System Analysis and Design is an active field that demonstrates continuously new techniques and approaches to develop systems more effectively and efficiently. Teaching System Analysis and Design courses are thus quite challenging fo r many reasons, such as rapid changes in the environment, changes in the nature of technology, demands and needs of the industry and new market trends. These factors will have a direct effect on the students learning and their effectiveness in the place of work. Th is paper examines this trend and considers Problem Based Learning as the best methodology to respond to them. It will imp rove the students understanding of the concepts, ideas and thus enhance their learning experience. This paper also examines the level of achievements in the course learning outcomes as outlined by Accreditation Board for Engineering and Technology.

Rhesus (Rh) isoimmunization commonly presents with anemia and jaundice of varying intensity in the early postnatal period and is usually treated with phototherapy and exchange transfusion. Rarely, babies with mild or no symptoms at birth may present later with severe hemolytic anemia. This report describes a newborn infant with no postnatal jaundice who presented during the second week of life with severe anemia. These findings indicate the importance of regular follow-up and close monitoring of Rh-isoimmunized infants during the first two months of life for delayed onset anemia.

Objective: To determine the frequency of electrolyte disorders, i.e., serum sodium and potassium and to evaluate its effect on mortality rate among children admitted at the pediatric intensive care unit. Study Design and Setting: This was a descriptive cross sectional study conducted at Pediatric Intensive care unit of Civil Hospital Karachi from April to December 2017 Methodology: Informed consent was obtained from 150 parents of the children who fulfill the inclusion criteria. Laboratory data (serum sodium and serum potassium) were recorded during the stay in the pediatric intensive care unit. Data was analyzed using SPSS version 20. Mean ± S.D was calculated for quantitative variables. Frequency and percentage were calculated for gender, electrolyte disorders and mortality. Effect modifiers were controlled by stratification of age, gender and electrolyte disorders (Hypernatremia, Hyponatremia, Hyperkalemia, and Hypokalemia). Post-stratification, Chi-squared test was applied. P-value = 0.05 was taken as significant. Results: Out of 150 patients, electrolyte disorders in terms of serum sodium and potassium, were found in 86(57.3%) children. Mortality in children with electrolyte disorders was found to be 46(53.5%) which was significantly higher (P<0.001) than patients without electrolyte disorders 40(46.5%). Hypernatremia was found in 48(32%), hyponatremia 24(16%), hyperkalemia 21(14%) and hypokalemia in 42(28%) patients.In comparison; of electrolyte disorders with mortality; significant association was found in hypernatremia (P<0.001), and hyperkalemia (P<0.001). Conclusion: The most common electrolyte abnormalities were hypernatremia and hypokalemia. Mortality was significantly higher in subjects with electrolyte disorders, especially hypernatremia and hyperkalemia

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In this data world, where users spawn their digital footprint and generate a huge amount of unstructured data continuously with each activity, data mining techniques help in discovering interesting patterns, establishing relationships and unravel the problems through analysis, in different aspects of life. Educational data mining is a multidisciplinary research area, in which data from various educational organizations, is explored and made operational, for various facets concerned with the students, like predicting academic performance, analyse the learning pattern, solving e-learning issues, predict employability, visualize the critical courses affecting performance, investigate the reasons for student's failure or drop out and thus make data-driven decisions to improve the institutions standards. This paper provides a brief overview of Data Mining tools and techniques, and its encroachment in the educational domain. It also proposes a simple framework using different variables which helps in predicting student's academic success using two different algorithms: Decision Trees and Bayesian Network. Finally, a comparative analysis of accuracy is done. The results show that Bayesian Network outperforms the Decision Tress and gives better accuracy.