Kodamaea ohmeri keratitis is an opportunistic pathogen seen in patients who have undergone invasive procedures and immunocompromised state. It has been identified in septicemia patients, resulting in mortality. To the best of our knowledge, we identified the first case of K. ohmeri keratitis following an injury with vegetative material. A 57-year-old woman with underlying, poorly controlled diabetes mellitus was gardening when a tree leaf accidentally poked her in the eye. Two weeks later, the patient presented with right eye pain, redness and progressive blurring of vision due to a traumatised right cornea. Slit-lamp examination showed a small inferior paracentral corneal stromal infiltrate with overlying epithelial defect. A corneal scraping sample yielded K. ohmeri from Analytical Profile Index (API) 20C yeast identification system. She was treated with intensive topical amphotericin B and fluconazole. After 6 weeks of treatment, the keratitis resolved with faint scar tissue, and her visual acuity improved.
The significant loss of surfactants during reservoir flooding is a challenge in oil field operations. The presence of clay minerals affects the surfactant performance, resulting in surfactant losses. This is because the mineralogical composition of the reservoir results in unpredicted adsorption quantity. Therefore, this paper seeks to investigate Aerosol-OT's adsorption on different quartz/clay mineral compositions during the flow. Also, it investigates adsorption mitigation by preflushing with lignin. The dynamic experiments were conducted on sand packs composed of quartz-sand and up to a 7% clay mineral content. The results obtained from the surfactant losses were compared with/without lignin preflush at different pH values. The main observation was the direct relationship between increasing the composition of clay minerals and the surfactant pore volume required to overcome the adsorption. The highest adsorption calculated was 46 g/kg for 7% kaolinite. Moreover, lignin successfully reduced the adsorption of Aerosol-OT by 60%. Therefore, the results demonstrate that the effects of the clay mineral content on adsorption could be efficiently minimized using lignin at a high pH.
Erosion of piping components, e.g., elbows, is a hazardous phenomenon that frequently occurs due to sand flow with fluids during petroleum production. Early prediction of the sand's erosion rate (ER) is essential for ensuring a safe flow process and material integrity. Some models have been applied to determine the ER of the sand in the literature. However, these models have been created based on specific data to require a model for application to wide-range data. Moreover, the previous models have not studied relationships between independent and dependent variables. Thus, this research aims to use machine learning techniques, namely linear regression and decision tree (DT), to predict the ER robustly. The optimum model, the DT model, was evaluated using various trend analysis and statistical error analyses (SEA) techniques, namely the correlation coefficient (R). The evaluation results proved proper physical behavior for all independent variables, along with high accuracy and the DT model robustness. The proposed DT method can accurately predict the ER with R of 0.9975, 0.9911, 0.9761, and 0.9908, AAPRE of 5.0%, 6.27%, 6.26%, and 5.5%, RMSE of 2.492E-05, 6.189E-05, 9.310E-05, and 5.339E-05, and STD of 13.44, 6.66, 8.01, and 11.44 for the training, validation, testing, and whole datasets, respectively. Hence, this study delivers an effective, robust, accurate, and fast prediction tool for ER determination, significantly saving the petroleum industry's cost and time.