In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

     A partial temporary immunity SIR epidemic model involv nonlinear treatment rate is proposed and studied. The basic reproduction number  is determined. The local and global stability of all equilibria of the model are analyzed. The conditions for occurrence of local bifurcation in the proposed epidemic model are established. Finally, numerical simulation is used to confirm our obtained analytical results and specify the control set of parameters that affect the dynamics of the model.

This research aims to investigate the approaches adopted by Iraqi newspapers in addressing the COVID-19 pandemic crisis. Employing a descriptive methodology and survey technique, the study conducts content analysis on articles published in three prominent newspapers: Al-Sabah, Al-Mada, and Tareeq Al-Shaab. A multi-stage sampling method was employed, encompassing 260 issues of the aforementioned newspapers. Data collection involved the use of a content analysis questionnaire, with the "How it was said?" method utilized to determine analysis categories.
The results showed that Al-Sabah newspaper adopted a positive approach in addressing COVID-19-related topics, while Al-Mada newspaper remained neutral, and Tare

The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model

Coronavirus disease (Covid-19) has threatened human life, so it has become necessary to study this disease from many aspects. This study aims to identify the nature of the effect of interdependence between these countries and the impact of each other on each other by designating these countries as heads for the proposed graph and measuring the distance between them using the ultrametric spanning tree. In this paper, a network of countries in the Middle East is described using the tools of graph theory.

Osteoarticular disease is a frequent complication of human brucellosis. Vaccination remains a critical component of brucellosis control, but there are currently no vaccines for use in humans, and no in vitro models for assessing the safety of candidate vaccines in reference to the development of bone lesions currently exist. While the effect of Brucella infection on osteoblasts has been extensively evaluated, little is known about the consequences of osteoclast infection. Murine bone marrow-derived macrophages were derived into mature osteoclasts and infected with B. abortus 2308, the vaccine strain S19, and attenuated mutants S19vjbR and B. abortus ∆virB2. While B. abortus 2308 and S19 replicated inside mature osteoclasts, the attenuated

Measuring the efficiency of postgraduate and undergraduate programs is one of the essential elements in educational process. In this study, colleges of Baghdad University and data for the academic year (2011-2012) have been chosen to measure the relative efficiencies of postgraduate and undergraduate programs in terms of their inputs and outputs. A relevant method to conduct the analysis of this data is Data Envelopment Analysis (DEA). The effect of academic staff to the number of enrolled and alumni students to the postgraduate and undergraduate programs are the main focus of the study.

This study aims to measure and analyze the direct and indirect effects of the financial variables, namely (public spending, public revenues, internal debt, and external debt), on the non-oil productive sectors with and without bank credit as an intermediate variable, using quarterly data for the period (2004Q1–2021Q4), converted using Eviews 12. To measure the objective of the study, the path analysis method was used using IBM SPSS-AMOS. The study concluded that the direct and indirect effects of financial variables have a weak role in directing bank credit towards the productive sectors in Iraq, which amounted to (0.18), as a result of market risks or unstable expectations in the economy. In addition to the weak credit ratings of borr