The objective of this study is to explore the role of artificial intelligence applications (AIA) in education. AI applications provide the solution in many ways to the exponential rise of modern-day challenges, which create difficulties in access to education and learning. They play a significant role in forming social robots (SR), smart learning (SL), and intelligent tutoring systems (ITS) to name a few. The review indicates that the education sector should also embrace the modern methods of teaching and the necessary technology. Looking into the flow, the education sector organizations need to adopt AI technologies as a necessity of the day and education. The study needs to be tested statistically for better understanding and to make the findings more generalized in the future.

The aim of the article is to explore the academic and administrative applications of Artificial Intelligence. Teachers have the main responsibility of teaching in any educational setting. But there are various other tasks to be performed by the teachers as well. Besides academic duty, most of the teacher’s time and educational resources are dedicated to administrative works. Artificial Intelligence Applications (AIA) are not only assisting education academically and administratively but also enhance their effectiveness. AIA provides help to teachers in various types of tasks in the shape of Learning Analytics (LA), Virtual Reality (VR), Grading/Assessments (G/A), and Admissions. It minimizes the administrative tasks of a teacher to invest more in teaching and guiding students. In the current era, where there are a lot of tasks associated with the teaching profession, AIA adds a significant contribution to enhance student learning, minimize the workload of a teacher, grade/assess the students effectively and easily, and to help in a lot of other administrative tasks. The study needs to be quantitatively checked to make it generalized and acceptable.

Adopting Artificial Intelligent Technology in an Educational Organization is often problematic due to many internal and external environmental reasons, and often fails to attain the desired goals. This study aims to design a framework for adopting AI technology in the education sector. Most of the research focuses on the acceptance of a particular technology and ignores the study of what else is needed for a technology acceptance. The framework in this study provides a step-by-step process of the Technological Transformation of an organization never designed before. We recommend that before making any technological changes in an organization, generally and in the educational organization particularly, the processes must be followed for the successful and meaningful adoption of AI technology.

This paper presents the integration of shunt capacitors in the radial distribution grids (RDG) with constant and time-varying load consideration for the reduction of power losses and total annual cost, which turns to enhance the voltage profile and annual net savings. To gather the stated goals, three objective functions are formulated with system constraints. To solve this identified problem, a novel optimization technique based on the modification of particle swarm optimization is proposed. The solution methodology is divided into two phases. In phase one, potential candidate buses are nominated using the loss sensitivity factor method and in phase two the proposed technique first selects the optimal buses for the capacitor placement among the potential buses then it decides the optimal sizing of the capacitors as well. To demonstrate the performance in terms of efficiency and strength, the proposed technique is tested on IEEE 15, 33, and 69 bus system for the optimal placement and sizing of capacitors (OPSC) problem. The results are achieved in terms of annual net savings for 15 bus (47.66${\%}_{case-1}$, 32.76${\%}_{case-2}$, 26.46${\%}_{case-3})$, 33 bus (33.09% ${}_{case-1}$, 27.06${\%}_{case-2}$, 24.15${\%}_{case-3}$), and 69 bus (34.51% ${}_{case-1}$, 29.43${\%}_{case-2}$, 25.83${\%}_{case-3}$) which are comparable to other state of the art methods, and it also indicates the success of the proposed technique.

Precise evaluation of risk and reliability is crucial for decision making and predicting the outcome of investment in a photovoltaic power system (PVPS) due to its intermittent source. This paper explores different methodologies for risk evaluation and reliability assessment, which can be categorized into qualitative, quantitative, and hybrid qualitative and quantitative (HQQ) approaches. Qualitative methods include failure mode analysis, graphical analysis, and hazard analysis, while quantitative methods include analytical methods, stochastic methods, Bayes’ theorem, reliability optimization, multi-criteria analysis, and data utilization. HQQ methodology combines table-based and visual analysis methods. Currently, reliability assessment techniques such as mean time between failures (MTBF), system average interruption frequency index (SAIFI), and system average interruption duration index (SAIDI) are commonly used to predict PVPS performance. However, alternative methods such as economical metrics like the levelized cost of energy (LCOE) and net present value (NPV) can also be used. Therefore, a risk and reliability approach should be applied together to improve the accuracy of predicting significant aspects in the photovoltaic industry.

The Electroluminescence (EL) technique has garnered significant interest over time for its reliable outcomes that enrich our understanding of the onset of electrical degradation in polymeric material. There is growing interest in combining both the EL technique and space charge measurement methods to obtain a more profound knowledge of the degradation and ageing of insulation. The generation of charge carriers in polymeric materials is widely acknowledged to be a crucial aspect of EL, involving injection, de-trapping, and field dissociation processes. This research investigates the variables that influence how EL emissions behave, including applied voltage or electric field, applied electrical frequency, ageing of material, and selection of materials and gases employed. It was found that these factors can be helpful in predicting the process of electrical ageing in insulation to avoid any catastrophes that may occur. A mathematical approach relating these factors and the intensity of EL is proposed through the aid of Dimensional Analysis method. A close relationship is obtained that suggests this mathematical approach can be utilised as a tool to predict electrical ageing of insulation material.