Due to the deliberate disposal of industrial waste, a great amount of petroleum hydrocarbons pollute the soil and aquatic environments. Bioremediation that depends on the microorganisms in the removal of pollutants is more efficient and cost-effective technology. In this study, five rhizobacteria were isolated from Phragmites australis roots and exposed to real wastewater from Al-Daura refinery with 70 mg/L total petroleum hydrocarbons (TPH) concentration. The five selected rhizobacteria were examined in a biodegradation test for seven days to remove TPH. The results showed that 80% TPH degradation as the maximum value by Sphingomonas Paucimobilis as identified with Vitek® 2 Compact (France).

The study searches for the possibility of using duckweed Lemna spp. to reduce the concentration of heavy metals (zinc and iron) in the wastewater of Baghdad by culturing two different densities of the plant with a fresh weights 5 and 10 g/l and without the plant under optimum uncontrolled conditions. The result showed that there was a significant differences at the possibility level of (p? 0.05) for the three treatments, as the highest percentages for zinc removal in the second day for the plant treatment of 5 g/l were 66.40%, while the highest percentage of iron removal were in the tenth days for the plant treatment 10 g/l were 80 %, and noticed that the increase of the heavy metals concentrations accumulated in the plant after bei

Cancer remains a leading cause of mortality and morbidity worldwide. Advances in cancer therapies—including immunotherapies (e.g., checkpoint inhibitors, gene-targeted therapies), antibody-based cancer toxins, chemotherapy, radiotherapy, and surgery—have significantly improved survival rates 1,2. However, this progress has led to a surge in the prevalence of cardiovascular disease (CVD) among cancer survivors, now recognised as a leading cause of mortality in this population 3,4. These intersecting burdens highlight the growing need to prevent, detect, and manage cardiovascular complications in cancer care pathways and call for important initiatives in establishing cardio-oncology services globally.

Transformers are a specific category of neural network design. Transformers often depend on extensive pre-training on a large scale and exhibit a notable degree of computational complexity. The disadvantage of using this method is a significant increase in computational complexity, which necessitates a significant commitment of time and computing resources in order to successfully work with these models. Transformer networks possess the desirable benefit of extracting distant characteristics effectively via their self-attention mechanism. In this paper, the Global Self-Attention Transformer module is applied to tackle these issues. The model is based on a segmentation problem called Brain-GS that works as a mechanism and encompasses

Recent advances in wireless communication systems have made use of OFDM technique to achieve high data rate transmission. The sensitivity to frequency offset between the carrier frequencies of the transmitter and the receiver is one of the major problems in OFDM systems. This frequency offset introduces inter-carrier interference in the OFDM symbol and then the BER performance reduced. In this paper a Multi-Orthogonal-Band MOB-OFDM system based on the Discrete Hartley Transform (DHT) is proposed to improve the BER performance. The OFDM spectrum is divided into equal sub-bands and the data is divided between these bands to form a local OFDM symbol in each sub-band using DHT. The global OFDM symbol is formed from all sub-bands together using

Short Multi-Walled Carbon Nanotubes functionalized with OH group (MWCNTs-OH) were used to synthesize flexible MWCNTs networks. The MWCNTs suspension was synthesized using Benzoquinone (BQ) and N, N Dimethylformamide alcohol (DMF) in specific values and then deposited on filter paper by filtration from suspension (FFS) method. Polypyrrole (PPy) conductive polymer doped with metallic nanoparticles (MNPs) prepared using in-situ chemical polymerization method. To improve the properties of the MWCNTs networks, a coating layer of (PPy) conductive polymer, PPy:Ag nanoparticles, and PPy: Cu nanoparticles were applied to the network. The fabricated networks were characterized using an X-ray diffractometer (XRD), UV-Vis. spectrometer, and Ato

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

The aim of the research is to apply fibrewise multi-emisssions of the paramount separation axioms of normally topology namely fibrewise multi-T0. spaces, fibrewise multi-T1 spaces, fibrewise multi-R0 spaces, fibrewise multi-Hausdorff spaces, fibrewise multi-functionally Hausdorff spaces, fibrewise multi-regular spaces, fibrewise multi-completely regular spaces, fibrewise multi-normal spaces and fibrewise multi-functionally normal spaces. Also we give many score regarding it.