This study employs wavelet transforms to address the issue of boundary effects. Additionally, it utilizes probit transform techniques, which are based on probit functions, to estimate the copula density function. This estimation is dependent on the empirical distribution function of the variables. The density is estimated within a transformed domain. Recent research indicates that the early implementations of this strategy may have been more efficient. Nevertheless, in this work, we implemented two novel methodologies utilizing probit transform and wavelet transform. We then proceeded to evaluate and contrast these methodologies using three specific criteria: root mean square error (RMSE), Akaike information criterion (AIC), and log

<p>Energy and memory limitations are considerable constraints of sensor nodes in wireless sensor networks (WSNs). The limited energy supplied to network nodes causes WSNs to face crucial functional limitations. Therefore, the problem of limited energy resource on sensor nodes can only be addressed by using them efficiently. In this research work, an energy-balancing routing scheme for in-network data aggregation is presented. This scheme is referred to as Energy-aware and load-Balancing Routing scheme for Data Aggregation (hereinafter referred to as EBR-DA). The EBRDA aims to provide an energy efficient multiple-hop routing to the destination on the basis of the quality of the links between the source and destination. In

An annular two-phase, steady and unsteady, flow model in which a conductingfluid flow under the action of magnetic field is concavely. Two models arepresented, in the model one; the magnetic field is perpendicular to the long side ofthe channel, while in the model two is perpendicular to the short side. Also, westudy, to some extent the single-phase liquid flow.It is found that the motion and heat transfer equations are controlled by differentdimensionless parameters namely, Reynolds, Hartmann, Prandtl, and Poiseuilleparameters. The Laplace transform technique is used to solve each of the motion andheat transfer equations. The effects of each of dimensionless parameters upon thevelocity and heat transfer is analyzed.A comprehensive study fo

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

An overall mathematical model for copper pipe corrosion in flowing water was derived based on mass transfer fundamentals where we introduced the effects of boundary layer velocity, bulk flow velocity and the surface oxide protective film on the corrosion rate. A set of experiments were conducted in a straight 10mm diameter copper pipe, flow of water include six velocities of maximum value 7.33m/sec at 200C and 350C. The good agreement between the calculated and experimental corrosion rate values were achieved , the agreement reached 92% .

Objectives: Umbilical cord blood can be taken at birth and largely gives indication of fetal and maternal conditions. The aim of the study was to investigate the relation between sex hormones in cord blood and birth weight of newborns and pregnancy complications. Methods: Fifty cord blood samples were collected from newborns at labor room of Baghdad Teaching Hospital between May and October 2018. Blood was withdrawn from their mothers for lead analysis. Five milliliters (ml) of cord blood was taken, 3 ml was used for testosterone and estradiol analysis (using enzyme-linked immunosorbent assay) and 2 ml for lead measurement by lead care analyzer. Newborns weight and head circumference were measured. Delivered women were divided into four gro

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

Hematological malignancies are important diseases that need more powerful therapeutics. Even with current targeting therapies, such as rituximab and other chemotherapeutic agents, there is a need to develop new treatment strategies. Combination therapy seems the best option to target the tumor cells by different mechanisms. Virotherapy is a very promising treatment modality, as it is selective, safe, and causes cancer destruction. The Iraqi strain of Newcastle disease virus (NDV) has proved to be effective both in vitro and in vivo. In the current work, we tested its ability on anti-hematological tumors and enhanced current treatments with combination therapy, and studied this combination using Chou–Talalay analysis. p53 concentration was