Comparison and Suggestions of Logistics Performance Index of Main Countries of Belt and Road Strategy Based on Bootstrap DEA Model.

As an important economic sector, logistics is becoming more important, if not crucial, in economic growth. In our nation, the logistics industry is booming, and it's just getting better. However, in addition to focusing on the positive aspects of our country's logistics industry's development, we should also analyze and address the negative aspects of our country's logistics industry's development. The overall logistics pattern has not yet been formed, and there is an urgent need for systematic construction. The regional development is extremely unbalanced. By comparing the logistics performance indices of various Belt and Road countries, this research aims to examine the major elements influencing overall logistics performance. Second, we introduce the Moran index to explore the geographical association of the subdivision indicators of the logistics performance index using the spatial econometric model. The bootstrap DEA analysis method examines and ranks the countries' logistics performance indexes, determines our country's advantages and disadvantages in comparison to other Belt and Road countries, and executes specific improvement strategies that will enhance logistics and boost the overall growth of our country's logistics sector.

Optimal location of logistics distribution centres with swarm intelligent clustering algorithms.

A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We employ three swarm intelligent algorithms to avoid these solutions. We propose a new algorithm for the clustering problem, the fruit-fly optimization K-means algorithm (FOA K-means). We designed a distribution centre location problem and three clustering indicators to evaluate the performance of algorithms. We compare the algorithms of K-means with the ant colony optimization algorithm (ACO K-means), particle swarm optimization algorithm (PSO K-means), and fruit-fly optimization algorithm. We find K-Means modified by the fruit-fly optimization algorithm (FOA K-means) has the best performance on convergence speed and three clustering indicators, compactness, separation, and integration. Thus, we can apply FOA K-means to improve the distribution centre location solution and the efficiency for distribution in the future.

Efficiency Analysis of New Rural Cooperative Medical System in China: Implications for the COVID-19 Era.

The sudden outbreak of coronavirus disease 2019 (COVID-19) has caused a huge impact on the Chinese residents' health and economic level. In the pandemic background, the country and its institutions have introduced pandemic-related insurance to stabilize the national situation. At this stage, insurance has played an increasingly important role in social life. With the popularization of insurance, the idea of buying insurance to avoid risk has gradually become popular among people. Among them, the New Rural Cooperative Medical System (NRCMS) has been farmers' common choice. The NRCMS, a mutual aid system created by farmers spontaneously in the country, plays a great role in guaranteeing farmers access to basic health services, alleviating poverty caused by disease and returning to poverty due to disease, and promoting poverty alleviation and rural revitalization. Given this backdrop, we study the efficiency of the NRCMS that can effectively promote poverty alleviation and rural revitalization and ensure the people's happy life. Implementing the Data Envelopment Analysis (DEA), we find that technological progress is one of the main factors influencing the efficiency of the NRCMS. Therefore, it is important to improve the technology for providing the efficiency of the NRCMS and promoting the happiness of the society.

Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models.

This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods.

COVID-19: Analysis of Factors Affecting the Economy of Hunan Province Based on the Spatial Econometric Model.

The COVID-19 pandemic has spread across the country negatively impacting on the economy. This paper uses the panel data of 14 prefecture-level cities from 2015 to 2020 in Hunan to determine the factors and effects of economic downturns based on the spatial econometric model. We calculate the Moran index, so-called the Moran's I, to analyse the impact of each factor on the economy. The results show that the spatial correlation of the cities around Chang-Zhu-Tan is high, and the economic growth of the entire province can be influenced by these cities. These cities should adopt strategies to improve the economy, such as reducing the tax revenues, improving the local financial revenues, and reducing the ineffective educational input. These results can also be helpful for policymakers, who will attempt to retransform the Hunan economy during the post-COVID era.

Who Can Get More Happiness? Effects of Different Self-Construction and Experiential Purchase Tendency on Happiness.

This study introduces the self-construction methods of consumers and the tendency characteristics of experiential purchase to study the effects of physical purchase and experiential purchase on wellbeing. The dependent self-builders obtain higher happiness from experiential purchase; however, the independent self-builders get higher happiness from physical purchase. Furthermore, consumers with a high purchase experience get higher happiness from experiential purchase. Consumers with high material consumption tendency get significantly higher happiness than physical purchase from experiential purchase. Consumers with high materialism tendency gain higher happiness in experiential purchase, which is in line with the expectations of self-construction and consumption theories. This study provides the first evidence for the impact of self-construction methods on wellbeing with different consumption choices.