The Fourth Industrial Revolution means the digital transformation of production systems. Cyber-physical systems allow for the horizontal and vertical integration of these production systems as well as the exploitation of the benefits via optimization tools. This article reviews the impact of Industry 4.0 solutions concerning optimization tasks and optimization algorithms, in addition to the identification of the new R&D directions driven by new application options. The basic organizing principle of this overview of the literature is to explore the requirements of optimization tasks, which are needed to perform horizontal and vertical integration. This systematic review presents content from 900 articles on Industry 4.0 and optimization as well as 388 articles on Industry 4.0 and scheduling. It is our hope that this work can serve as a starting point for researchers and developers in the field.
The Paris Climate Agreement and the 2030 Agenda for Sustainable Development Goals declared by the United Nations set high expectations for the countries of the world to reduce their greenhouse gas (GHG) emissions and to be sustainable. In order to judge the effectiveness of strategies, the evolution of carbon dioxide, methane, and nitrous oxide emissions in countries around the world has been explored based on statistical analysis of time-series data between 1990 and 2018. The empirical distributions of the variables were determined by the Kaplan–Meier method, and improvement-related utility functions have been defined based on the European Green Deal target for 2030 that aims to decrease at least 55% of GHG emissions compared to the 1990 levels. This study aims to analyze the energy transition trends at the country and sectoral levels and underline them with literature-based evidence. The transition trajectories of the countries are studied based on the percentile-based time-series analysis of the emission data. We also study the evolution of the sector-wise distributions of the emissions to assess how the development strategies of the countries contributed to climate change mitigation. Furthermore, the countries’ location on their transition trajectories is determined based on their individual Kuznets curve. Runs and Leybourne–McCabe statistical tests are also evaluated to study how systematic the changes are. Based on the proposed analysis, the main drivers of climate mitigation and evaluation and their effectiveness were identified and characterized, forming the basis for planning sectoral tasks in the coming years. The case study goes through the analysis of two counties, Sweden and Qatar. Sweden reduced their emission per capita almost by 40% since 1990, while Qatar increased their emission by 20%. Moreover, the defined improvement-related variables can highlight the highest increase and decrease in different aspects. The highest increase was reached by Equatorial Guinea, and the most significant decrease was made by Luxembourg. The integration of sustainable development goals, carbon capture, carbon credits and carbon offsets into the databases establishes a better understanding of the sectoral challenges of energy transition and strategy planning, which can be adapted to the proposed method.
A data-driven method to identify frequent sets of course failures that students should avoid in order to minimize the likelihood of their dropping out from their university training is proposed. The overall probability distribution of the dropout is determined by survival analysis. This result can only describe the mean dropout rate of the undergraduates. However, due to the failure of different courses, the chances of dropout can be highly varied, so the traditional survival model should be extended with event analysis. The study paths of students are represented as events in relation to the lack of completing the required subjects for every semester. Frequent patterns of backlogs are discovered by the mining of frequent sets of these events. The prediction of dropout is personalised by classifying the success of the transitions between the semesters. Based on the explored frequent item sets and classifiers, association rules are formed providing the estimates of the success of the continuation of the studies in the form of confidence metrics. The results can be used to identify critical study paths and courses. Furthermore, based on the patterns of individual uncompleted subjects, it is suitable to predict the chance of continuation in every semester. The analysis of the critical study paths can be used to design personalised actions minimizing the risk of dropout, or to redesign the curriculum aiming the reduction in the dropout rate. The applicability of the method is demonstrated based on the analysis of the progress of chemical engineering students at the University of Pannonia in Hungary. The method is suitable for the examination of more general problems assuming the occurrence of a set of events whose combinations may trigger a set of critical events.
This paper presents an algorithm for learning local Weibull models, whose operating regions are represented by fuzzy rules. The applicability of the proposed method is demonstrated in estimating the mortality rate of the COVID-19 pandemic. The reproducible results show that there is a significant difference between mortality rates of countries due to their economic situation, urbanization, and the state of the health sector. The proposed method is compared with the semi-parametric Cox proportional hazard regression method. The distribution functions of these two methods are close to each other, so the proposed method can estimate efficiently.
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