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All Journal Konfrontasi: Jurnal Kultural, Ekonomi dan Perubahan Sosial Budapest International Research and Critics in Linguistics and Education Journal (Birle Journal) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Budapest International Research in Exact Sciences (BirEx Journal) Britain International of Humanities and Social Sciences (BIoHS) Journal Britain International of Exact Sciences Journal (BIoEx Journal) Britain International for Linguistics, Arts and Education Journal (BIoLAE Journal) Polit Journal LingLit Journal
Belay Sitotaw Goshu
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Social Sciences Transportation Other

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Scaling Up Early Childhood Education: A Foundation for Long-Term Educational Success in Ethiopia Muhammad Ridwan; Belay Sitotaw Goshu
Budapest International Research and Critics in Linguistics and Education (BirLE) Journal Vol 1, No 1 (2012): Budapest International Research and Critics in Linguistics and Education, Februa
Publisher : BIRCU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birle.v1i1.8159

Abstract

The quality of early childhood education in Ethiopia faces significant challenges, particularly related to teacher training, resource availability, and parental involvement. This study aims to explore the key challenges in the Ethiopian early childhood education system and assess their impacts on both teacher effectiveness and student learning outcomes. The research employed a mixed-methods approach, combining quantitative data from surveys with qualitative interviews. Twenty education officials were interviewed to gather in-depth insights, and 200 instructors from various regions participated in the survey. Exploring the requirement for teacher training, determining the availability of resources, comprehending teacher motivation, analyzing the connection between class size and learning quality, and exploring the influence of parental engagement are the study's five main goals. The findings show a severe lack of educational resources and inadequate teacher preparation in rural areas. Additionally, learning outcomes were found to be adversely affected by large class sizes, and student achievement was significantly correlated with parental participation. The results emphasize the necessity of focused measures to close these inequalities, including bettering teacher preparation programs, allocating more funds, lowering class sizes, and encouraging family involvement in education. Tackling these issues will help Ethiopia's early childhood education level rise and promote a more efficient and equitable educational system.
Refining Biological Aging Clocks: Harnessing Multi-Organ, Multi-Omics Data with Machine Learning Belay Sitotaw Goshu
Budapest International Research and Critics in Linguistics and Education (BirLE) Journal Vol 8, No 4 (2025): Budapest International Research and Critics in Linguistics and Education, Novemb
Publisher : BIRCU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birle.v8i4.8151

Abstract

The image aging as a deeply personal story, where a gentle mirror reflects not just the years you’ve lived but the vibrant health pulsing through you, guided by cutting-edge aging clocks. This study dives into crafting and testing two aging clocks, basic and refined, using synthetic data from 1,200 unique souls across Groups A, B, and C. With 12 biomarkers as our guide, we shaped these clocks with linear and Ridge regression, achieving a heartwarming near-perfect match to biological age, missing by just 1.81 years on average, with an R² of 0.99 for both. In imagined clinical moments, a 30% dip in Delta Age after intervention and a strong bond between faster aging and lower therapy adherence (r = -0.587, p = 4.558e-112) showed these clocks’ promise. Response to help varied warmly, 68.6% for Group A, 67.4% for Group B, and 62.5% for Group C, hinting at our diverse human hues. The pre- and post-intervention shifts and adherence shaping age, while Table 1 unpacks a logistic model where chronological age (coefficient 0.0941, p < 0.001) steals the spotlight. Though our synthetic world softens life’s rough edges, these clocks offer a tender start for personalized care. They whisper hope for tailoring treatments and watching progress, though real-life validation will bring the full story to light.
Machine Learning-Enhanced Prediction of Lunar Crescent Visibility for Unified Hijri Calendar Determination: A Global and Regional Framework Belay Sitotaw Goshu; Muhammad Ridwan
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 9, No 2 (2026): Budapest International Research and Critics Institute May
Publisher : Budapest International Research and Critics University

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Abstract

The lunar Hijri calendar governs religious observances for approximately 1.9 billion Muslims worldwide, yet disunity in crescent sighting criteria leads to inconsistent Ramadan and Eid dates across regions. Traditional visibility criteria (Yallop, Odeh) rely on simplified parametric approximations that inadequately capture complex atmospheric and geographical interactions. This study develops and validates a machine learning-enhanced framework for predicting lunar crescent visibility to support unified Hijri calendar determination through global and regionally-adapted models. A comprehensive dataset of 7,488 observations spanning 13 years (2013–2025) across 24 countries and five geographical regions was compiled. Feature engineering created 15 predictive parameters including interaction terms and composite indices. Eight supervised learning algorithms were evaluated with hyperparameter optimization using randomized search, genetic algorithms, and particle swarm optimization. Ensemble methods including voting, stacking, and hybrid configurations were developed and validated using 5-fold cross-validation. Findings: The hybrid ensemble model achieved superior performance (AUC 0.906, F1-score 0.888), outperforming traditional criteria by 17–19%. Engineered interaction features (elongation × altitude, lag time × altitude) demonstrated highest predictive importance. Regional analysis revealed visibility rate variations from 97.7% (Oceania) to 98.7% (Asia), supporting geographically-calibrated models. Long-term Ramadan predictions (2027–2075) confirmed the 33-year lunar cycle with mean interval of 354.37 days. Conclusion: Machine learning provides robust, evidence-based crescent visibility prediction that exceeds traditional criteria accuracy while capturing complex parameter interactions. The framework supports both global unification and region-specific applications. Recommendation: Religious authorities should adopt probabilistic, multi-model ensemble predictions with confidence scoring for calendar determination, supported by continuous validation against global observational networks.
Co-Authors Abel Berhanu Sitotaw Arifulhak Aceh Ashenafi Taye Negewo Bikila Merga Deresa Kirubel Molla Melaku Masresha Woldeamanueal Melaku Masresha Woldemariam Muhammad Ridwan Muhammad Ridwan Muhammad Ridwan Syarifuddin Yonas Tadesse Alemu