Blockchain application in modern logistics information sharing: a review and case study analysis

Xiao-feng, XU; He, Yangyang

doi:10.1080/09537287.2022.2058997

Cited by 42 publications

(22 citation statements)

References 53 publications

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“…Jiangsu high-level university construction project requires participating universities to innovate talent cultivation mode, optimize, and reform curriculum and teaching methods. We encourage universities to pursue cultivating quality rather than cultivating quantity [31]. It seeks to cultivate high-level comprehensive talents at multiple levels through high-level university construction projects.…”

Section: Quality Of Student Source and Cultivationmentioning

confidence: 99%

A Fuzzy Comprehensive Evaluation Method on the Construction of High-Level Universities Based on Data from Jiangsu Province

Suo-ming

Wan

Chen

2022

Scientific Programming

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To respond to the call for the construction of a “double first-class university” in China and implement the “Focus on innovation, focus on enriching the people, and build a moderately prosperous society at a high level” development strategy. Jiangsu Province issued the first round of the “Jiangsu High-level University Construction Program” in 2016. Taking the deepening of the innovation-driven development strategy as the starting point, the main objective of promoting higher education is to strengthen its ability to support economic and social development. In this study, the fuzzy-ANP comprehensive evaluation model is used to create an index system for building high-level universities with Jiangsu Province features. Using this system, high-level university construction in Jiangsu Province is evaluated. The results of the study show that the first round of Jiangsu’s high-level university construction has reached an excellent level and is the most effective in improving the quality of research. In addition, the participating universities are better able to promote socioeconomic development. However, the resources and social reputation of the participating universities are close to the level of excellence. Furthermore, due to the problem of prioritizing research above teaching, the teaching and internationalization levels of the participating universities are rather low.

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Section: Quality Of Student Source and Cultivationmentioning

confidence: 99%

A Fuzzy Comprehensive Evaluation Method on the Construction of High-Level Universities Based on Data from Jiangsu Province

Suo-ming

Wan

Chen

2022

Scientific Programming

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“…Control variables: In this paper, 5 control variables are selected. Referring to the studies of other scholars in related fields [ 24 , 25 , 26 , 27 ], the degree of openness to the outside world ( Open ), enterprise size ( Scale ), level of economic development ( GDP ), investment in science and technology ( Tec ), and industrial structure ( STR ) were selected as control variables in this paper. The degree of openness to the outside world is measured by dividing total imports and exports by GDP, and trade activities have a technology diffusion effect, which may have an impact on green technology innovation of enterprises.…”

Section: Data Sources and Model Designmentioning

confidence: 99%

Carbon Emissions Trading and Green Technology Innovation—A Quasi-natural Experiment Based on a Carbon Trading Market Pilot

Liu

Qiu

Jia

et al. 2022

IJERPH

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Carbon emissions trading policy has received widespread attention from scholars as a core policy tool to reduce carbon emissions. While most scholars have previously focused on the carbon emission reduction effect, this paper investigates the impact of carbon emissions trading policy on green technology innovation using a differences-in-differences method based on provincial panel data from 2005–2019, using a carbon emissions trading pilot as a quasi-natural experiment. The findings show that the policy can significantly promote green technology innovation, but with a lagged effect, and this finding still holds in the robustness test. Further heterogeneity analysis reveals that the stronger the human capital, the stronger the intellectual property protection and the stronger the marketization with better policy effects. In addition, carbon emissions trading policy can indirectly affect green technology innovation by influencing research investment.

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“…To solve the problems above, this study applies the grey relational analysis (GRA) [16] to analyze the location correlation of substation equipment, calculates the correlation between temperatures at different test points, takes the ambient temperature as the seasonal influencing factor, and integrates the three types of data of equipment temperature in the past. In the prediction modeling stage, deep learning network is widely used in various research fields [17][18][19], and according to the existing research, Convolutional Neural Network (CNN) has strong advantages in depth feature extraction [20] and Long Short-term Memory Networks (LSTM) network is suitable for processing and predicting important events with relatively long interval and delay in time series [21]. However, there is still a problem when using LSTM for time series prediction modeling: it is unable to encode the information from back to front.…”

Section: Introductionmentioning

confidence: 99%

Substation Equipment Temperature Prediction Method considering Local Spatiotemporal Relationship

Sun

Chen

Jing³

et al. 2022

Scientific Programming

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Temperature prediction of substation equipment is one of the important means for intelligent inspection of substation equipment. However, there are still three challenges: (1) Limited extracted samples; (2) Typical nonlinearity, seasonality, and periodicity; (3) Changes in equipment and working conditions. To solve the problems above, a substation equipment temperature prediction method considering Spatio-temporal relationship (SETPM-CLSTR) is proposed. First, according to the time series of equipment temperature from two aspects of temporal and spatial, it is determined that the equipment temperature has seasonal, temporal, and spatial correlation; second, aiming at the problem that the spatial location correlation cannot be described quantitatively, grey relational analysis (GRA) is adopted to determine the spatial location monitoring points closely related to the prediction target; then, the daily maximum temperature and daily minimum temperature from the environment, the predicted target temperature from the past several times in time and the temperature from the spatial location monitoring point with close correlation in space are constructed as Spatio-temporal feature vectors; finally, CNN-BiLSTM double-layer depth network model is proposed to predict the equipment temperature. SETPM-CLSTR has applied to temperature prediction of phase A contact from primary equipment of a substation in Taizhou City, Zhejiang Province. Under the two prediction performance evaluation indexes of MASE and RMSE, compared with three correlation models of LSTM, BiLSTM, and CNN-LSTM from two aspects of different features and models, it is verified that SETPM-CLSTR in this study has better prediction performance.

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Blockchain application in modern logistics information sharing: a review and case study analysis

Cited by 42 publications

References 53 publications

A Fuzzy Comprehensive Evaluation Method on the Construction of High-Level Universities Based on Data from Jiangsu Province

A Fuzzy Comprehensive Evaluation Method on the Construction of High-Level Universities Based on Data from Jiangsu Province

Carbon Emissions Trading and Green Technology Innovation—A Quasi-natural Experiment Based on a Carbon Trading Market Pilot

Substation Equipment Temperature Prediction Method considering Local Spatiotemporal Relationship

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