Abstract:

 The research concerned the study of the railway transport sector in selected countries that sought to raise the efficiency of the railway network and develop it, after realizing the importance of this vital sector, which is a link between it and the rest of the other economic sectors.

The research sought to explain the methods, methods and procedures adopted by these countries for the development of the railway sector, and to benefit from these experiments to improve the efficiency of the railway transport sector in Iraq.

The railway transport sector in Iraq suffers from the erosion of railway lines and mobile units such as locomotives, pas

This review examines how artificial intelligence (AI) including machine learning (ML), deep learning (DL), and the Internet of Things (IoT) is transforming operations across exploration, production, and refining in the Middle Eastern oil and gas sector. Using a systematic literature review approach, the study analyzes AI adoption in upstream, midstream, and downstream activities, with a focus on predictive maintenance, emission monitoring, and digital transformation. It identifies both opportunities and challenges in applying AI to achieve environmental and economic goals. Although adoption levels vary across the region, countries such as Saudi Arabia, the UAE, and Qatar are leading initiatives that align with global sustainability targets.

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

In present work the effort has been put in finding the most suitable color model for the application of information hiding in color images. We test the most commonly used color models; RGB, YIQ, YUV, YCbCr1 and YCbCr2. The same procedures of embedding, detection and evaluation were applied to find which color model is most appropriate for information hiding. The new in this work, we take into consideration the value of errors that generated during transformations among color models. The results show YUV and YIQ color models are the best for information hiding in color images.

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

Gymnastics play from sports games that need to use appropriate methods and strategies that address mental abilities and that let the learner create and think about better performance with the supervision and guidance of the teacher. The researcher has chosen meditative thinking, which is a kind of thinking that needs to be taken care of. It is thinking about the situation in front of the individual, analysing it to his elements and drawing up plans that need to be understood with a view to reaching the results required by the situation and evaluating the results in the light of the plans. The analysis of the situation looks to different elements and look for internal relationships between these elements in this case. The problem is that fem

A study was conducted to evaluate the antibacterial effect of Phyllanthus emblica extract (ethanol:methanol, 1:1) against Pseudomonas aeruginosa, Staphylococcus aureus and Escherichia coli at different concentrations, i.e. 0.625, 1.25, 2.50, 5.0, 10.0 and 20.0 mg/ml. The antibacterial activity was determined by the agar well diffusion method to investigate the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). The alcoholic extract of Phyllanthus emblica had the highest antibacterial activity at 20 mg/ml and 5 mg/ml except for Pseudomonas aeruginosa where the value of inhibition was between 20 and 10 mg/ml. The MIC concentrations were mostly very high and ranged from 5 to 1.25 mg/ml, while the MBC range fro

In the present study, mixed ligand compounds of Mn(II), Ni(II), Co(II), Cu(II), Cd(II) and ‎Hg(II) were synthesized using new Ligand N1,N4-bis (pyrimidin-2-ylcarbamothioyl) ‎succinimide (NPS) derived from [Butanedioyl diisothiocyanate with 2- aminipyridine] as ‎first ligand, proline (pro) as second ligand and evaluation of their antioxidant activities for ‎ligand, nickel and cobalt complex towards 1.1-Di-phenyl-2picrylhydrazyl (DPPH) will be ‎compared to the standard anti-oxidants (i.e. the ascorbic acid). Those materials that have ‎been prepared provided results are a result of exhibiting different activities of the radical ‎scavenging for all of the compounds. Compounds were observed then confirmed through ‎the Fourier-tra