Ethiopian International Journal of Engineering and Technology

Innovative Track Infrastructure Solutions for Sustainable Railway Transportation

Tsion Wondwosen — 2025-08-20

The increasing demands for sustainable and resilient transportation systems necessities transformative innovations in railway infrastructure design and materials. This study explores the integration of recycled composite sleepers and geogrid-reinforced subgrades as a solution to enhance the mechanical performance, durability, and environmental sustainability of railway tracks. A 100-meter full-scale experimental track was constructed and instrumented to validate the performance of the proposed system under realistic operating conditions. The research employs finite element analysis (FEA) and full-scale field trials to evaluate the structural behavior of these materials under varying axle loads, dynamic train frequencies, and extreme climatic conditions. Key performance metrics such as track settlement, lateral stability, and maintenance intervals are analyzed, demonstrating that geogrid-reinforced subgrades reduce track deformation by up to 40% over 12 months compared to conventional designs. Recycled composite sleepers exhibit superior resistance to cracking, moisture ingress, and UV degradation, resulting in a25% reduction in lifecycle maintenance costs and a 30% decrease in carbon emissions. The lifecycle assessment (LCA) confirms that the proposed infrastructure solutions significantly lower resource consumption and greenhouse gas emissions. This research contributes to the circular economy, aligns with global sustainable development goals, and offers practical strategies for transitioning to green rail infrastructure. This finding provides a comprehensive framework for implementing sustainable materials in railway infrastructure, addressing contemporary challenges in transportation engineering while ensuring long-term operational efficiency and environmental stewardship.

Keywords: Geogrid reinforcement, life cycle assessment, recycled composites, railway

Kafi Noonoo To English Machine Translation Using Deep Learning Approaches

Ashagire Adinew — 2025-08-20

Kafi Noonoo is one of the Ethiopian languages that is spoken by the Kaffa people in the southwestern part of Ethiopia. Additionally, it is a morphologically rich language and has an indigenous name for prestige, cultural place, and cultural dejectedness, which has no equivalent meaning in other languages. Machine translation is a technique that automatically translates text’s or speech’s meaning from one language to another without human involvement to resolve information gaps. Various machine translation studies have been conducted for resource-rich languages like English, French, German, and others. However, the variety of linguistic patterns, the dominance of technologically developed languages, and the lack of machine translation from Kafi Noonoo to English will lead to the disappearance of Kafi Noonoo indigenous words among native speakers. To tackle such a problem, this article designed a Kafi Noonoo to English and vice versa machine translation solution by using deep learning approaches. The bidirectional long short-term memory, bidirectional gated recurrent unit with and without attention, and transformer were applied. In order to train the model, the bilingual parallel sentences were collected from Kafi Noonoo linguistic-related sources. Different experiments were applied to find out the optimal value of the proposed model. Based on the experiment’s result, the transformer performed better with an accuracy of 89% and a BLEU score of 6.34 and 5.42 for Kafi Noonoo to English and English to Kafi Noonoo, respectively. According to our experiment result, the transformer model was suitable for morphologically rich languages like Kafi Noonoo to English and vice versa, for machine translation. For a better result, there is a necessity to generate parallel corpora in order to conduct comparable research.

Keywords: Kaffa, Kafi Noonoo, Low-resource machine Translation, Transformer

Experimental and Numerical Approaches: Performance Optimization of a Baffled Solar Cabinet Dryer for Mango Slices

Ermias A. Tesema — 2025-08-20

During peak seasons, postharvest loss of mangoes remains a significant challenge for smallholder farmers in Ethiopia, driven by high production volumes, limited market access, and a lack of preservation methods. The objective of this research was to develop and enhance the performance of a solar cabinet dryer for mango slices through Computational Fluid Dynamics (CFD) simulations. A solar cabinet dryer incorporating a rectangular baffle solar collector was developed, built, and experimentally validated. The CFD simulation results showed a temperature difference of 4.35% and a relative humidity deviation of 7.8%, indicating good agreement with the experimental data. The model optimized the baffle design and inlet air velocity to maximize outlet temperature and minimize pressure drop. The optimal design was identified using Response Surface Methodology (RSM) in conjunction with ANSYS software. The best-performing configuration featured four baffles with 25% cut openings and an inlet air velocity of 1 m/s. The porous domain of mango slices was incorporated into the model to predict moisture transport with experimental effective diffusivity of (5.224 × 10⁻⁷ m²/s). Results showed that the designed dryer achieved uniform temperature and moisture distribution, with higher drying rates observed near the lower trays. .The moisture content in mango slices was reduced from 45942.17 mol/m³ to 2,677 mol/m³ within two hours of drying. The CFD model showed strong predictive accuracy with only minor discrepancies observed in the simulation of temperature, relative humidity, and moisture movement. The developed dryer provides a sustainable and cost-effective solution to reduce postharvest mango losses with an efficient design that enhances food security and farmer livelihoods.

Keywords: CFD modeling, heat and mass transfer, mango slices, postharvest loss, solar dryer