Search Results (1,397)

To address the challenge of the synergistic optimization of carbon reduction and economic operation in the integrated energy systems (IES) of industrial parks, this paper proposes an optimization scheduling model that incorporates carbon trading and supply–demand response (SDR) coordination mechanisms. This model is based on an IES coupling power-to-gas (P2G) and carbon capture and storage (CCS) technologies. First, the K-means clustering algorithm identifies three typical daily scenarios—transitional season, summer, and winter—from annual operation data. Then, we construct a synergistic optimization model that integrates a carbon trading mechanism, tiered carbon quota allocation, and SDR coordination. The model is solved via mixed-integer linear programming (MILP) to minimize total system operating costs. Systematic comparative analysis across six scenarios quantifies the incremental benefits: P2G–CCS coupling achieves a 15.2% cost reduction and 49.3% emission reduction during transitional seasons; supply–demand response contributes 3.5% cost and 5.6% emission reductions; technology synergies yield an additional 21.6 percentage points of emission reduction beyond individual contributions. The integrated system achieves 100% renewable energy utilization and optimizes peak-to-valley differences across electricity, heating, and cooling loads. Carbon price sensitivity analysis reveals three response stages—low sensitivity, rapid reduction, and saturation—with the saturation point at 200 CNY/t (28.6 USD/t), providing quantitative guidance for tiered carbon pricing design. This research provides theoretical support and practical guidance for achieving low-carbon economic operations in industrial parks. Full article

This experiment was designed to systematically evaluate the immunomodulatory effect of water extract of Artemisia annua L. (WEAA) on sheep, both in vivo and in vitro, and to determine the involvement of the TLR4/NF-κB signaling pathway in mediating these effects. In experiment 1, 32 female sheep (Dorper × Han, 3 months old, 24 ± 0.09 kg each) were designated to 4 groups, with each group receiving a basal diet supplemented with, respectively, 0 (control group), 500, 1000, and 1500 mg/kg WEAA. The serum, liver, and spleen immune indicators and related gene expressions were measured. In experiment 2, the peripheral blood lymphocytes (PBLs) were processed with WEAA (0, 25, 50, 100, 200, and 400 μg/mL), with six replicates assigned to each concentration group, then the cell viability, immune function, and related gene expressions were measured, and the optimal concentration of WEAA was determined. In experiment 3, the experimental groups consisted of PBLs subjected to treatments with or without PDTC (NF-κB inhibitor) and with or without WEAA, forming four distinct treatment groups (six replicates/group): PDTC(−)/WEAA(−) group, PDTC(−)/WEAA(+) group, PDTC(+)/WEAA(−) group and PDTC(+)/WEAA(+) group. The immune indexes and TLR4/NFκB pathway related indexes were determined. The results were as follows: WEAA dose-dependently enhanced the content of immunoglobulins (IgA, IgG, IgM) and cytokines (IL-1β, IL-2, IL-4) in the serum, liver, and spleen tissues, among which IgA, IgG, and IL-4 were the most significantly affected core indicators (p < 0.05). Meanwhile, WEAA dose-dependently upregulated the expression of TLR4/NF-κB pathway-related genes (TLR4, IKKβ, IκBα, NF-κBp65) and their downstream cytokine-related genes (IL-1β, IL-4) in liver and spleen tissues (p < 0.05). Of these genes, liver IL-4, IκBα, and spleen IL-4 were the most prominently regulated core genes (p < 0.05), The optimal supplementary dose of WEAA was determined to be 1000 mg/kg. In addition, adding 100 μg/mL WEAA to the culture medium of PBLs significantly enhanced immune function and cell viability. The underlying mechanism involved the TLR4/NF-κB pathway; that is to say, WEAA enhanced sheep’s immune indicators by upregulating TLR4/NF-κB pathway genes, thereby coordinately regulating humoral and innate immunity, thereby improving the immune indices of sheep. This study provided compelling experimental support for the prospective utilization of WEAA as a functional feed supplement in intensive meat-type sheep production systems. Full article

Understanding the pathways through which digital innovation contributes to carbon emission reduction is crucial for designing effective climate policies. Existing studies generally find a negative association between digitalization and carbon emissions, but they often treat this relationship as a “black box” and pay insufficient attention to the distinct local and spatial mechanisms through which digital innovation operates. This paper investigates the impact of digital innovation on city-level carbon emissions in 283 Chinese cities from 2010 to 2020 and decomposes the total effect into a local synergistic effect and a spatial spillover effect using a Spatial Durbin Model. We further conduct an empirical test of the underlying mechanisms, including energy efficiency gains and industrial structure upgrading for the local synergy pathway, and green technology diffusion for the spatial spillover pathway. The results indicate that (1) digital innovation significantly reduces city-level carbon emissions, confirming an overall negative effect; (2) spatial decomposition reveals two simultaneous pathways, with a significant local synergistic effect within cities and a spatial spillover effect to neighboring cities; (3) the mechanism analysis shows that the local synergy is significantly associated with improvements in energy efficiency and industrial upgrading, whereas the spatial spillover is significantly associated with the diffusion of green patents; and (4) the effects are especially pronounced in technology-intensive industries and cities in more advanced regions. These findings imply that carbon reduction driven by digital innovation occurs through both intra-city optimization and inter-city technology diffusion. Therefore, policies should not only motivate cities to strengthen their own digital capacities, but also promote interregional collaboration to enhance positive spillovers and achieve cost-effective and well-coordinated carbon neutrality. Full article

Background: Robotic assistive devices are increasingly used in post-stroke gait rehabilitation, yet quantitative evaluations of synchronization between human and robotic joint motion remain limited. This study examined gait kinematics in post-stroke hemiplegic patients using two exoskeleton-type devices—HAL^® (Cyberdine Inc., Tsukuba, Japan) and curara^® (AssistMotion Inc., Ueda, Japan)—based on synchronized IMU measurements. Methods: Two post-stroke patients performed treadmill walking under non-assisted and assisted conditions with HAL^® and curara^®. Only the paretic knee joint was analyzed to focus on the primary control joint during gait. Inertial measurement units (IMUs) simultaneously recorded human and robotic joint angles. Synchronization was assessed using Bland–Altman (BA) analysis, root mean square error (RMSE), and mean synchronization jerk (MSJ). The study was designed as an exploratory methodological case study to verify the feasibility of synchronized IMU-based human–robot joint measurement. Results: Both assistive devices improved paretic knee motion during gait. RMSE decreased from 7.8° to 4.6° in patient A and from 8.1° to 5.0° in patient B. MSJ was lower during curara-assisted gait than HAL-assisted gait, indicating smoother temporal coordination. BA plots revealed reduced bias and narrower limits of agreement in assisted conditions, particularly for curara^®. Differences between HAL^® and curara^® reflected their distinct control strategies—voluntary EMG-based assist vs. cooperative gait-synchronization—rather than superiority of one device. Conclusions: Both devices enhanced synchronization and smoothness of paretic knee motion. curara^® demonstrated particularly smooth torque control and consistent alignment with human movement. IMU-based analysis proved effective for quantifying human–robot synchronization and offers a practical framework for optimizing robotic gait rehabilitation. The novelty of this study lies in the direct IMU-based comparison of human and robotic knee joint motion under two contrasting assistive control strategies. Full article

With the irregular integration of small-capacity distributed generators (DG) and single-phase loads, rural low-voltage distribution transformers are faced with issues such as three-phase imbalance, light-heavy loading, and feeder terminal voltage excursions, impacting the safe and stable operation of the system. To address this issue, a coordinated control strategy based on direct power control (DPC) for low-voltage substation series-parallel coordination is proposed. A flexible interconnection topology for multi-substation series-parallel coordination is designed to achieve coordinated optimization of alternating current–direct current (AC-DC) power quality. Addressing the three-phase imbalance, light-heavy loading, and feeder terminal voltage excursions in rural low-voltage distribution transformers, a series-parallel coordinated optimization control strategy is proposed. This strategy incorporates a DC bus voltage control strategy based on sequence-separated power compensation and a closed-loop control strategy based on phase-separated power compensation, effectively addressing three-phase imbalances and load balancing in each power distribution areas. Furthermore, a series-connected phase compensation control strategy based on DPC is proposed, efficiently mitigating feeder terminal voltage excursions. A corresponding circuit model is established using Matlab/Simulink, and simulation results validate the effectiveness of the proposed strategy. Full article

The capacity of traditional distribution networks is limited. After large-scale distributed power sources are connected, it is difficult to consume them at the same voltage level, which can lead to transformer reverse overloading and voltage limit violations. Although the unified power flow controller (UPFC) excels in flexible power flow regulation and power quality optimization, existing research on it is mostly focused on the transmission grid, focusing on device topology, power flow control, etc. Fault protection is also targeted at high-voltage and ultra-high-voltage domains and only covers a single overvoltage or overcurrent fault. Research on the protection of the unified power flow controller in a distribution network (D-UPFC) remains scarce. A key challenge is the absence of fault protection schemes that are compatible with the unified power flow controller in a distribution network, which cannot meet the requirements of the distribution network for monitoring and protecting multiple fault types, rapid response, and equipment economy. This paper first designs a protection device centered on the distribution thyristor bypass switch (D-TBS), completes the thyristor selection and transient energy extraction, optimizes the overvoltage protection loop parameter, then builds a three-level coordinated protection architecture, and, finally, verifies through functional and system tests. The results show that the thyristor control unit trigger is reliable and the total overvoltage response delay is 1.08 μs. In the case of a three-phase short-circuit fault in a 600 kVA/10 kV system, the distribution thyristor bypass switch can rapidly reduce the secondary voltage of the series transformer, suppress transient overcurrent, achieve isolation protection of the main equipment, provide a reliable guarantee for the engineering application of the distribution network unified power flow controller, and expand its distribution network application scenarios. Full article

Urban metro systems face increasing pressure to reconcile passenger service quality with infrastructure maintenance demands during late-night operations. This study proposes a coordinated optimization framework that integrates train timetabling with a flexible and selective skip-stop strategy. A mixed-integer programming model is formulated to jointly maximize passenger Origin–Destination (OD) accessibility and extend available maintenance windows. To solve the high-dimensional and computationally intensive model efficiently, a hybrid GWO-CNN algorithm is designed, where a Convolutional Neural Network (CNN)-based surrogate model replaces the time-consuming fitness evaluation process in the Grey Wolf Optimizer (GWO). A real-world case study on the Beijing metro network demonstrates that the proposed method increases OD accessibility by 23.60% and extends maintenance window by 8310 s. Compared to the conventional GWO, the GWO-CNN algorithm achieves superior solution quality with a 98.4% reduction in computation time. Sensitivity analyses further reveal the trade-offs between skip-stop rates, objective weight settings, and optimization outcomes, offering practical insights for metro operators in tailoring late-night scheduling strategies to both passenger demand and maintenance priorities. Full article

The increasing deployment of hybrid renewable energy systems has introduced significant challenges in optimal energy dispatch and load balancing due to the intrinsic stochasticity and temporal variability of renewable sources, along with the multi-dimensional optimization requirements of simultaneously achieving economic efficiency, grid stability, and environmental sustainability. This paper presents GreenMind, a scalable Deep Reinforcement Learning framework designed to address these challenges through a hierarchical multi-agent architecture coupled with Long Short-Term Memory (LSTM) networks for predictive energy management. The framework employs specialized agents responsible for generation dispatch, storage management, load balancing, and grid interaction, achieving an average decision accuracy of 94.7% through coordinated decision-making enabled by hierarchical communication mechanisms. The integrated LSTM-based forecasting module delivers high predictive accuracy, achieving a 2.7% Mean Absolute Percentage Error for one-hour-ahead forecasting of solar generation, wind power, and load demand, enabling proactive rather than reactive control. A multi-objective reward formulation effectively balances economic, technical, and environmental objectives, resulting in 18.3% operational cost reduction, 23.7% improvement in energy efficiency, and 31.2% enhancement in load balancing accuracy compared to state-of-the-art baseline methods. Extensive validation using synthetic datasets representing diverse hybrid renewable energy configurations over long operational horizons confirms the practical viability of the framework, with 19.6% average cost reduction, 97.7% system availability, and 28.6% carbon emission reduction. The scalability analysis demonstrates near-linear computational growth, with performance degradation remaining below 9% for systems ranging from residential microgrids to utility-scale installations with 2000 controllable units. Overall, the results demonstrate that GreenMind provides a scalable, robust, and practically deployable solution for predictive energy dispatch and load balancing in hybrid renewable energy systems. Full article

In the optimal design and operation of moistube irrigation systems, a wetted body and its components are important factors. This study presents an analytical characterization of the soil wetted body under horizontal moistube irrigation. In the laboratory experiment, the temporal and spatial changes in the wetted body during irrigation were observed. Specifically, the maximum wetting distances in the horizontal, vertical upward, and vertical downward directions on the soil profile were measured every 30 min. Additionally, images documenting the wetted body’s changes at different time points were recorded throughout the experiment. On this basis, by locating the soil profile of the wetted body in a coordinate system, the main motion equations describing the temporal and spatial changes in the wetted body’s soil profile were derived. Through integral processing of these motion equations, an analytical model for the wetted body under horizontal moistube irrigation was constructed. Finally, the model was validated using the experimental data. The results show that the model outcomes are consistent with the natural movement of water in the soil. Therefore, when characterizing the size of the wetted body under horizontal moistube irrigation using the soil profile area, the proposed method, which involves analyzing the shape and components of the wetted body’s soil profile at different time points and determining its soil profile size by integrating four distinct parabolas, is feasible. Full article

Autonomous Unmanned Aerial Vehicles (UAVs) are widely used in smart agriculture, logistics, and warehouse management, where precise trajectory prediction is important for safety and efficiency. Traditional approaches require complex physical modeling including mass properties, moment of inertia measurements, and aerodynamic coefficient calculations, which creates significant barriers for custom-built UAVs. Existing trajectory prediction methods are primarily designed for motion forecasting from dense historical observations, which are unsuitable for scenarios lacking historical data (e.g., takeoff phases) or requiring trajectory generation from sparse waypoint specifications (4–6 constraint points). This distinction necessitates architectural designs optimized for spatial interpolation rather than temporal extrapolation. To address these limitations, we present a segmented LSTM framework for complete autonomous flight trajectory prediction. Given target waypoints, our architecture decomposes flight operations, predicts different maneuver types, and outputs the complete trajectory, demonstrating new possibilities for mission-level trajectory planning in autonomous UAV systems. The system consists of a global duration predictor (0.124 MB) and five segment-specific trajectory generators (∼1.17 MB each), with a total size of 5.98 MB and can be deployed in various edge devices. Validated on real Crazyflie 2.1 data, our framework demonstrates high accuracy and provides reliable arrival time predictions, with an Average Displacement Error ranging from 0.0252 m to 0.1136 m. This data-driven approach avoids complex parameter configuration requirements, supports lightweight deployment in edge computing environments, and provides an effective solution for multi-UAV coordination and mission planning applications. Full article

The growing pressure to achieve carbon neutrality has exposed major limitations in current industrial processes, which often operate in isolation, rely on simplified mass-balance assumptions, and struggle to manage increasingly complex material and energy flows. Traditional industrial symbiosis and circular economy strategies have improved resource efficiency, yet they rarely capture molecular-level interactions or enable coordinated optimization across multiple facilities, restricting their ability to support large-scale decarbonization. In this context, Carbon–Hydrogen–Oxygen Symbiosis Networks (CHOSYNs) have emerged as an advanced framework that integrates atomic-level targeting with multi-scale process systems engineering to identify synergies, valorization pathways, and cross-sector exchanges that conventional approaches overlook. This review consolidates the theoretical foundations, historical development, and recent applications of CHOSYNs, illustrating how it can enhance efficiency, reduce emissions, and strengthen resilience in energy systems, chemical industries, and circular resource management. Although the literature remains limited, existing studies demonstrate the promise of CHOSYNs as a unifying methodology for designing low-carbon industrial ecosystems. Key challenges related to scalability, validation, governance, and operational robustness are examined, and a roadmap is proposed to guide the evolution and practical deployment of CHOSYNs toward 2035. Full article

With the rapid growth of residential electric vehicles, synchronized charging during peak periods can induce severe load ramping and exceed distribution network capacity limits. To mitigate these issues, governments have promoted a unified build-and-operate community model that enables centralized coordination of community charging and ensures real-time responsiveness to grid dispatch signals. Targeting this emerging operational paradigm, a dual-dimensional compensation mechanism for real-time electric vehicle (EV) demand response is proposed. The mechanism integrates two types of compensation: power regulation compensation, which rewards users for providing controllable power flexibility, and state-of-charge (SoC) loss compensation, which offsets energy deficits resulting from demand response actions. This dual-layer design enhances user willingness and long-term engagement in community-level coordination. Based on the proposed mechanism, a bi-level optimization framework is developed to realize efficient real-time regulation: the upper level maximizes the active response capacity under budget constraints, while the lower level minimizes the aggregator’s total compensation cost subject to user response behavior. Simulation results demonstrate that, compared with conventional fair-share curtailment and single-compensation approaches, the proposed mechanism effectively increases active user participation and reduces incentive expenditures. The study highlights the mechanism’s potential for practical deployment in unified build-and-operate communities and discusses limitations and future research directions. Full article

The development of onshore wind energy is linked to the conditions for landscape protection and development established during the implementation of the European Landscape Convention (ELC). In Poland, the implementation of the ELC results in the designation and protection of priority landscapes, which may restrict the construction of new wind farms. The widespread ratification of the ELC by European countries where wind farms are being developed makes the possibility of limiting wind energy development through ELC implementation an important issue from a research and practical perspective. Experience from Poland can be helpful in optimizing the implementation process without impacting the total installed capacity of wind farms. Using GIS tools and a multi-criteria assessment of the conditions for excluding areas from wind energy development in Poland, the scale of territorial barriers was assessed in the variants without and with priority landscapes. The resources available for wind farms and the reduction in these resources associated with the implementation of the ELC were assessed quantitatively and spatially. The amount of capacity that can be connected and that will be limited by the implementation of the ELC was estimated. The analysis was conducted for the country, its regions, and zones with varying wind conditions predisposing to wind energy development. An approximately 5% reduction in the territorial potential for onshore wind energy development was observed due to the implementation of the ELC. Significant spatial variation was observed, including regional limitations on wind farms associated with the implementation of the ELC. Spatial barriers to development result not only from the presence of high-quality landscapes but also from varying regional policies for their protection and shaping. There is a lack of national coordination of regional policies for the implementation of the ELC, and there is no coordination of this process with other plans and strategies, including energy transformation and security. Despite the identified limitations to wind energy development, no threat to the achievement of strategic wind energy development goals related to connecting new capacity by 2030 and 2040 has been identified. However, in the longer term, as areas available for wind energy development become increasingly scarce, implementing ELC may pose a significant barrier to energy transition. Research indicates that the ELC implementation model in Poland, which emphasizes landscape protection rather than landscape planning and sustainable management, is not beneficial for onshore wind energy. It is necessary to integrate landscape protection policy with energy transition policy, particularly in zones with the most favorable wind (economic) conditions for onshore wind energy development. Full article

With the large-scale integration of new energy and power electronic devices into power systems, frequency stability has become an increasingly critical concern. To maintain frequency stability while mitigating the high capital expenditure of energy storage systems (ESSs), this paper develops a control framework centered on edge energy management terminals (EEMTs). The design is based on a demonstration project in which distributed energy resources (DERs) and flexible loads collaboratively provide frequency regulation. A monitoring station is implemented to make fast frequency response (FFR) resources dispatchable, detectable, measurable, and tradable. Furthermore, a control strategy tailored for building- and factory-level applications is proposed. This strategy enables real-time optimal scheduling of DERs and flexible loads through coordinated communication between EEMTs and net load units (NLUs). Two field tests further demonstrate the effectiveness and advantages of the proposed approach. In addition, this paper proposes a coordinated scheme in which wind farms and NLUs jointly participate in frequency regulation, aiming to mitigate the response delay of NLUs and the secondary frequency drop observed in wind farms. The feasibility and benefits of this scheme are validated through experimental tests. Full article

To achieve low-carbon collaborative operation of active distribution networks (ADNs) and microgrid clusters, this paper proposes a signal-guided collaborative optimization method. Firstly, a spatiotemporal carbon intensity equilibrium model (STCIEM) is constructed, overcoming the limitations of centralized carbon emission flow models in terms of data privacy and equitable distribution, and enabling distributed and precise carbon emission measurement. Secondly, a dual-market mechanism for carbon and electricity is designed to support peer-to-peer (P2P) carbon quota trading between microgrids and ADN-backed clearing, enhancing market liquidity and flexibility. In terms of scheduling strategy optimization, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is incorporated into the carbon-electricity cooperative game framework, enabling differentiated energy scheduling under constraints. Simulation results demonstrate that the proposed method can effectively coordinate the operation of energy storage, gas turbines, and demand response, reduce system carbon intensity, improve market fairness, and enhance overall economic performance and robustness. The study shows that this framework provides theoretical support and practical reference for future distributed energy consumption and carbon neutrality paths. Full article