One of the most important concerns in the planning and operation of an electric power generation system is the effective scheduling of all power generation facilities to meet growing power demand. Economic load dispatch (ELD) is a phenomenon where an optimal combination of power generating units is selected in such a way as to minimize the total fuel cost while satisfying the load demand, subject to operational constraints. Different numerical and metaheuristic optimization techniques have gained prominent importance and are widely used to solve the nonlinear problem. Although metaheuristic techniques have a good convergence rate than numerical techniques, however, their implementation seems difficult in the presence of nonlinear and dynamic parameters. This work is devoted to solving the ELD problem with the integration of variable energy resources using a modified directional bat algorithm (dBA). Then the proposed technique is validated via different realistic test cases consisting of thermal and renewable energy sources (RESs). From simulation results, it is observed that dBA reduces the operational cost with less computational time and has better convergence characteristics than that of standard BA and other popular techniques like particle swarm optimization (PSO) and genetic algorithm (GA).

The depletion of natural resources and the intermittence of renewable energy resources have pressed the need for a hybrid microgrid, combining the benefits of both AC and DC microgrids, minimizing the overall deficiency shortcomings and increasing the reliability of the system. The hybrid microgrid also supports the decentralized grid control structure, aligning with the current scattered and concentrated load scenarios. Hence, there is an increasing need to explore and reveal the integration, optimization, and control strategies regarding the hybrid microgrid. A comprehensive study of hybrid microgrid’s performance parameters, efficiency, reliability, security, design flexibility, and cost-effectiveness is required. This paper discusses major issues regarding the hybrid microgrids, the integration of AC and DC microgrids, their security and reliability, the optimization of power generation and load management in different scenarios, the efficient management regarding uncertainty for renewable energy resources, the optimal placement of feeders, and the cost-effective control methodologies for the hybrid microgrid. The major research areas are briefly explained, aiming to find the research gap that can further improve the performance of the grid. In light of the recent trends in research, novel strategies are proposed that are found most effective and cost-friendly regarding the hybrid microgrid. This paper will serve as a baseline for future research, comparative analysis, and further development of novel techniques regarding hybrid microgrids.

Interpretive Structural Modeling (ISM) is a technique to establish the interrelationships between elements of interest in a specific domain through experts’ knowledge of the context of the elements. This technique has been applied in numerous domains and the list continues to grow due to its simplistic concept, while sustainability has taken the lead. The partially automated or manual application of this technique has been prone to errors as witnessed in the literature due to a series of mathematical steps of higher-order computing complexity. Therefore, this work proposes to develop an end-to-end graphical software, SmartISM, to implement ISM technique and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)), generally applied along with ISM to classify variables. Further, a scoping review has been conducted to study the applications of ISM in the previous studies using Denyer and Tranfield’s (2009) framework and newly developed SmartISM. For the development of SmartISM, Microsoft Excel software has been used, and relevant algorithms and VBA (Visual Basic for Applications) functions have been illustrated. For the transitivity calculation the Warshall algorithm has been used and a new algorithm reduced conical matrix has been introduced to remove edges while retaining the reachability of variables and structure of digraph in the final model. The scoping review results demonstrate 21 different domains such as sustainability, supply chain and logistics, information technology, energy, human resource, marketing, and operations among others; numerous types of constructs such as enablers, barriers, critical success factors, strategies, practices, among others, and their numbers varied from 5 to 32; number of decision makers ranged between 2 to 120 with a median value of 11, and belong to academia, industry, and/or government; and usage of multiple techniques of discourse and survey for decision making and data collection. Furthermore, the SmartISM reproduced results show that only 29 out of 77 studies selected have a correct application of ISM after discounting the generalized transitivity incorporation. The outcome of this work will help in more informed applications of this technique in newer domains and utilization of SmartISM to efficiently model the interrelationships among variables.

The Accreditation Board for Engineering and Technology (ABET) accredits the tertiary education programs in the areas of applied and natural science, computing, engineering, and engineering technology. ABET offers accreditation in the United States and other regions in the world that lack such entities such as Gulf Cooperative Counties (GCC). Though ABET accreditation is voluntary, graduates of the ABET-accredited programs are considered equivalent in knowledge, behaviors, and attitude with global standards. The process of ABET accreditation takes months or years depending upon the gap with readiness and resources. The objective of this study is to compile and prioritize the list of critical success factors (CSFs) to commit resources optimally for sustained academic quality assurance and ABET accreditation. The triangulation research designed has been employed. Firstly, the observation of the ABET accreditation process of multiple programs at King Khalid University (KKU) helped in identifying 11 CSFs in three categories namely Program design and execution, Quality culture and excellence, and Institutional infrastructure and support. Further, these CSFs have been explored in the literature in the area of ABET accreditation. Finally, the research employs a fuzzy analytical hierarchy process (Fuzzy AHP) and full consistency method (FUCOM) to rank the relative importance of these CSFs and their dimensions for sustained academic quality assurance and ABET accreditation. The incorporation of these CSFs will help institutions in the GCC and other regions to get their academic programs ABET-accredited in an optimal manner.

Machine learning is emerging nowadays as an important tool for decision support in many areas of research. In the field of education, both educational organizations and students are the target beneficiaries. It facilitates the educational sector in predicting the student’s outcome at the end of their course and for the students in deciding to choose a suitable course for them based on their performances in previous exams and other behavioral features. In this study, a systematic literature review is performed to extract the algorithms and the features that have been used in the prediction studies. Based on the search criteria, 2700 articles were initially considered. Using specified inclusion and exclusion criteria, quality scores were provided, and up to 56 articles were filtered for further analysis. The utmost care was taken in studying the features utilized, database used, algorithms implemented, and the future directions as recommended by researchers. The features were classified as demographic, academic, and behavioral features, and finally, only 34 articles with these features were finalized, whose details of study are provided. Based on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability to predict the students’ performance based on specified features as categorized and can be used by students as well as academic institutions. A specific machine learning model identification for the purpose of student academic performance prediction would not be feasible, since each paper taken for review involves different datasets and does not include benchmark datasets. However, the application of the machine learning techniques in educational mining is still limited, and a greater number of studies should be carried out in order to obtain well-formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.