Increasing power demand from passive distribution networks has led to deteriorated voltage profiles and increased line flows. This has increased the annual operations and installation costs due to unavoidable reinforcement equipment. This work proposes the reduction in annual costs by optimal placement of capacitors used to alleviate power loss in radial distribution networks (RDNs). The optimization objective function is formulated for the reduction in operation costs by (i) reducing the active and reactive power losses, and (ii) the cost and installation of capacitors, necessary to provide the reactive power support and maintain the voltage profile. Initially, the network buses are ranked according to two loss sensitivity indices (LSIs), i.e., active loss sensitivity with respect to node voltage (LSI1) and reactive power injection (LSI2). The sorted bus list is then fed to the particle swarm optimization (PSO) for solving the objective function. The efficacy of the proposed work is tested on different IEEE standard networks (34 and 85 nodes) for different use cases and load conditions. In use case 1, the values finalized by the algorithm are selected without considering their market availability, whereas in use case 2, market-available capacitor sizes close to the optimal solution are selected. Furthermore, the static and seasonal load profiles are considered. The results are compared with recent methods and have shown significant improvement in terms of annual cost, losses and line flows reduction, and voltage profile.
Measuring and analyzing the student's visual attention are significant challenges in the e-learning environment. Machine learning techniques and multimedia tools can be used to examine the visual attention of a student. Emotions play a vital impact in understanding or judging the attention of the student in the class. If the student is interested in the lecture, the teacher can judge it by reading his emotions, and the learning has increased, and students can pay more attention to the classroom, authors say. The study explores the effect on the brand reputation of universities of information and communication technology (ICT), e-service quality, and e-information quality by focusing on the e-learning and fulfillment of students.
El studio investiga la idoneidad de las pruebas de idioma para contribuir no solo a mejorar los procedimientos de evaluación del idioma inglés adoptados en Pakistán por la Junta de Educación Intermedia y Secundaria, Punjab, sino también para investigar su conformidad con cualquier modelo de prueba. Se realiza una revision histórica bibliográfica con un análisis cualitativo de documentos de preguntas de los últimos cinco años del decimo grado en dicho examen de la junta. Los resultados mostraron que los cuestionarios no cumplían con los objetivos generales del idioma de destino, que se debe prestar más atención a las pruebas de idioma inglés utilizadas en los exámenes de la junta, y que el formato de pruebas debe revisarse para que pueda responder a las demandas de estudiantes y profesores de inglés.
ICT tools and machine learning tools are used to analyze the visual attention of the student. The student's attention score is calculated for the analysis of the visual attention of the student. For this purpose, the authors have developed a software package (i.e., Visual Attention Tool [VAT]) based on the ICT that extracts the frames from a video stream that is taken through the webcam attached to the student's laptop. Each image is converted into a grayscale image, enhanced by image processing, then face detection is performed by following eye detection. This real-time processing of video produces a dataset by tracking the faces and eyes. It measures the attention level of the student with the timestamp. A manual observer also calculates the student's attention score focusing face and eye contact and produces a dataset manually. Then a comparative analysis of both datasets is performed in statistical and machine learning tools.
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