Search

Today, data storage technology is also gradually improving. Various industries can store massive amounts of data for analysis. The global climate change and the bad ecology led to frequent occurrence of natural disasters. Therefore, it is necessary to establish an effective emergency materials distribution system. The neural network model is used to calculate and the optimal emergency distribution route is analyzed according to the historical information and the data. Considering backpropagation, this paper further disposing a method to further improve the calculation of neural network algorithm. From the perspective of structural parameters of neural network algorithms, this paper uses genetic algorithms to construct predictions, and combines the actual purpose of material distribution after disasters. Considering the capacity constraints of distribution centers, time constraints, material needs of disaster relief points and different means of transportation, a dual-objective path planning with multiple distribution centers and multiple disaster relief points with the shortest overall delivery time and lowest overall delivery cost is constructed. By establishing an emergency material distribution system, it can maximize the prompt and accurate delivery after a natural disaster occurs, and solves the urgent needs of the people. [ABSTRACT FROM AUTHOR]

Aiming at the problem of global climate change and energy crisis, wind power has become the focus of energy sustainable development in all countries. The wind turbines (WTs) power system is connected to the grid via the power electronic converter, causing the system inertia level to drop. In this paper, the direct-drive WT system is considered as the research object, and the whole-system frequency response model is established. The inertia and damping characteristics of the WT converter systems with virtual inertia control are analyzed. With the support of fan rotor kinetic energy and the energy saved in a capacitor, the simple control can also make the system exhibit different degrees of inertia and damping features. The results show that the equivalent inertia and the WT inertia time constant, capacitance parameters and virtual control parameters kd are related; the equivalent damping parameter is related to the steady-state operating point parameters and the virtual control parameter kp; the equivalent synchronization parameter is related to the steady-state operating point parameters and the virtual inertia control parameter ki. Finally, the correctness of the inertial and damping characteristics of the WT grid-connected system is verified by simulation, which provides a theoretical reference for studying the inertial damping of power electronic dominant systems. [ABSTRACT FROM AUTHOR]

Recently, it has been observed that the weather is changing constantly because of global warming. The government is urging everyone, including scientists and the general public, to help address the severe challenges caused by climate change. Addressing the pivotal issue of carbon emissions stemming from transportation, this manuscript delves into the development of an efficient and coordinated management system. The proposed solution involves a green solid transportation system employing a two-stage network to implement a carbon cap and trade policy. A mathematical model is introduced to underscore the significance of this approach. Because of market fluctuations, supply and demand constraints are not always the same. Therefore, a two-folded uncertainty is included in this article for a better realistic outcome. A ranking defuzzification approach is employed to convert this uncertainty into a deterministic measure. Two illustrative numerical case studies are presented to underscore the effectiveness and feasibility of the proposed approaches. Then, three multi-objective techniques are employed to obtain Pareto-optimal solutions for the addressed problem. After that, a comparative study among these techniques is introduced and a sensitivity analysis is added to explore how the objective functions are influenced by potential changes in supply and demand. In conclusion, the paper offers important insights and identifies areas for future research in this field. [ABSTRACT FROM AUTHOR]

Artificial neural networks are widely applied for different forecasting applications including rainfall forecasting. The climate input features and parameters for neural networks highly affect the overall performance of the prediction model. Therefore, an appropriate approach for the selection of features and parameters is needed. In this paper, a novel approach is proposed to select the input features and neural network parameters. A hybrid genetic algorithm that combines natural reproduction and particle swarm optimization characteristics was developed to select the best input features and network parameters for each month. The developed model was compared against alternative climate and network parameters feature selection model, climate feature selection model and climatology where a better accuracy was recorded with the proposed model. The skill score against the three alternative climate models was 17.41, 21.68 and 32.12%, respectively. The aggregated time series of the proposed model showed a root-mean-square error of 141.67 mm for a location with 3553.00 mm annual average. [ABSTRACT FROM AUTHOR]

Climate changes and the number of people in the world are increasingly affecting the environment. In order to reduce this impact, there are more and more alternatives to cars with internal combustion. Currently, the most used alternative is electric cars. This research aimed to rank electric cars according to their characteristics. It was selected 13 criteria according to which 20 alternatives were ranked. For this purpose, it was used two methods, DNMEREC (Double normalization Method based on the Removal Effects of Criteria) used to determine criterion weights objectively and DNCRADIS (Double normalization Compromise Ranking of Alternatives from Distance to Ideal Solution) method used to rank alternatives. Here, classical methods for multi-criteria decision-making (MCDM) are extended to contribute to a more stable ranking of alternatives. Unlike similar approaches, the same normalization has been used here, but in two ways, which represents an innovative approach in MCDM. The results of this approach have shown that the best-ranked alternative is A6 (Sono Sion), while the worst-ranked alternative is A2 (Smart EQ forfour). These results were confirmed with a comparative analysis of the results obtained using other MCDM methods and sensitivity analysis. The validation of the results and the application of the Spearman correlation coefficient have shown that the ranking of the alternatives is uniform and more stable when double normalization is applied than when classical methods with their normalization are used. In addition, this decision-making provides support to potential buyers for choosing electric cars. [ABSTRACT FROM AUTHOR]