Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to enhance efficiency and maximize the output power of PV module, regardless the variation of temperature, irradiation, and the electrical characteristics of the load. A new MPPT system has been presented in this research, consisting of a synchronous DC-DC step-down Buck converter controlled by an Arduino microcontroller based unit. The MPPT process with Perturb and Observe method is performed with a DC-DC converter circuit to overcome the problem of voltage mismatch between the PV modules and the loads. The proposing system has high efficiency, lower cost and can be easily modified to handle more energy sources. The test results indicate that the u

Removing Congo red (CR) is critical in wastewater treatment. We introduce a combination of electrocoagulation (EC) and electro-oxidation (EO) to address the elimination of CR. We also discuss the deposition of triple oxides (Cu–Mn–Ni) simultaneously on both anodic and cathodic graphite electrodes at constant current density. These electrodes efficiently worked as anodes in the EC-EO system. The EC-CO combination eliminated around 98 % of the CR dye and about 95 % of the Chemical Oxygen demand (COD), and similar results were obtained with the absence of NaCl. Thus, EC-EO is a promising technique to remove CR in an environmentally friendly pathway.

. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

     The Non-thermal plasma has a wide range of applications in different areas. In this study, the oxidation of a model fuel containing organo-sulfur compounds (benzothiophene and dibenzothiophene) was investigated using plasma. The process involved ozone generation through dielectric barrier discharge, followed by extraction using acetonitrile. The results demonstrated the efficient oxidation and removal of dibenzothiophene and benzothiophene by non-thermal plasma. The desulfurization efficiency reached 93.78% under the optimum conditions, including a voltage of 11Kv, temperature of 50ºC, duration of 4 hours, and a flow rate of 75 ml/min.

   Increasingly, public organizations in the federal state are required to work together, as well as to work with others to achieve their objectives. In Iraq there are two levels of organizations, including federal and local, and these organizations have been forced to work for many years in an environment in which the responsibility for service delivery is shared between policy makers and service providers, and between local governments and the federal government. It is sometimes difficult to manage the relationship between these organizations (federal and local) and do not always provide the best possible outcome of this relationship. This paper reviews how to manage the relationship between local administrations and

<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in

Background: Kidney failure is the leading cause of early death for diabetic patients and is regarded as the most dangerous consequence of the disease. This can only be reversed through early detection and intervention. The incidence of type 1 diabetes, which comprises 5-10 per cent of all diabetic diseases, is rising across the globe with a rate of 15 per 100,000 people per year. Methods: 90 samples were collected and used in this study. Thirty healthy children served as controls (aged 5-15 years), 30 patients with Type 1 Diabetes Mellitus (T1DM, aged 5-15 years) and 30 patients with type 1 diabetic nephropathy (DN), aged 5-20 years). Results: highly significant increase in Urea and Creatinine levels when compared control with DN al