Electrical Energy is an essential commodity which significantly contributes to the economic development of any country. Many non-linear factors contribute to the final output of electrical energy demand. In order to efficiently predict electrical energy demand, many time-series analysis and multivariate techniques have been suggested. In order for these methods to accurately work, an enormous quantity of historical dataset is essential which sometimes are not available, inadequate and inaccurate. To overcome some of these challenges, this paper presents an Artificial Neural Network based method for Electrical Energy Demand Forecasting using a case study of Lagos state, Nigeria. The predicted values are compared with actual values to estimate the performance of the proposed technique.

The increased role of wind turbine systems makes it important for its operational states to be con-tinuously monitored and optimized. This goal can be achieved using existing methods, which re-lies on closed-form expressions. The use of these methods, however, becomes challenging when interacting parameters cannot be fully presented with closed form expressions. In this paper, an artificial neural network (ANN) based algorithm is proposed as a solution to this problem. This algorithm is used to estimate wind turbine systems operational state and reliability. The proposed method is able to provide a more holistic approach to manage a wind turbine system with respect to the problem mentioned above. Simulation results show that the developed ANN can predict the average number of failures per year, distribution of failure and average downtime per failure with good accuracy. This was achieved using an ANN model with 5-15-3 architecture. The model generates mean square errors of 4.6 × 10^-3, 4.2 ×10-3, and 4.0 × 10^-3 at the training, validation, and testing stages, respectively. The study is beneficial to wind turbine practitioners and manufacturers as its findings can provide in-depth understandings of reliability issues of the system.

Supply chain management in oil and gas industry plays an important role for the success of these companies in most countries. A reliable supply chain helps on time delivery of goods and services and leads to better performance of the firms and yields higher profitability. This paper presents an empirical investigation to measure the relative efficiency of different oil distribution companies in Iran. The proposed study uses a five-stage Supply-Chain Operations Reference (SCOR) technique to measure the relative efficiencies of 40 distribution oil companies. The study designs a questionnaire based on four balanced scorecard perspectives and distributes it among various experts who were familiar with supply chain issues. The results indicate that the network performed relatively efficient since the study did not detect any unit with low performance and most of them maintained relatively high scores.

Supply chain provides continual turbulence with unpredictable disruptions potential for enterprises with complex network infrastructure. Supply chain in enterprises needs to be resilient and sustain-able to provide effective response, removing vulnerabilities and minimizing the impact of negative disturbances. This review article is based on evaluating the role of supply chain management in automobile sector of India. The overall findings derived from review of refined articles state that, in countries like India, automobile industry needs to have appropriate management commitment for sustainability and resilience of supply chain rather than rules and regulations of government. The review article examined 914 articles related to evaluating supply chain management practices in automobile sector of India. Through the analysis of the collected articles, it is evident that DEMATEL is effective technique for providing inter-relationship in automobile industry in India. Rather than economic influence in automobile sector, managerial activity is a major concern for effective sustainable and resilience practices in automobile industry of India.

In real world multi criteria decision making (MCDM) problem, it is tough to solve a decision matrix with vague and imprecise data. The degree of impreciseness depends on the kind of data avail-able. For interval valued data this impreciseness is less and interval-valued MCDM methods can be effectively used to solve the problem. A flexible manufacturing system (FMS) selection prob-lem was taken into consideration to find the best FMS among available alternatives. An interval extension of CODAS method is proposed in this paper which was used to solve the problem along with two other interval-valued decision-making methods i.e. interval-valued TOPSIS, interval-valued EDAS. All the three methods are distance-based approaches and it was found that the interval-valued CODAS method gave the exact same ranking with that of interval-valued TOPSIS and interval-valued EDAS.

The purpose of this research is to study the role of the social media for knowledge sharing. The study presents a comprehensive review of the researches associated with the effect of knowledge management in social media. The study uses Scopus database as a primary search engine and covers 1858 of highly cited articles over the period 1994-2019. The records are statistically analyzed and categorized in terms of various criteria using an open source software package named R. The findings show that researches have grown exponentially during the recent years and the trend has continued at relatively stable rates. Based on the survey, knowledge management is the keyword which has carried the highest citations followed by social media and social networking. Among the most cited articles, papers published by researchers in United States have received the highest citations, followed by United Kingdom and China.

The purpose of this research is to investigate the status and the evolution of the scientific studies for the effect of social networks on big data and usage of big data for modeling the social net-works users’ behavior. This paper presents a comprehensive review of the studies associated with big data in social media. The study uses Scopus database as a primary search engine and covers 2000 of highly cited articles over the period 2012-2019. The records are statistically analyzed and categorized in terms of different criteria. The findings show that researches have grown exponentially since 2014 and the trend has continued at relatively stable rates. Based on the survey, decision support systems is the keyword which has carried the highest densities followed by heuristics methods. Among the most cited articles, papers published by researchers in United States have received the highest citations (7548), followed by United Kingdom (588) and China with 543 citations. Thematic analysis shows that the subject nearly maintained an important and well-developed research field and for better results we can merge our research with “big data analytics” and “twitter” that are important topics in this field but not developed well.

Social media has grown very quickly and has affected all dimensions of the world’s community. The purpose of this article is to review valid articles on the relationship between social media and promotion using manual and bibliometrics analysis methods and identify top themes in these articles. We review the papers published between 2007 and the first month of 2019 in Scopus. 1,840 articles were published in the mentioned period. In this article, we review various charts including word dynamics, the contributions of different countries, country scientific production, corresponding author's country, the frequency distribution of sources, collaboration network and country collaboration map. The study indicates that Canada, Australia and France were the most productive countries in this area.

This paper presents a comprehensive scientometric study for the impact of social networks on advertisement. The study uses the Scopus database as a search engine to accomplish the survey. To better understand the evolution and identity of this category, the study covers 1216 most cited data over the period 1983-2019. Qualitative and quantitative data analysis techniques are applied to determine author distribution, country, individual and institutional-level productivity rankings. In terms of keywords, the study indicates that social media was jointly studied with gender and be-havior and researchers from the United States maintained the highest rate of contribution. The survey also indicates that there were strong collaboration between the researchers from China and United States. Moreover, there were also remarkable collaborations between the researchers in United States from one side and other countries.

Social media has grown steadily during the last decade and it is now considered as a new opportunity to use for different purposes such as decision making. The primary objective of this paper is to review articles related to social media and decision making using manual and bibliometrics anal-ysis methods, and to identify top themes in these articles. We have reviewed the papers published between 2008 and the first month of 2019 in Scopus where 1,159 articles were published in this period. These articles come from 733 sources and 3,459 authors. According to our survey, United States is the most productive country. Moreover, most collaborations occurred between two coun-tries of United States and United Kingdom as well as between United States and China. The bibliometrics analysis examines global research in this field from the different point of views.