Search Results (12)

Climate change, resource scarcity, and ecological degradation have become critical bottlenecks constraining socio-economic development. Basin cities serve as key nodes in China’s ecological security pattern, playing indispensable roles in ecological civilization construction. This study established an evaluation index system spanning five dimensions to assess the effectiveness of ecological civilization construction. This study employs the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Back-Propagation (BP) neural network methods to evaluate the level of ecological civilization construction in the Yellow River Basin from 2010 to 2022, to analyze its indicator weights, and to explore the spatio-temporal evolution characteristics of each city. The results demonstrate the following: (1) Although the ecological civilization construction level of cities in the Yellow River Basin shows a steady improvement, significant regional development disparities persist. (2) The upper reaches are primarily constrained by ecological fragility and economic underdevelopment. The middle reaches exhibit significant internal divergence, with provincial capitals leading yet demonstrating limited spillover effects on neighboring areas. The lower reaches face intense anthropogenic pressures, necessitating greater economic–ecological coordination. (3) Among the dimensions considered, Territorial Space and Eco-environmental Protection emerged as the two most influential dimensions contributing to performance differences. According to the ecological civilization construction performance and changing characteristics of the 48 cities, this study proposes differentiated optimization measures and coordinated development pathways to advance the implementation of the national strategy for ecological protection and high-quality development in the Yellow River Basin. Full article

In tennis matches, “momentum” describes the situation where players are inspired by positive factors during the game. This paper focuses on the quantification of momentum, the impact of momentum on match trends, and the optimisation of players’ match strategies based on momentum. In this research, the Markov chain is used to quantify the momentum, and the “momentum score” of players is obtained. The Eta and Spearman correlation coefficients are used to study the impact mechanism of momentum on match trends. The results show that, within a 95% confidence interval, momentum has a strong correlation with the results of the current game of players, but a weak correlation with the result of each current point. In addition, in this paper, we construct a match strategy optimisation model for players based on momentum scores. On the one hand, the entropy weight method and TOPSIS are combined to evaluate players’ performances. On the other hand, a BP neural network prediction model is established based on a multiple linear regression model with 13 indicators in 10 categories to predict match trends. According to the evaluation and prediction results, a series of strategy optimisation suggestions are put forward for players to cope with matches. Full article

Dempster–Shafer Theory (DST) relies significantly on belief and plausibility measures to handle ambiguity and uncertainty; however, DST has been extended to fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs) with only a few extensions focusing on belief and plausibility intuitionistic fuzzy distance (BP-distance) and similarity (BP-similarity) until now. In this work, we propose a novel framework for the belief and plausibility of intuitionistic fuzzy sets (BP-IFSs) and their BP-distance and BP-similarity measures. We modified steps 4 and 5 of the classical TOPSIS method, utilizing both distance and similarity measures to rank the alternatives that satisfy all necessary axioms of distance and similarity. We present numerical examples involving pattern recognition, linguistic variables, and clustering to illustrate the efficiency of these measures, and we develop belief and plausibility TOPSIS (BP-TOPSIS) using the proposed criteria and apply it to complex multicriteria decision-making (MCDM) challenges. The results demonstrate the practicality and effectiveness of our approach. Full article

To improve the water resources carrying capacity of the Xiangjiang River Basin and achieve sustainable development, this article evaluates and predicts the Xiangjiang River Basin’s water resources carrying capacity level based on county-level units. This article takes 44 county-level units in the Xiangjiang River Basin as the evaluation target, selects TOPSIS and the entropy weight method to determine weights, calculates the water resources carrying capacity level of the evaluation sample, uses a BP neural network model to calculate the predicted water resources carrying capacity level for the next 5 years, and adds the GIS method for spatiotemporal analysis.(1) The water resources carrying capacity of the Xiangjiang River Basin has remained relatively stable for a long period, with overloaded areas being the majority. (2) There are relatively significant spatial differences in the carrying capacity of water resources: Zixing City, located upstream of the tributary, is far ahead due to its possession of the Dongjiang Reservoir; the water resources carrying capacity in the middle and lower reaches (northern region) is generally higher than that in the upper reaches (southern region). (3) According to the BP neural network model prediction, the water resources carrying capacity of the Xiangjiang River Basin will maintain a stable development trend in 2022, while areas such as Changsha and Zixing City will be in a critical state, and other counties and cities will be in an overloaded state.This study has important references value for the evaluation and early warning work of the Xiangjiang River Basin and related research, providing a scientific and systematic evaluation method and providing strong support for water resource management and planning in Hunan Province and other regions. Full article

To address the issues of workpiece distortion and excessive material melting caused by heat accumulation during laser cutting of thin-walled sheet metal components, this paper proposes a segmented optimization method for process parameters in sheet metal laser cutting considering thermal effects. The method focuses on predetermined perforation points and machining paths. Firstly, an innovative temperature prediction model $T_p\left(r,t\right)$ is established for the nth perforation point during the cutting process, with a prediction error of less than 10%. Secondly, using the PSO-BP-constructed prediction model for laser cutting quality features and an empirical model for processing efficiency features, a multi-objective model for quality and efficiency is generated. The NSGA II algorithm is employed to solve the objective optimization model and obtain the Pareto front. Next, based on the predicted temperature at the perforation point using the model $T_p\left(r,t\right)$, the TOPSIS decision-making method is applied. Different weights for quality and efficiency are set during the cutting stages where the temperature is below the lower threshold and above the upper threshold. Various combinations of machining parameters are selected, and by switching the parameters during the cutting process, the thermal accumulation (i.e., temperature) during processing is controlled within a given range. Finally, the effectiveness of the proposed approach is verified through actual machining experiments. Full article

This study focuses on a small agricultural diesel engine fueled with B20 (20% biodiesel and 80% diesel by volume) blend fuel in a plateau area. The combustion chamber’s structural parameters and fuel injection angle were taken as variables at peak torque conditions. First, a full factorial design was used for experimental design. Second, the back-propagation (BP) neural network was employed to predict the indicated thermal efficiency and the indicated specific NO_x emission. Third, the non-dominated sorting genetic algorithm-II (NSGA-II) was utilized to optimize the indicated thermal efficiency and the indicated specific NO_x emission. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) method was applied to obtain optimal solutions, and a three-dimensional numerical simulation was conducted to verify the optimization results. The optimization results indicate that the shape characteristics of the combustion chamber have a certain influence on the engine’s performance. The optimized design significantly reduces NO_x emissions, by 22.83%, compared to the original engine, whilst maintaining the engine’s performance. Full article

Immediately and accurately assessing the risk of coal spontaneous combustion and taking targeted action are crucial steps in coal spontaneous combustion prevention and control. A new model, AEM-AHP-LSTM, was proposed to solve the weight calculation problem of multiobjective evaluation in the process of coal spontaneous combustion. Firstly, the key indicators of coal spontaneous combustion were analyzed and used as risk factors to establish an evaluation system. Next, the objective and subjective weights were calculated using AEM and AHP, respectively. The objective and subjective weights were then combined, and TOPSIS was used to calculate the score of the evaluation sample. Finally, the obtained evaluation samples were trained with the BP, RBF, and LSTM model to resolve the problem of model overdependence on historical data and achieve the auto-adapt adjustment of weight with data change. Additionally, data from 15 typical Chinese coal mines were applied to the model. The results indicate that, compared with the BP and RBF neural networks, the LSTM model has higher prediction accuracy, stronger generalization ability, and stronger practicability. The modeling and application findings show that the AEM-AHP-LSTM model was better appropriate for the risk assessment of coal spontaneous combustion. This method can potentially be further applied as reliable approach for the assessment of mine disaster risk. Full article

The efficiency and emission levels of taxi operations are influenced by taxi drivers’ empirical judgments of hotspot travel areas. In this study, we exploited vehicle specific power (VSP) approaches and taxi trajectory data in a 1000 × 1000 m grid to calculate emission and revenue efficiency-related indicators and explored their spatial and temporal characteristics. Then, the entropy weight TOPSIS method was employed to identify the grids with the top comprehensive ranking of the indicators in the period to replace the driver experience. Finally, the k-means clustering method was utilized to identify the recommended road segments in the hotspot grid. The data from Nanchang City in China showed the following. (1) The study area was divided into 7553 grids, and the main travel and emission areas were located in the West Lake, Qingyunpu and Qingshan Lake districts (less than 200 grids). However, revenue efficiency-related indicators in this region are at a moderately low level. For example, the order revenue was about 0.9–1.2 RMB/min, and the average was 1.3–1.5 RMB/min. Areas with high trip demand had low revenue efficiency. (2) Five indicators related to emissions and revenue efficiency were selected. Of these, grid boarding points (G-bp) maintained the highest weight, reaching a maximum of 0.48 from 7:00 a.m. to 9:00 a.m. The ranking of secondary indicators was time varying. Hotspot grids and road segments were identified within each period. For example, from 1:00 a.m. to 3:00 a.m., (66,65), (68,65) were identified as hotspot grids. People’s Park North Gate near the road was identified as the recommended section from 1:00 a.m. to 3:00 a.m. This study can provide recommended grids and sections for idle cruising taxis. Full article

The combine harvester is the main machine for fieldwork during the harvest season. When the harvester fails and cannot continue to work, this indirectly affects the harvest time and the yield in the field. The emergency maintenance service of agricultural machinery can be optimized through the dynamic planning of harvester maintenance tasks, using the operation and maintenance platform. According to the scene, a priority scheme for the operation and maintenance tasks, based on the improved Q-learning algorithm, was proposed. The continuous approximation capability of the model was improved by using the BP neural network algorithm and the Q function value, in iterations, was updated continuously. At the same time, the improved TOPSIS method, based on Mahalanobis distance, was used to calculate the closeness of each harvester maintenance task, so as to determine the priority of the equipment maintenance tasks. An operation and maintenance service platform for combine harvesters was developed based on the B/S architecture, with the goal of minimizing the operation and maintenance costs and improving the tasks’ complete efficiency. In this research process, dynamic scheduling rules were formulated. Operation and maintenance resources were optimized and rationally allocated through dynamic optimization scheduling methods, and feasible solution information was generated from the operation and maintenance service platform. Finally, the actual data from the enterprise were used for verification and analysis. The verification showed the following: through a comparison of algorithm performance, it was seen that the improved BP-Q-Learning algorithm can quickly find the operation and maintenance scheduling scheme in the maintenance scheduling; the priority rules can improve the efficiency of task execution, to a certain extent; the cost of the tasks’ execution can be significantly reduced; and the maintenance distance can be shortened. This research has reference significance for the formulation and optimization of agricultural machinery maintenance for cross-regional operations. Full article

The evaluation and trend prediction of tourism economic vulnerability (TEV) in major tourist cities are necessary for formulating tourism economic strategies scientifically and promoting the sustainable development of regional tourism. In this study, 58 major tourist cities in China were taken as the research object, and an evaluation index system of TEV was constructed from two aspects of sensitivity and adaptive capacity. On the basis of the entropy weight method, TOPSIS model, obstacle diagnosis model, and BP neural network model, this study analyzed the spatiotemporal patterns, obstacle factors, and future trends of TEV in major tourist cities in China from 2004 to 2019. The results show three key findings: (1) In terms of spatiotemporal patterns, the TEV index of most of China’s tourist cities has been on the rise from 2004 to 2019. Cities throughout the coast of China’s Yangtze River Delta and the Pearl River Delta urban agglomeration show high vulnerability, whereas low vulnerability has a scattered distribution in China’s northeast, central, and western regions. (2) The proportion of international tourists out of total tourists, tourism output density, urban industrial sulfur dioxide emissions per unit area, urban industrial smoke and dust emission per unit area, and discharge of urban industrial wastewater per unit area are the five major obstacles affecting the vulnerability degree of the tourism economy. (3) According to the prediction results of TEV from 2021 to 2030, although the TEV of many tourist cities in China is increasing year by year, cities with low TEV levels occupy the dominant position. Research results can provide reference for tourist cities to prevent tourism crises from occurring and to reasonably improve the resilience of the tourism economic system. Full article

Energy security has become a worldwide issue in recent years. Coal resources security (CRS), an important part of energy security, has been an emerging concern in many countries, due to the diminishing fossil energy reserve and unbalanced energy structure. However, there is no universally agreed method of constructing indicator system for CRS assessment. Subjectivity in the process of evaluation also affects the results of assessment. Moreover, CRS is a complex system that should be evaluated scientifically under diverse methods. Therefore, we constructed an indicator system and evaluation model of CRS and used a case study of China and 31 provinces in its mainland to evaluate CRS at both national and provincial levels. The indicator system included two subsystems—long-term CRS and short-term CRS. We also chose a few elements and factors that are consistent with China’s reality. Different research methods were used: the entropy-weight-based TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is applied to evaluate the degree of CRS, which avoids the subjectivity of weight determination and reflects the relative merit of each indicator; the BP (Back-Propagation) Neural Network method is used to analyze the sensitivity of CRS to each index. The results show that the national level of CRS dropped in the early years but slowly picked up with the help of government intervention. Investment in coal industry development resulted in the immediate effect of improving CRS. The positive impact of maintaining environmental sustainability is stable over either the short, medium, or long term. The degrees of CRS vary significantly across provinces, even between those with similar coal stock levels. Extra attention should be paid to the transportation of coal resources among provinces and intervention to balance supply and demand within the regions. Full article

Air pollution has become one of the key environmental concerns in the urban sustainable development. It is important to evaluate the impact of air pollution on socioeconomic development since it is the prerequisite to enforce an effective prevention policy of air pollution. In this paper, we model the impact of air pollution on the urban economic development as a Multiple Criteria Decision Making (MCDM) problem. In particular, we propose a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis framework to evaluate multiple factors of air pollutants and economic development. Our method can overcome the drawbacks of conventional TOPSIS methods by using Bayesian regularization and the Back-Propagation (BP) neural network to optimize the weight training process. We have conducted a case study to evaluate our proposed framework. Full article