Approach to the Problem of Operation Task and Platform Resource Matching based on MPLDS and PWS

AbstractThe Problem of operation tasks and platform resources matching(MPoTP) is the main topic in the preparation phase of battle. In order to consider the loss of the platform capacity in the process of combat, the loss coefficient is introduced in the process of problem modeling, and the problem model can be more conformable with actual combat. An approach to the problem model based on the multi-PRI list dynamic scheduling (MPLDS) and pair-wise exchange (PWE) is proposed. In the basic of the solution which is obtained by MPLDS algorithm, the PWE method is imported which improves the solution by considering all possible task assignment sequences obtained by exchanging the task at the current place in the assignment sequence with some other task. At last, the superiority and applicability of this approach are illuminated by case analysis

Analysis on the Evolution of Rural Settlement Pattern and Its Influencing Factors in China from 1995 to 2015

Since the early 1990s, China has experienced rapid industrialization and urbanization. As cities have expanded rapidly, the spatial patterns of rural settlements also changed significantly. This study uses land use data from satellite imagery interpretation, socioeconomic statistics, and field survey data, together with techniques including landscape pattern analysis, kernel density estimation, and spatial measurement models, to analyze the evolving spatial patterns of rural settlements influencing factors in China from 1995 to 2015. The results indicate the following: First, China’s rural settlements experienced significant changes in the period 1995–2015, as 88.92% of Prefectural-level administrative district units saw an increase in rural settlement area, with total settlement size increasing by 1.35 million hectares, and settlement area sprawl index values can be summarized as “high in the west and low in the east”. Second, in the two-decade study period, the population agglomeration capacity of rural settlements in China continuously weakened, and the shape and structure of rural settlement became more complex and irregular. The scale and scope of the disappearance of rural settlement areas in the northeast and southeast regions was relatively drastic, and the kernel density value of settlements dropped significantly. Third, the increase in rural settlement land area is concentrated in low-altitude and low-slope areas, with a significant tendency to be near water and roads. Fourth, social and economic factors, such as per capita net income of rural residents, the proportion of the population employed in agriculture, the size and structure of the permanent rural population, local fiscal revenue, and urbanization level, are the main factors that cause changes of rural settlement patterns. The results of this study can serve as a reference for promoting regional rural sustainable development policies and advancing rural spatial governance and comprehensive revitalization

The Adjustment of China’s Grain Planting Structure Reduced the Consumption of Cropland and Water Resources

Driven by technological progress and market demand, the optimization and adjustment of grain planting structure played an important role in increasing grain output. Due to the great difference between the yield per unit area of different types of food crops, the consumption of cropland and water resources has a significant change during the grain growth. From the perspective of structural adjustment, rather than the usual productive factor input, we analyze the process of adjustment for grain planting structure in China and its effect on the consumption of cropland and water resources by using the scenario comparative analysis method. The results show that: (1) From 2003 to 2019, China’s grain output has increased steadily and the planting structure has changed greatly. Rice was replaced by corn to become the grain crop with the maximum proportion of planting area since 2007. The increase of corn planting structure proportion is concentrated in the northern regions. (2) At the national level, according to the adjustment of grain planting structure, the saving of cropland and water resources consumption showed a “cumulative effect” as time went on. (3) The saving effects of structural adjustment in the northern regions on cropland and water resources consumption are better than that in the southern regions, such as Northeast China Plain, Northern arid and semiarid region and Loess Plateau. (4) In reality, although the adjustment of grain planting structure saved lots of cropland and water resources, the continuous growth of grain output has increased the pressure on the ecological environment in the northern regions according to theirs water limits. Therefore, it is necessary to continuously optimize the grain planting structure and restrict land reclamation in northern China. In addition, to ensure food security, it is feasible to encourage the southern regions with abundant water and heat resources to increase the grain planting area and meet its self-sufficiency in grain demand

Dynamic identification of important nodes in complex networks based on the KPDN–INCC method

Abstract This article focuses on the cascading failure problem and node importance evaluation method in complex networks. To address the issue of identifying important nodes in dynamic networks, the method used in static networks is introduced and the necessity of re-evaluating node status during node removal is proposed. Studies have found that the methods for identifying dynamic and static network nodes are two different directions, and most literature only uses dynamic methods to verify static methods. Therefore, it is necessary to find suitable node evaluation methods for dynamic networks. Based on this, this article proposes a method that integrates local and global correlation properties. In terms of global features, we introduce an improved k-shell method with fusion degree to improve the resolution of node ranking. In terms of local features, we introduce Solton factor and structure hole factor improved by INCC (improved network constraint coefficient), which effectively improves the algorithm’s ability to identify the relationship between adjacent nodes. Through comparison with existing methods, it is found that the KPDN–INCC method proposed in this paper complements the KPDN method and can accurately identify important nodes, thereby helping to quickly disintegrate the network. Finally, the effectiveness of the proposed method in identifying important nodes in a small-world network with a random parameter less than 0.4 was verified through artificial network experiments

Temporal and Spatial Changes of Rural Settlements and Their Influencing Factors in Northeast China from 2000 to 2020

Rural settlements in Northeast China have undergone significant changes in the process of rapid urbanization, which has profoundly affected food production and the process of sustainable rural development. Based on multi-period remote sensing interpretation data of land use and economic statistics, this study quantitatively analyzes the temporal and spatial pattern change characteristics and influential factors of rural settlements in Northeast China from 2000 to 2020. The results show that: (i) Between 2000 and 2020, the area of rural settlements in Northeast China increased by 190,603.03 hectares, which accounts for 7.62% of the total rural settlements area, and 129 counties (cities) (70.88%) increased the area of rural settlements; (ii) The expanded state of rural settlements presents a low spatial distribution pattern in the northwest and high spatial distribution pattern in the southeast. The core density value of rural settlements in the north decreases, and the core density value in the southeast increases slightly. In addition, the landscape pattern of rural settlement expansion is irregular and there was increased disturbance from settlement expansion; (iii) A total of 81.6% of the land occupied by the expansion of rural settlements comes from cultivated land. The soil’s organic matter content is 10.0 g/kg–20.0 g/kg, and the high-quality cultivated land occupied by the expansion is 218,274.17 hectares. However, it is interesting that the expansion of rural settlements coincides with the increasing number of hollow villages. From 2000 to 2020, the utilization degree of rural settlements in Northeast China decreased by 56.97%; (v) The main factors affecting the changes of rural settlements in Northeast China are water resource conditions, terrain conditions, traffic location, and the level of county economic development. In areas with superior agricultural production conditions, the influence of various factors on the change of rural settlements is more obvious

An Understanding of Education in Supporting Cotton Production: An Empirical Study in Benin, West Africa

Benin is an underdeveloped country whose economy is dependent on agriculture, principally cotton production. Kandi is a community in the country’s northeast region, and is regarded as one of Benin’s top four cotton-producing communities. This community has a deficient level of education. The present paper aimed to investigate whether education could contribute to increasing cotton production in Kandi. A questionnaire was distributed to educated, uneducated, and organic farmers in the research area to achieve this goal. A linear regression strategy was applied, with the key components of the research areas being the usage of agricultural chemical inputs (pesticides and fertilizers), miscellaneous factors, and the level of education of farmers. The data collected were utilized to compare the different groups polled (educated and uneducated farmers). Organic farmers are used in this paper as a model of suitable agriculture development in the study area. The key finding is that primary education affects agricultural production. Solutions are presented with a focus on organic farmers

Analysis of the Spatial–Temporal Pattern of the Newly Increased Cultivated Land and Its Vulnerability in Northeast China

Ensuring compliance with China’s “1.8 billion mu” (120 million hectares) cultivated land preservation policy is a fundamental goal of land policy. Northeast China has experienced significant cultivated land expansion due to rigorous compensation policies over the past two decades, resulting in sustainable increases in grain output. This research employs remote sensing data to examine the spatial–temporal pattern and vulnerability of newly increased cultivated land expansion in Northeast China and its potential impact on food security. Results indicate a 3.08% increase in newly increased cultivated land from 2000 to 2020, with the majority located in the Sanjiang Plain’s humid area and Inner Mongolia’s arid and semi-arid regions. The low quality of newly added cultivated land makes it highly vulnerable. Temperature instability significantly and negatively correlates with cultivated land expansion. The vulnerability of cultivated land is negatively and significantly related to grain yield, suggesting an adverse impact on national food security. This study focuses on the marginal impact of newly increased cultivated land and proposes policy recommendations

A Multidimensional Elastic–Plastic Calculation Model of the Frame Structure with Magnetorheological Damper

To analyze the multidimensional elastic–plastic response of the frame structure with magnetorheological (MR) dampers under strong seismic excitations, the test of the MRD was performed, the location matrix of the MRD in the frame structure was derived, and the multidimensional elastic–plastic calculation models of the frame structure with and without an MRD were established based on the three-segment variable stiffness beam. Taking a five-story reinforced concrete (RC) frame structure as an example, the multidimensional elastic–plastic calculation models were developed by MATLAB software and the dynamic time history analyses were performed under strong seismic excitations. The results show that under the seismic wave, after the MRD is installed in the structure, the maximum horizontal displacements of the top-story node of the structure in X and Y directions is reduced by 51.87% and 39.59%, respectively, and the maximum horizontal accelerations are reduced by 36.67% and 47.86%. The maximum displacements and the story drift ratios of each story of the structure are significantly reduced, and the reduction in the maximum accelerations of each story is small relatively. In the frame structure without an MRD, plastic hinges appear at the ends of most columns, and the structure is characterized by a column hinge yield mechanism. The maximum residual displacement angles of the column end in X and Y directions which reach 1.628 × 10−3 rad and 2.101 × 10−3 rad, respectively. After setting the MRD, the number of plastic hinges in X and Y directions at the column end are both reduced by 37.5%, and the residual displacement angle at some column ends are reduced to 0. The results show that the complied calculation model programs of the frame structure can effectively simulate the multi-dimensional seismic response of the structure with and without MRD

Dynamic QoS Prediction Algorithm Based on Kalman Filter Modification

With the widespread adoption of service-oriented architectures (SOA), services with the same functionality but the different Quality of Service (QoS) are proliferating, which is challenging the ability of users to build high-quality services. It is often costly for users to evaluate the QoS of all feasible services; therefore, it is necessary to investigate QoS prediction algorithms to help users find services that meet their needs. In this paper, we propose a QoS prediction algorithm called the MFDK model, which is able to fill in historical sparse QoS values by a non-negative matrix decomposition algorithm and predict future QoS values by a deep neural network. In addition, this model uses a Kalman filter algorithm to correct the model prediction values with real-time QoS observations to reduce its prediction error. Through extensive simulation experiments on the WS-DREAM dataset, we analytically validate that the MFDK model has better prediction accuracy compared to the baseline model, and it can maintain good prediction results under different tensor densities and observation densities. We further demonstrate the rationality of our proposed model and its prediction performance through model ablation experiments and parameter tuning experiments