This research presents an analysis of the five-position angle in both single-axis (one-axis tracking) and dual-axis (two-axis tracking) solar tracking systems. The study compares these tracking systems, where four solar panels move simultaneously, with a fixed solar panel system. The findings revealed that the five-position angle Sun-tracking technique resulted in lower energy consumption by the tracking mechanism than in the case of an all-time solar tracking system. The key component of the implemented system is a light-dependent resistor (LDR) sensor for controlling the motion of the motor for five positions on the vertical axis and horizontal axis, processed by a microcontroller to ensure the necessary solar tracking always moves in a perpendicular direction. According to the results, the voltage, current, and power increased with both one-axis and two-axis tracking compared to those of the fixed solar panel system under the same conditions. However, when evaluating the total energy with numerical integration methods, one-axis and two-axis provided 183.12 Wh and 199.79 Wh, respectively. Consequently, the energy production of the one-axis tracking system and the one-axis tracking system was found to be 16.71% and 24.97%, respectively, when compared to the fixed-axis system. Thus, the five-position angles of the sun-tracking technique resulted in lower energy consumption than is the case of an all-time solar tracking system.

This study examines the impact of students’ anxiety, due to online learning, in different learning environments: a synchronous (Zoom) and asynchronous learning environment (YouTube) to compare students’ conceptual understanding of electric circuits. Multiple linear regression and factor analyses were conducted to examine the factor of students’ anxiety and conceptual understanding. A sample of 99 vocational students participated in the study, including YouTube (n = 49) and Zoom (n = 50) groups. The DIRECT was used to diagnose test for conceptual understanding in the electric circuits, and OTAI was used to assess anxiety in online learning test. The OTAI consists of three factors: psychological, physiological, and online. The results showed that students’ anxiety, in some factors, affected their conceptual understanding of the electric circuits in both groups. However, there was a significant increase in conceptual understanding in both treatment groups. Although the students’ conceptual understanding had a slight increase, online learning has to improve to reduce the anxiety of learners.

The lifetime of a cast-resin transformer mainly depends on the condition of insulation material. Partial discharge (PD) is an important reason for insulation deterioration in cast-resin transformers. Identifying the position of PD is very necessary for damage assessment while the transformer is still operating, and the transformer is covered by housing. This paper proposes the investigation of a cast-resin transformer using an AE sensor and HFCT sensor to specify the precise source of PD. In this study, four AE sensors were used to find PD sources, and the high-frequency current transducer (HFCT) technique was used to identify the PD source and the criteria level. The experiment, in the first two parts, identified the possibility of PD, which includes the position of PD. The final part of the experiment verified the position of the PD source of a cast-resin transformer and confirmed the inspection results. AE and HFCT sensors can be used to detect the location of PD sources, confirming the position of the PD source by sensor detection. In addition, the evident partial discharge picture on the insulator surface of high voltage side. The process successfully and accurately identifies and locates the PD source.

The Black Soldier Fly (BSF), scientifically known as Hermetia Illucens L., effectively reduces organic waste and decreases harmful gas emissions more efficiently than landfill or composting methods. However, it can still produce harmful gases during the rearing of BSF larvae, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ammonia (NH3), which contribute directly and indirectly to the greenhouse effect. The pH level of the food provided to the larvae affects their biomass and leads to varying levels of gas emissions. This study aims to investigate the impact of greenhouse gas emissions when different pH levels of food are used, utilizing IoT technology to measure greenhouse gases and control the environmental conditions for rearing BSF larvae. The greenhouse gases monitored in this study include carbon dioxide (CO2) and methane (CH4), with environmental factors such as air temperature, humidity, soil moisture, and light intensity being controlled. The experiment tested pH levels ranging from 2.0, 4.0, 6.0, 7.0, and 8.0 to 10.0. The results show that different pH levels in food affect the emission of CO2 and CH4. Initially, a large amount of CO2 is released, gradually decreasing over time, while CH4 emissions steadily increase. In the early days, CH4 emissions are unstable, fluctuating before stabilizing. Food with a pH of 2.0 results in the lowest average CO2 emissions (430.53 PPM) from BSF larvae, and the lowest CH4 emissions (16.11 PPM) are also observed with a pH of 2.0.

For several years, the cathode spot model has been used as a mechanism for analysis of the instability phenomena in vacuum arcs with numerical methods. Artificial neural networks (ANNs) can accurately predict all disciplines. In this study, the ANN model was used for the prediction of instability phenomena in vacuum arc through the cathode spot model. The traditional method was experimentally used for numerical methods, to determine parameters which are related to the instability of phenomena analysis. The ANN model was used for TRAINBR of the training function. The transfer function was Tan-Sigmoid (Tag-Sig), while the input was two variables, the hidden layer was 39 units, and the output data were ten variables. As a result, the percentage error of mean absolute was 0.1635%. All parameters of the instability phenomena in vacuum arc were similar between the traditional method and the ANN model compared. However, the traditional method was only presented by approximation of the arc current in instability phenomena. Moreover, the advantage of the ANN model is that that the lowest arc current was immediately displayed before instability occurred. In addition, the ANN model can possibly be applied to predict the effects of the instability phenomena in vacuum arc from changing some parameters more quickly than the traditional method.

Dye-sensitized solar cells (DSSCs) are among the most widely studied thin-film solar cells because of their cost-effectiveness, low toxicity, and simple fabrication method. However, there is still much scope for replacing current DSSC materials due to their high cost, low volume, and lack of long-term stability. Accordingly, indium tin oxide (ITO)-nanorod films were fabricated by electron (E)-beam evaporation using the glancing angle deposition method in this study. Then, the ITO-nanorod was treated with oxygen plasma via a bias-magnetron radio-frequency (RF) sputtering process to improve the efficiency of DSSCs under a varying gas flow rate of 20, 40, 60, 80, and 100 sccm. The field emission scanning electron microscopy (FE-SEM) investigation of the ITO film structure revealed that the obtained nanorod structures have slightly different diameters. At the same time, an increase in the oxygen flow rate resulted in a rougher film surface structure. In this, the lower sheet resistance was received because of rougher morphology. When comparing the DSSCs efficiency (η) test results, we found that at a gas flow rate of 100 sccm, the highest efficiency value showed 9.5%. On the other hand, the ITO-nanorod without plasma treatment exhibited the lowest η. Hence, plasma technology can be practically applied to improve the η of DSSC devices. This study will be a prototype of a highly advanced solar cell manufacturing method for the solar cell industry.