Search Results (6)

Amid persistent global food security challenges, the efficient utilization of cultivated land resources has become increasingly critical, as optimizing Cultivated Land Utilization Efficiency (CLUE) is paramount to ensuring food supply. This study introduced a cultivated land utilization index (CLUI) based on Fractional Vegetation Cover (FVC) to assess the spatiotemporal variations in Henan Province’s CLUE. The Theil–Sen slope and the Mann–Kendall test were used to analyze the spatiotemporal variations of CLUE in Henan Province from 2000 to 2020. Additionally, we used a genetic algorithm optimized Artificial Neural Network (ANN) and a particle swarm optimization-based Random Forest (RF) model to assess the comprehensive in-fluence between topography, climate, and human activities on CLUE, in which incorporating Shapley Additive Explanations (SHAP) values. The results reveal the following: (1) From 2000 to 2020, the CLUE in Henan province showed an overall upward trend, with strong spatial heterogeneity across various regions: the central and eastern areas generally showed decline, the northern region remained stable with slight increases, the western region saw significant growth, while the southern area exhibited complex fluctuations. (2) Natural and economic factors had notable impacts on CLUE in Henan province. Among these factors, population and economic factors played a dominant role, whereas average temperature exerted an inhibitory effect on CLUE in most parts of the province. (3) The influenced factors on CLUE varied spatially, with human activity impacts being more concentrated, while topographical and climatic influences were relatively dispersed. These findings provide a scientific basis for land management and agricultural policy formulation in major grain-producing areas, offering valuable insights into enhancing regional CLUE and promoting sustainable agricultural development. Full article

With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm–particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels. Full article

Global optimization challenges are frequent in scientific and engineering areas where loads of evolutionary computation methods i.e., differential evolution (DE) and particle-swarm optimization (PSO) are employed to handle these problems. However, the performance of these algorithms declines due to expansion in the problem dimension. The evolutionary algorithms are obstructed to congregate with the Pareto front rapidly while using the large-scale optimization algorithm. This work intends a large-scale multi-objective evolutionary optimization scheme aided by the determinantal point process (LSMOEA-DPPs) to handle this problem. The proposed DPP model introduces a mechanism consisting of a kernel matrix and a probability model to achieve convergence and population variety in high dimensional relationship balance to keep the population diverse. We have also employed elitist non-dominated sorting for environmental selection. Moreover, the projected algorithm also demonstrates and distinguishes four cutting-edge algorithms, each with two and three objectives, respectively, and up to 2500 decision variables. The experimental results show that LSMOEA-DPPs outperform four cutting-edge multi-objective evolutionary algorithms by a large margin. Full article

The flutter phenomenon is a potentially destructive aeroelastic vibration studied for the design of aircraft structures as it limits the flight envelope of the aircraft. The aim of this work is to propose a heuristic design of a piezoelectric actuator-based controller for flutter vibration suppression in order to extend the allowable speed range of the structure. Based on the numerical model of a three degrees of freedom (3DOF) airfoil and taking into account the FEM model of a V-stack piezoelectric actuator, a filtered PID controller is tuned using the population decline swarm optimizer $P_{D}SO$ algorithm, and gain scheduling (GS) of the controller parameters is used to make the control adaptive in velocity. Numerical simulations are discussed to study the performance of the controller in the presence of external disturbances. Full article

This paper utilizes the generalized Fisher index (GFI) to decompose the factors of carbon emission and exploits improved particle swarm optimization-back propagation (IPSO-BP) neural network modelling to predict the primary energy consumption CO₂ emissions in different scenarios of Beijing-Tianjin-Hebei region. The results show that (1) the main factors that affect the region are economic factors, followed by population size. On the contrary, the factors that mainly inhibit the carbon emissions are energy structure and energy intensity. (2) The peak year of carbon emission changes with the different scenarios. In a low carbon scenario, the carbon emission will have a decline stage between 2015 and 2018, then the carbon emission will be in the ascending phase during 2019–2030. In basic and high carbon scenarios, the carbon emission will peak in 2025 and 2028, respectively. Full article

Carbon emissions are the major cause of the global warming; therefore, the exploration of carbon emissions reduction potential is of great significance to reduce carbon emissions. This paper explores the potential of carbon intensity reduction in Beijing in 2020. Based on factors including economic growth, resident population growth, energy structure adjustment, industrial structure adjustment and technical progress, the paper sets 48 development scenarios during the years 2015–2020. Then, the back propagation (BP) neural network optimized by improved particle swarm optimization algorithm (IPSO) is used to calculate the carbon emissions and carbon intensity reduction potential under various scenarios for 2016 and 2020. Finally, the contribution of different factors to carbon intensity reduction is compared. The results indicate that Beijing could more than fulfill the 40%–45% reduction target for carbon intensity in 2020 in all of the scenarios. Furthermore, energy structure adjustment, industrial structure adjustment and technical progress can drive the decline in carbon intensity. However, the increase in the resident population hinders the decline in carbon intensity, and there is no clear relationship between economy and carbon intensity. On the basis of these findings, this paper puts forward relevant policy recommendations. Full article