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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia
(2023)
Hazrin N.A
;
Chong K.L
;
Huang Y.F
;
Ahmed A.N
;
Ng J.L
;
Koo C.H
;
Tan K.W
;
Sherif M
;
El-shafie A
In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model. The following were the developed best models for their respective study areas: In Peninsular Malaysia, the interactions linear regression model was the best at Pulau Langkawi (RMSE = 19.066), the Matern 5/2 gaussian process regression model at Geting (RMSE = 49.891), and the trilayered artificial neural network at Pulau Pinang (RMSE = 20.026), while the linear regression model was the best at Sandakan in Sabah, East Malaysia (RMSE = 14.054). Other metrics, such as MAE and R-square, were also at their best values, each providing its best values, further substantiating the RMSE respectively, at each of the study areas. These empirical statistics (or metrics) also revealed that despite employing sea level as the sole parameter, results obtained were exceptional better when utilizing a 7-day lag, regardless of the model used. Notably, lag variables with less than a 7-day lag could degrade the model's accuracy in representing ground reality. The study emphasizes the importance of thorough training and testing of ML to aid decision-makers in developing mitigation actions for the climate change phenomena of sea level rise through reliable ML. © 2023
5
Simulation of argon-excited microwave plasma reactor for green energy and CO2 conversion application
(Elsevier Ltd, 2024)
Ong M.Y.
;
Chia S.R.
;
Milano J.
;
Nomanbhay S.
;
Chew K.W.
;
Yusaf T.
;
Show P.L.
;
57191970824
;
57194081866
;
57052617200
;
57217211137
;
57192980692
;
23112065900
;
47861451300
Microwave plasma as a potential tool to convert CO2 has been extensively studied in recent years. A simulated study on the plasma parameters via the variation of the operating pressure of a microwave plasma model has been performed in this study. The establishment of the model was based on the finite element method to analyse the spatial distribution of plasma parameters in the plasma torch over a period of time. Plasma parameters such as electron potential, density, and temperature were investigated at three different pressures, and the growth of electron potential and density were associated with time. The distribution of molecular ions was observed to be located more on the enter port of the microwave or waveguide near the location of the magnetron at the initial stage. The electron density was found to be constant after it reached maximum value for all the determined pressures. However, the electron temperature behaved differently as compared to the electron potential and density, the distribution of high electron temperature did not enhance during the processing time. The analysis of microwave plasma parameters is beneficial for plasma reactor designing, particularly for CO2 conversion. ? 2023 The Authors
17
Drivers and Barriers Affecting Metaverse Adoption: A Systematic Review, Theoretical Framework, and Avenues for Future Research
(Taylor and Francis Ltd., 2024)
Al-Sharafi M.A.
;
Al-Emran M.
;
Al-Qaysi N.
;
Iranmanesh M.
;
Ibrahim N.
;
57196477711
;
56593108000
;
57205206257
;
55226710300
;
9337335600
The Metaverse holds immense potential for individuals, organizations, and society, providing immersive and innovative experiences. However, its adoption is a complex process influenced by various factors yet to be thoroughly understood. Therefore, this systematic review aims to identify and classify the drivers and barriers affecting Metaverse adoption. Of the 279 papers gathered from the Web of Science and Scopus databases, 29 studies fulfilled the eligibility requirements and underwent a detailed analysis. The identified factors in the selected studies were classified into distinct categories, including motivational and psychological factors, social factors, technological and Metaverse-related characteristics, learning experience-related factors, inhibitors, privacy and security factors, conditional factors, quality factors, economic-related factors, and personalization and immersion-related factors. We have then proposed a comprehensive Metaverse adoption framework based on this taxonomy to guide future empirical studies. We have also suggested several agendas as a road map for future research on Metaverse adoption. Based on these findings, the review presents several theoretical contributions and practical implications for Metaverse developers and marketers. ? 2023 Taylor & Francis Group, LLC.
9
Recent advancement and assessment of green hydrogen production technologies
(Elsevier Ltd, 2024)
Zainal B.S.
;
Ker P.J.
;
Mohamed H.
;
Ong H.C.
;
Fattah I.M.R.
;
Rahman S.M.A.
;
Nghiem L.D.
;
Mahlia T.M.I.
;
57200914760
;
37461740800
;
57136356100
;
55310784800
;
58776756000
;
57201359295
;
36778460100
;
56997615100
Hydrogen energy has garnered substantial support from industry, government, and the public, positioning it as a pivotal future fuel source. However, its commercial realisation faces significant hurdles, including slow infrastructure growth and the high cost of producing clean hydrogen. This review uniquely emphasises the different colour codes of hydrogen, which have been rarely discussed in the literature to date. Hydrogen production methods are classified by colour codes, with green hydrogen, produced from renewable sources such as wind and solar, being the most desirable option. The demand for green hydrogen across various sectors is expected to surge. This review comprehensively evaluates the major hydrogen production methods based on cost, environmental impact, and technological maturity. Recent data confirm the increased efficiency, cost-competitiveness, and scalability of green hydrogen production technologies. The cost of green hydrogen has declined significantly, making it competitive with blue hydrogen (produced from fossil fuels with carbon capture). The review also scrutinises several recent hydrogen production technologies, highlighting their advantages, disadvantages, and technological readiness. Among these, the solid oxide electrolysis cell (SOEC) currently outperforms others, with anion exchange membrane (AEM) and electrified steam methane reforming (ESMR) also showing promise. This review also succinctly summarises global progress in hydrogen infrastructure and policies. By spotlighting the diverse colour codes of hydrogen and discussing the crucial takeaways and implications for the future, this review offers a comprehensive overview of the hydrogen energy landscape. This unique focus enriches the literature and enhances our understanding of hydrogen as a promising energy source. ? 2023 Elsevier Ltd
6
Energy and battery management systems for electrical vehicles: A comprehensive review & recommendations
(SAGE Publications Inc., 2024)
Challoob A.F.
;
Bin Rahmat N.A.
;
A/L Ramachandaramurthy V.K.
;
Humaidi A.J.
;
58698724700
;
58698327900
;
58698859900
;
57192640171
Electric vehicle technology has recently drawn a lot of interest on a global scale due to improved performance in its efficiency and the capability to solve the problems of carbon emission. As such, electric vehicles are the key to achieving sustainable development goals. This review article analyzes deeply the previous technical developments of electric vehicles, focusing on important topics like battery management systems, technologies of power electronics, techniques of charging, and the relevant algorithms and improvements. In addition, several critical problems, and difficulties are presented in order to pinpoint the gaps in the literature. To address the analysis of battery behavior, battery condition monitoring, real-time control design, temperature control, fault diagnostics, and efficiency of battery model are considered. This study highlighted the estimation techniques that predict the internal battery conditions such as internal temperature, state of health, and state of charge, which are difficult to be directly monitored and determined. A lithium-ion battery, a super-capacitor, and related bidirectional DC/DC converters constitutes the infrastructure of a hybrid power system. This review offers useful and practical recommendations for the future development of electric vehicle technology which in turn help electric vehicle engineers to be acquainted with effective techniques of battery storage, battery charging strategies, converters, controllers, and optimization methods to satisfy the requirements of sustainable development goals. Accordingly, this review article will be a platform and future guide for those who are interesting in the field of energy management and its development. ? The Author(s) 2023.
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