This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

This paper describes the digital chaotic signal with ship map design. The robust digital implementation eliminates the variation tolerance and electronics noise problems common in analog chaotic circuits. Generation of good non-repeatable and nonpredictable random sequences is of increasing importance in security applications. The use of 1-D chaotic signal to mask useful information and to mask it unrecognizable by the receiver is a field of research in full expansion. The piece-wise 1-D map such as ship map is used for this paper. The main advantages of chaos are the increased security of the transmission and ease of generation of a great number of distinct sequences. As consequence, the number of users in the systems can be increased. Rec

A perturbed linear system with property of strong observability ensures that there is a sliding mode observer to estimate the unknown form inputs together with states estimation. In the case of the electro-hydraulic system with piston position measured output, the above property is not met. In this paper, the output and its derivatives estimation were used to build a dynamic structure that satisfy the condition of strongly observable. A high order sliding mode observer (HOSMO) was used to estimate both the resulting unknown perturbation term and the output derivatives. Thereafter with one signal from the whole system (piton position), the piston position make tracking to desire one with a simple linear output feedback controller after ca

Background and Aim: due to the rapid growth of data communication and multimedia system applications, security becomes a critical issue in the communication and storage of images. This study aims to improve encryption and decryption for various types of images by decreasing time consumption and strengthening security. Methodology: An algorithm is proposed for encrypting images based on the Carlisle Adams and Stafford Tavares CAST block cipher algorithm with 3D and 2D logistic maps. A chaotic function that increases the randomness in the encrypted data and images, thereby breaking the relation sequence through the encryption procedure, is introduced. The time is decreased by using three secure and private S-Boxes rather than using si

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

One of the most important environmental issues is the responsible effective and economic treatment of drilling waste especially oily waste.
In this research two fungal isolates named Pleurotus ostreatus and Trichoderma harzianum were chosen for the first time to treat biologically the oily drilled cuttings contaminated with diesel which resulted from drilling oil wells use oil based muds (OBMs).
The results showed that the fungi under study utilized the hydrocarbon of contaminated soil as a source of nutrient and growth and that both fungi can be considered hydrocarbon degrading microorganisms. The used biotreatment is cost effective process since most of the materials used in the cultivation and growth of the present fungi were av

    Clopidogrel is a prodrug that must be transformed into an active metabolite by hepatic cytochrome P450 (CYP) isoenzymes to prevent platelet clotting. Polymorphisms of the CYP2C19 gene can cause a reduction or complete loss of CYP2C19 enzyme activity resulting in inhibiting clopidogrel metabolism, effectiveness and increase stroke recurrence risk in ischemic stroke patients. This study aims to investigate the correlation between genetic polymorphisms in CYP2C19*2 and*3 and recurrent risk in patients with ischemic stroke taking clopidogrel 75mg in Kurdistan region –Iraq. This retrospective case-control study was carried out at Kurdistan, Erbil, Medicina medical center, and Rizgary general hospital from January 2021 to

Introduction to Medical and Biological Statistics for Pharmacy Students and Medical Groups (Undergraduate & Postgraduate) - ISBNiraq.org