Search Results (398)

Exercise activates many metabolic and signaling pathways in skeletal muscle and other tissues and cells, causing numerous systemic beneficial metabolic effects. Traditionally recognized for their principal role in oxygen (O₂) transport, erythrocytes have emerged as dynamic regulators of vascular homeostasis. Beyond their respiratory function, erythrocytes modulate vascular tone through crosstalk with other cells and tissues, particularly under hypoxia and physical exercise. This regulatory capacity is primarily mediated through the controlled release in the bloodstream of adenosine triphosphate (ATP) and nitric oxide (NO), two potent vasodilators that contribute significantly to matching oxygen supply with tissue metabolic demand. Emerging evidence suggests that many other erythrocyte-released molecules may act as additional factors involved in tissue-erythrocyte crosstalk. This review highlights erythrocytes as active contributors to exercise-induced adaptations through their exocrine signaling. Full article

Under the dual backdrop of the Healthy China strategy and the concept of sustainable development, optimizing the spatial layout of primary healthcare facilities is important for fairly distributing healthcare resources and strengthening the resilience of the public health system in a sustainable way. This study introduces an innovative 3D spatial resilience evaluation framework, covering transmission (service accessibility), diversity (facility type matching), and stability (supply demand balance). Unlike traditional accessibility studies, the concept of “resilience” here highlights a system’s ability to adapt to sudden public health events through spatial reorganization, contrasting sharply with vulnerable systems that lack resilience. Method-wise, the study uses an improved Gaussian two-step floating catchment area method (Ga2SFCA) to measure spatial accessibility, applies a geographically weighted regression model (GWR) to analyze spatial heterogeneity factors, combines network analysis tools to assess service coverage efficiency, and uses spatial overlay analysis to identify areas with supply demand imbalances. Harbin is located in northeastern China and is the capital of Heilongjiang Province. Since Harbin is a typical central city in the northeast region, with a large population and clear regional differences, it was chosen as the case study. The case study in Harbin’s main urban area shows clear spatial differences in medical accessibility. Daoli, Nangang, and Xiangfang form a highly accessible cluster, while Songbei and Daowai show clear service gaps. The GWR model reveals that population density and facility density are key factors driving differences in service accessibility. LISA cluster analysis identifies two typical hot spots with supply demand imbalances: northern Xiangfang and southern Songbei. Finally, based on these findings, recommendations are made to increase appropriate-level medical facilities, offering useful insights for fine-tuning the spatial layout of basic healthcare facilities in similar large cities. Full article

Socialized services for rural characteristic industries are becoming a key support for promoting rural industries’ transformation and upgrading. They are permeating the development process of modern agricultural service technologies, achieving significant progress in specialized fields such as mechanized operations and plant-protection services. However, challenges remain, including low efficiency in matching service resources and limited spatiotemporal coordination capabilities. With the deep integration of spatiotemporal information technology and knowledge graph technology, the enormous potential of semantic-level feature spatial representation in intelligent scheduling of service resources has been fully demonstrated, providing a new technical pathway to solve the above problem. This paper systematically analyzes the technological evolution trends of socialized services for rural characteristic industries and proposes a collaborative scheduling framework based on semantic feature space and spatiotemporal maps for characteristic industry service resources. At the technical architecture level, the paper aims to construct a spatiotemporal graph model integrating geographic knowledge graphs and temporal tree technology to achieve semantic-level feature matching between service demand and supply. Regarding implementation pathways, the model significantly improves the spatiotemporal allocation efficiency of service resources through cloud service platforms that integrate spatial semantic matching algorithms and dynamic optimization technologies. This paper conducts in-depth discussions and analyses on technical details such as agricultural semantic feature extraction, dynamic updates of rural service resources, and the collaboration of semantic matching and spatio-temporal matching of supply and demand relationships. It also presents relevant implementation methods to enhance technical integrity and logic, which is conducive to the engineering implementation of the proposed methods. The effectiveness of the proposed collaborative scheduling framework for service resources is proved by the synthesis of principal analysis, logical deduction and case comparison. We have proposed a practical “three-step” implementation path conducive to realizing the proposed method. Regarding application paradigms, this technical system will promote the transformation of rural industry services from traditional mechanical operations to an intelligent service model of “demand perception–intelligent matching–precise scheduling”. In the field of socialized services for rural characteristic industries, it is suggested that relevant institutions promote this technical framework and pay attention to the development trends of new technologies such as knowledge services, spatio-temporal services, the Internet of Things, and unmanned farms so as to promote the sustainable development of rural characteristic industries. Full article

It is essential to balance energy supply and demand in residential buildings through accurate forecasting of energy use due to varying daily and seasonal residential building loads. This study demonstrates a data-driven Virtual Energy Replication Framework (VERF) to predict the behavior of residential buildings using weather forecast data. The framework integrates supervised machine learning models and time-ahead weather parameters to estimate photovoltaic (PV) power production, heat pump energy consumption, and indoor thermal comfort. The accuracy of prediction models is validated using TRNSYS simulations of a typical household in Saarbrucken, Germany, a temperate oceanic climate region. The XGBoost model exhibits the highest reliability, achieving a root mean square error (RMSE) of 0.003 kW for PV power generation and 0.025 kW for heat pump energy use, with R² scores of 0.94 and 0.87, respectively. XGBoost and random forest regression models perform well in predicting PV generation and HP electricity load, with mean prediction errors of 5.27–6% and 0–7.7%, respectively. In addition, the thermal comfort index (PPD) is predicted with an RMSE of 1.84 kW and an R² score of 0.80 using the XGBoost model. The mean prediction error remains between 2.4% (XGBoost regression) and −11.5% (lasso regression) throughout the forecasted data. Because the framework requires no real-time instrumentation or detailed energy modelling, it is scalable and adaptable for smart building energy systems, and has particular value for Building-Integrated Photovoltaics (BIPV) demonstration projects on account of its predictive load-matching capabilities. The research findings justify the applicability of VERF for efficient and sustainable energy management using weather-informed prediction models in residential buildings. Full article

As the global demand for clean and efficient energy continues to grow, the development of advanced energy storage technologies is becoming increasingly important. This study explores the influence of the dead volume coefficient and pulse-width modulation (PWM) control strategy on the performance of a piston expander in a micro-compressed air energy storage system. Simulation results showed that low dead volume values, combined with short air supply durations with PWM values between 0.1 and 0.2, led to improved energy utilization. This was achieved through complete piston strokes and stable power output. In contrast, high dead volume values and high PWM settings, such as 0.9, resulted in incomplete air expansion, excessive air consumption, and a significant reduction in overall system efficiency, even though peak power output may increase. Sensitivity analysis confirmed that PWM had a major impact on efficiency, with the highest value of 0.76 achieved for a dead volume coefficient of 0.05 and a PWM value of 0.2. Under these operating conditions, the expander delivered a generated power output of 970 W. Additionally, PWM enabled flexible control of power output, without requiring modifications to the system’s physical design. The study highlights the importance of adjusting the air admission strategy to match the internal volume characteristics. Full article

In the context of low-growth urban development, the interaction between the supply–demand structure of community public service facilities and the housing market has increasingly become a key research concern. Yet, systematic investigations into how supply–demand dynamics influence market value remain limited. To fill this gap, this study takes Wuchang District of Wuhan as the empirical case and establishes an integrated framework of “supply–demand evaluation—value effects” to assess both the equity of facility allocation and its capitalization effects. The results indicate that: (1) all categories of public service facilities in Wuchang District have Gini coefficients above 0.6, indicating substantial imbalance. Among them, elderly care, infant care, and child recreation facilities exceed 0.7, reflecting particularly severe inequality. (2) The “accessibility–housing price” quadrant model further reveals typical mismatch patterns, with “low accessibility–high price” and “high accessibility–low price” zones together accounting for 45.08%, suggesting that mismatches are widespread in the study area. (3) MGWR results show that different facility types exert differentiated effects across locations, with some even displaying opposite positive and negative effects, underscoring significant spatial heterogeneity. Overall, this study uncovers the intrinsic links between facility supply–demand structures and market value, clarifies the differentiated roles of facility types in shaping spatial value, and provides empirical evidence to support improvements in urban public service systems. Full article

To address the issue of spatial mismatch of real estate resources at its source, we incorporate the multidimensional urbanization speeds into the real estate market stock-flow model, simulate the regional real estate resource allocation process from a dynamic perspective, and explore the impact of urbanization on housing prices, as well as the characteristics that a coordinated multidimensional urbanization should possess. Utilizing data on population flow, economic development, and the relative increment of newly built housing units that meet delivery standards from 2008 to 2022 in 35 large- and medium-sized cities in China. We employ the dynamic panel system GMM approach to estimate the direct effect of single-dimensional urbanization on housing prices, and utilize the threshold effect model to examine the comprehensive effect of multidimensional urbanization on housing prices. The findings reveal that population, economic, and spatial urbanization influence housing prices by altering the flow of real estate supply and demand, with their effects being significantly shaped by the scarcity of stock real estate resources. The dynamic coordination of multidimensional urbanization ρ has a significant threshold effect on housing price changes. When Vsu and Vpu reach the optimal match, the real estate market achieves dynamic equilibrium, and housing prices remain relatively stable. This not only underscores the significance of multidimensional urbanization as a driver of urban housing price variations but also provides valuable insights for cities on how to adjust the quantity of new residential construction (or land supply) during the dynamic urbanization process, thereby enhancing the spatial allocation rationality of real estate resources from the source. Full article

During the continuous operation of trailing suction hopper dredger (TSHD), equipment workload exhibits significant time-varying characteristics. Maintaining dynamic symmetry between power generation and consumption is crucial for ensuring system stability and preventing power supply failures. Key challenges lie in dynamic perception, accurate prediction, and real-time power management to achieve this equilibrium. To address this issue, this paper proposes and constructs a “prediction-driven dynamic power management method.” Firstly, to model the complex temporal dependencies of the workload sequence, we introduce and improve a dilated convolutional long short-term memory network (Dilated-LSTM) to build a workload prediction model with strong long-term dependency awareness. This model significantly improves the accuracy of workload trend prediction. Based on the accurate prediction results, a dynamic power management strategy is developed: when the predicted total power consumption is about to exceed a preset margin threshold, the Power Management System (PMS) automatically triggers power reduction operations for adjusfigure loads, aiming to maintain grid balance without interrupting critical loads. If the power that the generator can produce is still less than the required power after the power is reduced, and there is still a risk of supply-demand imbalance, the system uses an Improved Grey Wolf Optimization (IGWO) algorithm to automatically disconnect some non-critical loads, achieving real-time dynamic symmetry matching of generation capacity and load demand. Experimental results show that this mechanism effectively prevents generator overloads or ship-wide power failures, significantly improving system stability and the reliability of power supply to critical loads. The research results provide effective technical support for intelligent energy efficiency management and safe operation of TSHDs and other vessels with complex working conditions. Full article

Quantitative analysis of ecosystem services (ESs) supply–demand dynamics, and identifying its dominant drivers and the spatial non-stationarity of driving mechanisms, is a crucial prerequisite for effective regional ESs management and the formulation of scientific ecological conservation plans. Previous related studies have primarily focused on the supply–demand balance of specific ESs and the driving analysis of ESs supply. Comprehensive analysis of ESs supply–demand dynamics and research on their spatially heterogeneous response mechanisms remain relatively scarce. In this study, we assessed the supply, demand, and supply–demand matching relationships of ESs on the Loess Plateau (LP) from 1990 to 2023 using a land-cover-based ESs supply–demand quantitative matrix. We then employed Geodetector and Geographically weighted regression model to explore the dominant driving factors and their spatially varying effects on ESs supply–demand relationships. The results revealed that over the past three decades, the continuous decline in ESs supply coupled with the annual increase in ESs demand has led to a worsening trend in ESs supply–demand relationships towards deficit. Fortunately, the LP still maintained a supply-surplus state at present. The proportion of construction land, population density, GDP density, and the proportion of forestland and grassland were identified as key drivers of changes in ESs supply–demand relationships. The expansion of construction land was the most crucial driver of the deterioration in ESs supply–demand relationships on the LP, exhibiting a universally negative inhibitory effect. The proportion of forestland and grassland exerted a regionally wide positive spatial effect, highlighting the critical role of vegetation restoration in improving ESs relationships. The influences of population density and GDP density exhibited a coexistence of positive promoting and negative inhibitory effects across space. Our results emphasize that ESs management policies on the LP must account for the spatial heterogeneity of driving mechanisms, requiring more localized and targeted land use strategies and management policies to enhance ESs sustainability. Full article

Against the backdrop of rapid new urbanisation and the ongoing integration of urban and rural areas, the evolving spatial dynamics between public service facilities and population distribution have increasingly garnered scholarly interest. The present study employs a grid-based spatial unit and a coupling coordination model as a foundation. This model integrates POI data, Baidu heat maps, and other sources of spatial and temporal information. The objective is to explore the dynamic matching pattern of public service facilities and population distribution. The study’s findings are as follows: The population within the core urban area displays a strong propensity for agglomeration during the morning and evening peak hours, thereby forming a highly coordinated public service network characterised by high-density and piecemeal distribution of public service facilities. The population residing within the transition zone between urban and rural areas is commuting in a substantial number, and the relationship between the supply of and demand for facilities demonstrates cyclical fluctuations. Local areas are subject to time-periodic pressure on the supply of and demand for facilities. In rural areas, due to the continuous population outflow and dispersed residence, the layout of service facilities is fragmented, exhibiting the island effect. The study reveals a structural contradiction between traditional homogeneous planning and the gradient difference between urban and rural areas, providing a scientific basis for Shandong Province to promote new urbanisation and rural revitalisation strategies in an integrated manner. Full article

Against the backdrop of ongoing degradation of ecosystem services and the increasing demand for sustainable development, the scientific delineation of ecological management zones has become a critical means by which to balance human wellbeing and ecological conservation. This study takes Jiangxi Province as the research area and selects four typical ecosystem services—food production, water supply, carbon storage, and soil retention—to systematically evaluate their supply–demand relationships from both static and dynamic dimensions. By introducing the entropy weight method to construct a comprehensive supply–demand index and integrating a coupling coordination degree model with a four-quadrant dynamic evolution model, this paper proposes a coupled “static–dynamic” analytical framework. The findings reveal significant spatial heterogeneity in various ecosystem services; high-supply areas are concentrated in the southern and peripheral mountainous regions while demand is closely linked to population distribution, exhibiting a pattern of high demand in the central areas and high supply in the peripheral areas. Our supply–demand matching analysis uncovers a distinct gradient distribution characterized by core imbalance and peripheral coordination, with prominent supply–demand conflicts in urban expansion areas and enhanced coordination in peripheral ecological barrier zones. Based on these insights, we divide Jiangxi Province into five types of ecological management zones: Degraded Restoration, Conflict Mitigation, Coordination Enhancement, Potential Development, and Maintenance Conservation, with tailored management strategies proposed for each zone type. As a result, this study not only provides scientific support for regional ecological spatial optimization but also offers a new methodological paradigm for ecosystem services management. Full article

The ultrasonic power supply constitutes the core component of an ultrasonic welding system, and its main function is to convert the industrial frequency electricity into resonant high-frequency electricity in order to achieve mechanical energy conversion. However, factors such as changes in ambient temperature or component aging may cause the resonant frequency of the transducer to drift, thus detuning the resonant system and seriously affecting system performance. Therefore, an ultrasonic welding system requires high-frequency tracking in real time. Traditional frequency tracking methods (such as acoustic tracking, PID control, etc.) have defects such as poor stability, narrow bandwidth, or cumbersome parameter setting, making it difficult to meet the demand for fast tracking. To address these problems, this study adopts a T-matching network and utilizes sliding mode control for frequency tracking. In order to solve the problems of slow convergence and obvious jitter in sliding mode control (SMC), a Hippopotamus Optimization (HO) algorithm is introduced to simulate hippopotamuses’ group behavior and predation mechanisms, thereby optimizing the control parameters. It is verified through simulation that the SMC algorithm optimized by the HO algorithm (HO-SMC) is able to suppress frequency drift more effectively and demonstrates the advantages of fast response, high accuracy, and strong robustness in the scenario of sudden load changes. Full article

Intercropping is an eco-friendly, sustainable agricultural model that significantly improves yield stability, nutrient use efficiency, and soil health through spatiotemporal niche complementarity, increases biodiversity, and improves soil health. Water and nitrogen play crucial roles in limiting and regulating efficient resource utilization and ecological sustainability in intercropping systems. Synchronizing water and nitrogen inputs to match crop demands optimizes the spatiotemporal distribution of these resources, alleviates interspecific competition, and promotes mutualistic interactions, which significantly impacts crop growth, yield, and soil environment. This paper reviews the mechanisms of intercropping and water–nitrogen coupling regulation, aligning water and nitrogen supply with crop growth patterns, spatial configuration parameters, irrigation management techniques, and environmental climate change, and explores the response mechanisms of water–nitrogen coupling on crop growth, yield, and soil environmental adaptation. It can provide some references for researchers, extension agents, and policymakers. Research indicates that water–nitrogen coupling can enhance photosynthetic efficiency, promote root development, optimize nutrient uptake, and improve soil water dynamics, nitrogen cycling, and microbial community structures. Intercropping enhances the climate resilience of agricultural systems by leveraging species complementarity for resource utilization, strengthening ecosystem stability, and improving buffering capacity against climate change impacts such as extreme precipitation and temperature fluctuations. Future studies should further elucidate the differential effect of water–nitrogen coupling across regions and climatic conditions, focusing on multidimensional integrated administration strategies. Combining precision agriculture technologies and climate change predictions facilitates the development of more adaptive water–nitrogen coupling models to provide theoretical support and technical guarantees for sustainable agriculture. Full article

Solar thermal technology is an important component of low-carbon energy systems, but its application potential is constrained by two key factors: the inherent limits of energy flux density and the temporal mismatch between supply and demand. This study examined efficiency losses in building heating systems in Northwest China caused by the mismatch between supply and demand in intermittent solar thermal storage systems. Three typical building heating models (Day–Night Intermittent Mode, Day–Night + Monthly Intermittent Mode, and Composite Intermittent Mode (Day–Night + Weekly + Monthly)) were constructed through SketchUp, integrating the Transient System Simulation Tool (TRNSYS) with improved calculation methods in an innovative way. The study first examined regional energy consumption patterns and the temporal characteristics of building occupancy and then proposed a collaborative optimization framework for thermal collection and storage, focused on improving the dynamic matching algorithm of the thermal collection area ratio and the tank volume ratio and establishing a tank capacity calculation model that considers the time-varying characteristics of heat demand and fluctuations in thermal collection efficiency during the intermittent heating cycle. The results show that compared with continuous operation, the intermittent strategy reduces the annual cumulative heat load by 13–33%, among which the Day–Night Intermittent Mode shows the daily peak load reaches 1.8 times the normal value during restart, while the daily fluctuation amplitude of the Day–Night + Monthly Intermittent Mode decreases by 42%. The corresponding solar energy guarantee rate reaches 86–88%, and the heat storage loss is reduced by 19–27%. The time-varying coupling design method established in this study provides an optimization path that takes into account both system efficiency and economy for intermittent heating scenarios. The proposed dynamic capacity configuration criterion has universal guiding value for the design of solar district heating systems. Full article

With the increasing penetration of renewable energy in power systems, it is vital to adopt methods to enhance the acceptance capacity of renewable energy. Energy-intensive loads have excellent potential for regulating the utilization of renewable energy. Existing studies have often overlooked the regulatory potential of energy-intensive industrial loads. The coordinated optimization of source, load, and storage can improve the matching degree between power supply and load demand and achieve on-site consumption of renewable energy. This paper proposes a coordinated optimization method for source–load–storage integrated systems, utilizing for regulation energy-intensive industrial loads such as electrolytic aluminum load and polysilicon load. The operational characteristics and regulatory ability of electrolytic aluminum load and polysilicon load were analyzed in the production process. Operation models of energy-intensive loads are proposed. A coordinated operation model of a source–load–storage integrated system is established. The operation schemes of thermal units, energy storage, and energy-intensive loads are jointly optimized to guarantee power supply capacity and renewable energy consumption. In addition, power purchase from the bulk power system and the time-of-use electricity price are considered to ensure a reliable power supply for energy-intensive loads. The case results showed that on the premise of ensuring that the production meets the requirements, the flexibility and economy of system operation were effectively improved. Reasonably rated power and capacity for the energy storage system can improve the regulation ability and reduce the operating costs of regional systems. Full article