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Many children with poor vision due to refractive error remain undiagnosed and perform poorly in school. The situation is worse in the Sub-Saharan Africa, including Ethiopia, and current information is lacking. The objective of this study is to determine the prevalence of refractive error among children enrolled in elementary schools in Gondar town, Ethiopia. This was a cross-sectional study of 1852 students in 8 elementary schools. Subjects were selected by multistage random sampling. The study parameters were visual acuity (VA) evaluation and ocular examination. VA was measured by staff optometrists with the Snellen E-chart while students with subnormal vision were examined using pinhole, retinoscopy evaluation and subjective refraction by ophthalmologists. The study cohort was comprised of 45.8% males and 54.2% females from 8 randomly selected elementary schools with a response rate of 93%. Refractive errors in either eye were present in 174 (9.4%) children. Of these, myopia was diagnosed in 55 (31.6%) children in the right and left eyes followed by hyperopia in 46 (26.4%) and 39 (22.4%) in the right and left eyes respectively. Low myopia was the most common refractive error in 61 (49.2%) and 68 (50%) children for the right and left eyes respectively. Refractive error among children is a common problem in Gondar town and needs to be assessed at every health evaluation of school children for timely treatment.

At present, the machine tool technology was in Title the United States is not in the state-of-the-art of leading international competitors. Conventional machine tools under use are being pushed to their machining accuracy limits. Such a pressing need calls for revitalizing the machine tool industry. In this dissertation, a mechatronic system has been proposed wazzu and developed for reducing tool vibration during machining. It consists of electrical and mechanical components, and is realized by placing electrically driven electrostrictive (PMN) actuators in a specially designed tool post mechanical structure. Analytical and experimental investigations are conducted to characterize the performance of the developed system. In the analytical investigation, a mathematical model is developed to describe the turning operation. The control mechanism is identified using experimental testing for the range of the disturbance frequency. Investigation using computer simulation is carried out in two phases. In phase 1, a linear neural network controller with an adaptive control strategy is examined. In phase 2, a nonlinear neural network with a learning control strategy is explored. The linear neural networks, namely, digital filters, are implemented using a signal processing board. The experimental investigation is conducted in two stages. In the first stage, a test bed is established to use an electro-magnetic shaker to resemble the excitation of cutting force acting on the tool. In the second stage, experiments were conducted using a lathe on the shop floor. In the experimental investigation, in-process vibration cancellation observed. In the laboratory experiment, a percent reduction in the 90% was possible using a feedforward scheme. The improvement in surface roughness during the turning operation was confirmed from measurements of surface roughness profiles. A cantilever machining operation gave a percent reduction of 30%. The main contributions of this thesis research are: (1) a successful implementation of PMN actuators for in-process vibration cancellation in the turning operation; (2) a successful implementation of linear neural network methodology for active machine tool vibration cancellation; (3) development of guidelines for identification of the neural structure for nonlinear neural network control.

Identification of indigenous fish species is crucial for sustainable fisheries management and biodiversity conservation. The traditional expert systems of fish identification are inconsistent and time-consuming. In an attempt to address this weakness, we leveraged cutting-edge deep learning techniques, namely various iterations of the You Only Look Once (YOLO) algorithm, i.e., YOLOv5, YOLOv7, YOLOv8, and YOLOv11. Our study introduces a new data set of 13,000 images of 16 indigenous species of fish acquired from the Bahir Dar Fishery and Other Aquatic Life Research Center in Ethiopia. We used a whole preprocessing pipeline that included image enhancement techniques, CSPDarkNet, Histogram of Oriented Gradients (HOG), and segmentation-based image feature extraction, along with Roboflow annotations. The new approach significantly improved the dataset quality and usability to train models. Besides, we experimented using various hyperparameters, such as activation functions (Softmax, ReLU), optimizers (AdamW, SGD), batch sizes (32, 16), learning rates (0.01, 0.001), and epochs (100, 50). Our experiment confirms that YOLOv11n achieved the best mean Average Precision (mAP) of 94.7% when trained using 100 epochs with the AdamW optimizer. This study confirms that the combination of a well-designed dataset, sophisticated preprocessing techniques, and effective deep learning architectures provides a robust and practical paradigm for indigenous fish species identification, thereby enhancing aquatic biodiversity conservation.

Research on plant phenology offers insights into the growth and developmental patterns of plants, as well as the impact of environmental conditions and selective pressures on their flowering and fruiting behaviors. The phenological behaviors of ten indigenous tree species were examined over a three-year period in the dry Afromontane Tara Gedam Natural Forest in northwestern Ethiopia, aiming to fill a data gap for this ecosystem. A total of 100 reproductively mature trees (10 per species) were selected for monitoring their leafing, flowering, and fruiting stages. The region experiences an extended rainy season (May–October) and a dry season (November–April). Most flowering occurred annually and seasonally during the dry season, while fruiting was less seasonal, occurring throughout the year. Peak reproductive events occurred later in the dry season or at the onset of the rainy season. Correlation analysis showed that both temperature and rainfall significantly influenced the flowering and fruiting patterns; however, temperature was found to primarily influence leaf phenology. This research is significant for conservation, providing foundational information for developing strategies for species conservation and managing tree seed production in the region.

The production of seedlings from selected indigenous tree species is influenced by the type of potting media and soil ratios. A nursery experiment was conducted in the Libo Kemekem District of Addis Zemen, northwest Ethiopia, to assess the growth performance and biomass of Olea africana, Albizia gummifera, Cordia africana, Millettia ferruginea , and Grewia ferruginea . Seeds were initially sown in seedbeds and later transplanted into polythene pots of varying sizes (8, 10, and 12 cm in diameter) filled with a mixture of forest soil, farmyard manure, and fine sand in three different ratios (3:3:1, 3:2:1, and 3:1:1). A factorial experiment using a randomized complete block design with three replications was implemented. Growth and biomass data were analyzed using the general linear model in SPSS software (Version 26). Significant variations were observed in root collar diameter and shoot height among potting media, pot sizes, and their interactions at level of 0.05 but not in shoot and root biomass. The most vigorous seedlings of G. ferruginea, C. africana, M. ferruginea, A. gummifera , and Olea europaea thrived in a 3:3:1 soil ratio with a 12‐cm pot size. While shoot and root dry weights showed minimal variation across treatments, M. ferruginea excelled in the 3:3:1 mixture and 12‐cm size. The findings indicate that the growth performance of indigenous tree seedlings is significantly influenced by the composition of potting media and pot size. The optimal mixture of forest soil, farmyard manure, and fine sand, particularly with a higher proportion of organic matter, enhances seedling vigor. Therefore, selecting the right substrate is crucial for enhancing the growth of indigenous tree species in nursery settings, thereby contributing to the restoration of degraded Afromontane forests.