Background: The repair of bone defects remains a major clinical challenge in dentistry. Bone is a highly vascularized tissue reliant on the close spatial and temporal connection between blood vessels and bone cells to maintain skeletal integrity. The health promotive , preventive, and curative properties of herbs were recognized by the ancient and the present pharmacist and physicians to form the theoretical foundations in Medicine. Objective: Immunohistochemistry of osteocalcin and histological study to prove that symphytum officinale oil when applied locally on generated bone defect healing in rat tibia, it was very effectiveness. Patients and Methods: 0ur study fourty male rats , weighting (250-350) grams ,aged (5 7)months ,was

ABSTRACT : The restoration of bone continuity and bone union are complex processes and their success is determined by the effectiveness of osteosynthesis. The use of plants for healing purposes predates human history and forms the source of current modern medicine. This research was planned to study the histological and immunohisto-chemistry of osteocalcin to evaluate of effect of local application of lepidium sativum oilon healing of induced bone defect in rat tibia. In this study, fourty albino male rats, weighting (300-400) gram, aged (6-8) months, will be used under control conditions of temperature, drinking and food consumption. The animals will subject for a surgical operation of medial side of tibiae bone, in control group the bone

The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.

In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete

The use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree mode

This research basically gives an introduction about the multiple intelligence
theory and its implication into the classroom. It presents a unit plan based upon the
MI theory followed by a report which explains the application of the plan by the
researcher on the first class student of computer department in college of sciences/
University of Al-Mustansiryia and the teacher's and the students' reaction to it.
The research starts with a short introduction about the MI theory is a great
theory that could help students to learn better in a relaxed learning situation. It is
presented by Howard Gardener first when he published his book "Frames of
Minds" in 1983 in which he describes how the brain has multiple intelligen

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca