Addis Ababa Science and Technology University (Amharic: አዲስ አበባ ሳይንስና ቴክኖሎጂ ዩኒቨርስቲ), or AASTU, is an Ethiopian higher institute in Addis Ababa, Ethiopia. The main campus is located in the Akaky Kaliti subcity, Kilinto area.The concept of Addis Ababa Science and Technology University had a direct and reasonable connection with the Five-Year Growth and Transformation Plan (2010-2015) of the government of the Federal Democratic Republic of Ethiopia. As it was stated in the plan, the establishment of well institutionalized and strong science and technology universities and institutes of technology will serve as a cornerstone to build an economically developed and industrialized state of Ethiopia. As a result, AASTU was founded in 2011 under the Directive of the Council of Ministers No. 216/2011 by admitting the first batch (2000 students) in November 2011.Currently, the university has enrolled more than 8000 undergraduate (under regular and continuing education program) and close to 700 postgraduate students under its nine applied sciences, technology, engineering and ICT focused schools. AASTU is a university in the making, and much of its short-term plans aim at establishing academic infrastructures and facilities, staff recruitment and manpower development. So far, the university managed to recruit 472 academic staff and 391 administrative staff..
Recycling waste materials into useful products is an effective strategy for reducing environmental pollution while providing cost-effective alternatives. This study highlights the novelty of using 100
Determining the geoid gravimetrically poses challenges which are complex mathematical computation and variability representation of the Earth, particularly when precision is necessary. This study aimed to compare the geoid determination methods namely: least squares collocation (LSC), Stokes integral, and Hotine integral methods. Shuttle Radar topography mission (SRTM), gravity anomaly, gravity disturbance, Earth gravity model 2008 (EGM2008), and global navigation satellite systems with levelling (GNSS/Levelling) data were used for the purpose of geoid determination methods. The LSC method was applied using the RCR technique. Whereas Stokes and Hotine integrals methods were computed based on KTH approach with essential corrections, including topographic, atmospheric, ellipsoidal, and downward continuation refinements. All three approaches yielded consistent geoid models with validation against benchmarks produced standard deviations of 0.069 m for LSC, 0.062 m for Stokes, and 0.061 m for Hotine. The findings demonstrate that the LSC provides reliable geoid approximations, Stokes and Hotine integrals attain marginally higher precision. This study highlights the significance of integrating airborne gravity data with global geopotential models and determined the gravimetric geoid model to contribute the development of an accurate nationwide vertical reference for Ethiopia.
Vital sign assessment is a central step in clinical decision-making for monitoring and handling patient health status. However, the lack of interoperability among heterogeneous vital sign monitoring devices remains a major challenge in healthcare systems. To address this issue, this study recommends a Semantic Web of Things (SWoT)–based framework for vital sign interoperability, including the development of a semantic model that defines relevant medical domain classes, relationships, and attributes, along with an implementation approach. A prototype application was developed using a mobile application as frontend and a web-based backend, and its applicability was validated in a real-world clinical setting. Following ethical approval from the Addis Ababa Health Office, the validation was conducted at Tirunesh Beijing Hospital, Addis Ababa, Ethiopia, with informed consent obtained from all participating patients. Real-time data were collected from outpatients for hypertension classification using two wearable devices (for body temperature and heart rate measurement) and one non-wearable device (for blood pressure measurement). Patients were classified into four categories: normal, elevated, stage I hypertension, and stage II hypertension. This study presents validation of a SWoT model using real-world hospital data for hypertension classification. The results demonstrate the practicality of the proposed framework and its ability to enable unified access to heterogeneous devices and data formats, highlighting the potential of SWoT to improve interoperability in healthcare applications.
This study investigates the impact of Land Use/Land Cover (LULC) changes on streamflow and sediment yield within the Gidabo River Catchment of the Rift Valley Basin, Ethiopia. This study applied the Soil and Water Assessment Tool (SWAT) to evaluate LULC conditions between 1990 and 2013. Sensitivity, calibration, validation, and uncertainty analyses were performed using the SUFI-2 algorithm in SWAT-CUP. To isolate the specific hydrologic responses driven by historical land-cover transitions, a systematic parameter transfer approach (’fixing-changing’ method) was employed, separating LULC-dependent and LULC-independent variables. The model calibration and validation employed a monthly, multi-site approach. Calibration results demonstrated a strong alignment between observed and simulated streamflow and sediment yield, with the model successfully capturing 63 ^3 s ^-1 for the Aposto station and +0.44 m ^3 s ^-1 for the Bedessa station. An increase of cultivated area during the study period resulted in an increase in sediment yield by 84.62 t km ^-2 for the Aposto station and 22.92 t km ^-2 for the Bedessa station. Notably, the annual sediment load reaching the Gidabo dam site was estimated to be 435.26 t km ^-2 year ^-1 . Furthermore, the study identified spatial variability in sediment yield based on validated outputs, highlighting specific hot spot sub-basins. These findings serve as critical indicators for resource analysis and are essential for developing effective strategies to ensure sustainable natural resource management in the region and the country at large.
Civet coffee is a premium specialty coffee known for its unique flavor, traditionally produced via spontaneous fermentation in the digestive system of civet cats (Paradoxurus hermaphrodites). This study aimed to replicate and optimize the fermentation process in vitro using eight bacterial strains isolated from civet gut, identified as Bacillus, Enterobacter, and Acinetobacter via 16 S rRNA sequencing. Fermentation conditions were optimized using Response Surface Methodology (RSM) with a Box-Behnken Design, assessing the effects of temperature (25–35 °C), time (24–48 h), and inoculum concentration (5–15
