Search Results (1,237)

A mathematical model of sustainable resource allocation in a competitive economy is developed and studied, taking into account transaction costs and technological constraints. The model describes the interaction of producers and consumers and introduces a technological set and price dynamics through demand–supply imbalance. Using the theory of covering mappings and variational methods, the existence of equilibrium prices is proven. Issues of stability, numerical algorithms, and macroeconomic interpretation of the obtained results are considered. Full article

Two-speed transmissions can improve battery electric vehicle (BEV) drivetrain efficiency. However, the additional losses caused by shifting actuators offset these efficiency gains. Particularly hydraulic actuated wet running multi-plate clutches, which enable powershifts, typically require rotary feedthroughs. Commonly used rectangular sealing rings (RSR) demand continuous hydraulic power due to leakage and cause friction torque. This leads to high RSR temperatures, especially at high angular velocities of electric machines. This article introduces a two-speed BEV transmission concept using wet running multi-plate clutches actuated via a rotating 5/3-way valve that can shut off, i.e., lock up the actuating pressure directly in the rotating system. Consequently, the rotary feedthrough is depressurized and contactless gap seals are usable. This reduces supply pressure requirements and minimizes hydraulic and friction losses while retaining powershift capability. Component-level tests evaluate leakage, pressure shut off, actuator dynamics and power consumption. Results show that actuating pressure in a shut-off clutch is maintained for longer than 60 min and electrical actuator power consumption is less than 20 W. During overlapping gearshifts, gap seal leakage is less than 1 L/min at 10 bar and sufficient pressure dynamics are achieved. These findings confirm the feasibility of the proposed actuator for multi-plate clutches in two-speed BEV transmissions. Full article

Global climate change results in increasing challenges to the structural security of China’s food system, while pronounced spatial heterogeneities in provincial production and consumption intensify the risk of supply-demand imbalance. This study examines the resilience of China’s inter-provincial staple food flow network from a systemic perspective and identifies its key drivers. Inter-provincial food flows are first inferred using a cost-minimization optimization model. Network resilience is then evaluated by integrating complex network analysis with ecological network resilience theory. Finally, econometric analysis is applied to quantify the relative contributions of multiple structural factors to resilience dynamics. The results reveal an overall decline in the resilience of aggregated staple food, alongside persistently low resilience in soybeans network, indicating heightened structural vulnerability. Substantial heterogeneity is observed across staples in both resilience levels and underlying mechanisms. In general, greater connectivity and diversity of flow paths enhance system resilience, although this effect is markedly weaker for soybeans due to concentrated and import-dependent supply structures. By explicitly linking flow, network structure, and resilience, this study provides system-level insights into the functioning of inter-provincial food flow networks. The proposed analytical framework offers a transferable tool for assessing interregional food flow resilience and supports evidence-based strategies for validating system robustness under uncertainties. Full article

The accelerating pace of urbanization and the pressing need for sustainability have compelled cities worldwide to integrate renewable energy into their infrastructure. While solar, wind, and hydro sources offer cleaner alternatives to fossil fuels, their inherent variability creates challenges in maintaining balance between supply and demand in urban energy systems. Traditional statistical forecasting methods are often inadequate for capturing the nonlinear, weather-driven dynamics of renewables, highlighting the need for advanced artificial intelligence (AI) approaches that deliver both accuracy and interpretability. This paper proposes a spatio-temporal framework for smart city energy management that combines a Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) for renewable energy generation forecasting, a Gradient Boosting Machine (GBM) for urban demand prediction, and Particle Swarm Optimization (PSO) for cost-efficient energy allocation. The framework was first validated using Spain’s national hourly energy dataset (2015–2018). To rigorously test its generalizability, the methodology was further validated on a separate dataset for the German energy market (2019–2022), proving its robustness across different geographical and meteorological contexts. Results indicate strong predictive performance, with solar generation achieving a 99.03% R² score, wind 96.46%, hydro 93.02%, and demand forecasting 91.56%. PSO further minimized system costs, reduced reliance on fossil-fuel generation by 18.2%, and improved overall grid efficiency by 12%. These findings underscore the potential of AI frameworks to enhance reliability and reduce operational costs. Full article

The significant transformation of agricultural production and operation models has reshaped the supply-demand structure of rural land, providing growth opportunities for new agricultural management entities characterized by large-scale operation. Their large-scale land demand has directly driven an upward trend in the transfer prices of contracted land management rights. By analyzing this practical phenomenon, this study explores the intrinsic logic behind the rising transfer prices of contracted land management rights under the participation of new agricultural management entities, aiming to provide references for further regulating the formation mechanism of transfer prices and promoting the healthy development of the land transfer market. Based on the sample survey data of farmers from the Songnen Plain and Sanjiang Plain in Northeast China, this study adopts the cluster-robust Ordinary Least Squares (OLS) model and moderating effect model for analysis. The results show that the participation of new agricultural management entities exerts a positive impact on the transfer price of contracted land management rights; the impact of new agricultural management entities’ participation on the transfer price is positively moderated by agricultural production efficiency; and the impact also presents heterogeneity across different villages and land parcels. Compared with remote villages and paddy parcels, the participation of new agricultural management entities has a more significant impact on the transfer price of contracted land management rights in township-adjacent villages and dryland parcels. Therefore, to reasonably standardize the transfer price of contracted land management rights, efforts should focus on further strengthening policy guidance to standardize the participation mechanism of new agricultural management entities, regulating the transfer market to establish a dynamic monitoring mechanism for transfer prices, and strengthening the training and guidance for new agricultural management entities to connect and drive farmers so as to improve the agricultural production efficiency of individual farmers. Full article

Modern deep neural network (DNN) accelerators must sustain high throughput while avoiding performance degradation from supply voltage (VDD) droop, which occurs when large arrays of multiply–accumulate (MAC) units switch concurrently and induce high peak current ($IC{C}_{max}$) transients on the power delivery network (PDN). In this work, we focus on ASIC-class DNN accelerators with tightly synchronized MAC arrays rather than FPGA-based implementations, where such cycle-aligned switching is most pronounced. Conventional guardbanding and reactive countermeasures (e.g., throttling, clock stretching, or emergency DVFS) either waste energy or incur non-trivial throughput penalties. We propose SparseDroop, a unified hardware-conscious framework that proactively shapes instantaneous current demand to mitigate droop without reducing sustained computing rate. SparseDroop comprises two complementary techniques. (1) SparseStagger, a lightweight hardware-friendly droop scheduler that exploits the inherent unstructured sparsity already present in the weights and activations—it does not introduce any additional sparsification. SparseStagger dynamically inspects the zero patterns mapped to each processing element (PE) column and staggers MAC start times within a column so that high-activity bursts are temporally interleaved. This fine-grain reordering smooths $ICC$ trajectories, lowers the probability and depth of transient VDD dips, and preserves cycle-level alignment at tile/row boundaries—thereby maintaining no throughput loss and negligible control overhead. (2) SparseBlock, an architecture-aware, block-wise-structured sparsity induction method that intentionally introduces additional sparsity aligned with the accelerator’s dataflow. By co-designing block layout with the dataflow, SparseBlock reduces the likelihood that all PEs in a column become simultaneously active, directly constraining $IC{C}_{max}$ and peak dynamic power on the PDN. Together, SparseStagger’s opportunistic staggering (from existing unstructured weight zeros) and SparseBlock’s structured, layout-aware sparsity induction (added to prevent peak-power excursions) deliver a scalable, low-overhead solution that improves voltage stability, energy efficiency, and robustness, integrates cleanly with the accelerator dataflow, and preserves model accuracy with modest retraining or fine-tuning. Full article

Common wheat (Triticum aestivum ssp. vulgare) is one of the three basic cereal crops worldwide that plays a key role in global food security. A key factor affecting the yield and traits of common wheat is an adequate nitrogen supply. Improving the efficiency of soil nitrogen use can be achieved through the application of appropriate mineral fertilizers and the proper selection of cultivars. The aim of this study was to determine the impact of residual nitrogen (N_res) after maize cultivation (the preceding crop) on the yield and chemical composition of winter and spring wheat grain. It was shown that both the variety selection and the type of nitrogen carrier had a significant impact on the characteristics related to wheat yield and grain quality. The most stable effect of the type of nitrogen, regardless of the type of corn variety, was recorded for ammonium nitrate with N-Lock. The average yield was approximately 6.1 t ha⁻¹. With the exception of the variant with N-Lock, the most progressive reaction to the type of fertilizer occurred in the stand with a three-line corn hybrid (TC, stay green). The advantage of this corn variety as a winter wheat forecrop results from the value of the site in a site without nitrogen. In the nitrogen control, the increase in yield compared to the single corn hybrid (SC) was 14%. However, in the U + N-Lock variant, it was 17%, and SG Stabilo as much as 32%. The increase in the weight of 1000 wheat grains in the stands after the SC and TC hybrid compared to stay green + roots power indicates a compensatory mechanism that became visible in the grain filling phase. Current challenges in agriculture caused by population growth and the need to ensure sufficient food production require greater awareness and knowledge regarding improved nitrogen management, including recognizing the role of residual nitrogen remaining in the soil after the preceding crop. A major advantage of slow-release fertilizers is that the nutrient (N) is released in response to the dynamic demand of the crop. This, on the one hand, increases grain yield and, on the other, does not negatively impact the agrosystem (eutrophication). Full article

Real estate inventory dynamics exhibit distinct temporal patterns and spatial heterogeneity, and precise identification of these trends serves as a prerequisite for effective policy formulation. Research on the spatiotemporal evolution patterns and influencing factors of real estate inventory holds significant academic and practical value. By employing ESDA, the Boston Matrix, and geographically weighted regression models to analyze 2017–2022 data from 287 Chinese cities, this study reveals a cyclical shift in China’s real estate inventory management—from “destocking” to “restocking”. The underlying drivers have transitioned from policy-led interventions to fundamentals-driven factors, including population dynamics, income levels, and market expectations. China’s real estate inventory and its changes exhibit significant spatiotemporal differentiation and spatial agglomeration patterns, demonstrating a spatial structure characterized by “multiple clustered highlands with peripheral lowlands” led by urban agglomerations. The influencing mechanism of China’s real estate inventory constitutes a complex system shaped by three key dimensions: macro-level drivers, regional differentiation, and structural contradictions. Policymakers should reorient destocking policies from “short-term stimulus” to “long-term coordination”, from “industrial policy” to “spatial policy”, and from addressing market “symptoms” to tackling “root causes”. This study argues that effective destocking policies constitute a systematic engineering challenge, demanding policymakers demonstrate profound analytical depth. They must move beyond simplistic sales metrics and perform multi-dimensional evaluations encompassing economic geography, demographic trends, fiscal systems, and land supply mechanisms. This paradigm shift from “symptom management” to “root cause resolution” and “systemic regulation” is essential for achieving sustainable real estate market development. Full article

This study proposes two optimization models and a column-generation algorithm, applied at the root node, to support tactical planning in sawmills by dynamically generating log cutting patterns aligned with sustainability and efficiency objectives. Starting from an industrial dataset containing 160 cutting patterns, the methodology iteratively incorporates new geometrically feasible configurations guided by the dual prices of a primary model, explicitly considering log supply, product demand, and alternative tactical criteria. Three computational experiments were conducted. The first assesses the convergence behavior of the algorithm and shows reductions in total log consumption of up to 31% as new patterns are generated. The second demonstrates that strategies aimed at minimizing log usage and residues can achieve near-optimal solutions with only 20–25 patterns, since additional configurations provide marginal improvements while increasing setup time and operational complexity. The third experiment confirms that near-optimal performance can be reached with a moderate number of active patterns, facilitating practical implementation in industrial settings. Overall, the proposed methodology offers a flexible and sustainability-oriented decision-support tool for sawmill tactical planning, improving raw-material utilization, reducing residues, and enhancing alignment between supply and demand while maintaining operational feasibility. Full article

Traditional product demand forecasting has typically been modeled as a single time series problem relying exclusively on temporal information. However, temporal features alone are insufficient to capture complex demand dynamics in modern, interconnected markets. To address this limitation, we propose a product demand forecasting method based on a Spatiotemporal Hypergraph Attention Network (STHA), which jointly models temporal dependencies and higher-order spatial interactions among multiple market entities to enhance forecasting accuracy and robustness. STHA integrates a Long Short-Term Memory (LSTM) network with a Hawkes attention mechanism to capture temporal patterns and constructs a hypergraph to represent multi-entity relationships. It further incorporates hypergraph convolution and a hypergraph attention mechanism to dynamically aggregate higher-order spatial information and weight relational importance. Experiments on the Corporación Favorita sales dataset demonstrate that STHA substantially outperforms single time series benchmarks (ARIMA, LSTM, TCN, Transformer, and PatchTST), achieving notable reductions in MAE, RMSE, and MAPE—with improvements in MAPE exceeding 15 percentage points for certain stores. Compared with the graph-based STGCN model, STHA also exhibits superior robustness. These results demonstrate the effectiveness of STHA for complex multi-market-entity demand forecasting and highlight its potential as a reliable framework for improving inventory management and supply chain decision-making. Full article

Decarbonizing heavy-duty freight transport is essential for achieving climate neutrality targets. Although internal combustion engine (ICE) trucks currently dominate logistics, they contribute substantially to greenhouse gas emissions. Zero-emission alternatives, such as battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (H2), provide different decarbonization pathways; however, their relative roles remain contested, particularly in small economies. While BEVs benefit from technological maturity and declining costs, hydrogen offers advantages for high-payload, long-haul operations, especially within energy-intensive cold supply chains. The aim of this paper is to examine the gradual transition from ICE trucks to hydrogen-powered vehicles with a specific focus on cold-chain logistics, where reliability and energy intensity are critical. The hypothesis is that applying a system dynamics forecasting approach, incorporating investment costs, infrastructure coverage, government support, and technological progress, can more effectively guide transition planning than traditional linear methods. To address this, the study develops a system dynamics economic model tailored to the structural characteristics of a small economy, using a European case context. Small markets face distinct constraints: limited fleet sizes reduce economies of scale, infrastructure deployment is disproportionately costly, and fiscal capacity to support subsidies is restricted. These conditions increase the risk of technology lock-in and emphasize the need for coordinated, adaptive policy design. The model integrates acquisition and maintenance costs, fuel consumption, infrastructure rollout, subsidy schemes, industrial hydrogen demand, and technology learning rates. It incorporates subsystems for fleet renewal, hydrogen refueling network expansion, operating costs, industrial demand linkages, and attractiveness functions weighted by operator decision preferences. Reinforcing and balancing feedback loops capture the dynamic interactions between fleet adoption and infrastructure availability. Inputs combine fixed baseline parameters with variable policy levers such as subsidies, elasticity values, and hydrogen cost reduction rates. Results indicate that BEVs are structurally more favorable in small economies due to lower entry costs and simpler infrastructure requirements. Hydrogen adoption becomes viable only under scenarios with strong, sustained subsidies, accelerated station deployment, and sufficient cross-sectoral demand. Under favorable conditions, hydrogen can approach cost and attractiveness parity with BEVs. Overall, market forces alone are insufficient to ensure a balanced zero-emission transition in small markets; proactive and continuous government intervention is required for hydrogen to complement rather than remain secondary to BEV uptake. The novelty of this study lies in the development of a system dynamics model specifically designed for small-economy conditions, integrating industrial hydrogen demand, policy elasticity, and infrastructure coverage limitations, factors largely absent from the existing literature. Unlike models focused on large markets or single-sector applications, this approach captures cross-sector synergies, small-scale cost dynamics, and subsidy-driven points, offering a more realistic framework for hydrogen truck deployment in small-country environments. The model highlights key leverage points for policymakers and provides a transferable tool for guiding freight decarbonization strategies in comparable small-market contexts. Full article

Rising residential land prices push up housing prices and worsen credit misallocation. These patterns emerge amid cyclical real estate fluctuations and heavy land-based public finance. Such pressures undermine macroeconomic stability and sustainable land-use. The land price circuit breaker is widely applied with a price cap and state dependence, yet its trigger mechanism and interaction with inflation targeting remain underexplored. This study addresses three core questions. First, how does the circuit breaker’s discrete trigger and rule-switching logic differ from traditional static price ceilings? Second, can the mechanism, via the collateral channel, restrain excessive land price hikes, improve credit allocation, and, thereby, stabilize land price dynamics and long-run macroeconomic performance? Third, how does the circuit breaker interact with inflation targeting, and through which endogenous channels does a strict target dampen housing prices and raise activation probability? This study develops a multi-sector DSGE model with an embedded land price circuit breaker. The price cap is modeled as an occasionally binding constraint. A dynamic price band and trigger indicator capture the policy’s switch between slack and binding states. The framework incorporates interactions among local governments, the central bank, developers, and households. It also links firms and the secondary housing market. Under different inflation-targeting rules, this study uses impulse responses, an event study, and welfare analysis to assess trigger conditions and macroeconomic effects. The findings are threefold. First, a strict inflation target increases the probability of a circuit breaker being triggered. It channels housing-demand shocks toward land prices and creates a “nominal anchor–relative price constraint” linkage. Second, once activated, the circuit breaker narrows the gap between land price and house-price growth. It weakens the procyclicality of collateral values. It also restrains credit expansion by impatient households. These effects redirect credit toward firms, improve corporate financing, reduce the decline in investment, and accelerate output recovery. Third, the circuit breaker limits new land supply and shifts demand toward the secondary housing market. This generates a supply-side effect that releases existing stock and stabilizes prices, thereby weakening the amplification mechanism of housing cycles. This study identifies the endogenous trigger logic and cross-market transmission of the land price circuit breaker under a strict inflation target. It shows that the mechanism is not merely a price-management tool in the land market but a systemic policy variable that links the real estate, finance, and fiscal sectors. By dampening real estate procyclicality, improving credit allocation, and stabilizing macroeconomic fluctuations, the mechanism offers new insights for sustainable land-use policy and macroeconomic stabilization. Full article

Traditionally, the dynamics of power systems have been governed by synchronous generators and their associated rotating masses. However, with the increasing penetration of renewable generation and power electronic interfaces, the inertia contributed by rotating machines has been gradually displaced. This makes it imperative to study alternative elements capable of mitigating the reduction in inertia in modern power systems. This article addresses the problem of optimal sizing and placement of Battery Energy Storage Systems to enhance frequency response in power grids through the application of optimization techniques such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Several inertia scenarios are analyzed, where the algorithms determine the optimal locations for Battery Energy Storage Systems units while minimizing the total installed Battery Energy Storage Systems capacity. As key contributions, this study models Battery Energy Storage Systems units, which emulate inertial responses based on the system’s Rate of Change of Frequency, and evaluates the impact of Battery Energy Storage Systems on frequency stability by analyzing parameters such as the frequency nadir, zenith, and steady-state frequency according to the installed Battery Energy Storage System’s size and location. A comparative analysis of the optimization scenarios shows that the Particle Swarm Optimization algorithm with 50% rotational inertia is the most efficient, requiring the lowest total installed power (277.11 MW). It is followed by the Particle Swarm Optimization algorithm with 100% rotational inertia (285.79 MW) and Genetic Algorithms with 50% rotational inertia (285.57 MW). In contrast, Genetic Algorithms with 25% rotational inertia demand the highest total installed Battery Energy Storage Systems power (307.44 MW), a result directly associated with a significant reduction in system inertia. Overall, an inverse relationship is observed between the available inertia level and the required Battery Energy Storage Systems capacity: the lower the inertia, the greater the power that the Battery Energy Storage Systems must supply to keep the system frequency within acceptable operational limits. Full article

As a key node of the energy internet, the park-level integrated energy system undertakes the dual functions of improving energy supply reliability and promoting low-carbon development in the transformation of the global energy structure. The need to simultaneously meet terminal energy demand and market regulation requirements constrains operational optimization due to factors such as energy price fluctuations. Future research should focus on supply–demand coordination mechanisms and energy efficiency improvement strategies to advance the high-quality development of such systems. To this end, this study constructs a collaborative optimization framework integrating demand response based on a dual-compensation mechanism and dynamic multi-energy pricing and incorporates it into a Stackelberg game-based low-carbon economic dispatch model. By incorporating a dynamic multi-energy pricing mechanism, the model coordinates and optimizes the interests of the upper-level park integrated energy system operator (PIESO) and the lower-level park users. On the supply side, the model couples a two-stage power-to-gas (P2G) device with a stepwise carbon trading mechanism, forming a low-carbon dispatch system enabling source–grid–load coordination. On the demand side, an integrated demand response mechanism with dual compensation is introduced to enhance the coupling intensity of multi-energy flows and the adjustability of price elasticity. The simulation results show that, compared with traditional models, the proposed optimization framework achieves improvements in three dimensions: carbon emissions, economic benefits, and user costs. Specifically, the carbon emission intensity is reduced by 28.04%, the operating income of the PIESO is increased by 29.53%, and the users’ energy consumption cost is decreased by 13.05%, which verifies the effectiveness and superiority of the proposed model. Full article

With the rapid expansion of data centers and the growing demand for cloud computing, their share in total electricity consumption has surged, making them a major high-power load in power systems. Consequently, accurately modeling their energy consumption and quantifying the feasible region have become critical research challenge. Existing studies have focused on energy consumption models for single data centers and single time periods, while limited attention has been given to multi-data centers energy optimization that considers spatiotemporal workload migration. This paper presents an energy consumption model for multi-data centers that accounts for the spatiotemporal transfer flexibility of delay-tolerant workloads. By enabling task migration across data centers (spatial dimension) and workload deferral within each center (temporal dimension), the model dynamically adjusts the operational states of IT equipment to minimize overall system operating costs while satisfying computational demands. To address the computational challenges caused by the large number of integer variables, the sliding window method and equipment aggregation method are employed to ensure the model can be efficiently solved. To further capture the flexibility of data center energy consumption, a method for computing the energy consumption elasticity space is proposed based on multi-parametric programming. This elasticity space characterizes the feasible range of energy consumption under operational constraints and provides boundary conditions for power system dispatch optimization. Simulation studies using real operational data from a large-scale Internet enterprise show that the proposed model reduces the total operational cost by approximately 3.4% compared to the baseline model without flexibility, decreases the frequency of IT equipment state transitions, and enhances the flexibility of data centers in supporting power system supply-demand balance and renewable energy integration. Full article