This paper is concerned with the numerical solutions of the vorticity transport equation (VTE) in two-dimensional space with homogenous Dirichlet boundary conditions. Namely, for this problem, the Crank-Nicolson finite difference equation is derived.  In addition, the consistency and stability of the Crank-Nicolson method are studied. Moreover, a numerical experiment is considered to study the convergence of the Crank-Nicolson scheme and to visualize the discrete graphs for the vorticity and stream functions. The analytical result shows that the proposed scheme is consistent, whereas the numerical results show that the solutions are stable with small space-steps and at any time levels.

Renewable energy technology is growing fast especially photovoltaic (PV) system to move the conventional electricity generation and distribution towards smart grid. However, similar to monthly electricity bill, the PV energy producers can only monitor their energy PV generation once a month. Any malfuntion in PV system components may reduce the performance of the system without notice. Thus, developing a real-time monitoring system of PV production is very crucial for early detection. In addition, electricity consumption is also important to be monitored more frequently to increase energy savings awareness among consumers. Hardware based Internet-of-Thing (IoT) monitoring and control system is widely used. However, the implementation of

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Incremental sheet forming (ISF) is a metal forming technology in which small incremental deformations determine the final shape. The sheet is deformed by a hemispherical tool that follows the required shape contour to deform the sheet into the desired geometry. In this study, single point incremental sheet forming (SPIF) has been implemented in dentistry to manufacture a denture plate using two types of stainless steel, 304 and 316L, with an initial thickness of 0.5mm and 0.8mm, respectively. Stainless steel was selected due to its biocompatibility and reasonable cost. A three-dimensional (3D) analysis procedure was conducted to evaluate the manufactured part's geometrical accuracy and thickness distribution. The obtained results confirm

1267 Objectives Aim to evaluate 198Au nanoparticles (AuNP) biodistribution and uptake in a human prostate model for treatment. Many phytochemicals are known to have anti-tumor properties but have short half-lives in vivo. We hypothesized that using these phytochemicals to formulate and coat AuNP would inhibit enzyme cleavage and enhance their anti-tumor properties. Initial evaluations were performed in SCID mice bearing PC3 tumors. Methods : 198AuNP were formulated with the following gum Arabic, epigalocatechin gallate (EGCg) pomegranate extract and mangiferin extract. The resultant nanoparticles were evaluated in normal mice and in human prostate bearing SCID mice. The tumor bearing mice were injected intratumorally with 3-5 uCi of 198A

Coated sand (CS) filter media was investigated to remove phenol and 4-nitrophenol from aqueous solutions in batch experiments. Local sand was subjected to surface modification as impregnated with iron. The influence of process variables represented by solution pH value, contact time, initial concentration and adsorbent dosage on removal efficiency of phenol and 4-nitrophenol onto CS was studied. Batch studies were performed to evaluate the adsorption process, and it was found that the Langmuir isotherm effectively fits the experimental data for the adsorbates better than the Freundlich model with the CS highest adsorption capacity of 0.45 mg/g for 4-nitrophenol and 0.25 mg/g for phenol. The CS was found to adsorb 85% of 4-nitrophenol and

The main aim of this study is to evaluate the remaining oil in previously produced zones, locate the water productive zone and look for any bypassed oil behind casing in not previously perforated intervals. Initial water saturation was calculated from digitized open hole logs using a cut-off value of 10% for irreducible water saturation. The integrated analysis of the thermal capture cross section, Sigma and Carbon/oxygen ratio was conducted and summarized under well shut-in and flowing conditions. The logging pass zone run through sandstone Zubair formation at north Rumaila oil field. The zones where both the Sigma and the C/O analysis show high remaining oil saturation similar to the open hole oil saturation, could be good oil zones that

Quality control is an effective statistical tool in the field of controlling the productivity to monitor and confirm the manufactured products to the standard qualities and the certified criteria for some products and services and its main purpose is to cope with the production and industrial development in the business and competitive market. Quality control charts are used to monitor the qualitative properties of the production procedures in addition to detecting the abnormal deviations in the production procedure. The multivariate Kernel Density Estimator control charts method was used which is one of the nonparametric methods that doesn’t require any assumptions regarding the distribution o

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob