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Existing researches on the quality of life have mostly used socio-economic data for analysis and less research on the use of multi-source data; more research on large cities and less research on small and medium-sized regions; more research on single years and less research on evolution. This article uses Shandong province as an empirical case, using multiple data sources such as remote sensing data, PM2.5 meteorological data, socio-economic data, by selecting key indicators, a county-level quality of life indicator system based on the three dimensions of economy, society, and the environment was constructed, and using a variety of spatial analysis methods to analyze the quality of life in 137 counties (cities) in Shandong Province in the four years from 2000 to 2015. The results are as follows: (1) The overall spatial difference in the quality of life of counties in Shandong Province is quite large, and the polarization is severe. (2) Unbalanced development in all dimensions; unbalanced development of quality of life in municipal and non-municipal districts. (3) Radiation power is not strong in high quality of life areas. lack of core cities in low quality of life areas. According to the status quo, this article proposes reasonable targeted measures and suggestions.

An urban agglomeration (UA), similar to a megalopolis or a metropolitan area, is a region where cities and people are concentrated, and where air pollution has adversely impacted on sustainable and high quality development. Studies on the spatio-temporal trends and the factors which influence PM 2.5 concentrations may be used as a reference to support air pollution control policy for major UAs throughout the world. Nineteen UAs in China covering the years 2000–2016 were chosen as the research object, the PM 2.5 concentrations being used to reflect air pollution and being estimated from analysis of remote sensing images. The Exploratory Spatial Data Analysis method was used to study the spatio-temporal trends for PM 2.5 concentrations, and the Geodetector method was used to examine the factors influencing the PM 2.5 concentrations. The results revealed that (i) the temporal trend for the average values of the PM 2.5 concentrations in the UAs followed an inverted U-shaped curve and the inflection points of the curve occurred in 2007. (ii) The PM 2.5 concentrations in the UAs exhibited significant global spatial autocorrelation with the high–high type and the low–low type being the main categories. (iii) The rate of land urbanization and the structure of energy consumption were the main factors which influenced the PM 2.5 concentrations in the UAs.

With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction.

As the “Outline for Building a Leading Transportation Nation” puts forward, the overall requirement of high quality development, the measurement work of transportation industry plays a much more important role. This paper introduces the measurement technology state of the FWD of the highway pavement bearing capacity measuring equipment, the technical state and error analysis of the equipment are introduced, and the calibration and comparison methods are sorted out, which provide a reference for the next generation of the equipment.

This article summarized the mechanism of the impact of real estate tax on city output path, on the basis of selecting the Chinese provincial panel data from 2005 to 2019, by using the PD - GMM method. The real estate tax was taken as the core explanation variable, and was added a lot of control variables to establish the impact of real estate tax on city output 7 panel data models. We estimated and analyzed the robustness through Eviews software. Research results showed that: (1) from the national level, the real estate industry tax would have a significant effect on urban output, and there is a \"U\" shaped relationship between the output and the real estate tax and cities. At the beginning of the urban development, the city real estate tax inhibited output, when the urban economy was in the period of high quality development. The real estate tax on urban output result, had obvious function. (2) At the regional level, the real estate tax in the eastern region had a negative inhibitory effect on the urban revenue, while the real estate tax in the western region had a significant promoting effect on the urban revenue, and the influence degree was shown as the eastern>western>middle. (3) The more real estate investment and human capital are tilted to the west, the more favorable the development of the western region, while the impact on the eastern and central regions was not significant.

The Vietnam’s new general education curriculum is transferring from content orientation to students’ capacity and quality development orientation. STEM (is acronym words of Science, Technology, Engineering, and Mathematics) education contributes to the development of 21 st century skills of students. In order to successfully implement STEM education, in addition to leaders, managers and programmers, teachers have an important role in directly building and organizing STEM education. And pre-service teachers need to be trained to deal with complexities of education. The purpose of this research is to assess the pre-service chemistry teachers? existing STEM teaching capacity in Vietnam. Research method was survey through a set of unstructured and structured questions, and participants of this research are 57 pre-service chemistry teachers (who are 3 rd and 4 th grade learners of 5 Universities of Education representing all three regions of Vietnam). Content analysis was used to analyze the data from unstructured questions while the data from structured questions were transformed into frequency, percentage and descriptive statistic analyze. The findings indicated that most of participants have conceptualized the aspects of STEM in accordance with a number of researches in the world. However, they were less capable in designing their teaching STEM and performing teaching STEM in the classroom, beside assessment abilities were not good and lack diversity. They also suggested that there should be a specific guide material on how to design and conduct STEM teaching. Therefore, the study also suggested measures to develop STEM teaching competences for pre - service chemistry teachers in Vietnam: 1) redesign the teacher training program to include STEM educational modules, 2) provide STEM teaching instruction modules from STEM fields in Chemistry, and 3) combine Blended Learning approach to enhance their ICT ability.

Wisdom supply chain is a new supply chain model developed in the new generation of information technology revolution, which makes the organization and management mode of supply chain undergo an inevitable change. As an important intersection connect “One Belt and One Road” and the Yangtze river economic belt, integrating wisdom supply chain industry into the regional resource construction system and cultivating wisdom supply chain management talents are the urgent requirements for the Yangtze River Delta urban agglomeration to adapt to the adjustment and upgrading of industrial structure and pursue higher quality development. This survey analyzes the new characteristics of wisdom supply chain and the new demand for human resources by using the methods of literature analysis and enterprise research. Then, explores the demand characteristics of market-oriented wisdom supply chain management talents by mining the recruitment information of supply chain and logistics posts in the Yangtze River Delta and its surrounding areas based on clustering, statistics, text mining and other data analysis methods.

Management is also productive. In a way, management is more important than other elements. Education and management of big data is a new stage in the development of higher education management. We can find value through big data. Big data makes universities adopt a more intelligent way to stimulate and produce new wisdom. There are fundamental differences between big data in university education management and big data in the business field. The big data in Colleges and universities take the correlation relation as the breakthrough, finally seeks the special correlation relations. The use of large data, cloud computing and Internet of things technology is an important tool and basis for higher education to improve the efficiency of running a school and to promote the transformation of higher education management from incremental development to quality development. At present, there are not many systematic research results on the combination of big data and university education management in the academic field, and the depth and breadth are not enough, and there is also lack of specific empirical research.

Risk management is a vital factor for ensuring better quality software development processes. Moreover, risks are the events that could adversely affect the organization activities or the development of projects. Effective prioritization of software project risks play a significant role in determining whether the project will be successful in terms of performance characteristics or not. In this work, we develop a new hybrid fuzzy-based machine learning mechanism for performing risk assessment in software projects. This newly developed hybridized risk assessment scheme can be used to determine and rank the significant software project risks that support the decision making during the software project lifecycle. For better assessment of the software project risks, we have incorporated fuzzy decision making trial and evaluation laboratory, adaptive neuro-fuzzy inference system-based multi-criteria decision making (ANFIS MCDM) and intuitionistic fuzzy-based TODIM (IF-TODIM) approaches. More significantly, for the newly introduced ANFIS MCDM approach, the parameters of ANFIS are adjusted using a traditional crow search algorithm (CSA) which applies only a reasonable as well as small changes in variables. The main activity of CSA in ANFIS is to find the best parameter to achieve most accurate software risk estimate. Experimental validation was conducted on NASA 93 dataset having 93 software project values. The result of this method exhibits a vivid picture that provides software risk factors that are key determinant for achievement of the project performance. Experimental outcomes reveal that our proposed integrated fuzzy approaches can exhibit better and accurate performance in the assessment of software project risks compared to other existing approaches.