KE Sharquie, AA Noaimi, AF Hameed, Journal of Cosmetics, Dermatological Sciences and Applications, 2013 - Cited by 11

Background: Clinicians and investigators consider the normal range of bowel habit and frequency as between 3 to 21 motions per week . Stool frequency out side the normal range may be unusual but may not be abnormal in the sense of a disease . And according to the consistency, the normal stool ranges from porridge like to hard and pellety .Objectives: To establish a basic data about the bowel habits (consistency and frequency) in a sample of healthy Iraqi population; in addition to learn about their definition of constipation and diarrhea.Methods: Prospective study from Jan 2000- Jun 2000 at Al-Yarmouk teaching hospital, Baghdad. Questionnaires were distributed to 950 healthy persons of different age group .The questionnaire included: Det

Clinicians and investigators consider the normal range of bowel habit and frequency as between 3 to 21 motions per week. Stool frequency outside the normal range may be unusual but may not be abnormal in the sense of a disease, and according to the consistency, the normal stool ranges from porridge like to hard and pellety.
Objectives: To establish a basic data about the bowel habits (consistency and frequency) in a sample of healthy Iraqi population; in addition to learn about their definition of constipation and diarrhea.
Methods: Prospective study from Jan 2000- Jun 2000 at Al-Yarmouk teaching hospital, Baghdad. Questionnaires were distributed to 950 healthy persons of different age group .The questionnaire included: Detailed hi

  

To evaluate and compare serum Leptin hormone level between Iraqi male & female and the relation between this hormone & BMI in these two groups.

A total of 44 normal male & female subjects were included in this study

{Group 1 : 22 female } , { Group 2 : 22 male}.

Serum Leptin hormone ,BMI &fasting blood glucose were measured for both groups.

   Serum Leptin level in group 1 was (8.82 + 2.9 μg/L) where as in group 2 it was (4.65 + 3.2 μg/L) . These changes were statistically significant. Fasting blood glucose levels were technically within the normal value (

Software-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they accou

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines