A cross-sectional study was conducted on 80 type 2 diabetic patients aged 20-60 years in Baghdad and 20 non diabetic persons as controls. Laboratory assessment of glucose related parameters; Fasting blood sugar (FBS), Glycated hemoglobin (HbA1c), Insulin and Insulin resistance (IR), renal function test; Blood urea, serum creatinine, Calcium (Ca) and Phosphorus (P), Calcium regulating hormones; Parathyroid hormone (PTH), calcitonin and vitamin D, cytokines, Adiponectin and Tumor necrosis factor (TNF-α) and comparison these parameters between patients and controls. The results: a high significant (p˂0.01) increase in FBG level in the patients (211.34 ± 11.20 mg/dl) as compared with control (85.89 ± 3.07 mg/dl). A high significant (p˂0.01

Hand gestures are currently considered one of the most accurate ways to communicate in many applications, such as sign language, controlling robots, the virtual world, smart homes, and the field of video games. Several techniques are used to detect and classify hand gestures, for instance using gloves that contain several sensors or depending on computer vision. In this work, computer vision is utilized instead of using gloves to control the robot's movement. That is because gloves need complicated electrical connections that limit user mobility, sensors may be costly to replace, and gloves can spread skin illnesses between users. Based on computer vision, the MediaPipe (MP) method is used. This method is a modern method that is discover

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 load shedding  scheme has been extensively implemented as a fast solution for unbalance conditions. Therefore, it's crucial to investigate supply-demand balancing in order to protect the network from collapsing and to sustain stability as possible, however its implementation is mostly undesirable. One of the solutions to minimize the amount of load shedding is the integration renewable energy  resources, such as wind power, in the electric power generation could contribute significantly to minimizing power cuts as it is ability to positively improving the stability of the electric grid. In this paper propose a method for shedding the load base on the priority demands with incorporating the wind po

With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

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

Background: For patients with coronavirus disease(COVID-19), continuous positive airway pressure (CPAP) has been considered as a useful treatment. The goal of CPAP therapy is to enhance oxygenation, relieve breathing muscle strain, and maybe avoid intubation. If applied in a medical ward with a multidisciplinary approach, CPAP has the potential to reduce the burden on intensive care units. Methods: Cross-sectional design was conducted in the ALSHEFAA center for crises in Baghdad. Questionnaire filled by 80 nurses who work in Respiratory Isolation Unit who had chosen by non-probability (purposive) selection collected the data. Then the researcher used an observational checklist to evaluate nurses’ practice. The data was analyzed us