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Bamboo culm has been regarded as a traditional element in construction; meanwhile, it has great potential for the construction of rural houses to achieve green and low-carbon development. However, traditional bamboo houses are usually constructed according to previous experience, and it is hard to design bamboo houses in a standard way. Structural grading of the bamboo culm is an essential work to achieve standardization design. Grading the Moso bamboo culm (P. edulis) based on its minimum external diameter is proposed in this paper. The geometric, physical and mechanical properties of 883 Moso bamboo culms with three different treatment processes were measured and analyzed, namely untreated, with chemical preservatives and heat treatment. It was found that the external diameter of the Moso bamboo culm could be determined by the perimeter in practice. The treatment process has a great influence on the geometric, physical and mechanical properties. Bamboo culms with three different treatment processes could be divided into five, five and four grades, respectively. Meanwhile, based on measurement data, the characteristic values of each grade are presented, including the wall thickness, external and internal taper, linear mass, nominal density and compressive strength. The minimum chemical treatment factor is 0.785, 0.662 and 0.649, while the minimum heat treatment factor is 0.722, 0.644 and 0.877 for wall thickness, linear mass and nominal density, respectively. The treatment factor for compressive strength is 1.12 and 1.52 of chemical treatment and heat treatment, respectively. This study may aid establishing technical specifications and a standard design method for Moso bamboo structural building.

Bamboo culm has been regarded as a traditional element in construction; meanwhile, it has great potential for the construction of rural houses to achieve green and low-carbon development. However, traditional bamboo houses are usually constructed according to previous experience, and it is hard to design bamboo houses in a standard way. Structural grading of the bamboo culm is an essential work to achieve standardization design. Grading the Moso bamboo culm (P. edulis) based on its minimum external diameter is proposed in this paper. The geometric, physical and mechanical properties of 883 Moso bamboo culms with three different treatment processes were measured and analyzed, namely untreated, with chemical preservatives and heat treatment. It was found that the external diameter of the Moso bamboo culm could be determined by the perimeter in practice. The treatment process has a great influence on the geometric, physical and mechanical properties. Bamboo culms with three different treatment processes could be divided into five, five and four grades, respectively. Meanwhile, based on measurement data, the characteristic values of each grade are presented, including the wall thickness, external and internal taper, linear mass, nominal density and compressive strength. The minimum chemical treatment factor is 0.785, 0.662 and 0.649, while the minimum heat treatment factor is 0.722, 0.644 and 0.877 for wall thickness, linear mass and nominal density, respectively. The treatment factor for compressive strength is 1.12 and 1.52 of chemical treatment and heat treatment, respectively. This study may aid establishing technical specifications and a standard design method for Moso bamboo structural building.

As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge–Nelson (B-N) decomposition approach, the Least Square Support Vector Machine (LSSVM), and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA). For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM) was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM), and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM), it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.

As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge–Nelson (B-N) decomposition approach, the Least Square Support Vector Machine (LSSVM), and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA). For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM) was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM), and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM), it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.

The sustainable development of the aquaculture industry is of great significance for national food security within the context of increasing consumer demand, scarce land resources, and continuous ecological and environmental deterioration. China is a major shrimp farming country. It is crucial to investigate the spatiotemporal variations in the characteristics of shrimp aquaculture for the stability and sustainable development of China’s shrimp aquaculture industry, as well as to explore an optimization strategy for the spatial layout of this industry. Existing research mainly focuses on the fluctuations in shrimp production and their influencing factors, while studies on the spatiotemporal variations of shrimp aquaculture and their influencing mechanisms are few. In this study, we analyzed the patterns of spatiotemporal variation in shrimp aquaculture in China and further explored their influencing factors using standard deviational ellipse and Geodetector, based on China’s shrimp aquaculture production data from 2003 to 2020. The results show that (1) there has been a steady upward trend in shrimp aquaculture production in China over the past 18 years, with a growth rate of 128% and an average annual growth rate of 5.2%; (2) the spatial distribution range of shrimp farms has expanded, showing a zonal distribution pattern from the northeast to southwest, and the center of the shrimp aquaculture industry has generally shifted southward; and (3) the level of development within the fishing industry, labor quantity, and per capita income are the main factors affecting the spatial distribution variability of shrimp aquaculture. The degree of influence of each different factor on spatial distribution of shrimp aquaculture varied temporally.

The sustainable development of the aquaculture industry is of great significance for national food security within the context of increasing consumer demand, scarce land resources, and continuous ecological and environmental deterioration. China is a major shrimp farming country. It is crucial to investigate the spatiotemporal variations in the characteristics of shrimp aquaculture for the stability and sustainable development of China’s shrimp aquaculture industry, as well as to explore an optimization strategy for the spatial layout of this industry. Existing research mainly focuses on the fluctuations in shrimp production and their influencing factors, while studies on the spatiotemporal variations of shrimp aquaculture and their influencing mechanisms are few. In this study, we analyzed the patterns of spatiotemporal variation in shrimp aquaculture in China and further explored their influencing factors using standard deviational ellipse and Geodetector, based on China’s shrimp aquaculture production data from 2003 to 2020. The results show that (1) there has been a steady upward trend in shrimp aquaculture production in China over the past 18 years, with a growth rate of 128% and an average annual growth rate of 5.2%; (2) the spatial distribution range of shrimp farms has expanded, showing a zonal distribution pattern from the northeast to southwest, and the center of the shrimp aquaculture industry has generally shifted southward; and (3) the level of development within the fishing industry, labor quantity, and per capita income are the main factors affecting the spatial distribution variability of shrimp aquaculture. The degree of influence of each different factor on spatial distribution of shrimp aquaculture varied temporally.

The combination of car-sharing and electric vehicles can increase the acceptance of electric vehicles and facilitate car-sharing to be a more sustainable means of transport. However, this also poses more challenges for the good planning of electric car-sharing systems. To assist car-sharing companies in improving the planning decisions, this paper developed an evaluation framework from a comprehensive view. In the first step, four evaluation criteria were identified according to the planning process: construction of stations; routine inspection; vehicle usability and relocation management; and the maintenance and replacement of stations. Then, a combinatorial method based on analytic hierarchy process (AHP), cost-benefit analysis (CBA), and Voronoi diagram (VD) is developed to determine the relative weight of the four criteria and evaluate the alternative. Finally, the evaluation framework was applied in a realistic case of EVCARD, which is the most influential electric car-sharing company in China. The performance of two different operational districts of EVCARD—Jingan and Changning—were compared. The results showed that vehicle usability and relocation management is the greatest criterion influencing the planning performance of the electric car-sharing system in China, and that routine inspection is a negligible but important factor. According to the relative scores, Jiagan District performed better than Changning district.

The combination of car-sharing and electric vehicles can increase the acceptance of electric vehicles and facilitate car-sharing to be a more sustainable means of transport. However, this also poses more challenges for the good planning of electric car-sharing systems. To assist car-sharing companies in improving the planning decisions, this paper developed an evaluation framework from a comprehensive view. In the first step, four evaluation criteria were identified according to the planning process: construction of stations; routine inspection; vehicle usability and relocation management; and the maintenance and replacement of stations. Then, a combinatorial method based on analytic hierarchy process (AHP), cost-benefit analysis (CBA), and Voronoi diagram (VD) is developed to determine the relative weight of the four criteria and evaluate the alternative. Finally, the evaluation framework was applied in a realistic case of EVCARD, which is the most influential electric car-sharing company in China. The performance of two different operational districts of EVCARD—Jingan and Changning—were compared. The results showed that vehicle usability and relocation management is the greatest criterion influencing the planning performance of the electric car-sharing system in China, and that routine inspection is a negligible but important factor. According to the relative scores, Jiagan District performed better than Changning district.