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Preparing course timetables for universities is a search problem with many constraints. Exhaustive search techniques in theory can be used to develop course timetables for academic departments, but unfortunately these techniques are computation intensive, since the search space is very large and therefore are impractical. In this paper, Genetic Algorithms (GA’s) are utilized to build an automated course timetable system. The system is designed for any academic department. The proposed timetabling system requires minimal effort from the administration staff to prepare the course timetable. Moreover, the prepared course timetable considers faculties’ desires, students' needs and available resources, such as classrooms and laboratories with optimal utilization. The proposed timetabling process was divided into three stages. The first stage is the data collection stage. In this stage, the administrative staff; usually the head of the department, is responsible for preparing the required data, such as the names of the faculty personnel and their desires of courses and laboratories ordered with some priority scheme. Number and type of theoretical and practical courses are also fed to the system based on some statistics about student numbers and previous course timetable history. The system is also fed with number of lecture rooms allocated for the department and number of labs with information about theoretical courses they are able to serve. In the second stage, the program generates an initial set of suggested schedules (chromosomes). Each chromosome represents a solution to the problem, but usually is not satisfactory. Finally, the proposed timetabling system starts the search for a good solution that satisfies best interests of the department according to a cost function. GA is applied in search for a satisfactory course timetable based on a pre-defined criterion. The system has been developed and tested utilizing benchmarked datasets developed by an international timetabling competition (ITC2007) and for the Computer Engineering Department at Yarmouk University. In both cases, the algorithm showed very satisfactory results.

It is unclear whether screening for sepsis using an electronic alert in hospitalized ward patients improves outcomes. The objective of the Stepped-wedge Cluster Randomized Trial of Electronic Early Notification of Sepsis in Hospitalized Ward Patients (SCREEN) trial is to evaluate whether an electronic screening for sepsis compared to no screening among hospitalized ward patients reduces all-cause 90-day in-hospital mortality. This study is designed as a stepped-wedge cluster randomized trial in which the unit of randomization or cluster is the hospital ward. An electronic alert for sepsis was developed in the electronic medical record (EMR), with the feature of being active (visible to treating team) or masked (inactive in EMR frontend for the treating team but active in the backend of the EMR). Forty-five clusters in 5 hospitals are randomized into 9 sequences of 5 clusters each to receive the intervention (active alert) over 10 periods, 2 months each, the first being the baseline period. Data are extracted from EMR and are compared between the intervention (active alert) and control group (masked alert). During the study period, some of the hospital wards were allocated to manage patients with COVID-19. The primary outcome of all-cause hospital mortality by day 90 will be compared using a generalized linear mixed model with a binary distribution and a log-link function to estimate the relative risk as a measure of effect. We will include two levels of random effects to account for nested clustering within wards and periods and two levels of fixed effects: hospitals and COVID-19 ward status in addition to the intervention. Results will be expressed as relative risk with a 95% confidence interval. The SCREEN trial provides an opportunity for a novel trial design and analysis of routinely collected and entered data to evaluate the effectiveness of an intervention (alert) for a common medical problem (sepsis in ward patients). In this statistical analysis plan, we outline details of the planned analyses in advance of trial completion. Prior specification of the statistical methods and outcome analysis will facilitate unbiased analyses of these important clinical data.