Background A prospective clinical study was
performed to compare the efficacy of the use of lowmolecular-
weight heparin group (enoxparin group)
with control group in the prevention of deep-vein
thrombosis after total knee arthroplasty.
Aim of the study: to assess the prevalence of DVT
after total knee arthroplasty and evaluate the
importance of the use of low molecular weight
heparin in the prevention of this DVT.
Methods Thirty-three patients undergoing total
knee arthroplasty were randomly divided into two
groups. One group consisted of 12 patients who
received no prophylaxis with an anticoagulant (the
control group), other group consisted of 21 patients
who received the low-molecular-weight h

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

In this research, the influence of the fermentation treatments and baking in Iraqi’s flour type (Ibaa 99, Al-rashed, Tamus, Abu-grabe) and Turkish flour type (Muaamel) on phytic acid was investigated. In whole wheat flour, the phytic acid was (1500, 1290, 1450, 1230, 1440 ( mg/ 100 g flour respectively, and the inorganic phosphorous was (29.18, 25.15, 23.89, 20.85, 22.83) mg/100 g flour respectively. The dough prepared from flour with a higher phytic acid content also contained higher amount of phytic acid. During fermentation, degradation of phytic acid occurred. The cumulative loss of phytic acid after fermentation in all type of dough was ~ 23, 22, 34, 26 and 27% respectively،While increased of inorganic phosphorous occurred. The c

The aim of this research is to study the influence of additives on the properties of soap greases, such as lithium, calcium, sodium, lithium-calcium grease, by adding varies additives, such as graphite, molybdenum disulfide, carbon black, corrosion inhibitor, and extreme pressure.
These additives have been added to grease to obtain the best percentages that improve the properties of grease such as load carrying, wear resistance, corrosion resistance, drop point, and penetration.
The results showed the best weight percentages to all types of grease which give good properties are 1.5% extreme pressure additive, 3% graphite, 1% molybdenum disulfide, 2.5% carbon black.
The other hand, the best weight percentage for corrosion inhibit

      In this study, different methods were used for estimating location parameter  and scale parameter for extreme value distribution, such as maximum likelihood estimation (MLE) , method of moment  estimation (ME),and approximation  estimators based on percentiles which is called white method in estimation, as the extreme value distribution is one of exponential distributions. Least squares estimation (OLS) was used, weighted least squares estimation (WLS), ridge regression estimation (Rig), and adjusted ridge regression estimation (ARig) were used. Two parameters for expected value to the percentile  as estimation for distribution f

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