Background: Edible oil manufacturing is a labor-intensive sector with significanttechnological demands, where employees face various occupational hazards.The use of personal protective equipment (PPE) is not only a legal obligationbut also a key measure for safeguarding workers against job-related injuriesand health risks. Despite these challenges, this industry often remainsunder-researched and overlooked.Objective: To assess utilization of personal protective equipment and its keyfactors among workers in the WA edible oil factory in Debre Markos town,Ethiopia, in 2024.Methods: A cross-sectional study was conducted among employees of theWA Edible Oil Factory in Debre Markos. Using a simple random samplingmethod, 387 workers were selected to participate. Data were collectedthrough an interviewer-administered structured questionnaire, focusing on theuse of protective equipment, as well as socio-demographic, work-related,environmental, and organizational characteristics. The data were analyzed usingSPSS version 26. Logistic regression analysis was employed to identify factorsinfluencing the use of protective equipment, with the strength of associationsexpressed as odds ratios at a 95% confidence level.Results: Out of the total workforce, 214 individuals (55.3%) reportedusing personal protective equipment while on duty. The study identifiedseveral significant factors influencing personal protective equipment utilization,including receiving safety training, having access to protective equipment,regular occupational health and safety inspections, the presence of workplacesafety protocols, having three or more years of work experience, and abstainingfrom alcohol consumption and smoking.Conclusion: The utilization level of personal protective equipment amongworkers at the WA edible oil factory was found to be moderate when comparedto findings from other developing countries. Key factors influencing personalprotective equipment usage included access to safety training, availability ofprotective gear, workplace supervision, the presence of safety protocols, workexperience, and lifestyle behaviors such as alcohol and tobacco use. To improvepersonal protective equipment utilization, it is recommended to strengthenworkplace supervision, offer comprehensive safety training, and ensure theconsistent availability of safety guidelines.KEYWORDSutilization, personal protective equipment, edible oil factory, factory worker, Ethiopia

This study presents a novel approach for optimizing UAV (unmanned aerial vehicle) Multicircularflight control by developing a fractional order proportional integral derivative (FOPID)-based hybridEagle strategy particle swarm optimization ant lion optimizer (HESPSOALO). The proposed algorithmcombines the strengths of particle swarm optimization (PSO) and the ant lion optimizer (ALO), whichare enhanced by the Eagle strategy to systematically fine-tune the FOPID controller parameters.This hybrid optimization method aims to improve system stability, responsiveness, and disturbancerejection in UAVs, particularly in challenging dynamic flight conditions. The proposed approachwas validated against traditional control methods that utilize FOPID (Base), the Base HESPSOALOalgorithm, the FOPID-based HPSOGWO (Hybrid Particle Swarm Optimization-Gray Wolf Optimizer),and the FOPID-based HGWOALO (Hybrid Gray Wolf Optimization-Ant Lion Optimizer) with a setof benchmark functions used in the analysis. The results demonstrate a minimization of positionand angular errors, reduced oscillations, and overall improved control stability for the FOPID-basedHESPSOALO compared with the other methods. Furthermore, a multicriteria decision-making(MCDM) framework is applied to evaluate the overall performance of alternative control strategiesutilizing the CRiteria importance through intercriteria correlation (CRITIC) and technique of orderpreference by similarity to ideal solution (TOPSIS) techniques. The MCDM analysis demonstratesthat among the evaluated criteria, Kp has the highest importance, with a weight of 0.244019,whereas Kd is deemed the least significant, with a weight of 0.161023. The ranking results revealthat the HESPSOALO algorithm (Base) is the best-performing controller method, with a rankingscore of 0.571161, indicating its superior control performance across major metrics. In contrast, theFOPID + HPSOGWO controller method ranks the lowest, with a score of 0.282794. The findings havesignificant industrial implications, particularly in sectors where UAVs are critical for precision tasks,such as logistics, agriculture, surveillance, and environmental monitoring. By optimizing the FOPIDcontroller parameters, the HESPSOALO algorithm enhances UAV stability, responsiveness, andreliability in dynamic environments, resulting in more precise control and robust performance undervarying conditions. This improvement may reduce operational risks and maintenance costs whileincreasing efficiency, prolonging UAV service life, and achieving energy savings. This study provides arobust solution for UAV control based on the potential of hybrid optimization algorithms to improveUAV precision and reliability in autonomous flight.Keywords: UAV multicircular flight control, FOPID, Hybrid optimization, CRITIC, TOPSIS

Foodborne illnesses as a result of poor food handling practicespose a significant threat to public health. The main objective of thissystematic review and meta-analysis was to pool the level ofhygienic food handling practices among food handlers working inpublic food establishments in the Amhara region, Ethiopia. Aninclusive search of databases was undertaken usingPubMed/MEDLINE, SCOPUS, Web of Science, and GoogleScholar from the 1st of January 2014 to the 30th of December 2023.Data was collected, entered into Excel, and finally exported toStata V.17 for analysis. Eyeball testing using forest plots, CochraneQ test statistics and I2 had been used to identify and measure het-erogeneity. The pooled prevalence of hygienic food handling prac-tices was estimated using a random effects model. The pooledprevalence of hygienic food handling practices of food handlers inthe Amhara region was 48% [95% confidence interval (CI): (43%,53%)] with significant heterogeneity (I2=94.39%, p

Introduction Nurse burnout negatively impacts patient care quality, safety, and outcomes, while harming nurses’mental health, job satisfaction, and retention. It also imposes financial burdens on healthcare organizations throughabsenteeism, reduced productivity, and higher turnover costs, highlighting the need for research to address thesechallenges. The umbrella review methodology was selected to integrate evidence from multiple systematic reviewsand meta-analyses, offering a broad and in-depth summary of existing research to guide practice and policy. Thisapproach equips stakeholders with a holistic understanding of the multifaceted impacts of nurse burnout, facilitatingthe design of effective interventions that support nurses, enhance healthcare delivery, and optimize patientoutcomes. Consequently, this umbrella review aims to evaluate the global prevalence and contributing factors ofnurse burnout.Methods This umbrella review included 14 systematic reviews and meta-analyses identified from various databases.The quality of each study was assessed using the Assessment of Multiple Systematic Reviews (AMSTAR II). Data wereextracted using Microsoft Excel and analyzed with STATA 17.0. Heterogeneity was measured using Higgin’s I2 Statistics,and summary prevalence estimates were calculated with the Der Simonian-Laird random-effects model. Meta-regression and subgroup analyses were conducted to identify the source of high heterogeneity. Publication bias wasassessed using funnel plots and Egger’s regression test, with the former providing a visual assessment of bias and thelatter offering a statistical method to detect asymmetry.Results The global prevalence of nurse burnout was evaluated in three areas: emotional exhaustion (33.45%, 95%CI 27.31–39.59), depersonalization (25.0%, 95% CI 17.17-33.00), and low personal accomplishment (33.49%, 95% CI28.43–38.55). Emotional exhaustion was most common among nurses working during the COVID-19 pandemic(39.23%, 95% CI 16.22–94.68). Oncology nurses experienced the highest rate of depersonalization (42%, 95% CI16.71–77.30), while nurses in intensive care units reported the highest rate of low personal accomplishment (46.02%,95% CI 43.83–48.28).
Conclusions Nurse burnout is prevalent worldwide, often marked by a sense of low personal accomplishment.Several factors contribute to this issue, including role conflict, negative emotions, family problems, moral distress,stress, commuting distance, predictability of work tasks, and workplace advancement.Keywords Nurse, Burnout, Determinant factors, And umbrella review

Background Microcephaly and macrocephaly, which are abnormal congenital markers, are associated withdevelopmental and neurologic deficits. Hence, there is a medically imperative need to conduct ultrasound imagingearly on. However, resource-limited countries such as Ethiopia are confronted with inadequacies such that access totrained personnel and diagnostic machines inhibits the exact and continuous diagnosis from being met.Objective This study aims to develop a fetal head abnormality detection model from ultrasound images via deeplearning.Methods Data were collected from three Ethiopian healthcare facilities to increase model generalizability.The recruitment period for this study started on November 9, 2024, and ended on November 30, 2024. Severalpreprocessing techniques have been performed, such as augmentation, noise reduction, and normalization.SegNet, UNet, FCN, MobileNetV2, and EfficientNet-B0 were applied to segment and measure fetal head structuresusing ultrasound images. The measurements were classified as microcephaly, macrocephaly, or normal using WHOguidelines for gestational age, and then the model performance was compared with that of existing industry experts.The metrics used for evaluation included accuracy, precision, recall, the F1 score, and the Dice coefficient.Results This study was able to demonstrate the feasibility of using SegNet for automatic segmentation,measurement of abnormalities of the fetal head, and classification of macrocephaly and microcephaly, with anaccuracy of 98% and a Dice coefficient of 0.97. Compared with industry experts, the model achieved accuracies of92.5% and 91.2% for the BPD and HC measurements, respectively.Conclusion Deep learning models can enhance prenatal diagnosis workflows, especially in resource-constrainedsettings. Future work needs to be done on optimizing model performance, trying complex models, and expandingdatasets to improve generalizability. If these technologies are adopted, they can be used in prenatal care delivery.Clinical trial number Not applicable.Keywords Microcephaly, Macrocephaly, Congenital abnormality, HC, BPD

Introduction: The magnitude of poor sleep quality among people with asthma is widespread and has detrimental consequences, including a higher chance of having poor work performance, an increase in the frequency of asthma attacks, an increase in the need for overnight hospitalization, and a worse health related quality of life. However, it has not been well studied, especially in low-income countries like Ethiopia. This study's objective was to assess the degree of sleep quality and related factors among people with asthma who had follow-up visits at public hospitals in the East Gojjam Zone.
Methods: An institutional-based cross-sectional study design was conducted among 406 people with asthma through consecutive sampling techniques at public hospitals in East Gojjam Zone from June 6 to July 1, 2022. Sleep quality was measured by the Pittsburgh Sleep Quality Index through a face-to-face interview, and the collected data were entered into Epi Data version 4.4.2 and exported to SPSS version 25 for analysis. Logistic regression was fitted to assess the association between dependent and independent variables. Variables with a P-value

Background: Children on the streets are still vulnerable to early and unsafe sexual experiences. Having multiple sexual partners and the limited use of condoms were major risk factors for the spread of sexually transmitted diseases among youths in Ethiopia.
Objective: This study aimed to explore the risk factors of sexual and reproductive health problems, service utilization, and challenges among street youths in the East Gojjam Zone.

This paper provides an in-depth analysis and performance evaluation of four Solar Radiance (SR)prediction models. The prediction is ensured for a period ranging from a few hours to several days ofthe year. These models are derived from four machine learning methods, namely the Feed-forwardBack Propagation (FFBP) method, Convolutional Feed-forward Back Propagation (CFBP) method,Support Vector Regression (SVR), and the hybrid deep learning (DL) method, which combinesConvolutional Neural Networks and Long Short-Term Memory networks. This combination results inthe CNN-LSTM model. Additionally, statistical indicators use Mean Squared Error (MSE), Root MeanSquared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), andNormalized Root Mean Squared Error (nRMSE). Each indicator compares the predicted output by eachmodel above and the actual output, pre-recorded in the experimental trial. The experimental resultsconsistently show the power of the CNN-LSTM model compared to the remaining models in terms ofaccuracy and reliability. This is due to its lower error rate and higher detection coefficient (R2 = 0.99925).Keywords: Artificial neural networks, Convolutional neural network, Convolutional feed-forward backpropagation, Deep learning, Feed-forward back propagation, Long short-term memory, Solar radianceforecasting

Journalists usually struggle to maintain private affairs with their professional responsibili-ties while practising journalism. This article scrutinizes the interplay between journal-ists’ professional and individual exposures and its impacts on the culture of journalismpractice in the Ethiopian state media perspective of Amhara Media Corporation (AMC).Qualitative research method, along with in-depth interviews and document analysis,was used to collect data. Semi-structured questions were forwarded to twelve purpo-sively selected journalists working in AMC. Individual-level analysis of the Hierarchy ofInfluences Model (HIM) and Individual Level of Branding were applied as theoreticaltemplates. Findings reveal that journalists’ journalistic contents are exposed to numer-ous occupational-level perspectives. Individual backgrounds and professional dilemmasjournalists face are the dominant challenges while practising professional journalism.Journalists’ academic qualifications and upbringing do have strong linkages with theirculture of journalism practice. The interface between sensitive reporting on politics, ethnic-ity, religion and professionalism is seen resulted in eroding journalistic integrity and creat-ing professional dilemmas among journalists in Ethiopia. It is recommended that mediaorganizations have comprehensible working guidelines and editorial policies to alleviate
he blurred lines between individual exposures and the professionalism of journalists inEthiopia.Keywords: hierarchy of influences model, ideological branding, individual-level analysis,journalist background, sensitive reporting, political stance, professional dilemma

Flooding exacerbates health challenges by spreading waterborne diseases like diarrhea throughthe destruction of sanitation infrastructure and contamination of drinking water sources. However,evidence on the prevalence and contributing factors of diarrheal diseases among under-five childrenin the Dasenech district is limited. This study aimed to assess the prevalence of acute diarrhea andits determinants among under-five children in flood-affected areas of the South Ethiopia region. Acommunity-based cross-sectional study was conducted from July 1 to July 15, 2024, in flood-affectedareas of the Dasenech district, involving 696 under-five children. Five kebeles were purposivelyselected, followed by the proportional allocation of households, after which a systematic samplingtechnique was applied to identify study participants. Data were collected using a pretested andstructured questionnaire administered by trained interviewers. Multivariable logistic regressionanalysis was performed to identify factors associated with acute diarrhea, with statistical significanceset at p < 0.05 and a 95% confidence interval (CI). The prevalence of acute diarrhea was 31.6% (95%CI: 28.7–34.3%). Significant predictors of acute diarrhea included non-adherence to exclusivebreastfeeding (AOR: 2.14, 95% CI: 1.65–3.98), lack of latrines (AOR: 12.08, 95% CI: 9.77–13.13), unsafedisposal of child excreta (AOR: 3.86, 95% CI: 2.38–6.26), home delivery (AOR: 6.02, 95% CI: 5.53–8.82),and a recent history of diarrhea among mothers or caregivers (AOR: 3.14, 95% CI: 1.33–5.66). Acutediarrhea is highly prevalent among under-five children in the Dasenech district. The findings underscorethe need for targeted public health measures, such as improving waste management, promotingexclusive breastfeeding, constructing and utilizing latrines, and addressing maternal and caregiverhealth, to mitigate the burden of diarrheal diseases in this vulnerable population.Keywords Acute diarrhea, Dasenech district, Ethiopia, Flood-affected, Under-five childrenDiarrhea is defined as an increase in bowel movement frequency or a change in stool consistency, commonlyidentified as the passage of three or more loose or watery stools within a 24-h period1. This condition oftenresults from intestinal infections caused by bacteria, viruses, or parasites, which are closely associated withlimited access to safe water and inadequate sanitation facilities2. Inadequate water and sanitation contributeto over 94% of the four billion annual cases of diarrhea worldwide3,4. This condition claims approximately twomillion lives each year, representing 4% of global mortality. Alarmingly, 1.3 million of these deaths occur amongchildren annually5.