Systems thinking (ST) is an interdisciplinary domain that offers different ways to better understand the behavior and structure of a complex system. Over the past decades, several publications can be identified in academic literature, focusing on different aspects of systems thinking. However, two critical questions are not properly addressed in the extant body of ST literature: (i) How to conduct the content analysis exclusively to derive the prominent statistics (i.e., influential journals, authors, affiliated organizations and countries) pertaining to the domain of ST? (ii) How to get better insights regarding the current and emerging trends that may evolve over time based on the existing body of ST literature? To address these gaps, the aim of this research study is to provide a comprehensive insight into the domain of systems thinking through bibliometric and network analysis. Beginning with over 6000 accumulated publications, the analysis narrowed down to 626 prominent articles with proven influence published over the past three decades. Leveraging rigorous bibliometric tools analysis, this research unveils the influential authors, leading journals and top contributing organizations and countries germane to the domain of systems thinking. In addition, citation, co-citation and page rank analysis used to rank top influential articles in the area of systems thinking. Finally, with the aid of the network analysis, key clusters in the existing literature are identified based on the research areas of systems thinking. The findings of this research will serve as a bluebook for practitioners and scholars to conduct future research within systems thinking context.

Due to the widespread of new technologies, the modern electric power system has become much more complex and uncertain. The Integration of technologies in the electric power system has increased the exposure of cyber threats and correlative susceptibilities from malicious cyber-attacks. To better address these cyber risks and minimize the effects of the power system outage, this research identifies the potential causes and mitigation techniques for the smart grid (SG) and assesses the overall cyber resilience of smart grid systems using a Bayesian network approach. Bayesian network is a powerful analytical tool predominantly used in risk, reliability, and resilience assessment under uncertainty. The quantification of the model is examined, and the results are analyzed through different advanced techniques such as predictive inference reasoning and sensitivity analysis. Different scenarios have been developed and analyzed to identify critical variables that are susceptible to the cyber resilience of a smart grid system of systems. Insight drawn from these analyses suggests that overall cyber resilience of the SG system of systems is dependent upon the status of identified factors, and more attention should be directed towards developing the countermeasures against access domain vulnerability. The research also shows the efficacy of a Bayesian network to assess and enhance the overall cyber resilience of the smart grid system of systems.

In this study, we used a structural equation modeling method to investigate the relationship between systems engineers and engineering managers’ Systems-Thinking (ST) skills preferences and their Personality Traits (PTs) in the domain of complex system problems. As organizations operate in more and more turbulent and complex environments, it has become increasingly important to assess the ST skills preferences and PTs of engineers. The current literature lacks studies related to the impact of systems engineers and engineering managers’ PTs on their ST skills preferences, and this study aims to address this gap. A total of 99 engineering managers and 104 systems engineers provided the data to test four hypotheses posed in this study. The results show that the PTs of systems engineers and engineering managers have a positive impact on their level of ST skills preferences and that the education level, the current occupation type, and the managerial experience of the systems engineers and engineering managers moderate the main relationship in the study.

Exponential technological-based growth in industrialization and urbanization, and the ease of mobility that modern motorization offers have significantly transformed social structures and living standards. As a result, electric vehicles (EVs) have gained widespread popularity as a mode of sustainable transport. The increasing demand for of electric vehicles (EVs) has reduced the some of the environmental issues and urban space requirements for parking and road usage. The current body of EV literature is replete with different optimization and empirical approaches pertaining to the design and analysis of the EV ecosystem; however, probing the EV ecosystem from a management perspective has not been analyzed. To address this gap, this paper develops a systems-based framework to offer rigorous design and analysis of the EV ecosystem, with a focus on charging station location problems. The study framework includes: (1) examination of the EV charging station location problem through the lens of a systems perspective; (2) a systems view of EV ecosystem structure; and (3) development of a reference model for EV charging stations by adopting the viable system model. The paper concludes with the methodological implications and utility of the reference model to offer managerial insights for practitioners and stakeholders.

This research paper presents the approach of machine learning analysis techniques on education. For the large dataset, data mining techniques are used to extract hidden information and create insight. We hypothesized that the prediction algorithm and dimensional reduction algorithm could be used on an educational dataset to extract the hidden information and analyze the information to create insight. Machine learning algorithms can be used to predict student academic performance. Since some of the features in our dataset are correlated so, before applying the prediction algorithm, we applied the dimensional reduction algorithm to reduce the dimension of our dataset and extract the important features. For the prediction analysis, we used three supervised machine learning algorithms, namely K-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression. Before we applied these machine learning algorithms, we applied the dimension reduction algorithm for the feature extraction purpose using two algorithms, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). We compared the performance of these machine learning algorithms. For the student academic performance, their final Examination result was taken as the target value, which was predicted by using the above-mentioned supervised algorithm. Our work shows that the dimensional reduction algorithm, followed by the prediction algorithm, achieved the acceptable prediction accuracy for determining student academic performance. Our result also highlights the advantage of employing machine learning techniques on educational data and explains how it helps to provide engineering education insight for the sustainable development of Engineering education as a whole.