Virtual Laboratory for Engineering Education: Review of Virtual Laboratory for Students Learning

Veza, Ibham; Sule, Ahmed; Putra, Nicky Rahmana; Idris, Muhammad; Ghazali, Ihwan; Irianto, -; Pendit, Ulka Chandini; Mosliano, Gipin; Arasmatusy,

doi:10.56741/esl.v1i02.138

Cited by 8 publications

(4 citation statements)

References 28 publications

(34 reference statements)

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“…The results obtained, in conjunction with comparable findings from other studies, suggest a favorable influence of utilizing a virtual laboratory on educational achievements (Veza et al, 2022;Gunawan et al, 2017). According to study findings (Gunadi et al, 2023;Nais et al, 2019), students can learn more independently and overcome time and space constraints in the learning process by utilizing technology and readily available information.…”

Section: Resultssupporting

confidence: 76%

Virtual Laboratory and Self-Efficacy in Optimizing Problem-Solving Skills: A Case in Applied Chemistry Learning in Vocational College

Ida Ayu Anom,

Ida Bagus Putra Manuaba,

I Ketut

et al. 2024

JTP

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The limitation of laboratory facilities in conducting practical chemistry learning activities has become one of the triggers for less optimal learning processes. Implementation of virtual laboratory simulation becomes a solution to overcome the limited equipment of practicum activities. It is possible to overcome some of the challenges that are currently being experienced in chemistry education by making effective use of virtual laboratories. The primary aim of this research is to investigate the impact of virtual laboratories and self-efficacy on the improvement of problem-solving skills among engineering students in the context of applied chemistry education. The research employs a quasi-experimental design to examine the impact of independent variables on dependent variables. Participants in this study are students enrolled in the Refrigeration and Air Conditioning Engineering and Utility Engineering Technology programs within the Mechanical Engineering Department of Bali State Polytechnic. The study utilized data collection instruments consisting of tests assessing problem-solving skills and questionnaires measuring self-efficacy. The current study employs analysis of covariance (ANCOVA) as a statistical method to examine the hypothesis.The hypothesis testing results show that using virtual laboratories has a better effect than problem-solving skill demonstrations when it comes to electroplating learning activities. The observed correlation between self-efficacy and improved problem-solving skills does not seem to result in a significant impact.

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Section: Resultssupporting

confidence: 76%

Virtual Laboratory and Self-Efficacy in Optimizing Problem-Solving Skills: A Case in Applied Chemistry Learning in Vocational College

Ida Ayu Anom,

Ida Bagus Putra Manuaba,

I Ketut

et al. 2024

JTP

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“…A very thin gas-phase interface is formed between the marine diesel and the hot surface, and the mode of heat transfer from the hot surface to liquid starts to change. Marine diesel is mainly composed of C 12 ~C23 alkane, which belongs to a multi-component mixture [45,46]. When the marine diesel leaks and comes into contact with the hot surface at a time lapse of 3000.0 ms, a large amount of white smoke and mixtures are produced.…”

Section: Hsi Characteristics Of Marine Fuel Under Lateral Airflow In ...mentioning

confidence: 99%

Effect of Lateral Airflow on Initial HSI and Flame Behavior of Marine Fuel in a Ship Engine Room: Experiment and Analysis

Wang,

Ming,

Liu

et al. 2023

JMSE

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The flame behavior of engine fires, such as those caused by leaked fuel coming into contact with an ignition source, is significant in practical applications, where flame detection is used to minimize the damage of the attendant ship fire safety problem. In this work, the flame behavior of hot-surface ignition (HSI) under crossflow was studied, with a particular focus on the difference in lateral airflow velocities for HSI-driven flame deviations at the windward and leeward sides of a ship engine room; a problem such as this has not previously been quantified. Full-scale experiments were conducted in a ship engine room using marine diesel and hydraulic oil as the fuel, and by adopting lateral airflow with the velocities of 0 m/s, 1.0 m/s, 3.0 m/s, and 5.0 m/s, together with an HSI mechanism consisting of marine diesel and hydraulic oil coming into contact with elevated hot-surface temperatures. The results show that the effects of disturbing the combustible gaseous mixture for marine fuel HSI, at both the windward and leeward sides, strengthened as the airflow velocity increased. The HSI position of the leaked marine fuel in the engine room was strongly dependent on ventilation, while that under the airflow condition decreased with the increase in the hot-surface temperature. A model was proposed to characterize this difference on the basis of the HSI height, which was defined as the ratio of the height during the initial HSI to the stationary period. The results indicate that the scale of the flame gradually increased in the horizontal direction, which was significantly different from the result in the scenario without mechanical ventilation. The results also revealed that the fluctuation of hydraulic oil through the temperature field was significant and lasted for a long time under a low HSI temperature.

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“…An ANN, which is a representative neural network algorithm, was selected as an alternative [62]. ANN is an ML algorithm inspired by the structure of the human brain and consists of an input, hidden, and output layer [63].…”

Section: Modeling Overviewmentioning

confidence: 99%

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Yang

Choi

2023

Energies

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The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models’ prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model’s performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG.

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Virtual Laboratory for Engineering Education: Review of Virtual Laboratory for Students Learning

Cited by 8 publications

References 28 publications

Virtual Laboratory and Self-Efficacy in Optimizing Problem-Solving Skills: A Case in Applied Chemistry Learning in Vocational College

Virtual Laboratory and Self-Efficacy in Optimizing Problem-Solving Skills: A Case in Applied Chemistry Learning in Vocational College

Effect of Lateral Airflow on Initial HSI and Flame Behavior of Marine Fuel in a Ship Engine Room: Experiment and Analysis

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

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