Background: Traditional teaching methods of cardiopulmonary resuscitation (CPR) are not effective for most learners today. They may lead to lack of retention of survival skills and poor outcomes. Various methods are adopted to provide optimal, effective, and attractive teaching methods. Application (app)-based teaching can be used as an alternative way for learners to develop their knowledge and skills. Despite the large number of professional and nonprofessional trainee members, the high quality of CPR is still not fulfilled. Technology-based learning can prove to be an effective way to teach medical subjects such as pediatric cardiac resuscitation, which require an optimal teaching environ

The statistical distributions study aimed to obtain on best descriptions  of variable sets phenomena, which each of them got one behavior of that distributions .  The estimation operations study for that distributions considered of important things which could n't canceled in variable behavior study, as result  this research came as trial for reaching to best method for information distribution estimation which is generalized linear failure rate distribution, throughout studying the theoretical sides by depending on statistical posteriori methods  like greatest ability, minimum squares method and Mixing method (suggested method).        

The research

Renewable energy technology is growing fast especially photovoltaic (PV) system to move the conventional electricity generation and distribution towards smart grid. However, similar to monthly electricity bill, the PV energy producers can only monitor their energy PV generation once a month. Any malfuntion in PV system components may reduce the performance of the system without notice. Thus, developing a real-time monitoring system of PV production is very crucial for early detection. In addition, electricity consumption is also important to be monitored more frequently to increase energy savings awareness among consumers. Hardware based Internet-of-Thing (IoT) monitoring and control system is widely used. However, the implementation of

Image is an important digital information that used in many internet of things (IoT) applications such as transport, healthcare, agriculture, military, vehicles and wildlife. etc. Also, any image has very important characteristic such as large size, strong correlation and huge redundancy, therefore, encrypting it by using single key Advanced Encryption Standard (AES) through IoT communication technologies makes it vulnerable to many threats, thus, the pixels that have the same values will be encrypted to another pixels that have same values when they use the same key. The contribution of this work is to increase the security of transferred image. This paper proposed multiple key AES algorithm (MECCAES) to improve the security of the tran

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Background: White-spot lesion is one of the problems associated with the fixed orthodontic treatment. The aims of this in-vitro study were to investigate enamel damage depth on adhesive removal when the adhesive were surrounded by sound, demineralized or demineralized enamel that had been re-mineralized prior to adhesive removal using 10% Nano-Hydroxy apatite and to determine the effect of three different adhesive removal techniques. Materials and methods: Composite resin adhesive (3M Unitek) was bonded to 60 human upper premolars teeth which were randomly divided in to three groups each containing ten sound teeth and ten teeth with demineralized and re-mineralized lesions adjacent to the adhesive. A window of 2 mm was prepared on the bucca

The purpose of this work was to study the effects of the Nd:YAG laser on exposed dentinal
tubules of human extracted teeth using a scanning electron microscope (SEM). Eighty 2.5mm-thick
slices were cut at the cementoenamel junction from 20 extracted human teeth with an electric saw. A
diamond bur was used to remove the cementum layer to expose the dentinal tubules. Each slice was
sectioned into four equal quadrants and the specimens were randomly divided into four groups (A to D ).
Groups B to D were lased for 2 mins using an Nd:YAG laser at 6 pulses per second at energy outputs of
80 , 100 and 120 mJ. Group A served as control. Under SEM observation, nonlased specimens showed
numerous exposed dentinal tubules. SEM o