Research Publications

Academic theses and dissertations are regularly published and widely disseminated in YUOE Journal, Monographs, Universities Research Journal (URJ), Myanmar Academy of Arts and Science (MAAS) Journal, AsTEN Research Journal and International online Journals for sharing knowledge and contributing to the development of our education.

Myanmar Academy of Art & Science List of Research

No. Researcher Title Abstract Year
2421 Khin Sandar Lin1, Chan Nyein Aung2, Lwin Maung3 MECHANICAL TENSILE STRENGTH ANALYSIS OF POLY VINYL ALCOHOL(PVA) POLYMER REINFORCED WITH WASTE SCRAPED TIRE RUBBER FIBERS The waste tire fibers were sourced and processed to reinforce Poly Vinyl Alcohol (PVA) polymer. The blended composite materials were prepared and tested for mechanical tensile strength analysis using a universal testing machine, EEU/20kN. The mechanical properties such as tensile strength, elongation at break, tensile modulus, yield strength and ultimate strength were determined by the Stress-Strain Analysis. Five PVA tested samples for each mix ratio are experimentally tested until the failure occurs by the Universal Material Testing Unit, EEU/20kN. The pure PVA has tensile strength is 48.71 MPa and the highest Scraped Tire Reinforced mixed with PVA by the 20 w/v% ratio has highest tensile strength is 52.218 MPa. The value of Elongation break (%), Tensile modulus (MPa), Yield strength (MPa) and Ultimate strength (MPa) are decreased as the filler percent increased. The waste tire fibers can be effectively utilized as a reinforcing agent in PVA composites, leading to improved mechanical properties. The significant implications for the development of sustainable composite materials and could contribute to reducing the environmental impact of waste tire disposal. 2025
2422 Kyaw Swar Min1, Kay Khing Oo2, Moe Sandar Win3, Hnin Nu Nu Aung4 Sitar Kyaw5 Study on Physical and Chemical Properties of Soil at Kalay University Campus Five soil samples were taken from Kalay University campus. Next, five soil samples were grounded with mortar for 1hr and then sieved by a 0.01cm sieve. Then, each sample was weighed 600 grams. To obtain more samples, 100 grams of each from five samples were re-weighed, mixed, and grounded by agate mortar. Finally, a total of six fine soil samples were obtained. Potential Hydrogen (pH) of all soil samples was measured to consider soil acidity or alkalinity. Electrical conductivities of soil samples were tested to investigate the amounts of salts in the soil. Measurements of soil content of Nitrogen(N), Phosphorus(P), and Potassium(K) of two fine samples were analyzed. Energy Dispersive of X-ray Fluorescence (EDXRF) measurements of two fine samples were carried out to determine the elemental contents of soils. 2025
2423 Hnin Ei Phyu1 APPLYING ONE-DIMENSIONAL DIFFERENTIAL TRANSFORM METHOD TO PARTIAL DIFFERENTIAL EQUATIONS In this paper, definitions of one-dimensional differential transform (reduced differential transform) and inverse differential transform are described. And then, properties of one-dimensional differential transform are expressed. After that, one-dimensional differential transform is utilized to solve the initial value problems for linear one-dimensional partial differential equations with constant coefficients and variable coefficients. Finally, one-dimensional differential transform is applied to solve the initial value problems for two-dimensional second-order partial differential equations. 2025
2424 Julie Marlar1, Wint Pa Pa Kyaw2, Soe Mya Mya Aye3 ANALYSIS OF TRAFFIC ACCIDENT BY USING MACHINE LEARNING In the case of road accidents, millions of people are dead each year, and the numbers of accident rates are rising all over the world. As a result, they have a great impact on society in terms of finance and economic. In this paper, Machine Learning (ML) models are used to analyze road accident severity based on road accident dataset. The dataset was collected from Myanmar’s Traffic Police Force which shares raw data on annual basis on Yangon- Naypyitaw expressway’s traffic accident data in 2022. The dataset is divided into training and testing dataset. The training dataset is used to train our model, and the test dataset is utilized to evaluate the predictions. In the proposed system, the collected data is preprocessed (data cleaning, encoding, transformation) and followed by data training, testing and comparison analysis on the analysis of the ML methods. By this experiment, the road accident contributing factors is vital. By using the continuous variables, this work is predicted road accidents with different accident types and applied with ML technique like Logistic Regression (LR), Adaptive Boosting (AdaBoost), Decision Tree, Adaboost using Decision Tree and Multinomial Naive Bayes. The experimental results showed that Logistic Regression classifier achieves the best accuracy than other classifiers. 2025
2425 Ei Ei Thwe1, Khin Sandar Myint2, Soe Mya Mya Aye3 PREDICTING STUDENT DROPOUT SYSTEM FOR BASIC EDUCATION HIGH SCHOOL BY K-MEANS CLUSTERING The prediction of Basic Education High School students’ dropout has been an important field for educational institutions. Recently, Educational Data Mining (EDM) has gained attention among educational researchers and information technology researchers. Developing a strategic plan helps every student to attend school with positive outcomes. Data mining technique called cluster can predict why students drop out. The proposed system of this study is to analyse the performance of data mining techniques and to predict students' dropout using the K-Means clustering algorithm. A pre-processing step for the student; parent; teacher survey data led to a higher level of accuracy due to data cleaning and data reduction using Principal Component Analysis. The proposed system processed the survey data of the Basic Education High School students in rural Pathein, Ayeyarwady Region. 2025
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2427 ? ????? ??? ?? ? ????????? ????? ??? ? ????? ???? ? ?? ???* ????? ???? ??? ? ? ? ?? ????????? ??? ?? ??? ? ????? ???? ? ? ??? ???? ? ?? ?? ?? ?????? ???? ???? ??? ??? ???? ? ??? ???? ??? ? ????? ? ????? ???? ? ? ??? ?? ???? ??????? ? ??? ? ????? ???? ? ? ???????? ? ??? ???? ? ??? ? ??? ?????? ??? ?????? ????????? ??????? ???????? ? ? ?? ?? ?? ? ?? ??????? ? ???????????? ???? ???? ????????? ???? ???? ??????? ? ????? ???? ? ? ???? ? ? ????????? ????? ? ??? ?? ?????? ?? ??? ? ? ?? ? ? ? ?? ? ? ??????? ? 2025
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2429 ????????? ? ? ???????????? “? ???????????????? ???? ???????? ?” ? ?? ???? ? ?????? “ ??????? ???????????????????????” ? ?? ? ?????? ??? ????? ????? ?? ???? ????? ???????????? ?????? ? ???????????????? ?? ?? ???????? ????????????? “? ?? ????????????? ????? ??? ?? ?”????? ????? ? ??????? “???? ?? ?????? ?? ?????? ??” ????? ?? ?????? ?? ????? ??? ??? ??? ????? ??? ? ?? ????? ?? ????? ??? ????? ? ? ? ?? ?? ?? ? ???????????????? ? ?? ????? ? ???? Lakoff (2004)? ?? ????? ??? ? ? ?? ?? ? ? ?? ?? ????? ?? (?)? ??? ? ???????? ?? ? ?????? ?????? ? ?(?)? ????? (?) ???? ????? ???? ????? ?(?)?? ?? ?? ???? ?????????? (?) ?????????? ? ?? ? ? ???? (?) ?????? ??????? (?)? ?? ?“???? ? ?”"? ???? ? ?" (?) ???? ? ????????? ???? ????? (?)? ? ????? ???? ?????????? (?)? ????? ?????? (?)? ?????? ????????? ????? ???? ???????? ?? ????? ???????????? ???????? ??? ??? ????????? ???????????? ?? ????? ??? ????? ? ? ? ?? ?? ???? ? ????????? ????? ??? ????? ? ? ? ?? ?? ? ?? ?????????? ? ?? ????????? ????????? ? ?? ????? ?????????? ? ??????????????? ?? ????? ??? ????? ? ? ? ?? ?? ? ?? ?????? ???? ? ? ?? ???????????? 2025
2430 ? ??? ???? ? ? ???????? ?? ??? ???? ???????????? ??????? ????????????????????????????? ?????????? ? ???????????????? ?? ?????????????? ? ???? ?????? ???? ??????? ??? ?????? ??????????? ?? ????? ?? ??? ?????????? ???? ????????????? ?? ????????????? ??????????? ?????????? ? ????????? ?????????? ???? ? ?? ???? ???????????????????? ?????????????????? ??????? ????????????????????? ? ??????????????? ????? ??? ?? ???? ?? ???? ??????? ??????????? ???? ??????? ??? ???? ????????????????????? ?????????????????? ???????????? ? ????? ?? ???? ? ? ?????????????????? ? ????(?)???????????????? ???????????????? ??????? ?????? ?????? ??? ?????????????????? ?????????????????????? ?????? ??? ???? (?)???? ???????????? ?????? ?????????? ? ????????? ?? ???? ??????? ????????????????????????? ???? ?? ???? ??????? ????????????????? ?????????????????? ????? ??????????????????? ? ??????????? ????????????????????????? ? ?????? ? ?????? ? ??? ????????????????????? ???? ? ??? ?????? ?? ???? ?????????????? ??????????????? ?????? ???????????????? ??????????? 2025