Need organizations today to move towards strategic thinking which means analyzing situations faced by particular challenges of change in the external environment, which makes it imperative for The Organization That to reconsider their strategies and orientations and operations, a so-called re-engineering to meet those challenges and pressures, to try to achieve improvement root in the installation of the organization and methodscompletion of its work towards achieving high levels of performance and that is reflected to achieve its objectives, and this is what aims to Current search to deal with implications characteristics of strategic thinking in the stages of application re-engineering business of the company General Industries

Thirteen isolates were collected from various clinical sources during the periodfrom 22/10/2017 to 22/12/2017. All the isolates were diagnosed based on the microscopic and biochemical propertiesby Vitek-2 Compact system. All isolates formed biofilm 100%, with 30% of isolatesbiofilm produced strongly and 70% on medium. The results of the present study have shown the presence of Curli fimbriae genes in E. cloacae bacteria from cases of urinary tract infections, infected patient with blood bacteremia and inflammation of wounds. Curli fimbriae is considered to be an important factor in the virulence of E.cloacae bacteria, which plays an important role in adhering and combining cells on solid surfaces to form the biofilmand helps in the adhesion

Abstract:

        Organizations need today to move towards strategic innovation, which means the analysis of positions, especially the challenges faced by the change in the external environment, which makes it imperative for the organization that you reconsider their strategies and orientations and operations, a so-called re-engineering to meet those challenges and pressures. Now this research dilemma intellectual two-dimensional, yet my account in not Take writings and researchers effect strategic innovation in re-engineering business processes, according to science and to inform the researcher, and after the application represented in the non-application of such resear

When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat

The present study aims to assess the effect of the Tharthar Canal as an outlet canal that feeds back from the Tharthar Lake on the quality of the Tigris water. Utilizing a Canadian Water Quality Index (CCME-WQI) for the protection of aquatic life Water samples were obtained every month from January to December of 2020. Six different sites were selected: four along the Tigris River and two on the Tharthar Canal. Seven ecological parameters were used to assess water quality depending on importance and availability: water temperature, Water Temperature, Turbidity, Dissolved Oxygen (DO), Total Dissolved Solids (TDS), pH, Nitrate (NO^3-) and Phosphate ( . The study demonstrated that the water quality of the Tharthr canal ranked as a

    The emergence of COVID-19 has resulted in an unprecedented escalation in different aspects of human activities, including medical education. Students and educators across academic institutions have confronted various challenges in following the guidelines of protection against the disease on one hand and accomplishing learning curricula on the other hand. In this short view, we presented our experience in implementing e-learning to the undergraduate nursing students during the present COVID-19 pandemic emphasizing the learning content, barriers, and feedback of students and educators. We hope that this view will trigger the preparedness of nursing faculties in Iraq to deal with this new modality of learning and improve it should t

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the l

In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med