Noorul Islam Centre for Higher Education

The need for powerful energy storage systems is rising. Supercapacitors are a highly appropriate approach for fulfilling the needs related to energy storage. They have better qualities than batteries, such as more energy per unit than ordinary capacitors, greater power density levels, and better cycle stability. These benefits make them appropriate for smaller-scale applications in macro-to-micro devices utilized for compact electrical components, as well as for large-scale devices needing higher energy and power density. Because of its intrinsic qualities, conducting polymers—a recently identified material—have drawn attention from scientists studying energy storage. Metal oxides are frequently added to conducting polymers to provide them improved or unique properties for usage in supercapacitors. Our study presents a unique approach to making and analyzing Polyaniline–lead oxide (PANI–Pb3O4) composites at a lower cost. To the best of our knowledge, the electrochemical characteristics of the PANI– Pb3O4 composite have been comparatively less explored, and this study aims to provide further insights into their potential for supercapacitor applications. In the present study, a straightforward, new one-step approach has been demonstrated for the synthesis of Polyaniline (PANI), a conducting polymer that is utilized to make composites with Pb3O4 with different weight

The rapid evolution of Smart Grid (SG) technologies necessitates robust control and monitoring systems. This study introduces an effective SG energy monitoring system utilizing Type 2 Fuzzy Logic Controller (T2FLC), enhanced with the Mayfly Optimization Algorithm (MOA). The MOA plays a crucial role, drawing inspiration from the natural mating behavior of mayflies to perform an efficient search space exploration, thus optimizing the T2FLC parameters including membership functions and rule weights. The system utilizes a Wireless Sensor Network (WSN) at its core for real-time data acquisition of key electrical parameters. The system comprehensively manages both Photovoltaic (PV) systems and Wind Energy Conversion Systems (WECS), ensuring stable power output using the innovative Mayfly Algorithm optimized Type 2 Fuzzy Logic Controller (MF-T2FLC). Its primary function is to compute and generate reference power for the associated DC-DC and AC-DC converters connected to the PV system and WECS, respectively. The efficacy of the MF-T2FLC is thoroughly verified through both laboratory prototype implementations and MATLAB simulations, particularly under variable environmental conditions. The MF-T2FLC showcases remarkable performance through improvements in settling times and steady-state errors, with zero overshoot, outperforming conventional controllers. Moreover, the real-time energy monitoring system is instrumental in enhancing SG performance, contributing to power delivery optimization and resource utilization.

In recent years, the field of educational technology has experienced significant advancements, with innovative methods being developed to assess and improve student performance. Traditional approaches developed for student performance analysis face challenges, such as simple grading or standardized testing, often fail to capture the complex, multifaceted nature of learning. These methods do not manage dynamic factors like student persistence, engagement, and cognitive development, which play a crucial role in shaping learning outcomes. To address this limitation, an enhanced deep learning approach was developed. The data are collected from students’ academic records. Initially, the data undergoes pre-processing through decimal scaling normalization, the relative density factor, and missing value imputation using extreme gradient boosting. Multi-Dimensional Scaling (MDS) is applied to extract relevant features from the pre-processed data. The GritNet approach predicts students’ academic performance, with its hyperparameters optimized using the Hippopotamus algorithm to enhance accuracy. The experimental results demonstrate the effectiveness of this integrated approach, achieving impressive performance metrics and attaining 95

The necessity for improved dielectric materials has grown due to the swift advancement of 5G networks and high-frequency communication technologies (mmWave, IoT, and satellite communications). Dielectric materials with high dielectric permittivity, low dielectric loss, and excellent thermal stability are necessary for modern communication devices. To address these issues, this research focuses on creating innovative dielectrics with better permittivity, enhanced thermal stability, and environmentally benign synthesis. Advancing such materials are critical for developing sustainable and energy-efficient technologies in telecommunications, power electronics, and electric cars. In this study, Copper oxide nanoparticles (CuO NPs) were produced in an ecologically acceptable manner, and their dielectric characteristics were thoroughly investigated. Rare earth elements (Samarium and Cesium) were added to improve these qualities by doping and co-doping process. The structural properties of the prepared nanoparticles were investigated using X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were used to study their morphology. Additionally, thermogravimetric analysis (TGA) and differential thermal analysis (DTA) were used to determine their thermal stability. Dielectric studies in the 50 Hz—1 MHz range and at 40, 80 and 120 °C found that Sm-Ce co-doped CuO had the maximum dielectric permittivity of 789 at 50 Hz and 120 °C, as well as the lowest dielectric loss of 0.99 (< 1). Low dielectric loss obtained in this present study is one of the major advantages for using CuO as dielectric material, which exhibit high dielectric loss due to its semiconducting nature. Electric modulus investigation revealed more consistent relaxation behavior with distinctive features about 105 Hz, validating its potential for high-frequency applications. Moreover, individual doping of rare earth produced high dielectric constant with minimal loss when compared with other metal oxides, vital for high-power applications to preserve signal integrity.

Insulators are the components that offer support to the conductors in the transmission lines and also provide the insulation to these conductors. It is important for power transmission lines. Hence, conducting inspections to verify the safety of a depends extensively on the inspection of insulator systems. There are standard practices for inspecting Insulators may involve hand inspections or inspections carried out by helicopter. However, many a time, this type of inspection may turn out to be extremely expensive, and time-consuming. An alternative to performing conventional, in the context of insulator inspections of the transmission line, Autonomous Aerial Vehicles (AAV), which are (HD) cameras, used to perform comprehensive aerial inspection of the insulators. Inclusion of techniques using deep learning, artificial intelligence has even furthered defect detection, identification, and classification processes. Among these, the You Only Look Once (YOLO) object detection method is well known for its speed and accuracy. The use of YOLOv8 for insulator failure identification using AAV images is covered in this work. The methodology involves precise localization of insulators followed by crack detection. The following enhancements were made to increase the detection accuracy. In order to enhance global modelling capabilities, YOLO v8 first introduces the Contextual Transformer module and secondly introduces DRConv (dynamic region-aware convolution) to enhance convolutional format. A dataset of 3700 transmission line images taken under various lighting conditions, camera angles, and intricate natural backgrounds was used to train and assess the proposed model. To guarantee the accurate localization of insulator faults in practical situations, all samples were manually annotated using the Roboflow platform with oriented bounding boxes. The experimental findings show that the image classification network proposed in this study can compete with the traditional YOLOv8n network in terms of detection accuracy. It shows an increase of 4.1% in precision,5.5% increase in recall, and 4.1% increase in mAP compared to the original YOLOv8 model.