Random Fixed Point Theorems of Random Comparable Operators and an Application

We introduce the new concept of random comparable operators as a generalization of random monotone operators and prove several random fixed point theorems for such a class of operators in partially ordered Banach spaces. Part of the presented results generalize and extend some known results of random monotone operators. Finally, as an application, we consider the existence of the solution of a random Hammerstein integral equation

Extraction Optimization of Water-Extracted Mycelial Polysaccharide from Endophytic Fungus Fusarium oxysporum Dzf17 by Response Surface Methodology

Water-extracted mycelial polysaccharide (WPS) from the endophytic fungus Fusarium oxysporum Dzf17 isolated from Dioscorea zingiberensis was found to be an efficient elicitor to enhance diosgenin accumulation in D. zingigerensis cultures, and also demonstrated antioxidant activity. In this study, response surface methodology (RSM) was employed to optimize the extraction process of WPS from F. oxysporum Dzf17 using Box-Behnken design (BBD). The ranges of the factors investigated were 1–3 h for extraction time (X1), 80–100 °C for extraction temperature (X2), and 20–40 (v/w) for ratio of water volume (mL) to raw material weight (g) (X3). The experimental data obtained were fitted to a second-order polynomial equation using multiple regression analysis. Statistical analysis showed that the polynomial regression model was in good agreement with the experimental results with the determination coefficient (R2) of 0.9978. By solving the regression equation and analyzing the response surface contour plots, the extraction parameters were optimized as 1.7 h for extraction time, 95 °C for extraction temperature, 39 (v/w) for ratio of water volume (mL) to raw material weight (g), and with 2 extractions. The maximum value (10.862%) of WPS yield was obtained when the WPS extraction process was conducted under the optimal conditions

Soil Erosion Features by Land Use and Land Cover in Hilly Agricultural Watersheds in Central Sichuan Province, China

Abstract: The study assessed soil erosion features in hilly agricultural watersheds in central Sichuan Province, China. Relationships among area percentages of land covers, soil erosion modulus, soil loss rate, and area percentage of erosion in the watersheds were examined by multivariate regression analysis. The first two regression models were constructed with area percentages of land covers as independent variables and soil erosion modulus or soil loss rate as dependent variables. The third one was built with soil erosion modulus as dependent variable and area percentage of the four erosion intensity classes as independent variables. Results showed that for flat and slope fields, forests, shrubs, abandoned fields, and other land covers, percentage of soil loss were -6%, 89%, -4%, 11%, 12%, and -3%, and area percentage of erosion were 1%, 72%, 0%, 18%, 9%, and 0%. The slope fields were the main source of soil loss. In contrast, flat crop fields, forests and other land covers could conserve soil from eroding. Changing land covers and reducing area of slope fields to 7% and of abandoned fields to 0%, were proposed as a management strategy for soil conservation in the watersheds. As completion of the land cover conversion, percentages of the land covers should be 35%, 7%, 5%, 45%, 0%, and 8% for flat and slope fields, forests, shrubs, abandoned fields, and other land covers. The study suggested that statistical models could be successfully used for processing inventory data for make decisions in soil conservation

Data-Driven Kernel Extreme Learning Machine Method for the Location and Capacity Planning of Distributed Generation

For the integration of distributed generations such as large-scale wind and photovoltaic power generation, the characteristics of the distribution network are fundamentally changed. The intermittence, variability, and uncertainty of wind and photovoltaic power generation make the adjustment of the network peak load and the smooth control of power become the key issues of the distribution network to accept various types of distributed power. This paper uses data-driven thinking to describe the uncertainty of scenery output, and introduces it into the power flow calculation of distribution network with multi-class DG, improving the processing ability of data, so as to better predict DG output. For the problem of network stability and operational control complexity caused by DG access, using KELM algorithm to simplify the complexity of the model and improve the speed and accuracy. By training and testing the KELM model, various DG configuration schemes that satisfy the minimum network loss and constraints are given, and the voltage stability evaluation index is introduced to evaluate the results. The general recommendation for DG configuration is obtained. That is, DG is more suitable for accessing the lower point of the network voltage or the end of the network. By configuring the appropriate capacity, it can reduce the network loss, improve the network voltage stability, and the quality of the power supply. Finally, the IEEE33&69-bus radial distribution system is used to simulate, and the results are compared with the existing particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM). The feasibility and effectiveness of the proposed model and method are verified

Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles

The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehicles’ charging power as training data, we propose a capacity selection model based on the kernel extreme learning machine (KELM). The model accuracy is evaluated by using the root mean square error (RMSE). The stability of the network is evaluated by voltage stability evaluation index (Ivse). The IEEE33 node distributed system is used as simulation example, and gives results calculated by the kernel extreme learning machine that satisfy the minimum network loss and total investment cost. Finally, the results are compared with support vector machine (SVM), particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to verify the feasibility and effectiveness of the proposed model and method

fu niu shan yao yong zhi wu zhi 伏牛山药用植物志

v.

Research on the Influence of Battery Cell Static Parameters on the Capacity of Different Topology Battery Packs

The parameter inconsistency of the battery cells and the series-parallel connection mode are closely related to the battery pack capacity. Studying the degree of influence of battery pack capacity by battery cell parameters is of great significance to the series-parallel design of battery packs. This paper establishes battery cell models and battery pack models with different topologies. In the MATLAB/Simulink environment, simulation studies were conducted to study the influence of the battery pack capacity by the monomer parameters as the number of cells in series and parallel in the topology changes. Then, from a statistical point of view, the simulation results were analyzed in principle. Finally, a small-scale battery pack experimental platform was built in the laboratory environment to verify the correctness of the simulation conclusions and theoretical analysis