Many therapeutic methods used to encourage reparative processes of cartilage and accelerate their healing such as drugs, magneto-laser and so on. Twenty four adult New Zealand rabbits used in this study. They were divided in to two groups; control and treaded with He –Ne laser. A square skin flap done on the medial aspect of both auricles followed by pealing a square piece of cartilage from the auricle then the flaps sutured .The site of the operation in the rabbits of the treated group were irradiated with He –Ne laser 5mw power for seven days began after the operation directly. 3 rabbits from each group used for collection of specimens for histopathological examination at the 1, 2, 4 & 6 weeks post the operation. Significant

The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modifie

The syntheses, characterization and experimental solid state X-ray structures of five low-spin paramagnetic 2-pyridyl-(1,2,3)-triazole-copper compounds, [Cu(Ln)2Cl2], are presented in this study, for the following five Ln ligands: L1 = 2-(1-(p-tolyl)-1H-(1,2,3-triazol-4-yl)pyridine), L2 = 2-(1-(4- chlorophenyl)-1H-(1,2,3-triazol-4-yl)pyridine), L3 = 4-(4-(pyridin-2-yl)-1H-(1,2,3-triazol-4-yl)benzonitril), L4 = 2-(1-phenyl-1H-(1,2,3-triazol-4-yl)pyridine) and L5 = 2-(1-(4-(trifluoromethyl)phenyl)-1H-(1,2,3- triazol-4-yl)pyridine). These five [Cu(Ln)2Cl2] complexes each contain two bidentate 2-pyridyl-(1,2,3)- triazole (Ln) and two chloride ions as ligands, with the Cu–N(pyridine) bonds, Cu–N(triazole) and Cu–Cl bonds trans to each othe

With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica

Type 2 diabetes mellitus (T2DM) is the most frequent endocrinal disease commonly associated with thyroid disorders .The study is conducted at the Specialized Center for Endocrinology and Diabetes in Baghdad ,during December 2014 up to October 2015.This study was done to investigate the prevalence of anti- thyroid peroxidase (Anti-TPO) antibody in patients suffered from type 2 diabetes with thyroid disorders .The study groups included a total number of 80 subjects consisting of 60 type 2 diabetic patients divided into 20 hyperthyroidism subjects (group 1) ,20 hypothyroidism subjects (group 2), 20 euthyroidism subjects (group 3) and 20 healthy controls (group 4) . The fasting blood samples were analyzed for (T3,T4,TSH) by using Vitek Immuno d

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac

תקציר :

 המחקר הזה הוא ניסיון לשפוך אור על נושא מרכזי וחשוב בחייהם של היהודים, "הממד הדתי" אצל היהודים, מחקרי הנקרא "הממד הדתי בסיפור העברי המודרני" גם מתייחס להשפעת התרבות הדתית של המספר והחוג המשפחתי שחי בו, ואיך שיקף המספר את כל הדברים האלה ביצירותיו הסיפורית .

המספר בוחר במילים ובמונחים בעלי משמעויות דתיות או מביא את הסיפור הזה אשר קרוב אל נושא הסיפור ההולך באותה מגמה .גם כן השפעת התיאולג

The accurate identification of internal and external pressures in thick-walled hyperelastic vessels is a challenging inverse problem with significant implications for structural health monitoring, biomedical devices, and soft robotics. Conventional analytical and numerical approaches address the forward problem effectively but offer limited means for recovering unknown load conditions from observable deformations. In this study, we introduce a Graph-FEM/ML framework that couples high-fidelity finite element simulations with machine learning models to infer normalized internal and external pressures from measurable boundary deformations. A dataset of 1386 valid samples was generated through Latin Hypercube Sampling of geometric and l