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Recent Submissions
Adherence to iron deficiency interventions among pregnant women attending antenatal clinics in Ubungo municipality, Dar es Salaam, Tanzania
(Bulletin of the National Research Centre, 2025-01-22) Glory Benjamin; Ezra J. Mrema; Nchang’wa Nhumba; Albert Burudi Wakoli; Hussein H. Mwanga
Abstract
Background
Iron deficiency anemia in pregnant women remains a public health concern despite iron deficiency interventions that have been implemented. This study investigated adherence to iron deficiency interventions and the associated factors among pregnant women attending antenatal clinics in Ubungo Municipality.
Methods
This cross-sectional study used a systematic random sampling technique to obtain 503 participants from the surveyed clinics. Interviews were conducted by using interviewer-administered questionnaires. Data were analyzed using Stata version 17. The study employed binary and multivariable logistic regression analysis to determine factors associated with adherence to iron deficiency interventions.
Results
In this study, 72% of participants were non-adherent and 28% were adherent to the interventions. In multivariable regression analysis, participants who forgot to take their iron tables on most days (AOR 2.35; 95% CI 1.23–4.48) and those who reported that not enough time was spent on education and counseling during antenatal clinic visits (AOR 3.87; 95% CI 1.08–13.84) were more likely to be non-adherent to iron deficiency interventions.
Conclusions
Majority of pregnant women in Ubungo Municipality were non-adherent to iron deficiency interventions. Non-adherence was associated with a tendency to forget taking iron tablets, and lack of enough time in providing health education and counseling. Improving the quality of health education and counseling could increase adherence to iron deficiency interventions and reduce maternal–child morbidity and mortality rates.
From the Classroom into Virtual Learning Environments: Essential Knowledge, Competences, Skills and Pedagogical Strategies for the 21st Century Teacher Education in Kenya
(2021-04-14) Catherine Adhiambo Amimo
As teachers in Kenya begin to migrate from the classroom to virtual learning
spaces following COVID 19 pandemic, there is pressing need to realign Teacher
Education to requisite Knowledge, competences, skills, and attitudes that will support
online teaching. This chapter explores these needs using a combination of lived
experiences and literature review that captured a meta-analysis of research trends
on e-learning. While trends in Teacher Education indicate progression towards
adoption of technology, there are disparities between the theory and practice.
Evidence from recent research and reports; and the recollected experiences confirmed
knowledge, competence, skills and pedagogical gaps in the implementation
of online learning, that have been exacerbated by COVID-19. The researcher recommends
that teacher education should sensitize and train teacher trainees on how to
access, analyze and use new knowledge emerging with technology; they also should
be coached on how learners learn with technology and on fundamentals of the communication
process. Particularly the course on educational technology, should focus
on how to create and manage online courses. The 5-stage E-Moderator Model and
Universal Design for Learning (UDL) are recommended as effective pedagogical
scaffold for online teaching.
Numerical Study of Shear Banding in Flows of Fluids Governed by the Rolie-Poly Two-Fluid Model via Stabilized Finite Volume Methods
(Processes | An Open Access Journal from MDPI, 2020-07-09) Jade Gesare Abuga; Tiri Chinyoka
The flow of viscoelastic fluids may, under certain conditions, exhibit shear-banding
characteristics that result fromtheir susceptibility to unusual flowinstabilities. In thiswork, we explore
both the existing shear banding mechanisms in the literature, namely; constitutive instabilities and
flow-induced inhomogeneities. Shear banding due to constitutive instabilities is modelled via either
the Johnson–Segalman or the Giesekus constitutive models. Shear banding due to flow-induced
inhomogeneities is modelled via the Rolie–Poly constitutive model. The Rolie–Poly constitutive
equation is especially chosen because it expresses, precisely, the shear rheometry of polymer solutions
for a large number of strain rates. For the Rolie–Poly approach, we use the two-fluid model wherein
the stress dynamics are coupled with concentration equations. We follow a computational analysis
approach via an efficient and versatile numerical algorithm. The numerical algorithm is based on the
Finite VolumeMethod (FVM) and it is implemented in the open-source software package, OpenFOAM.
The efficiency of our numerical algorithms is enhanced via two possible stabilization techniques,
namely; the Log-Conformation Reformulation (LCR) and the Discrete Elastic Viscous Stress Splitting
(DEVSS) methodologies. We demonstrate that our stabilized numerical algorithms accurately simulate
these complex (shear banded) flows of complex (viscoelastic) fluids. Verification of the shear-banding
results via both the Giesekus and Johnson-Segalman models show good agreement with existing
literature using the DEVSS technique. A comparison of the Rolie–Poly two-fluid model results with
existing literature for the concentration and velocity profiles is also in good agreement.
Mathematical Modelling and Simulation of Aluminium Filling in Conical Pipe and Cylindrical Pipe under High Pressure
(Physical Science & Biophysics Journal, 2022-08-10) Abuga JG
The desired technology for manufacturing light-weight components from metal alloys mostly aluminum and magnesium alloy
is Die casting. High pressure die casting requires the liquid metal to be forced at high speed and pressure through a metal pipe.
In our study, we seek to study Aluminum filling under high pressure in two different pipes, cylindrical and conical pipes. Two
cases are considered for the cylindrical pipe, when the pipe vertical and when the pipe in inclined at an angle of 450 with the
horizontal. The governing equations are obtained and the results are compared. The governing equations are obtained and
modeling is done using ANSYS FLUENT.
The results show that inclining the cylindrical pipe causes a shift in the oscillations and the inclined pipe has slightly lower
amplitude of oscillation implying a greater loss of energy due to the inclination. The inclined cylindrical pipe has higher
damping compared to the vertical cylindrical pipe. It is also evident that the conical pipe has higher oscillations than the
cylindrical pipe implying a greater loss of energy for the conical cylindrical.
FRAUD DETECTION IN BANKING USING MACHINE LEARNING
(The European Academic Journal (EAJ), 2024-03-28) Jade Gesare Abuga; Editah Hadassa Abuto; Roy Kuria
Financial institutions, particularly banks, have a challenge of fraud detection. Fraud poses a substantial financial risk to both institutions and their customers since fraudulent activities can result in significant monetary losses and erode customer trust. Recent research has shown that machine learning techniques can be used to detect fraud in the banking sector.
In this project, we applied logistic regression, random forest, K-Nearest Neighbours, and decision trees to detect fraudulent transactions to the problem of fraud detection in the banking industry. The dataset was obtained from Kaggle and has 31 variables. Logistic regression had the lowest performance metrics with an accuracy of 87.91% while the decision tree had the highest
performance metrics with an accuracy of 97.17%.