Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors

The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.Scopu

Investigating Determinants of Student Satisfaction in the First Year of College in a Public University in the State of Qatar

Purpose. A first-year student's life is a web of interrelated academic and social experiences. Most universities have rigorous processes to achieve excellence or reach high-quality standards, with "Student Satisfaction" being the central focus of all of higher education aims for excellence. This study examined the influence of various academic, social, and environmental aspects on the overall satisfaction of first-year students. Design. A questionnaire was designed and administered to first-year students, and the resulting data were analyzed using correlation, linear regression, binary logistic regression, and artificial neural networks. Findings. The findings suggested that three of the five factors explored-100-level course satisfaction, a sense of belonging, and citizenship knowledge and skills-were the best determinants of the level of first-year student satisfaction. Originality. This study examined the influence of academic, social, and environmental factors on overall student satisfaction with the college experience. Many studies have focused on how factors such as student attitudes, perceptions, and academic and social engagements impact first-year student success and retention; however, few studies have attempted to explore the influence these factors have on student satisfaction and their overall perceptions of the college experience. Discussion and Conclusion. This study has provided a snapshot of some of the key determinants of the overall student satisfaction of the first-year experience. This study can assist college administrators and instructors in their quality assurance initiatives which may include reviewing the current system, setting college priorities, and planning and allocation of future resources to better achieve higher levels of student satisfaction. - 2018 Bothaina Al-Sheeb et al.Scopu

Burden and risk factors for Pseudomonas aeruginosa community-acquired pneumonia:a Multinational Point Prevalence Study of Hospitalised Patients

Pseudornonas aeruginosa is a challenging bacterium to treat due to its intrinsic resistance to the antibiotics used most frequently in patients with community-acquired pneumonia (CAP). Data about the global burden and risk factors associated with P. aeruginosa-CAP are limited. We assessed the multinational burden and specific risk factors associated with P. aeruginosa-CAP. We enrolled 3193 patients in 54 countries with confirmed diagnosis of CAP who underwent microbiological testing at admission. Prevalence was calculated according to the identification of P. aeruginosa. Logistic regression analysis was used to identify risk factors for antibiotic-susceptible and antibiotic-resistant P. aeruginosa-CAP. The prevalence of P. aeruginosa and antibiotic-resistant P. aeruginosa-CAP was 4.2% and 2.0%, respectively. The rate of P. aeruginosa CAP in patients with prior infection/colonisation due to P. aeruginosa and at least one of the three independently associated chronic lung diseases (i.e. tracheostomy, bronchiectasis and/or very severe chronic obstructive pulmonary disease) was 67%. In contrast, the rate of P. aeruginosa-CAP was 2% in patients without prior P. aeruginosa infection/colonisation and none of the selected chronic lung diseases. The multinational prevalence of P. aeruginosa-CAP is low. The risk factors identified in this study may guide healthcare professionals in deciding empirical antibiotic coverage for CAP patients

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Usage of Non-Linear Regression for Modeling the Behavior of Motor Vehicle Crash Fatality (MVF) Rate

Data analysis for vehicular crash counts is essential for transportation and traffic management systems (TTMS) to develop practical and innovative road safety interventions. The crash trend analysis, in particular, is the most popular technique for extracting an underlying trend or pattern of behavior in crash data. The recent years have seen a growing concern in the State of Qatar of the consequences of motor vehicle crashes (MVCs) and their associated fatalities (MVFs) on the economy, society, and the performance of the whole road network. This paper reports on the results of using nonlinear regression for crash trend analysis highlighting the substantial enhancement of road safety level in the State of Qatar during the period between 2003 and 2015. One of the critical findings of the study is the notable decline in the increasing tendency of both the MVF/100,000 population and the MVF/100,000 car over the last thirteen years in the State of Qatar. The matter that makes this finding worthy of comment is that it occurs over the period in which the State of Qatar is witnessing a significant growth in the population density and traffic volume. Several valuable contributions and recommendations were drawn and reported. � IEOM Society International.ATTADAMOUNE MICRO - FINANCE;EATON Powering Business world wide;informs;LINDO SYSTEMS INC;SIEMENSScopu

Sonographic evaluation of fetal abdominal circumference and cerebroplacental Doppler indices for the prediction of fetal macrosomia in full term pregnant women. Cohort study

Objectives: The aim of the study was to explore the relationship between cerebroplacental Doppler ratio and birth weight in cases of suspected fetal macrosomia. Methods: The pulsatility indices of the umbilical (UA-PI) and middle cerebral (MCA-PI) arteries, the cerebroplacental pulsatility index ratio (CPR) and the estimated fetal weight (EFW) were obtained in a cohort of 150 ultrasound-dated pregnancies at ⩾ 37 weeks’ gestation divided into two groups as follows; large for gestational age (LGA, n = 50) and average for gestational age (AGA, n = 100). Results: There is a significant difference between groups in abdominal circumference (AC), head circumference (HC), biparital diameter (BPD), estimated fetal weight (EFW) and actual fetal weight with a mean difference of 92.7 g in the LGA group and 84 g in the AGA group. MCA-RI and PI were significantly lower in the LGA group with no difference in UA-RI, PI and CPR-PI between both groups. Conclusions: CPR-PI could not differentiate between LGA and AGA

Penalized Conway-Maxwell-Poisson regression for modelling dispersed discrete data: The case study of motor vehicle crash frequency

Statistical modelling of road crashes has been of extreme interest to researchers over the last decades. Such models are necessary for the investigation of the opportunities for road safety improvement. The motor vehicle crash frequency (MVC-F) is probably the most important count of road crashes. In practice, like many of other discrete variables, this count is often diagnosed with over- or underdispersion, i.e. the variance is greater or less than the mean. The traditional regression models, especially those based on the Poisson distribution, are inefficient in modelling dispersed count data. On the contrary, the Conway-Maxwell-Poisson (COM-Poisson) distribution has been proven powerful in modelling count data with a wide range of dispersion. In crash data modelling, many situations may give rise to collinearity between contributory crash factors. Under this situation, the maximum likelihood estimates of the coefficients of the COM-Poisson GLM become increasingly unreliable as the collinearity among the model predictors increases. This paper addresses this issue and proposes a penalized likelihood scheme to be used with the COM-Poisson GLM regression for improving its prediction performance. For better GLM regression output, we suggest implementing the penalized COM-Poisson GLM regression under a K- fold cross-validation framework. A real-world crash example is provided, showing the performance of the penalized COM-Poisson GLM regression compared to the Poisson and the classical COM-Poisson GLM regressions. - 2019 Elsevier LtdScopu

Control charts for variability monitoring in high-dimensional processes

Monitoring process variability is associated with detecting changes in the covariance matrix of a multivariate normal process. Most monitoring methods estimate the sample covariance matrix and compare it with the in-control covariance matrix that is mostly priori known based on the sufficient historical data. However, when the sample size is smaller than the number of variables, the sample covariance matrix is not applicable to estimate the covariance matrix, since the matrix may not be positive semi-definite. In this paper, we propose a new control chart for monitoring changes in the covariance matrix when the sample size is smaller than the number of variables. The proposed chart is based on the ridge penalized likelihood ratio. It detects general changes, without sparsity assumption, in the covariance matrix efficiently when the sample size is small, while other existing penalized likelihood-based methods are expected to detect only sparse changes in the covariance matrix. The superiority of the proposed chart is demonstrated through an average run length performance to variety in shift patterns. The proposed chart also maintains a low computational complexity. These differentiated properties of the proposed chart were proved through numerous simulation studies and in a real example from the semiconductor industry.This publication was made possible by the NPRP award [ NPRP 05-563-2-142 ] and [ NPRP-7-1040-2-393 ] from the Qatar National Research Fund (a member of The Qatar Foundation).Scopu

A Real Case-Based Study Exploring Influence of Human Age and Gender on Drivers’ Behavior and Traffic Safety

The demand for understanding the functional relationship between changes in traffic safety level and human-related factors has increased remarkably due to a steady increase in population and traffic volume. Motor vehicle crashes can cause a serious impact on the victims, families, and the community resulting in instability of family and large socio-economic costs to society and healthcare service. Statistical studies of drivers’ behavior as a function of some of the human characteristics, in particular, have become increasingly important for establishing an evidence-based framework through which transportation and healthcare authorities can develop innovative solutions to mitigate serious consequences of risky driving. This paper uses a real-world dataset to analyze the relationship between the human characteristics and both the behavior and risk-level. In addition to that, this paper conducts several analytical studies to investigate the extent to which the risky driving behavior may affect the severity of the crash and human injury.Scopu

An adaptive thresholding-based process variability monitoring

In high-dimensional processes, monitoring process variability is considerably difficult due to the large number of variables and the limited number of samples. Monitoring changes in the covariance matrix of a multivariate process is often used for monitoring process variability under the assumption that only a few elements in the covariance matrix are changed simultaneously from the in-control values. The existing LASSO-based covariance monitoring charts in the high-dimensional settings provide good performance in detecting some shift patterns depending on the prespecified tuning parameter. In practice, control charts that perform reasonably well over various shift patterns are desired when shift patterns are unknown. In this article, we propose a control chart based on an adaptive LASSO-thresholding for monitoring changes in the covariance matrix. The performance of the proposed chart, which is called the ALT-norm chart, is evaluated for various shift patterns and compared with the existing penalized likelihood-based methods. The results show the effectiveness of the proposed chart. Finally, we illustrate the advantages of the ALT-norm chart through simulated and real data from both the semiconductor industry and a high-dimensional milling process. - 2019 American Society for Quality.This publication was made possible by the NPRP award [NPRP 05-563-2-142] and [NPRP-7 - 1040 - 2 - 393] from the Qatar National Research Fund (a member of The Qatar Foundation).Scopu