Background: This study aimed to assess the effect of tooth width in malocclusion in relation to normal, crowding, and spacing dentition. Materials and methods: The sample included dental casts of some dental students and orthodontic patients; their age was (18-25) years and having three groups normal, crowding, and spacing dentition groups. The sample was equally divided to three groups normal, crowding, and spacing dentition groups, each group contained 50 maxillary and 50 mandibular casts that were further subdivided by gender; all the stone casts were measured by highly sensitive digital vernier. Results and Conclusions: Non-significant side difference was found in both dental arches in the three studied groups. Males had higher mesiodis

Recently, research has focused on non-thermal plasma (NTP) technologies as a way to remove volatile organic compounds from the air stream, due to its distinctive qualities, which include a quick reaction at room temperature. In this work, the properties of the plasma generated by the dielectric barrier discharge (DBD) system and by a glass insulator were studied. Plasma was generated at different voltages (3, 4, 6, 7, 8 kV ) with a fixed distance between the electrodes of 5 mm, and a constant argon gas flow rate of (2.5) I/min. DBD plasma emission spectra were recorded for each voltage. The Boltzmann plot method was used to calculate the electron temperature in the plasma ( ), and the Stark expansion method was used to calculate the elec

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

Data generated from modern applications and the internet in healthcare is extensive and rapidly expanding. Therefore, one of the significant success factors for any application is understanding and extracting meaningful information using digital analytics tools. These tools will positively impact the application's performance and handle the challenges that can be faced to create highly consistent, logical, and information-rich summaries. This paper contains three main objectives: First, it provides several analytics methodologies that help to analyze datasets and extract useful information from them as preprocessing steps in any classification model to determine the dataset characteristics. Also, this paper provides a comparative st

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Complexes ofCo(ll),Cu(||),Ni(||),pt(|| ),and pd(||) with N3O-chelating Ligand Incorporating Azo and Shiff Base Moieties ;synthesis, spectroscopic ,Thermal Decomposition Theoretical

Landlocked countries are displayed geopolitical new geo-political and intended to
countries that do not have sea views, a phenomenon present in four continents of the world
are: Africa, Europe, and Asia, and South America and the number arrived at the present time
to the (44) state the largest number of them in the continent it arrived in Africa (16) countries
in Asia (13) countries and Europe (13) In the State of South America two. This phenomenon
emerged due to the division of federations and empires and colonial treaties and others. But
the negative effects suffered by these countries may vary from one country to another, since
these countries in the continent of Europe, for example, is different from the same cou