The study was conducted on female students who were 2005, 2006, 2007, and 2008 entries in the fields of Natural Science, Agriculture, and Social Science. From 1931 female students a sample of 605 was taken using stratified random sampling, Primary and secondary data were collected using questionnaire and analyzed using the Bayesian logistic regression analysis. The results showed that the percentage of graduation among 362 females who were enrolled in 2005, 2006, and 2007 was 72.1%. Similarly the retention rate among 243 females of 2008 entry was 75.7%. From the Bayesian logistic regression analyses, significant predicators of both graduation and retention were choice of field, preparatory average result, entrance exam score and first year cumulative GPA. Moreover pregnancy, organizing studying and leisure time, habit of chewing Khat, satisfaction with instructors, parent income, habit of smoking cigarette and using drugs, and feel safe to study at night in classrooms appeared as significant predictors of retention. The graduation rate and retention rate for the students who assigned to the field they did not choose were lower than that for those assigned to the field they chose. Those with first year CGPA less than 2.0 were having lower rates of graduation and retention than those having greater than 2.0. The graduation and retention rates for the students having higher preparatory average result and higher entrance exam score were higher than that for those having lower. The students having parents' income less than 500 were less likely to retain than those having parents' income greater than 1500. The retention rate for the students who were not satisfied with their instructors was lower than those were satisfied. The students who cannot organize their study and leisure time easily were less likely to retain than those can organize. In conclusion, the factors those mainly affect female students' graduation and retention were more of academic variables; hence we recommend that assigning to the field they choose by their interest may help female students' graduation and retention. The teaching method at secondary and preparatory schools should be designed to challenge and motivate them to adequately prepare them for Higher Education Institutions. Moreover, campus and Department administrators in collaboration with the students themselves and academic staff need to work hard to bring change in behavior, academics, and social aspects of female students at the University.
The objective of this study was to identify patterns of cough events for COPD patients. Simultaneously, the study was used to develop a Matlab based graphical user interface (GUI) that enables the user to analyze time-stamps of cough data. The time stamp data was received from Philips Research. They cover 17 data sets of 16 COPD patients and were determined using a semi-automated cough detection algorithm. Cough detection ran for multiple days in the living and bed rooms of the patients. The time stamp marks the event that a cough is assumed to occur. A descriptive statistics and a Markov Chain Model was used for analysis. A pattern of cough events was described by the probability that a COPD patient is in one of three possible states at a specific hour and in another state at the next hour. To define the states, the following three characteristics were used: 1) relative frequency, 2) average value-three times standard deviation band, 3) average value-three times inter-quartile range band. Relaxation time was determined to describe the dynamics of the cough event patterns. To be precise, pattern changes were characterized by considering the time it takes for the probabilities to reach stationarity. To reduce noise, the daily dynamics of the cough events over five day periods with a four day overlap were considered. From the results, we concluded that the distribution of cough events for all data sets was skewed to the right. The developed Matlab based graphical user interface allows the user to analyze the cough events of COPD patients together with their medical history. We conclude that the relaxation time and the stationary distribution of the Markov chain representation were typical characteristics of the patterns of cough events and the cough behavior of COPD patients was patient specific and varies over time.
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