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Atella is a by-product of Tella, a local brewed beverage. It is used as a food source to promote weight gain, development, and milk yield in cattle and sheep. The aim of this study was to analyze the physicochemical properties, nutritional composition, and mineral content of Atella. The samples of Atella were purposively collected from 25 Tella vendors in Jimma City, Ethiopia. The physicochemical properties and proximate analyses were performed using the AOAC method. The mineral contents of the samples were measured using flame atomic absorption spectrometry (FAAS). The results showed that the pH value was between 3.36 - 4.23, temperature (19.27 - 20.20 °C), conductivity (124.10 - 153.50 μS/cm), dry matter content (15.87 - 20.17%), crude protein content (16.47 - 18.72%), crude fat content (3.73 - 5.43%), crude fiber content (15.52 - 19.73%), total ash content (3.58 - 4.47%), carbohydrate content (35.02 - 40.50%), and gross energy (247.42 -271.13 kcal/g). The average concentrations of Ca, Mg, Fe, Cu, Cr, and Pb were 656.71, 274.74, 79.16, 7.73, 1.55, and 0.41 mg/kg, respectively. The results of the study show that Atella is a nutrient-rich source of essential nutrients, minerals, and energy. [ABSTRACT FROM AUTHOR]

Evaluating the level of Building Information Modeling (BIM) implementation in construction firms is critical yet challenging in the absence of a quantitative method. This study addresses this gap. The study begins with a literature review that identified 27 BIM implementation enablers, followed by interviews with three firms to score their performance on each enabler. A mathematical model was developed to score a firm's BIM implementation based on each enabler's score. For each firm, 1 million random scenarios are generated to simulate alternative ways by which a firm's enablers' score can be improved. Subsequently, in each simulated scenario, the firm's BIM implementation score is calculated. The simulation results are incorporated into a feature-pairing neural network that has been designed specifically to provide a customized best course of action for each firm's further BIM adoption. The first contribution of this research is providing a comprehensive analysis of the dynamics and interconnectedness of factors influencing BIM adoption in AEC firms, offering insights into effective BIM adoption. The second contribution is proposing a novel quantitative approach for measuring the current level of BIM implementation and providing data-driven advice for steering the BIM implementation process. This research offers a practical contribution by providing companies with a tool to compute their BIM implementation score, allowing comparisons and benchmarking against competitors. [ABSTRACT FROM AUTHOR]

In this study, several biowaste materials are screened for adsorptive removal of methylene blue (MB) from synthetic water. Among the tested adsorbents, barley (Hordeum vulgare) bran (BB) and enset (Ensete ventricosum midrib leaf, EVML) were selected for further evaluation of MB (a model cationic dye) adsorption. Batch MB adsorption performance of BB and EVML adsorbents was significantly high in a wide pH range (4-9). The well fitting of experimental data with pseudosecond-order kinetic model suggests a monolayer adsorption of MB. The MB adsorption onto both adsorbents was fit well with the Langmuir isotherm model with maximum MB adsorption capacities of 63.2 mg/g (BB) and 35.5 mg/g (EVML). The biowaste materials exhibit considerable adsorption capacity for cationic dye (MB), perform well under acidic and basic conditions, and are reusable. Therefore, the use of these materials as adsorbents may have an environmental benefit in terms of the conversion of wastes into valuable materials. Further studies are suggested to investigate the performance of these adsorbents in a continuous mode using real wastewater. [ABSTRACT FROM AUTHOR]