The operation of production planning is a difficult operation and it's required High effect and large time especially it is dynamic activity which it's basic variables change in continuous with the time, for this reason it needs using one of the operation research manner (Dynamic programming) which has a force in the decision making process in the planning and control on the production and its direct affect on the cost of production operation and control on the inventory.

Purpose: The main objective of this paper is, to determine the optimal no. of technicians’ men in a workshop crew of an Industrial System.   Theoretical framework: The purpose of applying these tools is to explore their ability to reduce costs and improvements that can be obtained in the process of providing services to the end customer.   Design/methodology/approach: The literature structure review was built from analyzing 12 of scientific papers and books, from web sciences and the Elsevier database. The papers were analyzed from descriptive, methodologic, and citation characteristics.   Finding:  By applying the equation model of the paper, the optimal no. of technician men in the crew of the workshop can be determined when

Psoriasis is a chronic autoimmune inflammatory skin condition characterized by uncontrolled keratinocyte proliferation and potential systemic manifestations. Its pathogenesis involves activation of both innate and adaptive immune responses, leading to an imbalance in inflammatory cytokines. Interleukin (IL)-12 and IL-23 are key cytokines in the pathophysiology of psoriasis and sustain chronic skin inflammation. Biologic therapies, such as ustekinumab (UST), have been developed to induce long-term remission in moderate-to-severe psoriasis. The objective of this study was to identify differences in serum levels of inflammatory biomarkers [erythrocyte sedimentation rate, high-sensitivity C-reactive protein (hs-CRP), IL-12, IL-17, IL-22, and IL

ZnO organic hybrid junction (electroluminescence EL device) was fabricated using phase segregation method. ZnO-nanoparticle (NPs) was prepared as a colloidal by self–assembly method of Zinc acetate solution with KOH solution. Nanoparticle is employed to form organic-inorganic hybrid film and generate white light emission, while N,N’–diphenyl-N,N’ –bis(3-methylphenyl)-1,1’-biphenyl 4,4’-diamine (TPD) and polymethyl methacrylate (PMMA) are adopted as the organic matrices. ZnO NPs was used to fabricate TPD: PMMA: ZnO NPs hybrid junction device. The photoluminescence (PL) and electroluminescence (EL) spectra of the TPD: PMMA: ZnO NPs hybrid device provided a broad emission band covering entirely the visible spectrum (∼350-∼700

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

          Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discrimina

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121