Woldia University

Parental involvement and emotional intelligence are widely recognized as important influences on academic success, yet their roles may vary across cultural and educational contexts. In Ethiopia, limited empirical work has examined how family engagement and emotional intelligence shape secondary school students’ performance. This study investigates the contributions of parental involvement and emotional intelligence to academic achievement at Woldia Millennium Secondary School, while also exploring potential gender differences. Using a quantitative correlational design, data were collected from 300 students through validated self-report questionnaires and academic records. Pearson correlation and hierarchical regression analyses examined the relationships and predictive contributions of parental involvement and emotional intelligence. The findings indicate that parental involvement is a strong positive predictor of academic achievement, emphasizing the importance of active family engagement. Emotional intelligence showed a negative association with achievement, while gender differences emerged: girls demonstrated higher academic performance and parental engagement, and boys scored higher in emotional intelligence. These findings underscore the central role of parents in supporting students’ learning and highlight the need for culturally sensitive emotional intelligence initiatives and gender-responsive educational strategies. They provide actionable guidance for teachers, school leaders, parents, and policymakers to enhance student outcomes in secondary education. • Parental involvement is a strong positive predictor of academic achievement among secondary school students in Ethiopia. • Emotional intelligence shows a context-dependent negative association with academic performance. • Female students exhibit higher academic achievement and parental engagement, while males score higher in emotional intelligence. • Combined parental support and emotional intelligence explain 23.9% of the variance in academic achievement. • Findings offer actionable guidance for teachers, parents, and policymakers on gender-responsive, culturally sensitive educational strategies.

Iron-Folic Acid (IFA) is a crucial process indicator, that helps to halt the burden of iron deficiency anemia worldwide, especially in low and middle income countries like Ethiopia. Iron-folic acid supplementation is a crucial method in Ethiopia to reduce maternal deaths from anemia and poor birth outcomes. Despite these initiatives, there is still extremely little adherence to IFA consumption for the 90-day or more WHO-recommended timeframe. Therefore, the aim of this study was to use the DHS 2016 information to create a predictive model for IFA consumption and figure out the key factors influencing it among Ethiopian pregnant women. A secondary data analysis of Ethiopian DHS from 2016 was performed. A total weight of sample of 3109 women (15–49) was included in this study. Data were extracted using STATA version 17 software, and imported to a Jupyter notebook for further analysis. Machin learning algorithm using different classification models were implemented. All analysis and calculation were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, xgboost, and shap packages. Random Forest demonstrated the best performance with accuracy of 92.93%, precision of 91.32%, recall of 94.77%, F1-score of 93.99% and AUC 94%. Region, distance to health facility, age at first marriage, household head, number of ANC visit, media exposure, community literacy, husband occupation, gravidity, family size were top predicting factors of IFA utilization among women in Ethiopia. Random Forest were best predictive models with improved performance. Highly predicting factors of IFA utilization were identified using machine learning algorithms particularly random forest. These findings can guide policymakers and program managers to target interventions more effectively, promote ANC attendance, enhance community literacy and address household-level determinants to improve IFA supplementation rates. However, as the analysis is based on cross-sectional survey data, causal inferences cannot be made.

ABSTRACT Ethnic conflicts have emerged as a central concern in contemporary global politics and represent a major challenge to international peace. As part of the conflict‐prone African continent, Ethiopia continues to face this pressing issue. The ethnic conflict in Ataye Town and its surrounding areas, though previously unstudied through rigorous academic inquiry, constitutes a significant case. Since identifying the actors in a conflict and their underlying interests is essential for resolution, this study aims to identify the major actors involved in the April 2021 inter‐ethnic conflict in Ataye Town and the surrounding areas of the Oromo Special Zone in the Amhara Region. It also examines how and why these actors became involved. To achieve these objectives, the study adopted a qualitative approach, drawing on primary and secondary data analyzed thematically. Following a thorough analysis, the study finds that the April 2021 inter‐ethnic conflict involved several actors, including the TPLF, OLA/Shene, the Amhara Special Force, arms and human traffickers, local and zonal administrators, and regional governments. This study demonstrates that while primordial and constructivist factors provide context, the conflict is most convincingly explained through an instrumentalist lens, wherein ethnicity is manipulated for political and economic gain. To prevent future conflicts, the study recommends addressing the root causes and engaging in genuine reconciliation regarding past conflict incidents.

This review critically examines the ballistic performance and impact resistance of composite fabrics, with a particular focus on their role in next-generation protective systems. While previous work has focused on general ballistic materials, relatively few studies have provided a systematic and integrated synthesis that links material selection, structural design parameters, and recent nanotechnology improvements. This work addresses this gap by offering a comprehensive classification of composite fabric configurations, considering key factors such as fiber types, fabric architecture, thickness, stacking sequence, and layer orientation. The influence of these parameters on deformation behavior, failure mechanisms, and energy absorption efficiency is discussed in detail. It also evaluates standardized testing methods, current reference benchmarks, and limitations of current testing protocols. Recent advances, including high-strength fibers, hybrid architectures, and multifunctional material interactions, are analyzed to highlight emerging trends and unresolved challenges. By organizingrecent progress within a unified analytical framework, this review offers researchers, engineers, and practitioners actionable insights for the design and optimization of advanced ballistic impact materials. Furthermore, it outlines key research gaps and future directions, particularly in the areas of multifunctional integration, lightweight design, and improved predictive modeling for high-strain-rate impact conditions.

In an era of digital disruption and hyper-personalized marketing, the integration of artificial intelligence (AI) into market segmentation and consumer behavior modeling has revolutionized strategic branding. This chapter explores how machine learning algorithms, particularly unsupervised clustering, neural networks, and predictive analytics, can uncover hidden consumer patterns and dynamically segment markets with unparalleled precision. By leveraging real-time data streams and behavioral insights, AI empowers brands to predict consumer intent, personalize engagement strategies, and optimize brand positioning. The chapter also addresses the ethical considerations, challenges of data governance, and the importance of human oversight in AI-driven marketing systems. Through illustrative case studies and emerging frameworks, this work highlights the transformative potential of AI in creating resilient, adaptive, and data-informed branding strategies suited for today's volatile and diverse consumer landscape.

Abstract The transition to a sustainable economy is driven by the urgent need to mitigate climate change, reduce environmental degradation, and promote resource efficiency. Global policies and green regulations play a pivotal role in guiding nations, industries, and businesses toward sustainable development by establishing frameworks for environmental protection, carbon reduction, and circular economy practices. This article provides a comprehensive analysis of key international agreements, such as the Paris Agreement, the Kyoto Protocol, and the European Green Deal, and their impact on shaping national sustainability strategies. A critical evaluation of carbon pricing mechanisms, including carbon taxes and cap-and-trade systems, is presented to highlight their effectiveness in reducing greenhouse gas (GHG) emissions. Additionally, the role of Extended Producer Responsibility (EPR), eco-labeling, and corporate Environmental, Social, and Governance (ESG) reporting is explored as essential drivers of sustainable business practices. The article also examines the increasing influence of digitalization and Artificial Intelligence (AI) in enforcing environmental regulations and optimizing resource efficiency. Further, this study investigates policy disparities between developed and developing nations, emphasizing challenges such as financing sustainable initiatives, technology transfer, and regulatory compliance. Case studies from leading countries—such as the European Union’s Circular Economy Action Plan (CEAP), China’s dual-carbon goals, and the United States’ Inflation Reduction Act (IRA)—illustrate successful policy implementations and their socioeconomic impacts. Finally, the paper proposes a roadmap for strengthening global green regulations through public–private partnerships (PPPs), international collaboration, and innovation-driven policies. By aligning regulatory frameworks with scientific advancements and economic incentives, nations can accelerate the shift toward a resilient, low-carbon, and resource-efficient future.

Diagnosing extrapulmonary and drug-resistant tuberculosis in children is challenging. Early recognition and treatment improve outcomes and reduce transmission. In this 9-year-old boy, diagnosis of drug-resistant subphrenic TB required a high index of suspicion, appropriate diagnostic testing, and timely initiation of second-line anti-tuberculosis therapy.

BackgroundAssessing or measuring adherence is the initial step toward comprehending it, or the lack thereof.ObjectiveThis study aims to translate and validate the Treatment Adherence Questionnaire TAQ among Yemeni dialysis patients with cardiorenal syndrome.MethodsThis cross-cultural adaptation study was conducted at two dialysis centers in Hadhramout Governorate, Yemen, between January 10 and February 15 of 2021. The English version of TAQ was translated into Arabic using the Beaton translation process. Psychometric tests of the TAQ were performed on 100 patients with cardiorenal syndrome. For face and content validity, the pre-final version of the TAQ Arabic was tested using a convenience sample of 10 patients. Individual interviews were used to investigate the meaning, comprehensibility, and acceptability. To evaluate the factor loadings, discriminant validity, and internal consistency of the TAQ Arabic version (the TAQ-Ar), one hundred patients were selected in the same manner and using the same inclusion criteria. Exploratory Factor Analysis (EFA) was utilized to ascertain the underlying factor structure of the Arabic version of the questionnaire, as its dimensionality had not been previously determined in this group. We employed principal component extraction with promax rotation to produce a clear and understandable factor solution. To assess reproducibility, 30 patients were chosen randomly from 100 to resubmit the questionnaire 2 weeks after the initial administration and under the same conditions.ResultsThe transcultural adaptation process revealed that all TAQ-Ar items and patient descriptions exhibited full semantic equivalence. During the psychometric validation phase, the scale-level Content Validity Index was 0.88, and cognitive interviews (n = 30) confirmed patient clarity. Exploratory factor analysis identified four variables, aligning with the structure suggested by the original instrument inventor. Based on Bandalos' standards, factors were kept if their eigenvalues were more than 1 and their factor loadings were higher than 0.30. The overall average factor loading was 0.683, which shows that the test was valid. Internal consistency was very good (Cronbach's alpha = 0.948), and test-retest repeatability showed significant stability (ICC = 0.776).ConclusionWe confirm that the TAQ-Ar is suitable for assessing treatment adherence in individuals with Arabic as their first language for cardiorenal syndrome disease.

Relapses from substance usage continue to be a major global public health concern. Many individuals find it difficult to sustain long-term recovery, often cycling between phases of recovery and relapse, even with improvements in treatments. This study employs a center-based phenomenological design to examine the experiences of adults who relapse from substance dependence. For in-depth interviews, nine patients were chosen. An interpretative phenomenological analysis (IPA) was used to analyze the data. The results revealed that a complex interplay of personal and environmental factors influences relapse. Self-motivation, relocating, maintaining supportive social networks, improving mental resilience, making plans for future stability, and actively engaging in spiritual healing were all effective coping strategies for reducing relapse. To prevent recurrence and encourage long-term recovery, the findings emphasize the need for comprehensive intervention strategies that address social influences, environmental triggers, and emotional regulation.

Abstract Background - The World Health Organization recommends integrase inhibitor dolutegravir as the first- and second-line treatment of choice for Human Immunodeficiency Virus in July 2019 for all populations. Since 2019, Ethiopia's Ministry of Health has advised adults, adolescents, and expectant mothers to use it as their initial antiretroviral treatment regimen. There are limited studies demonstrating the adherence, effectiveness, and safety of children and adolescent dolutegravir regimen therapy in Ethiopia. Methods - institutional based retrospective follow up study conducted by involving children and adolescent with dolutegravir regimen therapy at antiretroviral therapy clinic of Amhara Region Specialized Hospitals. Eligible children and adolescents were consecutively recruited at admission and followed up for one (1) year. The sample size of ART children and adolescents was 403 included in the study. A simple random sampling technique by lottery methods was used. Data were collecting using structured questionnaire from client medical record folder. The data was analysis by using SPSS Version 25 software and evaluate the data with Binary logistic regression Model. Descriptive statistics were computed depending on the nature of the variables. Binary logistic regression Model used to assess predictors. Predictors considered statistically significant if P -value was less than 0.05. Result - In total, 90.1% of participants adhered well to the treatment. At the start, 65.9% of those on DTG were in WHO Clinical Stage III. After follow-up, 67.4% were in Stage I and 32.6% in Stage II. Initially, 97.8% had viral loads above 200 copies/mL, but after one year on DTG, 87.4% were dropped below the threshold. CD4 counts improved significantly: 51.6% began below 200 cells/mm³, but with most now at 350 or higher. Additionally, 33.3% of patients reported adverse drug effects (ADEs) following the DTG regimen. Conclusion - This study indicated that the use of DTG-based ART in children and adolescents is safe with minor ADEs. In addition, the DTG regimen is successful in this population, especially in treatment-experienced children who had high viral load before converting to DTG-based therapy. However, the majority of kids and teenagers adhered well to DTG regimens.