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With the development of the urban industry in recent years, air pollution in areas such as factories and streets has become more and more serious. Air quality problems directly affect the normal lives of residents. Effectively predicting the future air condition in the area through relevant historical data has high application value for early warning of this area. Through the study of the previous monitoring data, it is found that the pollutant data of adjacent monitoring stations are correlated in more periods. Therefore, this paper proposes a hybrid model based on CNN and Bi-LSTM, using CNN to synthesize multiple adjacent stations with strong correlations to extract spatial features between data, and using Bi-LSTM to extract features in the time dimension to finally achieve pollutant concentration prediction. Using the historical data of 40 monitoring stations in different locations of Fushun city to conduct research. By comparing with the traditional prediction model, the results prove that the model proposed in this paper has higher accuracy and stronger robustness.

"Five-Year Plans" have been a cornerstone of Chinese social and economic development initiatives since 1953. During the thirteenth of these periods between 2016 and 2020, the global economy has experienced instability after the financial crisis, as well as political and economic reconfiguration. Drawing on modern economic theory, this book comprehensively discusses China's economic development in this crucial phase. The book analyzes the international economic environment, and asks how China’s continued reform and opening-up can fit with the new era of economic globalization. It also presents the difficulties China faces in such fields as urbanization, the coordination of regional development and urban-rural integration, economic reform, and the reform of factor markets and state-owned enterprises. The book outlines many medium-term development rules along with key characteristics of China's economy, helping international readers fully understand likely future trajectories for the Chinese economy.

In order to protect and promote the effective differentiation and rational utilization of grape germplasm resources, 34 grapevine varieties were selected from the National Germplasm Resources, Taigu Grape Nursery in China: 12 wine grapes (Group I), 14 table grapes (Group II) and 12 seedless grapes (Group III), 4 of which were in Group II, too. For the purpose of genetic characterization, 15 pairs of SSR (simple sequence repeat) primers were used. Sixty-six alleles were generated among Group I, including 64 polymorphic bands, with 1–8 alleles per locus. In Group II, a total of 54 alleles, from 1 to 8 (3.6 on average) alleles per locus, were identified. In Group III, 54 alleles with an average of 3.6 alleles per locus were identified and the percentage of polymorphic alleles was 94%. The 34 varieties clustered into two major clades in the dendrogram: V. vinifera with hybrid of V. vinifera and V. labrusca or hybrid of V. amurensisi and V. vinifera were clearly differentiated. After processing and filtering the raw data, we produced the molecular fingerprint code of each variety. These results showed that the SSR markers are useful for discrimination and analysis of genetic diversity of grapevine varieties. The SSR markers could be used to examine and distinguish the genetic resources among closely related varieties. This is also an effective tool for construction of a grapevine molecular fingerprinting system. The obtained data will be useful in grape breeding in the future.

At present, the construction dust has caused great losses to people's healthy life and national economic development. In order to solve the shortcomings of the existing real-time sensor network detection of construction dust such as poor accuracy, this paper proposes an automatic identification of construction dust based on computer vision and improved K-Means clustering algorithm. We extract the saturation of HSV color model of each image to form a text data set. We determine the median value of the data set as the initial centroid of clustering, reduce the number of iterations and get the global optimal solution. Mahalanobis distance is used as similarity measure to cluster, which reduces the difference between different feature measures, improves the accuracy, and realizes the automatic recognition of construction dust based on computer vision. For the improved K-Means algorithm, the precision, recall rate and harmonic mean value are used to analyze the clustering results. The experimental results show that the improved K-Means algorithm has good robustness and high accuracy, and the automatic recognition rate can reach 89.33%.

As the early stage of land planning and resource management, the evaluation of land use suitability provides a scientific basis for rational and effective use of limited land resources. This paper takes Chang’an District of Xi’an City, Shaanxi Province as an example, and the key of ecosystem services was taking as an important factor for developing the evaluation system of the suitability of construction land.The results show that the area of the highly suitable construction land in Chang’an District is 766.03km2, accounting for 48.18% of the total study area. The forbidden construction area is 585.00km2, accounting for 36.79%. Moderately suitable and low suitable construction regions account for 15.03%. The suitable construction regions are concentrated in the northern part of Chang’an District, which is suitable for large-scale development of cities and towns. The moderately and low suitable regions are mainly distributed along the northern foot of the Qinling Mountains. The Qinling Mountains have importance level of ecosystem services, which mostly are forbidden construction regions. The research results will provide a scientific reference for regional construction and development.

With the increase of power offtake requirements, it is proposed that the more electric engine (MEE) draws electrical power from a generator mounted directly on the gas turbine shaft-line without the use of a gearbox. It is expected that this arrangement will improve the power density and reliability of the whole system. The challenges include limited available space, high temperature, affordability, high power density and an exceedingly high reliability requirement. The segmented rotor flux switching machine is one of the promising topologies that are suitable for this application. The objective of this article is to investigate quantitatively the influence of several design parameters on the electromagnetic performance. The design parameters include, the excitation type, stator split ratio, segmented rotor length and load advance angle. Back EMF, cogging torque, average torque, torque ripple and magnetic force are considered to be the key evaluation criteria and are used to guide the search for the optimized design. The material discussed in this paper may be helpful to provide insight in the selection of key design parameters in the direct shaft-line gas turbine generators.