Perception of mathematics game’s design for primary school: based on teachers’ opinions

Unmistakable methods can be used for learning, and they can be looked at in a few viewpoints, particularly those identified with learning results. In this paper, we introduce an examination with a specific end goal to think about the design adequacy and development’s requirement of a game based learning (GBL) approach that is about to be used in LINUS screening for mathematics subject in primary school. The approach includes multiple interaction forms regarding addition and subtraction operation in mathematics based on LINUS constructs. Ten teachers from three different school located in Batu Pahat have participated in the study. The investigations involving survey activity by using questionnaire as the instrument. While breaking down the results, the outcomes demonstrated that the kids observed the amusement to be all the more fulfilling if there are less levels and more colours. Since the survey were conducted to a very common type of school in Malaysia, we believe game that is about to be built based on opinion gained could be utilized as an effective instrument in primary schools to strengthen pupils' lessons

Fuzzy logistic regression for detecting differential DNA methylation regions

“Epigenetics is the study of changes in gene activity or function that are not related to a change in the DNA sequence. DNA methylation is one of the main types of epigenetic modifications, that occur when a methyl chemical group attaches to a cytosine on the DNA sequence. Although the sequence does not change, the addition of a methyl group can change the way genes are expressed and produce different phenotypes. DNA methylation is involved in many biological processes and has important implications in the fields of biomedicine and agriculture. Statistical methods have been developed to compare DNA methylation at cytosine nucleotides between populations of interest (e.g., healthy and diseased) across the entire genome from next generation sequence (NGS) data. Testing for the differences between populations in DNA methylation at specific sites is often followed by an assessment of regional difference using post hoc aggregation procedures to group neighboring sites that are differentially methylated. Although site-level analysis can yield some useful information, there are advantages to testing for differential methylation across entire genomic regions. Examining genomic regions produces less noise, reduces the numbers of statistical tests, and has the potential to provide more informative results to biologists. In this research, several different types of logistic regression models are investigated to test for differentially methylated regions (DMRs). The focus of this work is on developing a fuzzy logistic regression model for DMR detection. Two other logistic regression methods (weighted average logistic regression and ordinal logistic regression) are also introduced as alternative approaches. The performance of these novel approaches are then compared with an existing logistic regression method (MAGIg) for region-level testing, using data simulated based on two (one plant, one human) real NGS methylation data sets”--Abstract, page iii

FUZZY LOGISTIC REGRESSION APPLICATION ON PREDICTIONS CORONARY HEART DISEASE

According to the World Health Organization (WHO) in 2015, 70% of cardiac deaths were caused by coronary heart disease (CHD). Based on WHO data in 2017, 17.5 million deaths were recorded, equivalent to 30% of the total deaths in the world caused by coronary heart disease. Coronary heart disease is a disorder of heart function caused by plaque that accumulates in arterial blood vessels so that it interferes with the supply of oxygen to the heart tissue. This causes reduced blood flow to the heart muscle and oxygen deficiency occurs. In more serious circumstances, it can result in a heart attack. Risk factors for coronary heart disease include age, gender, hypertension, cholesterol, heredity, diabetes mellitus, obesity, dyslipidemia, smoking and lack of physical activity. If a person's chances of suffering from coronary heart disease can be predicted early based on the existing risk factors, then the mortality rate of coronary heart disease can be suppressed. The objective of this study is to build a model that can predict the possibility of a patient suffering from coronary heart disease. The study used the Fuzzy Logistic Regression model. This model was used to maximize prediction results in which data size was limited. The least square method was used to estimate the value of the parameter. We obtained from National Cardiovascular Center Harapan Kita, Jakarta. Evaluation with the Mean Degree of Membership method showed that the model built was feasible and good enough to predict coronary heart disease. By using the confusion matrix, the accuracy of the prediction model is 80.00%, with a specificity of 42.85% and a sensitivity of 100%

Effect of Beloved Person’s Voice on Chest Tube Removal Pain in Patients undergoing Open Heart Surgery: Fuzzy Logistic Regression Model

Chest tube removal pain is one of the important complications after open heart surgery. The removal of a chest tube is a painful and frightening experience and should be managed with as little pain and distress as possible. The aim of this study is to assess the effect of beloved person’s voice on chest tube removal pain in patients undergoing open heart surgery. 128 patients were randomly assigned to two groups: one group listened to beloved person’s voice during the procedure, and the other did not. Since pain was measured by linguistic terms, a fuzzy logistic regression was applied for modeling. After controlling for the potential confounders, based on fuzzy logistic regression, the beloved person’s voice reduced the risk of pain. Therefore, using beloved person’s voice could be effective, inexpensive and safe for distraction and reduction of pain

A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income

Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio