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The education system in Malaysia employs classical method in evaluation students’ progress performances during their foundation level. The result was obtained by adding all the assessments scored and was graded based on grade A, A-, B+, B, …, and F. Somehow, non-classical methods may be applied such as fuzzy logic, where it can be applied to many forms of decision making. This paper proposed evaluation on students’ performances by using fuzzy logic approach. The assessment score obtained based on their assignment, midterm examination and final term examination of 26 Agriscience students, in Preparatory Centre for Science and Technology (PPST), University Malaysia Sabah, UMS was carried out with fuzzy logic. Their results are calculated based on classical method and fuzzy logic approach, which is then compared and analysed. The comparison shows variation between classical and fuzzy logic approach in terms of their overall results.

Quarantine, isolation, and social distancing were essential for preventing the transmission of the virus and protecting people's physical fitness during the ongoing COVID-19 pandemic. However, they may have had lasting negative impacts, notably on mental health and well-being, especially on higher education students, as they are in a pivotal life-transition stage linked to increased mental health crises. Therefore, the main objective of this study is to assess the influential factors associated with mental health crises among foundation-level students during the COVID-19 pandemic. This study involved 277 students from the foundation of science programme at the Preparatory Centre of Science and Technology, University of Malaysia Sabah. A questionnaire was originally constructed to analyse the factors impacting mental health crises. Overall results found that the range of the mean from the questionnaire was 4.56 to 4.80, indicating that depression, anxiety, stress, and eating disorders are undoubtedly the influencing factors of mental health crises among foundation-level students. The majority of students scored much higher on the items under the stress factor (4.73) compared to the other three factors, which suggests that most of the students suffered from stress during the COVID-19 pandemic. This is followed by depression (4.69), anxiety (4.64) and eating disorders (4.63). Thus, universities and lecturers play a crucial role in reducing stress, depression, and anxiety associated with academic commitments.

Since its accreditation by MQA in 2018, the UMS Foundation in Science Program, through PPST has continuously produced high quality standards for students furthering their studies in UMS. Clinging to its accredited program means that quality assurance of foundation’s teaching and learning will be maintained and monitored. Herein, this study reports the feedback given by the foundation program’s alumni whose continue in undergraduate study of UMS Engineering. The objective is to investigate the students’ perception on knowledge and skills they obtained throughout the foundation year in relation to their courses study. A questionnaire with open ended question was constructed through a google form and designed in accordance with the MQA program review and monitoring guideline with 4 Likert scale (1- strongly disagree, 2- disagree, 3-agree and 4-strongly agree). Among 35 student’s replies, almost everyone answered high percentage of number 3 and 4, indicating an agreement towards knowledge and skills aspects. 100% student agree that the foundation program give them new knowledge and strengthen the basic theories in their engineering courses. Meanwhile, 94% believed that the foundation courses strengthen the personal skills, contribute to the achievement in engineering course and assist them to apply the skills obtained in foundation for lifelong learning. In open-ended questions, two themes of answers: (i) strength; (ii) hopes and expectations were organized, which highly in line with the students’ feedback in the Likert scale scores. This is in accordance with the student agreement that Foundation in Science Programme of UMS does provide basic knowledge and skills that relevant to the Engineering course in UMS. Further suggestions received and changes to the programme in the new academic year will be continuously monitored and reviewed for academic quality assurance.

This paper investigates students’ academic achievements through e-learning approaches based on students’ preference. E-learning approaches can be considered as asynchronous, synchronous and blended learning. Asynchronous learning happens when there is no set time for it to occur. Allows students to learn whenever and wherever they want, at their own pace. Web conferencing and chatting are used to deliver organised and time-bound activities characterise synchronous e-learning. Due to the Movement Control Order (MCO), which went into effect on March 18, 2020, lecturers at the Preparatory Centre for Science and Technology, Universiti Malaysia Sabah (PPST, UMS) could teach either synchronous or asynchronous classes. The independent sample t-test will be used, and the outcomes reveal statistically significant difference in the mean of both online learning strategies. Therefore, from the results we can conclude that students that prefer asynchronous learning approach improves the academic performance of students. However, higher end-of-course grades in asynchronous courses do not necessarily indicate that the asynchronous mode of instruction was more effective. This is because based on students’ preference for both methods that asked in questionnaires. The results also can be affected by various factors such as blended learning that is implemented for learning.

Pandemic COVID-19 has given a significant impact on the education culture whereby online learning has become the main way in teaching and learning activities. In line with the important use of online learning, it is very important to identify whether the students are ready enough for an online class or not. The study on ‘An Assessment of UMS Pre-University Students’ Readiness on Online Examination’ sought to answer the questions about students’ readiness on the preferred mode of learning and the student's readiness on an online examination. The respondents of the study were UMS Pre-University students who enrolled in the Foundation in Science program (AUMS) in the year 2020. Specifically, there was 105 male, and 153 female students were included in the study. Through Google form online survey, the questionnaires were sent through their email and WhatsApp group. Then, the data were gathered, collected, and analysed. Descriptive statistics analysis and item level mean related to students’ internet connectivity is provided in the form of percentage, mean and standard deviations. The study reveals that AUMS students’ internet connectivity was moderately ready for their online examinations, as it depends mainly on their location. If the students are staying in an urban areas, then their internet connections should be good and vice versa. This study also reveals that problems in Wi-Fi connection are the main concern of students’ accessibility to online learning.

Video-based learning has recently gained popularity as one of the numerous choices in remote education approaches during the pandemic that differs from conventional face-to-face lecture-based education. However, video-based learning and online teaching were different and quite challenging for students, especially when studying statistics and it is conducted remotely. Therefore, the purpose of this study is to assess student satisfaction with video-based learning on statistical topics. This study was conducted with 357 foundations of science students in the Preparatory Centre for Science and Technology, Universiti Malaysia Sabah. An independently developed questionnaire focusing on four factors, namely teaching, learning, technology, and skills, was given to students, and the results were then analysed. Based on the questionnaire the range of the mean is 4.54 – 4.87, which suggested that the video-based learning on statistical topics had an overall positive outcome on student satisfaction. Mostly, students scored higher mean scores for the items under the factor of teaching than for the items under the other three factors. This study also discovered that video-based learning relied on teaching, learning, and skills rather than the technology itself. However, technology continues to be a critical platform for educators’ educational activities. Therefore, this study demonstrated the critical significance of meeting the rising demand for online education, particularly video-based learning, while still providing a compelling student learning experience.

The aim of the study was to find out how students preferred using asynchronous and synchronous e-learning tools. Asynchronous learning occurs when there is no predetermined time for it to take place. Learners can learn whenever and wherever they want, and they can take their time to learn what they need to know. Synchronous e-learning is characterized by structured and time-bound activities delivered via web conferencing and chatting. At the Preparatory Centre for Science and Technology, Universiti Malaysia Sabah (PCST, UMS) lecturers could conduct synchronous or asynchronous due to MCO which was enforced on March 18, 2020. As a result, this study was done to examine the impact of several learning styles on foundation UMS students during the COVID-19 crisis, including synchronous and asynchronous. The quantitative data analysis of research will be presented in this study. Microsoft Excel was used for data analysis. The male and female students' opinions were compared using an independent sample t-test. Additionally, the responses of students to various aspects of e-learning were represented using descriptive statistics. The findings found a significant difference in students' perceptions of the efficacy of asynchronous e-learning activities. Female students’ responses show that they found asynchronous is more effective than male students at the foundation education level. Students were found to have a greater interest in asynchronous and blended learning activities.

To cultivate STEM interest amongst high school students, a STEM carnival organized by PPST, UMS was conducted through Junior Innovator Competition (JIC) and Young Scientist Sci-Show Competition (YSSC). The competitions were participated by high school students from Sabah, Sarawak, and F.T Labuan. JIC requires a poster presentation for prototype innovation while YSSC requires a short video presentation about science knowledge learned in the school syllabus. The objective was to gauge JIC and YSSC effectiveness in students’ interest in STEM. A three-star rating Malaysia Research Assessment Instrument (MyRA) questionnaire resulted in 80% total feedback (Q1-Q6), as a principal in MyRA. The result indicated knowledge, attitude, skills, and aspiration (KASA) transformation amongst the students. Three main aspects were summarized from Q1-Q6: learning (Q1 & Q2), interpersonal skill (Q3 & Q4), and STEM interest (Q5 & Q6). Around 82% of students agreed that JIC and YSSC were able to increase their knowledge, interpersonal skills around 81%, as well as 84 - 88% in critical thinking. While 83.7% - 85.3% of students satisfactorily increased their STEM interest and 85% were willing to participate in future competitions. Interestingly, some ex-participants entered the PPST for Foundation in Science. Indeed, competitions such as JIC and YSSC have demonstrated an inclining STEM interest effect toward high school students.

Cardiovascular disease (CVD) is the number one killer among women in Malaysia and globally, with over two million deaths each year. In this study, two modelling approaches namely Bayesian approach and frequentist approach were considered to identify associated risk factors in CVD among female patients presenting with ST Elevation Myocardial Infarction (STEMI) and to obtain feasible model to fit the data. Comparisons were made to find the best model. A total of 874 STEMI female patients from 18 participating hospitals across Malaysia in the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Univariate and multivariate analysis were performed for both Bayesian and frequentist approaches. Six variables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killip class and age group were found to be significant at the multivariate analysis. The standard errors obtained from the Bayesian approach were much smaller than the frequentist approach. Also, the model fit using Bayesian approach was much better than the frequentist as the deviance value produced by the Bayesian approach was smaller. The Bayesian analysis provides a better alternative to the frequentist approach in the analysis of the risk factors associated with mortality among female CVD patients.

Background: Identifying risk factors associated with mortality is important in providing better prognosis to patients. Consistent with that, Bayesian approach offers a great advantage where it rests on the assumption that all model parameters are random quantities and hence can incorporate prior knowledge. Therefore, we aimed to develop a reliable model to identify risk factors associated with mortality among ST-Elevation Myocardial Infarction (STEMI) male patients using Bayesian approach. Methods: A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry for the years 2006-2013 were enrolled. In the development of univariate and multivariate logistic regression model for the STEMI patients, Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied. The performance of the model was assessed through convergence diagnostics, overall model fit, model calibration and discrimination. Results: A set of six risk factors for cardiovascular death among STEMI male patients were identified from the Bayesian multivariate logistic model namely age, diabetes mellitus, family history of CVD, Killip class, chronic lung disease and renal disease respectively. Overall model fit, model calibration and discrimination were considered good for the proposed model. Conclusion: Bayesian risk prediction model for CVD male patients identified six risk factors associated with mortality. Among the highest risks were Killip class (OR=18.0), renal disease (2.46) and age group (OR=2.43) respectively.

Cardiovascular disease (CVD) includes coronary heart disease, cerebrovasculardisease (stroke), peripheral artery disease, and atherosclerosis of the aorta. All femalesface the threat of CVD. But becoming aware of symptoms and signs is a great challengesince most adults at increased risk of cardiovascular disease (CVD) have no symptoms orobvious signs especially in females. The symptoms may be identified by the assessmentof their risk factors. The Bayesian approach is a specific way in dealing with this kindof problem by formalizing a priori beliefs and of combining them with the available ob-servations. This study aimed to identify associated risk factors in CVD among femalepatients presenting with ST Elevation Myocardial Infarction (STEMI) using Bayesian lo-gistic regression and obtain a feasible model to describe the data. A total of 874 STEMIfemale patients in the National Cardiovascular Disease Database-Acute Coronary Syn-drome (NCVD-ACS) registry year 2006-2013 were analysed. Bayesian Markov ChainMonte Carlo (MCMC) simulation approach was applied in the univariate and multivariateanalysis. Model performance was assessed through the model calibration and discrimina-tion. The final multivariate model of STEMI female patients consisted of six significantvariables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killipclass and age group. Females aged 65 years and above have higher incidence of CVD andmortality is high among female patients with Killip class IV. Also, renal disease was astrong predictor of CVD mortality. Besides, performance measures for the model wasconsidered good. Bayesian logistic regression model provided a better understanding onthe associated risk factors of CVD for female patients which may help tailor preventionor treatment plans more effectively.

Cardiovascular disease is the leading cause of death in Malaysia and globally. This study aimed to identify associated risk factors in cardiovascular disease among ST elevation myocardial infarction (STEMI) male patients and obtain a feasible model to describe the data. A total of 16,673 STEMI male patients from 18 participating hospitals across Malaysia in the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Univariate analysis was conducted. Significant variables from the univariate analysis were further analysed by a multivariate logistic analysis to identify the prognostic factors. The most prevalent risk factor for male patients was smoking (79.3%), followed by hypertension (54.9%) and diabetes mellitus (40.4%). At univariate level, this study is consistent with the findings from the Malaysian National Health and Morbidity Survey (NHMS) where smoking is a significant risk factor. After adjustment in multivariate logistic model, the risk factors for cardiovascular death among male patients are related to age, premorbid condition such as diabetes mellitus, hypertension, family history of CVD, Killip class, type of treatment such as percutaneous coronary intervention (PCI) and relevant comorbidity such as renal disease. Drastic efforts in the management of all risk factors in males is needed to improve adherence outcomes.

Coronary artery disease (CAD) continues to be one of the leading causes of morbidity and mortality globally. Of particular relevance for this issue is that major efforts should be focused on understanding the risk factor involved. In this study, three types of Bayesian models, each with different prior distribution were considered to identify associated risk factors in CAD among Malaysian male patients presenting with ST-Elevation Myocardial Infarction (STEMI) and to obtain a feasible model to fit the data. The results of the three models were compared to find the best model. A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Univariate and multivariate analyses for the three models were performed using one of the Bayesian Markov Chain Monte Carlo (MCMC) simulation approach known as Gibbs sampling. Models’ performances are evaluated through overall model fit. Bayesian model C which used both Beta and Dirichlet prior distributions, consisted of six significant variables namely diabetes mellitus, family history of cardiovascular disease, chronic lung disease, renal disease, Killip class and age group was considered as the best model. The same set of variables that were observed to be significant in the Bayesian model C was also found to be significant in models A and B which used single prior distribution, respectively. Model C has a better fit than models A and B as the deviance value produced was the smallest. This study showed that posterior estimation was mostly influenced by the existing prior knowledge. Though applying the non-informative prior which were both Beta and Dirichlet distribution priors, model C can minimise uncertainty in making effective clinical decisions and provides better parameters estimates of the posterior distribution.

A new compression algorithm used to ensure a modified Baptista symmetric cryptosystem which is based on a chaotic dynamical system to be applicable is proposed. The Baptista symmetric cryptosystem able to produce various ciphers responding to the same message input. This modified Baptista type cryptosystem suffers from message expansion that goes against the conventional methodology of a symmetric cryptosystem. A new lossless data compression algorithm based on theideas from the Huffman coding for data transmission is proposed.This new compression mechanism does not face the problem of mapping elements from a domain which is much larger than its range.Our new algorithm circumvent this problem via a pre-defined codeword list. The purposed algorithm has fast encoding and decoding mechanism and proven analytically to be a lossless data compression technique.

Cervical cancer is the fourth most common cancer affecting women worldwide, after breast, colorectal, and lung cancers with 528 000 new cases every year. It is also the fourth most common cause of cancer death with 266 000 deaths in 2012 among women worldwide. In Malaysia it remains to be a great concern among clinicians; yet published works on survival of cervical cancer patients are somewhat limited. In this study, two survival regression models which are parametric Stratified Weibull model and Weibull Accelerated Failure Time (AFT) model are considered as the alternative and improvement of the well-known Cox proportional hazard model to evaluate the prognostic factor that effect on survival of patients with cervical cancer. Comparisons were made to find the best model. Data were taken from Hospital University Science Malaysia (HUSM) over a period of 12 years. From the analyses it was found that the AFT model was the most appropriate. The AFT model has shown that the median survival time for patient at stage III & IV (14 months) is about one third that of those at stages I & II (40 months) for the same distant metastasis group. While, the median survival time for patient with distant metastasis (17 months) is half that of those without distant metastasis (34 months) for the same stage group.

In this study, sixth-order boundary value problems for linear and nonlinear differential equations have been solved by using Differential Transformation Method (DTM). The numerical solutions are given in several examples. For each example, the solution given by DTM is compared with the exact solution. Absolute relative error (ARE) for each iteration can be computed. Therefore, the maximum absolute relative error (MARE) of the DTM can be obtained. To show that the solution given by the DTM has higher level of accuracy, the absolute relative error of the DTM has been compared with the other methods such as Adomian decomposition method with Green’s function, modified decomposition method (MDM), homotopy perturbation method (HPM), Variational Iteration Method (VIM) and Quintic B-Spline Collocation Method. Comparison graphs are given at the end of this paper. The obtained result shows that the proposed method is able to provide better approximation in term of accuracy.