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A review on recent studies of buoyancy effect

AIP Conference Proceedings 2023年第1期2484卷

作者： Nur Syahmi Izzati Ali Othman Sunny Goh Eng Giap 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia

Vapor buoyancy effect is the moist air that is lighter than dry air at the same volume, temperature and pressure due to the molecular weight of water which is less than that of dry air. Even though this effect is well known however, the impact on system been overlooked. Therefore, in this paper, we review articles-based the effect of buoyancy that can be one of the factors for the environmental purposes under several conditions. Therefore, the review focused on the effect of buoyancy as the factor that affect the process of the system studied by the previous researchers. The studies discussed here primarily include the effect of buoyancy based on the findings, conditions and area of the applications. The results showed that the effect of buoyancy becoming the factor that affect the whole process of the system. The review found the effect of buoyancy possibly one of the factors that cause the fundamental problem in the model prediction of water, vapor and heat flux in the soils. Future research should focus on a complete understanding of the mechanisms of the effect of buoyancy in soils by developing design guidelines for better planning of model prediction.

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Design and Implement of REST API for Data Integration 8

Design and Implement of REST API for Data Integration

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Yusof, Mohd Kamir Man, Mustafa Ismail, Azli Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Terengganu Besut 22200 Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Terengganu Kuala Nerus21030 Malaysia Wisma Dataran Rimba Square Lieos SDN BHD Lot 37744 Tingkat 1 Terengganu Kuala Nerus21300 Malaysia

ISBN: (纸本)9781665491068

Data integration is currently most important to organization to integrate data from different data sources using different platform or programming languages. A suitable approach is required to allow one system send the data to another system using standard data format. REST API is one approach can used for data integration using JSON format. Three processes are involved in this approach. There are extract data, send data, received and dump data. In this approach, SAP system will be sending the data in JSON to FEMS system. FEMS system will be exploded and dump the data into database FEMS. Based the experiment, the result indicates, REST API is successfully implemented by showing less response time and CPU usage during sending and receiving the data in JSON format. © 2022 IEEE.

关键词： Data integration

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An intelligent autonomous surface vehicle manoeuvring analysis in restricted waters via neuroevolutionary method

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AIP Conference Proceedings 2023年第1期2484卷

作者： N. I. Jalal A. F. Ayob J. S. Norbakyah A. R. Salisa 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Malaysia

Autonomous surface vehicle (ASV) is beneficial to complete tasks that are beyond human limits such as offshore target detection, search-and-rescue (SAR) mission and offshore surveillance. The use of ASV may cater to lower the risks exposed to human operators in the shipping business for transporting cargo, handling congestion in waterways and harbours. Manoeuvring operations are complex and challenging for human pilots to conduct, in which with a slight error, may lead to marine incidents such as capsizing, grounding and collision. The application of ASV is advantageous as a human surrogate to operate in heavy traffic and restricted waters, therefore preserving human pilot safety and economic factors. ASV control is akin to the operation of human operators owing to the combination of mathematical models and artificial neural networks. This paper presented the capability of an ASV for decision-making to perform course-keeping and course-changing manoeuvring in restricted water. The neuroevolutionary method (combination single-layer artificial neural network (ANN) and genetic algorithm (GA)) is demonstrated in this work to utilize a simulation environment to train an ASV to operate without human intervention. The performance of the ASV to accomplish the piloting task faster compared to a human operator is highlighted as the result of the work. In this work, the movement of the ASV's rudder owing to an ANN bias due to imbalanced waterway turns setup (either to port or starboard direction). In future work, the training of the ASV may develop with implement the combination algorithms of multiple layer feedforward ANN and GA to perform autonomous navigation.

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Synthesis, Characterisation, and Corrosion Inhibition Performance of a β-Cyclodextrin/Melamine Supramolecular Complex on St20 Steel in Acidic Media

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Journal of Bio- and Tribo-Corrosion 2024年第4期10卷 1-22页

作者： Berdimurodov, Elyor Eliboev, Ilyos Dagdag, Omar Lal, Basant Berisha, Avni Rajabov, Yusufboy Mamatov, Jaykhun Wan Nik, W.B. Kholikov, Abduvali Akbarov, Khamdam Chemical & amp Materials Engineering New Uzbekistan University 54 Mustaqillik Ave. Tashkent100007 Uzbekistan Faculty of Chemistry National University of Uzbekistan Tashkent100034 Uzbekistan Uzbek-Finnish Pedagogical Institute Spitamen Street 166-Home Samarqand Uzbekistan Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Chemistry Institute of Applied Science and Humanities GLA University Mathura281406 India Department of Chemistry Faculty of Natural and Mathematics Science University of Prishtina Prishtina10000 Albania Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia Physics and Chemistry "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent100000 Uzbekistan Natural Science University of Tashkent for Applied Sciences Str. Gavhar 1 Tashkent100149 Uzbekistan Physics and Chemistry Western Caspian University BakuAZ-1001 Azerbaijan

Corrosion poses major challenges in various industrial sectors such as oil and gas, chemicals, and infrastructure. Traditional corrosion inhibitors have limitations in terms of toxicity and environmental compatibility. Supramolecular complexes are promising as green corrosion inhibitors, as the interaction between host and guest molecules can be tailored for different application needs. This study investigates a β-cyclodextrin (β-CD)/melamine (ME) supramolecular complex. The synthesis of the complex is confirmed by Raman spectroscopy, 1H NMR, and FT-IR methods. A range of characterisation techniques are used to analyse the complex’s structure. Gravimetric and electrochemical tests, including potentiodynamic polarisation and electrochemical impedance spectroscopy, evaluate its corrosion inhibition performance on steel in 0.5 M HCl solution at varying concentrations (25–100 mg/L) and temperatures (303 to 333 K). Computational modelling, including Density Functional Theory, Monte Carlo, and Molecular Dynamics simulations, provides atomic-scale insights into the mechanisms of inhibition. The synthesis, characterisation, and corrosion prevention performance of a novel supramolecular complex, β-cyclodextrin/melamine (β-CD/ME), on St20 steel, in acidic conditions, were the subjects of an inventive investigation that was described in this publication. The complex’s structural alterations, physical interactions, and structure were effectively disclosed, using Raman spectroscopy, 1H NMR, and FT-IR. The β-CD/ME complex’s ability to prevent corrosion in hostile solutions exceeded 92–94%, resulting in the formation of a thin protective film on the metal surface and acting as a mixed-type inhibitor. A novel mix of theoretical and experimental methods is used in this work to comprehend the underlying mechanisms, including the influence of structural factors on corrosion prevention effectiveness and the adsorption energies of the complex on the metal surface. Electrochemical tests

关键词： Molecular dynamics

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A hybrid classification model for predicting breast cancer survivability using public dataset

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AIP Conference Proceedings 2023年第1期2484卷

作者： N. H. Abd Rahim D. H. Awang Kadir M. S. H. Basri 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu 21030 Kuala Nerus Terengganu Malaysia

Breast cancer has been one of the deadliest diseases and becomes the leading cause of cancer deaths in women worldwide. It becomes an issue to any countries as the number of victims developing breast cancer is increasing, and the survivability rate of cancerous women is still ambiguous. In this study, a hybrid classification model is developed for predicting breast cancer survivability. The hybrid classification model is a combination of three single classification models, namely Naive Bayes, J48 Decision Tree and RBF Network. Stacking method has been used to combine those models. This hybrid model has been tested using Breast Cancer Wisconsin dataset which gives result 96.14% of accuracy, 96.30% of precision, 96.10% of recall and 96.20% of f-measure. These results are outperformed each of the single classifiers.

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A comparative study of lexical similarity approaches for Malay short text

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AIP Conference Proceedings 2023年第1期2484卷

作者： Madihah Mohammad Arifah Che Alhadi 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu 21030 Kuala Nerus Terengganu Malaysia

The short text retrieval is gaining tremendous attention due to rapid exposure and widespread usage of social networks. This type of text is widely used in various applications such as spam filtering, tweet classification, question-answer systems, web search and so forth. Thus, the effective analysis of these texts is of vital need. But, short text retrieval often becomes complex because they are usually contains repeated words, less features and overloaded with noise. Different lexical similarity approaches have been developed to tackle the problem of lexical analysis. However, most of them are in English, and the solutions for problems involving Malay are of limited availability. This paper examined the reliability of lexical similarity approaches focusing on term-based approaches for Malay short text analysis. The Cosine, Overlap coefficient and Jaccard similarity methods were used to quantify the similarities of some news title written in Malay news. Results show that the selected approaches term-based has potential to be used to analyze Malay short texts.

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On efficient fourth-order Runge-Kutta-Central schemes for modeling tsunami propagation and run-up prediction

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AIP Conference Proceedings 2023年第1期2484卷

作者： M. A. Puteri Nurul Fatihah K. C. Loy 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu 21030 Kuala Nerus Terengganu Malaysia.

In our work, one-dimensional shallow water equations are proposed to model the phenomena of tsunami propagation and run-up. This model is known to be reliable to make predictions on maximum run-up along the shorelines above the mean sea level induced by the tsunami waves. 1D shallow water equations in a depth-averaged manner can be derived from incompressible Navier-Stokes equations by the simplification of lateral velocity (z-direction) using boundary conditions imposed on both free surface and bottom topography. In our current work, we assume there is no motion in the y-direction. Further, 1D shallow water equations in a conservative form are solved using the fourth-order Runge-Kutta method for time and second-order central difference schemes in space. We observed that the resulting high-order method in time is efficient, fairly accurate for their smaller stencil sizes, and has a slightly better stability property than that of the conventional forward Euler in time. This high-order method allows us to take larger mesh size and time steps while still producing small discretization errors. Our method was implemented using a low-level C++ programming language which is essential to incorporate larger domains (consecutively larger degrees of freedom) and to render fast computations. Using a conceptual test problem, we validate the numerical error, reproduce the theoretical convergence rate in both time and space while observing its computational efficiency. We also verified our model using two test problems – one for wave propagation and another is for the run-up. Our numerical models reproduced the expected outcomes when a tsunami wave travels in the ocean just before it arrives at the shoreline in terms of a solitary wave and finally collapses.

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IR-Eclat: A new algorithm of incremental R-Eclat for infrequent itemset mining

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AIP Conference Proceedings 2023年第1期2484卷

作者： Mustafa Man Nurul Aqilah Ruslan Julaily Aida Jusoh Wan Aezwani Wan Abu Bakar 1Faculty of Ocean Engineering Technology & Informatics Universiti Malaysia Terengganu Terengganu Malaysia 2Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia

The key challenge of association rule mining is to discover and extract a valuable information from databases. However, mining association rule may require repetitious scanning of large databases that leads to the high memory usage and affects the running time. Rare Equivalence Class Transformation (R-Eclat) algorithm is specifically design for infrequent itemset mining. In response to the promising results of mining in speedy processing time and taking consideration of dynamic transaction of data in a database, a new incremental approach is introduced called Incremental R-Eclat (IR-Eclat). This model adopted via relational database engine management system; My Structured Query Language (MySQL) serves as a database engine for testing benchmark datasets. The experimental results on several benchmark datasets indicate that Incremental R-Eclat outperforms the R-Eclat by reducing its running time to process especially in dynamic database as the data is increasing in volume from time to time.

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Fi-Eclat: An enhancement of incremental Eclat algorithm

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AIP Conference Proceedings 2023年第1期2484卷

作者： Mahadi Man Masita Abdul Jalil Wan Aezwani Wan Abu Bakar 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia 2Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia

Nowadays, there is a large amount of data growth at all organizational scales. To find useful information, the right data mining technique needs to be used. One of the popular techniques is called the equivalence class transformation (Eclat) algorithm which is gaining the benefits of the vertical data format used in the association rule mining technique. However, like horizontal data representation, vertical data still suffering from huge memory consumption. The technique then has been extended by using the Incremental Eclat (i-Eclat) algorithm which is embedded with the incremental approach. Inspired by the dynamic data transactions in a database, a new algorithm has been adopted to optimize the performance of the current algorithm called the Fast Incremental (Fi-Eclat) algorithm. The research proposes an optimization of the performance of the i-Eclat algorithm. It relies on the incremental approach implemented on the database that consists of five (5) benchmark datasets from Frequent Itemset Mining (FIMI). The outcome of the experiment indicates an improvement of 32% in time execution for the proposed algorithm.

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Hybrid restarting-Lanczos-SVR for solving high dimensions of PDE engineering problems

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AIP Conference Proceedings 2022年第1期2472卷

作者： Rehana Thalib Maharani A. Bakar 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia 2Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia

Machine learning (ML) has shown a significant development in the application of several fields such as sciences, engineering, and technology. Its typical supervised and unsupervised learning makes ML desirable for us to use, since it is based on the availability of our data and the research problems. Regarding the prediction data, there are some methods under the ML that can be categorized as supervised learning, such as decision trees, regression analysis, and support vector regression (SVR). The latter one is a well-known regression or prediction tool which aims to find a function that approximates output to an actual target with minimum tolerance error. In this paper, we used SVR to predict a new approximate solution after we generated some iterates using an iterative method called Lanczos algorithm for solving high dimensional non-symmetric systems of linear equations (SLEs). Here, the SLEs are built from partial differential equations (PDEs) problems using the finite difference method (FDM). We solved the variety of PDEs which are typically used as the govern model in engineering problems. Numerical results showed that our proposed methods performed well in solving the high dimensional linear system with small residual norms.

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