International Research Progress and Evolution Trend of Interpersonal Trust—Prospects under COVID-19 Pandemic

Retrospecting articles on interpersonal trust is of great importance for understanding its current status and future development in the context of the COVID-19 pandemic, especially, with the widespread use of Big Data and Blockchain. In total, 1532 articles related to interpersonal trust were collected as research database to draw keyword co-occurrence mapping and timeline mapping by VOSviewer and CiteSpace. On this basis, the research content and evolution trend of interpersonal trust were systematically analyzed. The results show that: (1) Data cleaning by code was first integrated with Knowledge Mapping and then used to review the research of interpersonal trust; (2) Developed countries have contributed the most to the research of interpersonal trust; (3) Social capital, knowledge sharing, job and organizational performance, Chinese Guanxi are the research hotspots of interpersonal trust; (4) The research hotspots on interpersonal trust evolve from the level of individual psychology and behavior to the level of social stability and development and then to the level of organization operation and management; (5) At present, the research on interpersonal trust is in the outbreak period; fMRI technology and Big Data and Blockchain technology gradually become vital research tools of interpersonal trust, which provides significant prospects for the following research of interpersonal trust under the COVID-19 pandemic.

An Efficient Algorithm for Ocean-Front Evolution Trend Recognition

Marine hydrological elements are of vital importance in marine surveys. The evolution of these elements can have a profound effect on the relationship between human activities and marine hydrology. Therefore, the detection and explanation of the evolution laws of marine hydrological elements are urgently needed. In this paper, a novel method, named Evolution Trend Recognition (ETR), is proposed to recognize the trend of ocean fronts, being the most important information in the ocean dynamic process. Therefore, in this paper, we focus on the task of ocean-front trend classification. A novel classification algorithm is first proposed for recognizing the ocean-front trend, in terms of the ocean-front scale and strength. Then, the GoogLeNet Inception network is trained to classify the ocean-front trend, i.e., enhancing or attenuating. The ocean-front trend is classified using the deep neural network, as well as a physics-informed classification algorithm. The two classification results are combined to make the final decision on the trend classification. Furthermore, two novel databases were created for this research, and their generation method is described, to foster research in this direction. These two databases are called the Ocean-Front Tracking Dataset (OFTraD) and the Ocean-Front Trend Dataset (OFTreD). Moreover, experiment results show that our proposed method on OFTreD achieves a higher classification accuracy, which is 97.5%, than state-of-the-art networks. This demonstrates that the proposed ETR algorithm is highly promising for trend classification.

Spatial-Temporal Pattern and Evolution Trend of the Cultivated Land Use Eco-Efficiency in the National Pilot Zone for Ecological Conservation in China

The cultivated land use eco-efficiency (CLUE) is an important indicator to evaluate ecological civilization construction in China. Research on the spatial-temporal pattern and evolution trend of the CLUE can help to assess the level of ecological civilization construction and reveal associated demonstration and driving effects on surrounding areas. Based on the perspective of the CLUE, this paper obtains cultivated land use data pertaining to National Pilot Zones for Ecological Conservation in China and neighboring provinces from 2008 to 2018. In this study, the SBM-undesirable, Moran’s I, and Markov chain models are adopted to quantitatively measure and analyze the CLUE and its temporal and spatial patterns and evolution trend. The research results indicate that the CLUE in the whole study area exhibited the characteristics of one growth, two stable, and two decline stages, with a positive spatial autocorrelation that increased year by year, and a spatial spillover effect was observed. Geographical spatial patterns and spatial spillover effects played a major role in the evolution of the CLUE, and there occurred a higher probability of improvement in the vicinity of cities with high CLUE values. In the future, practical construction experience should be disseminated at the provincial level, and policies and measures should be formulated according to local conditions. In addition, a linkage model between prefecture-level cities should be developed at the municipal level to fully manifest the positive spatial spillover effect. Moreover, we should thoroughly evaluate the risk associated with CLUE transition from high to low levels and establish a low-level early warning mechanism.

Knowledge Mapping of Green Technology Visualization with Bibliometric Tools

An increasing number of research literature studies about green technology have been accepted by journals of different disciplines due to the rapid technical progress and innovation in all types of industry. This study uses bibliometric tools of CiteSpace and VOSviewer to analyse the key authors’ co-citation network, institution cooperation, the keyword clusters of green technology, and the evolution trend of green technology. We find that since 1960s, the number of research papers with green technology theme has been growing. These papers are mainly involved in fields of economics and business, engineering, and chemistry, in which there exist 13 highest cited papers from 2009 to 2020 based on the Web of Science database. In this study, we find the top 20 journals of green technology with the parameter of literature count and centrality. We find that the cooperation of authors is quite weak with the co-authorship analysis, and we obtain the top 12 institutions in 15 countries dominantly in green technology research through country and institution analysis. We conduct a cluster analysis of keywords related to green technology, and we obtain 10 clusters, with three economical clusters, five engineering clusters, and two chemical clusters. Finally, we summarise green technology with the aid of the timeline view function of CiteSpace system.

Convergence of Energy Carbon Emission Efficiency: Evidence From Manufacturing Sub-sectors in China

With China's economy entering the stage of high-quality development, manufacturing energy carbon emission efficiency has become the focus of academic attention. Based on the panel data of China's manufacturing sub-sectors, this paper measures and analyzes the evolution trend of manufacturing energy carbon emission and its efficiency. On this basis, this paper uses coefficient of variation and convergence model to test the convergence of manufacturing energy carbon emission efficiency. The results show that China’s manufacturing energy carbon emissions and its efficiency show an increasing trend. The coal was the main source of manufacturing energy carbon emissions. The manufacturing energy carbon emission efficiency does not exist σ convergence, but exists β convergence, and its convergence exists industry heterogeneity. The manufacturing energy carbon emission efficiency exits scale effect and technology effect, but not the effect of opening to the outside world and institutional effect, and its effect exists industry heterogeneity. By reducing carbon emissions, adopting differentiated policies, adjusting the industry scale, and enhancing the industry technology intensity, China's manufacturing can improve the energy carbon emissions efficiency and promote high-quality economic development.

The evolution trend of availability of China’s community-based care services and its impact on the cognitive function of elderly people: 2008-2018

Background To address the challenge of the aging population, community-based care services (CBCS) have been developed rapidly in China as a new way of satisfying the needs of elderly people. Few studies have described the evolution trend of availability of CBCS in rural and urban areas and evaluated their effectiveness. This study aims to show the availability of China’s CBCS and further analyze the effect of the CBCS on the cognitive function of elderly people. Methods Longitudinal analysis was performed using data from the 2008 to 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 23937 observations from 8421 elderly people were included in the study. The Chinese version of the Mini-Mental State Examination (MMSE) was used to assess cognitive function. We aggregated similar CBCS to generate three binary variable categories (daily life support, emotional comfort and entertainment services, medical support and health services) indicating the availability of CBCS (1 = yes, 0 = no). Multilevel growth models were employed to estimate the association between CBCS and cognitive function while adjusting for many demographic and socioeconomic characteristics. Results The availability of CBCS increased a lot from 2008 to 2018 in China. Although the availability of CBCS in urban areas was higher than that in rural areas in 2008, by 2018 the gap narrowed significantly. Emotional comfort and entertainment services (B = 0.331, 95% CI = 0.090 to 0.572) and medical support and health services (B = 1.041, 95% CI = 0.854 to 1.228) were significantly and positively associated with cognitive function after adjusting for the covariates. Conclusion There was a significant increase in the availability of CBCS from 2008 to 2018 in China. This study sheds light on the positive correlation between CBCS and cognitive function among Chinese elderly individuals. The results suggest that policymakers should pay more attention to the development of CBCS and the equity of the supply of CBCS in urban and rural areas.

A New Hybrid Method for Establishing Landslide Displacement Point Forecasting, Interval Forecasting and Probabilistic Forecasting

In addition to the inherent evolution trend, landslide displacement contains strong fluctuation and randomness, the omni-directional landslide displacement prediction is more scientific than single point prediction or interval prediction. In this work, a newly hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM) and gaussian process regression (GPR), was proposed for point, interval and probabilistic prediction of landslide displacement. The proposed model includes two parts: (i) predicting the inherent evolution trend of landslide displacement by DES-VMD-LSTM; (ii) evaluating the uncertainty in the first prediction based on the GPR model. In the first part, DES is used to predict the trend displacement, VMD is used to extract the periodic and stochastic displacement from the residual displacement, and then LSTM is used to predict them. The triggering factors of periodic and stochastic displacement are screened by maximum information coefficient (MIC), and the screened factors are decomposed into low- and high-frequency components by VMD, to predict periodic and stochastic displacement respectively. The first cumulative displacement prediction results are achieved by adding the predicted trend, periodic and stochastic displacement. By setting the first predicted displacement as input and actual displacement as expected output, the point, interval and probability prediction of displacement are realized in GPR model. The plausibility of this method was validated firstly with the data from Bazimen (BZM) and Baishuihe (BSH) landslide in the Three Gorges Reservoir area. This model has potential capacity to realize deterministic prediction of displacement and exhibit uncertainty contained in displacement. A comparing study shows that this method has a high performance at point, interval and probability prediction of displacement.