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

The process involved isolating E. faecium from the gut of honeybees, screening the bacterium for bacteriocin-like inhibitory substance (BLIS), evaluating its impact on the expression of the mexA gene in multidrug-resistant (MDR) P. aeruginosa, and determining the role of bacteriocin in treating infected wounds in mice through histopathological examination. After evaluating the best circumstances for producing BLIS, it was discovered that glucose was a superior carbon source and yeast extract was the best source of nitrogen. The pH was found to be 5, the ideal incubation time was 72 hours, and ammonium sulfate salt was used for partial purification at 80% saturation. The identification of MDR P. aeruginosa isolates from pus infection

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo

Background: Client satisfaction with the immunization service is used to evaluate the quality of the admitted service and at the same time it affects the goodness of the health care outcome.

Objectives: This study assessed the satisfaction with immunization services offered to children and factors affecting this satisfaction.

Methods: Exit interviews for clients were conducted in Baghdad, Al-Karkh in a representative sample of primary health care centers to assess clients’ satisfaction with immunization services. Clients are companions of children encountered at study settings.

Results: Among the 253 respondent clients, 183 (72.3%) reflected satisfaction with the immunization

هدفت الدراسة الى التعرف على درجة اتخاذ القرار وعلاقتها ببعض المهارات  الأساسية الهجومية بكرة السلة للطالبات ، اذ يعد اتخاذ القرار قدرة الفرد على التوصل لحل مشكلة اعتراضية أو موقف محير، وذلك باختيار حل من البدائل الموجودة أو المبتكرة، وهذا الاختيار يعتمد على المعلومات التي جمعها الفرد حول المشكلة وعلى القيم والعادات والخبرة والتعليم والمهارات الفردية وتمتاز لعبة كرة السلة بوجود الكثير من المواقف المتغ

Wellbore instability is one of the major issues observed throughout the drilling operation. Various wellbore instability issues may occur during drilling operations, including tight holes, borehole collapse, stuck pipe, and shale caving. Rock failure criteria are important in geomechanical analysis since they predict shear and tensile failures. A suitable failure criterion must match the rock failure, which a caliper log can detect to estimate the optimal mud weight. Lack of data makes certain wells' caliper logs unavailable. This makes it difficult to validate the performance of each failure criterion. This paper proposes an approach for predicting the breakout zones in the Nasiriyah oil field using an artificial neural network. It