The power generation of solar photovoltaic (PV) technology is being implemented in every nation worldwide due to its environmentally clean characteristics. Therefore, PV technology is significantly growing in the present applications and usage of PV power systems. Despite the strength of the PV arrays in power systems, the arrays remain susceptible to certain faults. An effective supply requires economic returns, the security of the equipment and humans, precise fault identification, diagnosis, and interruption tools. Meanwhile, the faults in unidentified arc lead to serious fire hazards to commercial, residential, and utility-scale PV systems. To ensure secure and dependable distribution of electricity, the detection of such ha

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

<span>Distributed denial-of-service (DDoS) attack is bluster to network security that purpose at exhausted the networks with malicious traffic. Although several techniques have been designed for DDoS attack detection, intrusion detection system (IDS) It has a great role in protecting the network system and has the ability to collect and analyze data from various network sources to discover any unauthorized access. The goal of IDS is to detect malicious traffic and defend the system against any fraudulent activity or illegal traffic. Therefore, IDS monitors outgoing and incoming network traffic. This paper contains a based intrusion detection system for DDoS attack, and has the ability to detect the attack intelligently, dynami

Solid dispersion (SD) is one of the most widely used methods to resolve issues accompanied by poorly soluble drugs. The present study was carried out to enhance the solubility and dissolution rate of Aceclofenac (ACE), a BCS class II drug with pH-dependent solubility, by the SD method. Effervescent assisted fusion technique (EFSD) using different hydrophilic carriers (mannitol, urea, Soluplus®, poloxamer 188, and poloxamer 407) in the presence of an effervescent base (sodium bicarbonate and citric acid) in different drug: carrier: effervescent base ratio and the conventional fusion technique (FSD) were used to prepare ACE SD. Solubility, dissolution rate, Fourier transformation infrared spectroscopy (FTIR), PowderX-ray diffraction

The research aims to highlight on the behavioural approach in accounting, and clarify the behavioural implications of the main activities of accounting, and clarify the concept of information inductance within the framework of the behavioural approach and its impact on preparing financial statements. And that the impact of financial information on the behaviour of investment decision-makers, and to achieve the goals of the research, the researcher prepared a questionnaire according to Likert five-step scale, and he took into consideration in preparing it in line with the characteristics of the study community, and that the target community for this questionnaire is the investors in the Iraq Stock Exchange. The researcher reached