Supply Chain Sustainability Management Through Blockchain Technology in Maritime Industry

Nowadays, integrating new technology into all management processes leads to significant evolution, particularly in the logistics business, which is one of the fourth industrial of Industry 4.0. Blockchain, an emerging idea, provides for the decentralized and unchangeable storing of verified data as integrated technology. The shipping industry has had to look for innovative ways to keep the accelerated growth of the planet on track, in the face of development threats and vulnerabilities from the mild development of foreign markets, expanding protectionism, correcting natural guidance, the current episode of COVID-19 pandemics. One of the successful innovations in blockchain technologies was to promote a computerized market change. It is also making the utilization of blockchain innovation in the maritime industry will empower quicker, more secure and more productive businesses. The objective of this study is to review sustainable in supply chain management through blockchain technology in the maritime industry. This work was done by discussion of literature by classifying the application according to the operation in the shipping process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Economic Impact of Pandemic Covid-19 Outbreak: Comparative Analysis of Three Major Economic Groups

The Pandemic Covid-19 outbreak cause a negative shock to the world economy, throwing many countries into economic uncertainty, facing an economic recession and if Covid-19 continuously actively spread possibly many countries face an economic depression. This study assessing the economic impact of Covid-19 by analyzing on the three main economic indicators which are GDP growth rate, inflation, and unemployment. This study using estimation proposed by Aditya and Acharyya (Aditya and Acharyya J. Int. Trade Econ. Dev. 22:959–992, 2013), applies generalized methods of moments (GMM) estimators. Data consist of 171 countries of the quarterly data set. The results of the study indicate that the most significant effect of the Covid-19 outbreak is on the GDP growth rate. However, the effect of the Covid-19 outbreak on inflation and unemployment is no exception. The findings suggest that the world economy can recovery or expand if policymakers and government focusing to stimulate investment through fiscal intervention which is likely to give a positive multiplier effect on economic activity. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach

COVID-19, CoronaVirus Disease - 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg's test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.