Nowadays, the emerging trend of utilizing the internet technology to improve teaching methods has caught the attention of many researchers. The education sector is the particular sector which has been getting the influence to implement more advanced teaching methods, and this can be seen through the application of the monitoring system, remote control, and even data acquisition through the Internet. Therefore, this study demonstrates the development of the interactive system via the Internet for the remote access to the equipment known as LD-Didactic temperature system which is used in the laboratory. This particular system is developed using an Arduino-Espresso8266 and LabVIEW platform which is integrated with Blynk software in order to remotely control the Proportional, Integral, and Derivative (PID) setting. Furthermore, the proposed framework is specifically focused on the PID temperature control system, which allows students to learn and simulate the concept of the control scheme by adjusting suitable parameter gains. Moreover, the system evaluation demonstrates the functionality and ability of direct controlling, monitoring, and tracking in order to achieve the targeted temperature control. These three strategies ensure the reproducibility of the experiment for the implementation in different systems. Last but not least, the designed system illustrates that full integration of the system with the internet platform provides students with the underlying knowledge of fundamentals, graphic visualization, and simulation practices. These will subsequently facilitate the learning process of the students.

In recent years, the installed capacity increment with regard to solar power generation has been highlighted as a crucial role played by Photovoltaic (PV) generation forecasting in integrating a growing number of distributed PV sites into power systems. Nevertheless, because of the PV generation’s unpredictable nature, deterministic point forecast methods struggle to accurately assess the uncertainties associated with PV generation. This paper presents a detailed structured review of the state-of-the-art concerning Probabilistic Solar Power Forecasting (PSPF), which covers forecasting methods, model comparison, forecasting horizon and quantification metrics. Our review methodology leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to systematically identify primary data sources, focusing on keywords such as probabilistic forecasting, Deep Learning (DL), and Machine learning (ML). Through an extensive and rigorous search of renowned databases such as SCOPUS and Web of Science (WoS), we identified 36 relevant studies (n=36). Consequently, expert scholars decided to develop three themes: (1) Conventional PSPF, (2) PSPF utilizing ML, and (3) PSPF using DL. Probabilistic forecasting is an invaluable tool concerning power systems, especially regarding the rising proportion of renewable energy sources in the energy mix. We tackle the inherent uncertainty of renewable generation, maintain grid stability, and promote efficient energy management and planning. In the end, this research contributes to the development of a power system that is more resilient, reliable, and sustainable.

To date, research efforts have demonstrated the stimulated need for the Internet of Things (IoT) based monitoring device in their laboratory. The benefits of remote laboratories in overcoming time constraints and the disadvantages of usability of conventional laboratories are well known. In addition to the current control engineering laboratories, a remote lab that incorporates an industry-relevant method has been established to assist in the understanding of data acquisition with cost-effective platform integration. However, one of the greatest challenges is the creation of a low-cost and user-friendly remote laboratory experiment that is ideal for interacting with the actual laboratory via a mobile device. The main objective of this work is therefore to build a remote laboratory system based on the IoT using the LabVIEW-Arduino interface with the example of proportional-integral-derivative (PID) tuning scheme for the LD-Didactic temperature plant. The practical work would include the implementation of the low-cost Arduino module connecting the actual plant to mobile devices. In addition, interfaces have been built using the Blynk application to allow communication between the end user and the laboratory equipment. In line with the Industrial Revolution 4.0 (IR 4.0), the proposed study structure called for the digitization of the current laboratory experiment method.

The adoption of precision agriculture in pineapple farming has a significant impact by increasing the yield and reducing the input resources while improving the management of pineapple crops. The intersection of advanced drone technology and cutting-edge artificial intelligence has reformed fruit crop management through revolutionary levels of automation, precision fruit detection, yield estimation, and crop health detection. However, the capability for obscuring the detection of subtle features to better manage occlusions and complex environments in images captured by drones at certain heights with drones is challenging to distinguish, thus hindering an accurate object analysis for fruit-environment differentiation. The proposed work uses Deep Learning (DL) techniques to classify pineapple fruit images captured ten meters above the ground. This is achieved specifically through the use of pretrained models and Faster Region-Based Convolutional Neural Networks (Faster R-CNNs) due to their ability to learn robust interpretations from images for object classification tasks. This paper evaluates the capabilities and accuracies of four pretrained models, namely ResNet-101, ResNet-50, Inception-ResNet-v2, and VGG-19, to detect and classify the pineapple fruit amidst the complex background and varying lighting conditions. By evaluating the pretrained models for pineapple fruit classification using comprehensive metrics (True Positive Rate (TPR), False Positive Rate (FPR), Accuracy (ACC), Recall (REC), Precision (PRE), F1-score), the results reveal that the Faster R-CNN architecture with the VGG-19 pretrained model outperformed the other architectures, demonstrating the best performance in pineapple fruit detection with an ACC of 0.7924 (79.24%), a PRE of 0.9990 (99.90%), a REC of 0.7930 (79.30%), and an F1-score of 0.8839 (88.39%). The effectiveness of this model in overseeing complex scenarios suggests potential improvements in classification accuracy compared to other pretrained models, while acknowledging performance variability across various architectures.

GIC (geomagnetic induced current) is a natural current that flows through a conductive substance. The purpose of this study is to provide bibliometric analysis on the computation of the GIC at the Power Network, since determining the backflow current’s threshold limit is crucial to avoid electrical equipment failure. The methodology of the study includes topics, scope, and eligibility, as well as screening and an analytical screen paper. From 1979 to 2021, we investigate the evolution of bibliometric studies on the assessment of the GIC at the power network. According to the statistics, there are 601 Scopus articles and 357 Web of Science (WoS) papers in the study on GIC at the power network that focus on estimation from 1979 to 2021. According to the data, the Engineering and Energy disciplines contribute the most to research on predicting the GIC at the Power Network. The words “geomagnetically induced current,” “reactive power,” and “geomagnetism” are commonly used instead of “magnetic storm,” “power grids,” and “geoelectric fields.” The bibliometric method encompasses themes, scope and eligibility, screening, and screen paper for all publications in a search for developing subjects based on Scopus and WoS to map the time-trend, disciplinary distribution, and high-frequency keywords.

To supplement conventional teaching methods, use of new technology have recently being adapted in the current education system. Such examples are implementing simulators and quiz kits in a classroom session to assist teachers in providing standardized evaluation or practical and more direct examples of concepts that are being taught. Additionally, the well-known issue of attracting student interest in a so-called uninteresting conventional lecture may be solved by using this technological method. The objective of this works to provide methods on developing a prototype training kit (named as e-Logic trainer kit or e-LTK) which comprises of an electronic simulator and quiz assessment module to evaluate subject knowledge on logic gates. The e-LTK is developed by using Arduino Mega 2560 as the base microcontroller, equipped with keypad and display module as the user input/output interface. The quiz assessment module contained in the prototype also allows conversion of inputs by combinatorial logic circuits into output in the form of timing diagram. This equipment enables electrical engineering students to simulate several combinations of logic circuit, reinforcing the understanding of logic circuit operation. The effectiveness of using this prototype in educational settings have been evaluated through a survey on students and instructors at Faculty of Technology in Engineering (FTK) in Universiti Teknikal Malaysia Melaka (UTeM). Response has been positive, with 60% of the correspondence has shown positive feedbacks, indicating the usefulness of the prototype kit.