In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurement

In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these

The current study is designed to achieve the goal of early detection of heart disease because it is the main risk of death. Some biomarkers were measured as well as the percentage of the effect of certain risk factors in people with myocardial infarction and heart failure. The study included 40 serum samples from people with heart disease. The effectiveness of the creatine kinase (CK-MB), as well as its temporal and albumin effects, as well as sodium ions in people with myocardial infarction and heart failure, were compared with the control group. as shown below:

-The first group consisted of 25 blood samples from people with myocardial infarction and 15 serum samples from people with heart failure. Blood

The present study aims to evaluate the effects of methotrexate (MTX) with and without vitamin A (Vit. A) on some biochemical parameters and histological structure in male rabbits liver. Twenty male rabbits weighing 1250-1480 gm were divided into four equal number groups. The first group was given 2 ml distilled water as control group. The second group was given MTX (20 mg/kg), the third group was given Vit. A (5000 IU), while the fourth group was given MTX (20 mg/kg) +Vit. A (5000 IU) in alternative days. Following four weeks of treatment, lipid profile total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), [low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL)]; in addition to thyroid hormones tr

In this work, corrosion parameters were evaluated using potentiodynamic polarization curves. In order to determine corrosion parameters of potential and current density of the interesting metal, carbon steel, environmental conditions of external corrosion of buried carbon steel pipeline in Iraqi soil were prepared in the laboratory using simulated prepared conditions. Solutions of sodium chloride at different concentrations (300, 1100, 1900, 2700, and 3500 ppm) were used. pH of solution were acidic at pH =5, and alkaline at pH = 9. Laboratory conditions were similar to those of Iraqi soil where the pipelines were buried. Temperature was constant at 20 °C. Potentiodynamic polarization curves, of potential vs. log current density, were ob

Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

Object tracking is one of the most important topics in the fields of image processing and computer vision. Object tracking is the process of finding interesting moving objects and following them from frame to frame. In this research, Active models–based object tracking algorithm is introduced. Active models are curves placed in an image domain and can evolve to segment the object of interest. Adaptive Diffusion Flow Active Model (ADFAM) is one the most famous types of Active Models. It overcomes the drawbacks of all previous versions of the Active Models specially the leakage problem, noise sensitivity, and long narrow hols or concavities. The ADFAM is well known for its very good capabilities in the segmentation process. In this

Introduction: Although soap industry is known from hundreds of years, the development accompanied with this industry was little. The development implied the mechanical equipment and the additive materials necessary to produce soap with the best specifications of shape, physical and chemical properties. Objectives: This research studies the use of vacuum reactive distillation VRD technique for soap production. Methods: Olein and Palmitin in the ratio of 3 to 1 were mixed in a flask with NaOH solution in stoichiometric amount under different vacuum pressures from -0.35 to -0.5 bar. Total conversion was reached by using the VRD technique. The soap produced by the VRD method was compared with soap prepared by the reaction - only method which

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings