Search Results (392)

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

To address the challenges of power generation rights trading and profit distribution in the integrated energy system of multi-park combined cooling, heating, and power (CCHP) with new energy grid integration, we constructed a hierarchical game model involving multi-energy system aggregators. By having aggregators price electricity, heat, cold, and carbon costs, the model establishes a hierarchical game framework with the linkage of the four prices (electricity, heat, cold, and carbon), achieving inter-park peer-to-peer (P2P) multi-energy dynamic price matching for the first time. It aims to coordinate distribution network dispatching, renewable energy, energy storage, gas turbine units, demand response, cooling–heating–power coupling, and inter-park P2P multi-energy interaction. With the goal of optimizing the profits of integrated energy aggregators, a hierarchical game mechanism is established, which integrates power generation rights trading models and incentive-based demand response. The upper layer of this mechanism is the profit function of integrated energy aggregators, while the lower layer is the cost function of park microgrid alliances. A hierarchical game mechanism with Two-Level Optimization, integrating the Adaptive Disturbance Quantum Particle Swarm Optimization (ADQPSO) algorithm and the branch and bound method (ADQPSO-Driven Branch and Bound Two-Level Optimization), is used to determine dynamic prices, thereby realizing dynamic matching of energy supply and demand and cross-park collaborative optimal allocation. Under the hierarchical game mechanism, the convergence speed of the ADQPSO-driven branch and bound method is 40% faster than that of traditional methods, and the optimization profit accuracy is improved by 1.59%. Moreover, compared with a single mechanism, the hierarchical game mechanism (Scenario 4) increases profits by 17.17%. This study provides technical support for the efficient operation of new energy grid integration and the achievement of “dual-carbon” goals. Full article

The agricultural products trade network is essentially a complex adaptive system formed by nonlinear interactions between countries. Based on the complex system theory, this study reveals the dynamic self-organization law of the RCEP regional agricultural products trade network by using the panel data of RCEP agricultural products export trade from 2000 to 2023, combining social network analysis (SNA) and the temporal exponential random graph model (TERGM). The results show the following: (1) The RCEP agricultural products trade network presents a “core-edge” hierarchical structure, with China as the core hub to drive regional resource integration and ASEAN countries developing into secondary core nodes to deepen collaborative dependence. (2) The “China-ASEAN-Japan-Korea “riangle trade structure is formed under the RCEP framework, and the network has the characteristics of a “small world”. The leading mode of South–South trade promotes the regional economic order to shift from the traditional vertical division of labor to multiple coordination. (3) The evolution of trade network system is driven by multiple factors: endogenous reciprocity and network expansion are the core structural driving forces; synergistic optimization of supply and demand matching between economic and financial development to promote system upgrading; geographical proximity and cultural convergence effectively reduce transaction costs and enhance system connectivity, but geographical distance is still the key system constraint that restricts the integration of marginal countries. This study provides a systematic and scientific analytical framework for understanding the resilience mechanism and structural evolution of regional agricultural trade networks under global shocks. Full article

Recent studies have shown benefits of using tidal stream and wave energy in the electricity generation mix to improve supply–demand balancing on annual/subannual timeframes. This paper investigates this further by considering the variability of solar photovoltaic, onshore and offshore wind, wave, and tidal stream over multiple years. It also considers their ability to match with electricity demand when combined. Variability of demand and generation can have a significant impact on results. Over the sample of five years considered (2015–2019), demand varied by around 3%, and the availability of each renewable technology differed by up to 9%. This highlights the importance of considering multiple years of input data when assessing power system impacts, instead of relying on an ‘average’ year. It is also key that weather related correlations between renewable resources and with demand can be maintained in the data. Results from an economic dispatch model of Great Britain’s power system in 2030 are even more sensitive to the input data year, with costs and carbon emissions varying by up to 21% and 45%, respectively. Using wave or tidal stream as part of the future energy mix was seen to have a positive impact in all cases considered; 1 GW of wave and tidal (0.57% of total capacity) reduces annual dispatch cost by 0.2–1.3% and annual carbon emissions by 2.3–3.5%. These results lead to recommended best practises for modelling high renewable power systems, and will be of interest to modellers and policy makers. Full article

The innovation demand and supply matching requires an accurate and time-consuming analysis of patents and the identification of their technological domains; since these tasks can be particularly challenging, this is why recent studies have evaluated the possibility of adopting Artificial Intelligence based on NLP techniques. Here, we present an automated workflow for patent analysis and classification devoted to the Italian patent scenario. High-quality data from the online platform KnowledgeShare (KS) were investigated: KS is the first patent management platform on the Italian innovation scene. A not secondary aspect consisted in determining which words mostly influenced patent classification, thus characterizing the corresponding research areas. Several models were compared to ensure the workflow’s robustness; Logistic Regression (LR) resulted in the best-performing model, and its performance compared well with the State of the Art. For each technological domain in the KS database, we evaluated and discussed its characteristic words; furthermore, a further analysis was focused on explaining why some domains, such as “Packaging” and “Environment,” were particularly confounding. This last aspect is of paramount importance to identify cross-contamination effects among research areas. Full article

This study examines whether adopting blockchain technology can enhance maritime supply chain performance by improving information sharing in the presence of mismatches between service capacity and demand. We analyze a maritime supply chain with one port and one carrier. Depending on whether the port and the carrier adopt blockchain technology to share forecast information, we consider two scenarios: neither party adopts the technology, or both the port and the carrier adopt it. We find that when the port’s ex ante expected demand is relatively low, the adoption of blockchain technology not only incentivizes the port to expand its service capacity but also increases the actual demand from the carrier. In addition, when the port has a high forecasting accuracy, it prompts both the port and the carrier to make more stable decisions on the service capacity and freight rates under demand uncertainty. Finally, while the port and the carrier exhibit conflicting incentives to adopt blockchain technology, these tensions can nonetheless be reconciled. This alignment becomes possible due to blockchain’s spillover effect: by enabling information sharing, it facilitates a closer match between the port’s service capacity and the carrier’s realized demand. Full article

Addressing the problem that energy supply and load demand cannot be matched due to the difference in inertia effects among multiple energy sources, and taking into account the thermoelectric load, this paper designs a two-stage operation optimization model of IES considering multi-dimensional thermal inertia and constructs an intelligent adaptive solution method based on a time scale-model base. Validation is conducted through an arithmetic example. Scenario 2 has 15.3% fewer CO₂ emissions than Scenario 1, 19.7% less purchased electricity, and 20.0% less purchased electricity cost. The optimal algorithm for the day-ahead phase is GA, and the optimal algorithm for the intraday phase is PSO, which is able to produce optimization results in a few minutes. Full article