scholarly journals The United States’ Clothing Imports from Asian Countries along the Belt and Road: An Extended Gravity Trade Model with Application of Artificial Neural Network

2020 ◽  
Vol 12 (18) ◽  
pp. 7433
Author(s):  
Danny Chi Kuen Ho ◽  
Eve Man Hin Chan ◽  
Tsz Leung Yip ◽  
Chi-Wing Tsang
Keyword(s):  
Neural Network ◽  
United States ◽  
Artificial Neural Network ◽  
Developing Countries ◽  
Trade Flow ◽  
The United States ◽  
Extended Gravity ◽  
Belt And Road ◽  
Artificial Neural ◽  
The Belt And Road

In 2013, China announced the Belt and Road Initiative (BRI), which aims to promote the connectivity of Asia, Europe, and Africa and deepen mutually beneficial economic cooperation among member countries. Past studies have reported a positive impact of the BRI on trade between China and its partner countries along the Belt and Road (B&R). However, less is known about its effect on the sectoral trade between the B&R countries and countries that show little support of the BRI. To address that gap, this study examines the changing patterns of clothing imports by the United States (US) from China and 14 B&R countries in Asia. An extended gravity model with a policy variable BRI is built to explain bilateral clothing trade flow. A panel regression model and artificial neural network (ANN) are developed based on the data collected from 1998 to 2018 and applied to predict the trade pattern of 2019. The results show a positive effect of the BRI on the clothing exports of some Asian developing countries along the B&R to the US and demonstrate the superior predictive power of the ANN. More research is needed to examine the balance between economic growth and the social and environmental sustainability of developing countries and to apply more advanced machine learning algorithms to examine global trade flow under the BRI.

scholarly journals Elements of global superiority of The People's Republic of China in the 21st century

Napredak ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 77-102
Author(s):  
Žarko Obradović
Keyword(s):  
United States ◽  
European Union ◽  
21St Century ◽  
The United States ◽  
People’S Republic Of China ◽  
The European Union ◽  
People's Republic Of China ◽  
Republic Of China ◽  
Belt And Road ◽  
The Belt And Road

The Chinese state has existed for more than five thousand years and in the history of human society it has always presented its own specific civilizational attainment, which exerted a considerable influence on the Asian region. In the years since its creation on October 1, 1949, and especially in the last decade, New China has stepped out beyond the region of Asia onto the global scene. With its economic power and international development projects (amongst which the Belt and Road projects stands out), China has become a leader of development and the promoter of the idea of international cooperation in the interests of peace and security in the world and the protection of the future of mankind. This paper will attempt to delineate the elements of the development of the People's Republic of China in the 21st century, placing a special focus on the realization of the Belt and Road initiative and the results of the struggle against the Covid-19 pandemic, all of which have made China an essential factor in the power relations between great global forces and the resultant change of attitude of the United States of America and the European Union towards China. Namely, China has always been a large country in terms of the size of its territory and population, but it is in the 21st century that the PR of China has become a strong state with the status of a global power. Such results in the organization of society and the state, the promotion of new development ideas and the achievement of set goals, would not have been possible without the Communist Party of China, as the main ideological, integrative and organizational factor within Chinese society. In its activities, the Chinese state sublimates the experiences of China's past with an understanding of the present moment in the international community and the need of Chinese citizens to improve the quality of life and to ensure stable development of the country. The United States and the European Union are taking various measures to oppose the strengthening of the People's Republic of China. These include looking after their interests and preserving their position in the international community, while simultaneously trying, if possible, to avoid jeopardizing their economic cooperation with China.

scholarly journals Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019 ◽  
Vol 9 (21) ◽  
pp. 4690 ◽  
Author(s):  
Fatemeh Davoudi Kakhki ◽  
Steven A. Freeman ◽  
Gretchen A. Mosher
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
The United States ◽  
Work Environments ◽  
Safety Risk ◽  
Grain Elevators ◽  
Manufacturing Operations ◽  
Safety Risks ◽  
Artificial Neural

The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

Western pushback against China will increase

2020 ◽  
Keyword(s):  
United States ◽  
The United States ◽  
Belt And Road Initiative ◽  
The West ◽  
Content Type ◽  
Positive View ◽  
Downward Spiral ◽  
Belt And Road ◽  
Public Views ◽  
The Belt And Road

Significance Senior US officials see Communist-led China as the foremost threat to the United States. The Trump administration’s campaign against it spans the spectrum of government actions: criticism; tariffs; sanctions; regulatory crackdowns; military intimidation; support for Taiwan; and restrictions on imports, exports, investment and visas. Impacts Beijing will have little success in driving a wedge between Washington and its major Western allies. The West is unlikely to produce a convincing alternative to the Belt and Road Initiative (BRI). Negative public views of China incentivise China-bashing by politicians, which in turn feeds negative public opinion in a downward spiral. Beijing will persist in its efforts to encourage a more positive view of China among Western publics.

Transport Energy Demand Modeling of the United States Using Artificial Neural Networks and Multiple Linear Regressions

2014 ◽  
Author(s):  
Arash Kialashaki ◽  
John Reisel
Keyword(s):  
Neural Network ◽  
United States ◽  
Energy Demand ◽  
Regression Models ◽  
The United States ◽  
Transportation Energy ◽  
Independent Variables ◽  
Transportation Sector ◽  
The Future ◽  
Artificial Neural

In 2009, the transportation sector was the second largest consumer of primary energy in the United States, following the electric power sector and followed by the industrial, residential, and commercial sectors. The pattern of energy use varies by sector. For example, petroleum provides 96% of the energy used for transportation but its share is much less in other sectors. While the United States consumes vast quantities of energy, it has also pledged to cut its greenhouse gas emissions by 2050. In order to assist in planning for future energy needs, the purpose of this study is to develop a model for transport energy demand that incorporates past trends. This paper describes the development of two types of transportation energy models which are able to predict the United States’ future transportation energy-demand. One model uses an artificial neural network technique (a feed-forward multilayer perceptron neural network coupled with back-propagation technique), and the other model uses a multiple linear regression technique. Various independent variables (including GDP, population, oil price, and number of vehicles) are tested. The future transport energy demand can then be forecast based on the application of the growth rate of effective parameters on the models. The future trends of independent variables have been predicted based on the historical data from 1980 using a regression method. Using the forecast of independent variables, the energy demand has been forecasted for period of 2010 to 2030. In terms of the forecasts generated, the models show two different trends despite their performances being at the same level during the model-test period. Although, the results from the regression models show a uniform increase with different slopes corresponding to different models for energy demand in the near future, the results from ANN express no significant change in demand in same time frame. Increased sensitivity of the ANN models to the recent fluctuations caused by the economic recession may be the reason for the differences with the regression models which predict based on the total long-term trends. Although a small increase in the energy demand in the transportation sector of the United States has been predicted by the models, additional factors need to be considered regarding future energy policy. For example, the United States may choose to reduce energy consumption in order to reduce CO2 emissions and meet its national and international commitments, or large increases in fuel efficiency may reduce petroleum demand.

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