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

Background: Periodontitis (PD) is well-known chronic disease affecting the periodontal ligament and alveolar bone, Osteoarthritis (OA) is a chronic joint disease with compound reasons characterized by synovial inflammation, subchondral bone remodeling, also the formation of osteophytes, that cause cartilage degradation. Chronic periodontitis and osteoarthritis are considered widely prevalent diseases and related to tissue destruction due to chronic inflammation in general health and oral health. The aim of this study is todetermine the association of chronic periodontitis and osteoarthritits in patients by analysing tumor necrosis factor alpha TNFα and high sensitive c-reactive protein (hsCRP) in the serum. Materials and Method: A tot

Seeking pharmacist advice about minor ailments is a common practice among Iraqi patients because such advice is free and quick. Unfortunately, the assessment and management of minor ailments by Iraqi pharmacists were inappropriate. Therefore, this study aimed to develop a model for a mobile application that can assist community pharmacists in the diagnosis and management of minor ailments.

The majority of real-world problems involve not only finding the optimal solution, but also this solution must satisfy one or more constraints. Differential evolution (DE) algorithm with constraints handling has been proposed to solve one of the most fundamental problems in cellular network design. This proposed method has been applied to solve the radio network planning (RNP) in the forthcoming 5G Long Term Evolution (5G LTE) wireless cellular network, that satisfies both deployment cost and energy savings by reducing the number of deployed micro base stations (BSs) in an area of interest. Practically, this has been implemented using constrained strategy that must guarantee good coverage for the users as well. Three differential evolution

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

The aim of human lower limb rehabilitation robot is to regain the ability of motion and to strengthen the weak muscles. This paper proposes the design of a force-position control for a four Degree Of Freedom (4-DOF) lower limb wearable rehabilitation robot. This robot consists of a hip, knee and ankle joints to enable the patient for motion and turn in both directions. The joints are actuated by Pneumatic Muscles Actuators (PMAs). The PMAs have very great potential in medical applications because the similarity to biological muscles. Force-Position control incorporating a Takagi-Sugeno-Kang- three- Proportional-Derivative like Fuzzy Logic (TSK-3-PD) Controllers for position control and three-Proportional (3-P) controllers for force contr

This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In ord

This study proposed control system that has been presented to control the electron lens resistance in order to obtain a stabilized electron lens power. This study will layout the fundamental challenges, hypothetical plan arrangements and development condition for the Integrable Optics Test Accelerator (IOTA) in progress at Fermilab. Thus, an effective automatic gain  control (AGC) unit has been introduced which prevents fluctuations in the internal resistance of the electronic lens caused by environmental influences to affect the system's current and power values ​​and keep them in stable amounts. Utilizing this unit has obtained level balanced out system un impacted with electronic lens surrounding natural varieties.