The goal of this research is to develop a numerical model that can be used to simulate the sedimentation process under two scenarios: first, the flocculation unit is on duty, and second, the flocculation unit is out of commission. The general equation of flow and sediment transport were solved using the finite difference method, then coded using Matlab software. The result of this study was: the difference in removal efficiency between the coded model and operational model for each particle size dataset was very close, with a difference value of +3.01%, indicating that the model can be used to predict the removal efficiency of a rectangular sedimentation basin. The study also revealed that the critical particle size was 0.01 mm, which means that most particles with diameters larger than 0.01 mm settled due to physical force, while most particles with diameters smaller than 0.01 mm settled due to flocculation process. At 10 m from the inlet zone, the removal efficiency was more than 60% of the total removal rate, indicating that increasing basin length is not a cost-effective way to improve removal efficiency. The influence of the flocculation process appears at particle sizes smaller than 0.01 mm, which is a small percentage (10%) of sieve analysis test. When the percentage reaches 20%, the difference in accumulative removal efficiency rises from +3.57% to 11.1% at the AL-Muthana sedimentation unit.
The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi
... Show MoreThis study was conducted to explore the effects of using ionized water on the productive and physiological performance of Japanese quails (Coturnix japonica). Our study was conducted at a private farm from 20th April, 2016 to 13th July, 2016 (84 d). One hundred 42-day-old Japanese quail chicks were used, divided randomly into 5 groups with 4 replicates. Treatments consisted in a control group (T1 - normal water:), alkaline (T2 - pH 8 and T3 - pH 9), and acidic water (T4 - pH 6 and T5 - pH 5). All birds were fed a balanced diet of energy and protein. The egg production ratio, egg weight, cumulative number of eggs, egg mass, feed conversion ratio, productivity per hen per week, and effects on plasma lipids, uric acid, glucose, calcium, and ph
... Show MoreDrilling fluid properties and formulation play a fundamental role in drilling operations. The Classical water-based muds prepared from only the Syrian clay and water without any additives((Organic and industrial polymers) are generally poor in performance. Moreover, The high quantity of Syrian clay (120 gr / l) used in preparing drilling fluids. It leads to a decrease in the drilling speed and thus an increase in the time required to complete the drilling of the well. As a result, the total cost of drilling the well increased, as a result of an increase in the concentration of the solid part in the drilling fluid. In this context, our study focuses on the investigation of the improvement in drilling mud Prepa
... Show MoreThe depletion of petroleum reserves and increasing environmental concerns have driven the development of eco-friendly asphalt binders. This research investigates the performance of natural asphalt (NA) modified with waste engine oil (WEO) as a sustainable alternative to conventional petroleum asphalt (PA). The study examines NA modified with 10%, 20%, and 30% WEO by the weight of asphalt to identify an optimal blend ratio that enhances the binder’s flexibility and workability while maintaining high-temperature stability. Comprehensive testing was conducted, including penetration, softening point, viscosity, ductility, multiple stress creep recovery (MSCR), linear amplitude sweep (LAS), energy-dispersive X-ray spectroscopy (EDX), F
... Show MoreCrime is a threat to any nation’s security administration and jurisdiction. Therefore, crime analysis becomes increasingly important because it assigns the time and place based on the collected spatial and temporal data. However, old techniques, such as paperwork, investigative judges, and statistical analysis, are not efficient enough to predict the accurate time and location where the crime had taken place. But when machine learning and data mining methods were deployed in crime analysis, crime analysis and predication accuracy increased dramatically. In this study, various types of criminal analysis and prediction using several machine learning and data mining techniques, based o