Adama University

About: Adama University is a education organization based out in Nazrēt, Ethiopia. It is known for research contribution in the topics: Population & Adsorption. The organization has 840 authors who have published 1010 publications receiving 5547 citations. The organization is also known as: Adama Science and Technology University & ቴክኖሎጂ ዩኒቨርሲቲ, አዳማ ሳይንስና ቴክኖሎጂ ዩኒቨርሲቲ.

Reservoir quality discrimination of the Albian-Cenomanian reservoir sequences in the Ivorian basin: a lithological and petrophysical study

Abstract: The Albian-Cenomanian reservoirs of the Ivorian sedimentary basin consist mainly of conglomeratic, pebbly, and very fine to coarse-grained sandstones. In the present study, the lithologic composition, flow and storage capacities, and reservoir quality parameters were studied in detail. Some fresh, clean and non-fractured samples were selected representative from FE-1 well in the depocenter of the basin, FE-2 well to the west, and FE-3 well to the east of the basin. Lithologic studies indicated that heterogeneity increases greatly to the east due to the implementation of diagenetic factors including cementation, silicification, authigenic clay minerals, and compaction. Though of the dominant reservoir quality-reducing factors, to the east, the reservoir quality increases due to increasing the grain size and the interstitial pore types. Porosity and permeability of samples were estimated by helium and nitrogen injection, respectively, whereas the reservoir quality was measured using different techniques, e.g., the FZI (flow zone indicator), the RQI (reservoir quality index), the effective pore radius (R35) of Winland, and the DRT (discrete rock type). The reservoir quality properties declared that the present plug samples can be clustered into six RRTs (reservoir rock types), with increasing reservoir quality from RRT6 (conglomeratic sandstone lithofacies) to RRT1 (deformed sandstone lithofacies).

Effect of Cultural Conditions on Protease Production by a Thermophilic Geobacillus thermoglucosidasius SKF4 Isolated from Sungai Klah Hot Spring Park, Malaysia.

Abstract: Major progress in the fields of agriculture, industry, and biotechnology over the years has influenced the quest for a potent microorganism with favorable properties to be used in scientific research and industry. This study intended to isolate a new thermophilic-protease-producing bacterium and evaluate its growth and protease production under cultural conditions. Protease producing bacteria were successfully isolated from Sungai Klah Hot Spring Park in Perak, Malaysia, and coded as SKF4; they were promising protease producers. Based on microscopic, morphological, and 16S rRNA gene analysis, isolate SKF4 was identified as Geobacillus thermoglucosidasius SKF4. The process of isolating SKF4 to grow and produce proteases under different cultural conditions, including temperature, pH, NaCl concentration, carbon and nitrogen sources, and incubation time, was explored. The optimum cultural conditions observed for growth and protease production were at 60 to 65 °C of temperature, pH 7 to 8, and under 1% NaCl concentration. Further, the use of casein and yeast extract as the nitrogen sources, and sucrose and fructose as the carbon sources enhanced the growth and protease production of isolate SKF4. Meanwhile, isolate SKF4 reached maximum growth and protease production at 24 h of incubation time. The results of this study revealed a new potent strain of thermophilic bacterium isolated from Sungai Klah Hot Spring Park in Perak, Malaysia for the first time. The high production of thermostable protease enzyme by G. thermoglucosidasius SKF4 highlighted the promising properties of this bacterium for industrial and biotechnological applications.

MASCA–PSO based LLRBFNN model and improved fast and robust FCM algorithm for detection and classification of brain tumor from MR image

Abstract: A novel modified adaptive sine cosine optimization algorithm (MASCA) integrated with particle swarm optimization (PSO) based local linear radial basis function neural network (LLRBFNN) model has been proposed for automatic brain tumor detection and classification. In the process of segmentation, the fuzzy C means algorithm based techniques drastically fails to remove noise from the magnetic resonance images. So, for reduction of noise and smoothening of brain tumor magnetic resonance image an improved fast and robust fuzzy c means algorithm segmentation algorithm has been proposed in this research work. The gray level co-occurrence matrix technique has been employed to extract features from brain tumor magnetic resonance images and the extracted features are fed as input to the proposed modified ASCA–PSO based LLRBFNN model for classification of benign and malignant tumors. In this research work the LLRBFNN model’s weights are optimized by using proposed MASCA–PSO algorithm which provides a unique solution to get rid of the hectic task of radiologist from manual detection. The classification accuracy results obtained from sine cosine optimization algorithm, PSO and adaptive sine cosine optimization algorithm integrated with particle swarm optimization based LLRBFNN models are compared with the proposed MASCA–PSO based LLRBFNN model. It is observed that the result obtained from the proposed model shows better classification accuracy results as compared to the other LLRBFNN based models.