to experimentally evaluate the system performance under various syngas compositions and equivalence ratios from 0.7 to 1.6 and ultimately identify the factors with the most impact on the system. As a complement to the experiments, a detailed numerical simulation was carried out integrating available kinetic mechanisms—chemical reaction sets and their rates—to support advancements in the understanding and optimization of ammonia/syngas co-firing dynamics. The sensitivity analysis results reveal that LBV is significantly enhanced by increasing the hydrogen content (>50%). Furthermore, the LBV of the gas mixture is found to increase with the use of a rich flame, higher mole fractions of syngas, and higher initial temperatures. The results indicate that higher pressure reduces LBV by 40% but at the same time enhances the adiabatic flame temperature (by 100 K) due to an equilibrium shift. The analysis was also extended to quantify the impact of syngas mole fractions and elevated initial temperatures. The kinetics of the reactions are analyzed through the reaction pathways, and the results reveal how the preferred pathways vary under lean and rich flame conditions. These findings provide valid quantitative design data for optimizing the combustion kinetics of ammonia/syngas blends, offering valuable design data for ammonia-based combustion systems in industrial gas turbines and power generation applications, reducing NOₓ emissions by up to 30%, and guiding future research directions toward kinetic models and emission control strategies. Full article

Trifluoroacetic acid (TFA) is a persistent degradation product of widely used fluorinated compounds such as hydrofluorocarbons, hydrofluoroolefins, hydrochlorofluorocarbons (HCFCs) and hydrochlorofluoroolefins. Its chemical stability, water solubility, and environmental persistence raise concerns about potential human and ecological risks. To provide an overview of current knowledge on TFA, we conducted a literature search (PubMed and Scopus, December 2024–January 2025) focusing on its environmental fate, human exposure, toxicokinetic, ecotoxicology, and regulation. A narrative approach was applied, prioritizing recent and high-quality evidence. TFA is ubiquitous in air, water, food, and consumer products. Human exposure occurs mainly through ingestion and inhalation. It is rapidly absorbed and excreted mostly unchanged in urine, with limited metabolic transformation. Though not bioaccumulated in fat, its environmental persistence and ongoing exposure raise concerns about long-term systemic effects. Ecotoxicological data show chronic toxicity in aquatic and terrestrial species, with environmental concentrations often exceeding safety thresholds. Currently, no binding EU limit exists for TFA, although several countries have proposed drinking water guidelines. TFA represents an emerging environmental contaminant with potential human health and ecological impacts. Strengthened monitoring, long-term toxicological studies, and precautionary regulatory action are urgently needed. Full article