The specialized Journalism plays an important role in our daily lives using all of its different ways, including news, caricature, commentary and dialogue. The specialized Journalism deserves vigilance and interest for it is caring about having a new media system. It is meant by “specialization”, first: identifying the areas of work in which the person concerned possesses great knowledge in the specialty resulting from a long experience, it also means being able to continuously develop required skills in that specialty. The specialization is not only a feature of the press, but also a feature of human development; thousands of years ago, the primitive society consisted of people from different specializations: fishermen, farmers, her

The objective of this review was to describe the COVID-19 complications after recovery.

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m

Background: Molars and premolars are considered as the most vulnerable teeth of caries attack, which is related to the morphology of their occlusal surfaces along with the difficulty of plaque removal. different methods were used for early caries detection that provide sensitive, accurate preoperative diagnosis of caries depths to establish adequate preventive measures and avoid premature tooth treatment by restoration. The aim of the present study was to evaluate the clinical sensitivity and specificity rates of DIAGNOdent and visual inspection as opposed to the ICDAS for the detection of initial occlusal caries in noncavitated first permanent molars. Materials and Methods: This study examined 139 occlusal surface of the first permanent

A vascular necrosis (AVN) is defined as cellular death of bone components due to interruption of the blood supply; the bone structures then collapse, resulting in bone destruction, pain, and loss of joint function. AVN is associated with numerous conditions and usually involves the epiphysis of long bones, such as the femoral head. In clinical practice, AVN is most commonly encountered in the hip. Early diagnosis and appropriate intervention can delay the need for joint replacement. However, most patients present late in the disease course. Without treatment, the process is almost always progressive, leading to joint destruction within 5 years. Treatment of a vascular necrosis depends mainly on early diagnosis which mainly based on clinical

Background: Birth weight is a powerful predictor of infant growth and survival. Evidence now shows that children born with low birth weight face an increased risk of chronic diseases and have many health problems including oral health. The aims of this study were to assess the salivary flow rate, viscosity, and salivary cortisol among low birth weight kindergarten children aged 5 years old in Hilla centre, in relation to dental caries and compares them with the normal birth weight children of the same age and gender. Materials and methods: The total sample involved 80 children (40 low birth weights and 40 normal birth weights) aged 5 years old. The diagnosis and recording of severity of dental caries was recorded through the application of

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an