Search Results (53,075)

This study aimed to investigate the production of nanoemulgels structured with flaxseed fiber, designed to simulate salad dressings. For this purpose, the influence of microfluidizer passes (from one to four) on physicochemical and rheological properties was determined, followed by an assessment of thermal behavior. Rotor–stator homogenization followed by microfluidization were employed to produce nanoemulgels, which were characterized using laser diffraction, multiple light scattering, and rheological measurements. The resulting systems exhibited monomodal particle size distributions with mean diameters below 220 nm. Increasing the number of microfluidizer passes from one to four led to slight reductions in particle size, although they were not statistically significant. The formulation with two passes demonstrated superior physical stability during aging studies. Rheological evaluation indicated enhanced gel-like behavior with up to three passes, whereas excessive energy input (four passes) slightly compromised structural integrity. The linear viscoelastic region decreased notably after the first pass but remained relatively stable thereafter. The two-pass nanoemulgel, identified as the optimal formulation, was further tested for thermal stability. Temperature increases (5–20 °C) led to minor decreases in viscosity and firmness, yet the structure remained thermally stable. These findings support microfluidization as an effective strategy for developing stable flaxseed fiber-based nanoemulgels, with potential applications in functional food systems. Full article

This study presents the development of an innovative battery-free, RF-powered implantable microdevice designed for intravesical chemotherapy delivery. The system utilizes a custom-designed RF energy harvesting module that enables wireless energy transfer through biological tissue, eliminating the need for internal power sources. Mechanical and electronic components were co-optimized to achieve full functionality within a compact, biocompatible housing suitable for intravesical implantation. The feasibility of the device was validated through simulation studies and ex vivo experiments using biological tissue models. The results demonstrated successful energy transmission, storage, and sequential actuator activation within a biological environment. The proposed system offers a promising platform for minimally invasive, wirelessly controlled drug delivery applications in oncology and other biomedical fields. Full article

This research investigates the responsiveness of climate and low-carbon (green) stock returns to oil prices and conventional energy stock returns, focusing on both contemporaneous and causal relationships, during an oil crisis. Two methodologies are used: vector auto-regressive (VAR) for testing the causal relationship, and ordinary least squares (OLS) for investigating the contemporaneous relationship. The main empirical results suggest that green stocks have a bidirectional positive contemporaneous relationship with oil prices and energy stock returns but no significant bidirectional causal relationship. The results reveal that oil prices and energy stock returns play a larger role in contemporaneous than causal relationships with green stock returns. In addition, green stock returns seem to have a stronger positive relationship with energy stock return than oil prices. Full article

Under the background of “double carbon” and sustainable development, aimed at the problem of resource capacity planning in the integrated energy system (IES), at improving the economy of system planning operation and renewable energy (RE) consumption, and at reducing carbon emissions, this paper proposes a multi-objective bi-level sustainability planning method for IES considering the bilateral response of supply and demand and hydrogen utilization. Firstly, the multi-energy flow in the IES is analyzed, constructing the system energy flow framework, studying the support ability of hydrogen utilization and the bilateral response of supply and demand to system energy conservation, emission reduction and sustainable development. Secondly, a multi-objective bi-level planning model for IES is constructed with the purpose of optimizing economy, RE consumption, and carbon emission. The non-dominated sorting genetic algorithm II (NSGA-II) and commercial solver Gurobi are used to solve the model and, through the simulation, verify the model’s effectiveness. Finally, the planning results show that after introducing the hydrogen fuel cells, hydrogen storage tank, and bilateral response, the total costs and carbon emissions decreased by 29.17% and 77.12%, while the RE consumption rate increased by 16.75%. After introducing the multi-objective planning method considering the system economy, RE consumption, and carbon emissions, the system total cost increased by 0.34%, the consumption rate of RE increased by 0.6%, and the carbon emissions decreased by 43.61t, which effectively provides reference for resource planning and sustainable development of IES. Full article

In the electrification of low-altitude aircraft, aviation hybrid power systems have become one of the core research areas in this field due to their significant advantages of low emissions and long endurance. The energy management strategy is an important part of the design of aviation hybrid power systems and has a significant impact on the performance and safety.This paper first develops a 200 kW dual DC-bus series hybrid power system prototype for low-altitude aircraft and its Simulink simulation model; then, it proposes a rule-based energy management strategy that uses the smoothness of the state of charge (SOC) of energy storage batteries as a coordination criterion. The strategy is validated via ground tests, where the battery SOC remains above 30%, the system response time is within 5 s, and the DC-bus voltage fluctuation is within 1%. These results demonstrate the strategy’s feasibility, providing a reference for designing and implementing series hybrid power systems. Full article

With increasing global pressure to decarbonize electricity systems, particularly in regions outside international carbon trading frameworks, it is essential to develop adaptive optimization tools that account for regulatory policies and system-level uncertainty. An emission-constrained power dispatch strategy based on an Adaptive Grouped Fish Migration Optimization (AGFMO) algorithm is proposed. The algorithm incorporates dynamic population grouping, a perturbation-assisted escape strategy from local optima, and a performance-feedback-driven position update rule. These enhancements improve the algorithm’s convergence reliability and global search capacity in complex constrained environments. The proposed method is implemented in Taiwan’s 345 kV transmission system, covering a decadal planning horizon (2023–2033) with scenarios involving varying load demands, wind power integration levels, and carbon tax schemes. Simulation results show that the AGFMO approach achieves greater reductions in total dispatch cost and CO₂ emissions compared with conventional swarm-based techniques, including PSO, GACO, and FMO. Embedding policy parameters directly into the optimization framework enables robustness in real-world grid settings and flexibility for future carbon taxation regimes. The model serves as decision-support tool for emission-sensitive operational planning in power markets with limited access to global carbon trading, contributing to the advanced modeling of control and optimization processes in low-carbon energy systems. Full article

Achieving carbon reduction is essential in advancing China’s dual carbon goals and promoting a green transformation in the transportation sector. Changes in energy structure and intensity constitute key drivers for sustainable and low-carbon development in this field. To explore the spatial spillover effects of the energy structure and intensity on the green transition of transportation, this study constructs a panel dataset of 30 Chinese provinces from 2007 to 2020. It employs a super-efficiency SBM model, non-parametric kernel density estimation, and a spatial Markov chain to verify and quantify the spatial spillover effects of green total factor productivity (GTFP) in the transportation sector. A dynamic spatial Durbin model is then used for empirical estimation. The main findings are as follows: (1) GTFP in China’s transportation sector exhibits a distinct spatial pattern of “high in the east, low in the west”, with an evident path dependence and structural divergence in its evolution; (2) GTFP displays spatial clustering characteristics, with “high–high” and “low–low” agglomeration patterns, and the spatial Markov chain confirms that the GTFP levels of neighboring regions significantly influence local transitions; (3) the optimization of the energy structure significantly promotes both local and neighboring GTFP in the short term, although the effect weakens over the long term; (4) a reduction in energy intensity also exerts a significant positive effect on GTFP, but with clear regional heterogeneity: the effects are more pronounced in the eastern and central regions, whereas the western and northeastern regions face risks of negative spillovers. Drawing on the empirical findings, several policy recommendations are proposed, including implementing regionally differentiated strategies for energy structure adjustment, enhancing transportation’s energy efficiency, strengthening cross-regional policy coordination, and establishing green development incentive mechanisms, with the aim of supporting the green and low-carbon transformation of the transportation sector both theoretically and practically. Full article

Biomolecular condensates (BCs), formed through liquid–liquid phase separation (LLPS), are membraneless compartments that dynamically regulate key cellular processes. Beyond their canonical roles in energy metabolism and apoptosis, Mitochondria harbor distinct BCs, including mitochondrial RNA granules (MRGs), nucleoids, and degradasomes, that coordinate RNA processing, genome maintenance, and protein homeostasis. These structures rely heavily on proteins with intrinsically disordered regions (IDRs), which facilitate the transient and multivalent interactions necessary for LLPS. In this review, we explore the composition and function of mitochondrial BCs and their emerging involvement in neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, Amyotrophic lateral sclerosis, and Huntington’s disease. We provide computational evidence identifying IDR-containing proteins within the mitochondrial proteome and demonstrate their enrichment in BC-related functions. Many of these proteins are also implicated in mitochondrial stress responses, apoptosis, and pathways associated with neurodegeneration. Moreover, the evolutionary conservation of phase-separating proteins from bacteria to mitochondria underscores the ancient origin of LLPS-mediated compartmentalization. Comparative analysis reveals functional parallels between mitochondrial and prokaryotic IDPs, supporting the use of bacterial models to study mitochondrial condensates. Overall, this review underscores the critical role of mitochondrial BCs in health and disease and highlights the potential of targeting LLPS mechanisms in the development of therapeutic strategies. Full article

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

Adoxophyes orana (Lepidoptera: Tortricidae) is a significant polyphagous leafroller that damages trees and shrubs in Rosaceae and other families. However, the molecular mechanisms by which this pest recognizes sex pheromones and host plant volatiles remain largely unknown. Tissue expression profiles indicated that two general odorant-binding proteins (AoraGOBP1 and AoraGOBP2) were more abundant in the antennae and wings of both sexes, with AoraGOBP1 being rich in the female head and abdomen. Temporal expression profiles showed that AoraGOBP1 was expressed at the highest level in 5 day-nmated adults, while AoraGOBP2 exhibited high expression in 5 day-unmated, 7 day-unmated, and mated female adults. Fluorescence competitive binding assays of heterologous expressed AoraGOBPs demonstrated that AoraGOBP2 strongly bound to the primary sex pheromone Z9-14:Ac, and two minor sex pheromones Z9-14:OH and Z11-14:OH, whereas AoraGOBP1 only showed a high binding affinity to Z9-14:Ac. What is more, AoraGOBP1 exhibited a broader binding spectrum for host plant volatiles than AoraGOBP2. Molecular dockings, molecular dynamic simulations, and per-residue binding free decompositions indicated that the van der Waals interaction was the predominant contributor to the binding free energy. Electrostatic interactions between aldehydes, or alcohols and AoraGOBPs stabilized the conformational structures. Phe12 from AoraGOBP1, and Phe13 from AoraGOBP2 were identified as the most important residues that contributed to bind free energy. Our findings provide a comprehensive insight into the molecular mechanisms of olfactory recognition in A. orana, facilitating the development of chemical ecology-based approaches for the control. Full article

Hydrogen is one of the future green energy sources that could resolve issues related to fossil fuels. The widespread use of hydrogen can be enabled by composite-overwrapped pressure vessels for storage. It offers advantages due to its low weight and improved mechanical performance. However, the safe storage of hydrogen requires continuous monitoring. Combining ultrasonic guided waves with interpretable machine learning provides a powerful tool for structural health monitoring. In this study, we developed a feature extraction approach based on a similarity method that enables interpretability in the proposed machine learning model for damage detection and localization in pressure vessels. Furthermore, a systematic optimization was performed to explore and tune the model’s parameters. This resulting model provides accurate damage localization and is capable of detecting and localizing damage on hydrogen pressure vessels with an average localization error of 2 cm and a classification accuracy of 96.5% when using quantized classification. In contrast, binarized classification yields a higher accuracy of 99.5%, but with a larger localization error of 6 cm. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

Urban carbon reduction is instrumental in enabling cities to realize their developmental sustainability objectives. However, regional disparities in economic development pose significant challenges to low-carbon transitions. This study utilizes panel data from 282 cities in China spanning 2006–2021, considering the energy saving and emission reduction (ESER) fiscal policy as an external shock. Using a multi-period difference-in-differences approach, we assess how ESER impacts urban carbon emissions. Our findings indicate that ESER significantly reduces municipal carbon emissions by an average of 23.3% compared to non-pilot cities. Mechanism analyses suggest that this effect operates through reduced energy consumption, improved industrial structure, and enhanced green innovation. ESER’s impact exhibits heterogeneity across cities with different levels of economic development, population size, innovation capacity, and industrial composition. Moreover, we find evidence of spatial spillover effects, as ESER benefits extend to neighboring regions. These results confirm the effectiveness of ESER in promoting low-carbon development and offer practical implications for enhancing environmental governance through green fiscal instruments. Full article

Coal gangue concrete (CGC), a recycled cementitious material derived from industrial solid waste, presents both opportunities and challenges for structural applications due to its heterogeneous composition and variable mechanical behavior. This study develops an ensemble learning framework—incorporating XGBoost, LightGBM, and CatBoost—to predict four key constitutive parameters based on experimental data. The predicted parameters are subsequently incorporated into an ABAQUS finite element model to simulate the compressive–bending response of CGC columns, with simulation results aligning well with experimental observations in terms of failure mode, load development, and deformation characteristics. To enhance model interpretability, a hybrid approach is adopted, combining permutation-based global feature importance analysis with SHAP (SHapley Additive exPlanations)-derived local explanations. This joint framework captures both the overall influence of each feature and its context-dependent effects, revealing a three-stage stiffness evolution pattern—brittle, quasi-ductile, and re-brittle—governed by gangue replacement levels and consistent with micromechanical mechanisms and numerical responses. Coupled feature interactions, such as between gangue content and crush index, are shown to exacerbate stiffness loss through interfacial weakening and pore development. This integrated approach delivers both predictive accuracy and mechanistic transparency, providing a reference for developing physically interpretable, data-driven constitutive models and offering guidance for tailoring CGC toward ductile, energy-absorbing structural materials in seismic and sustainability-focused engineering. Full article

This study employed a pot experiment to compare the effects of varying application rates of chili straw biochar on seed germination and seedling growth of alfalfa (Medicago sativa L.) cultivated in electrolytic manganese residue (EMR) and to elucidate the underlying mechanisms. We aimed to provide a theoretical basis for vegetation restoration and manganese pollution control at EMR disposal sites. Our results indicated that while chili straw biochar did not affect the seed germination rate, it significantly enhanced the germination energy. In addition, treatment with 5% biochar significantly increased the germination index. Biochar application increased alfalfa seedling height (6.13 cm in the control group vs. 6.63–7.20 cm in the treated groups). Concurrently, the aboveground fresh biomass significantly increased by 49–77% compared to the control. Additionally, biochar application elevated chlorophyll content and reduced malondialdehyde content in alfalfa leaves. Correlation analysis revealed that the primary mechanisms underlying biochar-mediated improvement in seed germination and seedling growth involved enhancing the organic matter, available nitrogen, and available phosphorus content in the EMR, while decreasing the available manganese content. Overall, the application of 5% biochar in EMR optimally improved alfalfa plant growth and development. Full article