University of Kerbala

Abdulridha A.A., Albo Hay Allah M.A., Makki S.Q., Sert Y., Salman H.E., Balakit A.A.

New Azo Schiff compound namely 4-((4-hydroxy-3-((pyridine-2-ylimino)methyl)phenyl)diazenyl)benzonitrile (5) which is denoted as AS was synthesized and characterized using FT-IR, 13C NMR and 1H NMR spectroscopy. The new compound was evaluated as corrosion inhibitor for carbon steel in 1 M H2SO4, using electrochemical and gravimetric techniques. Tween-80 surfactant was added to enhance the solubility of AS in the acidic medium. The inhibition efficiency was found to be dependent on the concentration of AS and temperature, the highest inhibition efficiency values (91.32% and 90.30% by potentiodynamic and weight loss measurements respectively) were recorded in the presence of relatively low concentration of AS (0.08 mM) at 303 K, and it acts as anodic inhibitor. To understand the mechanism of the corrosion inhibition, the adsorption of AS onto carbon steel surface was studied, the results indicated that the adsorption process obeys Langmuir adsorption isotherm, the calculated ΔGads values were found to be around −37 kJ mol−1 which indicates that AS is adsorbed on the carbon steel surface by chemical and physical interactions. For further investigations DFT studies were employed to explain the nature of interaction between the AS molecules (neutral and protonated) and metal surface. Finally, the morphology of both corroded and inhibited surfaces were studies by scanning electron microscopy (SEM) and atomic force microscopy (AFM) techniques which confirmed the high inhibition efficiency of AS at the optimum conditions, a significant reduction in the distortion and roughness of the surface were observed (the surface roughness was reduced from 17.10 nm to 2.15 nm as measured by AFM).

Tao H., Hameed M.M., Marhoon H.A., Zounemat-Kermani M., Heddam S., Kim S., Sulaiman S.O., Tan M.L., Sa’adi Z., Mehr A.D., Allawi M.F., Abba S.I., Zain J.M., Falah M.W., Jamei M., et. al.

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.

El-Sayed R.A., Jebur A.B., Kang W., El-Demerdash F.M.

Mycotoxins are potentially hazardous secondary metabolites produced by filamentous fungi (molds). These small molecular weight compounds (often less than 1 000 Da) are found in nature and are almost unavoidable. They can infiltrate our food chain either directly or indirectly through contaminated plant-based food components or toxigenic fungal development on food. Mycotoxins can build up in ripening corn, cereals, soybeans, sorghum, peanuts, and other food and feed crops in the field and during transportation. Humans and animals can get sick from eating mycotoxin-contaminated food or feed, which can result in acute or chronic poisoning. In addition to worries regarding direct consumption of mycotoxin-contaminated foods and feeds, the public is concerned about the possibility of ingesting mycotoxin residues or metabolites in animal-derived food products such as meat, milk, or eggs. Three fungal genera dominate mycotoxin production: Aspergillus, Fusarium , and Penicillium . Although more than 300 mycotoxins have been found, only six of them (aflatoxins, trichothecenes, zearalenone, fumonisins, ochratoxins, and patulin) are consistently detected in food, posing unpredictability and continuous food safety issues worldwide. This article focused on some of them, which are typically found in foods that have been contaminated by one or more of these mycotoxins.

Albo Hay Allah M.A., Balakit A.A., Salman H.I., Abdulridha A.A., Sert Y.

Hasan A.M., AL-Jawad M.M., Jalab H.A., Shaiba H., Ibrahim R.W., AL-Shamasneh A.R.

Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

Arif M., Rasool Abid H., Keshavarz A., Jones F., Iglauer S.

We conducted measurements of hydrogen adsorption on three coal samples of varying ranks at high pressure (0 to 102 bar) and elevated temperatures (303 K to 333 K) to assess their hydrogen storage potential. The excess adsorption capacity increased with increasing pressure but decreased with increasing temperature irrespective of coal rank. The highest hydrogen adsorption recorded was 0.721 mol/kg for the anthracite coal at 303 K and 102 bar. Furthermore, the hydrogen adsorption capacity correlated positively with coal vitrinite and fixed carbon contents (i.e. the high-rank coal exhibited greater adsorption), while all samples depicted predominantly type-I adsorption behavior for the entire pressure range. Micropore analysis and Fourier-transform infrared spectroscopy measurements were conducted to explore the microstructural and surface chemistry associated with these adsorption trends. The micropore content of the three samples followed the order: anthracite > sub-bituminous > bituminous, while H2 adsorption followed the trend: anthracite > bituminous > sub-bituminous - i.e., no direct correlation between coal micropore content and its H2 adsorption capacity - attributable to high clay content of bituminous coal which lowered its micropore content. Moreover, bituminous, and sub-bituminous samples exhibited an abundance of oxygen-containing functional groups, while anthracite coal depicted notable aromatic content - suggesting that the H2 adsorption capacity is a complex function of coal surface chemistry and micropore content. Overall, high-rank coal seams at high pressure and temperature showed the largest hydrogen adsorption i.e., analogous to CO2 adsorption potential albeitat lower absolute values. These results, therefore, provide preliminary data on the hydrogen storage potential of coal seams and the associated scientific understanding of the mechanisms causing hydrogen adsorption.

Salman M.M., Al-Obaidi Z., Kitchen P., Loreto A., Bill R.M., Wade-Martins R.

Neurodegenerative diseases (NDs) including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.

Marofi F., Rahman H.S., Al-Obaidi Z.M., Jalil A.T., Abdelbasset W.K., Suksatan W., Dorofeev A.E., Shomali N., Chartrand M.S., Pathak Y., Hassanzadeh A., Baradaran B., Ahmadi M., Saeedi H., Tahmasebi S., et. al.

Acute myeloid leukemia (AML) is a serious, life-threatening, and hardly curable hematological malignancy that affects the myeloid cell progenies and challenges patients of all ages but mostly occurs in adults. Although several therapies are available including chemotherapy, allogeneic hematopoietic stem cell transplantation (alloHSCT), and receptor-antagonist drugs, the 5-year survival of patients is quietly disappointing, less than 30%. alloHSCT is the major curative approach for AML with promising results but the treatment has severe adverse effects such as graft-versus-host disease (GVHD). Therefore, as an alternative, more efficient and less harmful immunotherapy-based approaches such as the adoptive transferring T cell therapy are in development for the treatment of AML. As such, chimeric antigen receptor (CAR) T cells are engineered T cells which have been developed in recent years as a breakthrough in cancer therapy. Interestingly, CAR T cells are effective against both solid tumors and hematological cancers such as AML. Gradually, CAR T cell therapy found its way into cancer therapy and was widely used for the treatment of hematologic malignancies with successful results particularly with somewhat better results in hematological cancer in comparison to solid tumors. The AML is generally fatal, therapy-resistant, and sometimes refractory disease with a disappointing low survival rate and weak prognosis. The 5-year survival rate for AML is only about 30%. However, the survival rate seems to be age-dependent. Novel CAR T cell therapy is a light at the end of the tunnel. The CD19 is an important target antigen in AML and lymphoma and the CAR T cells are engineered to target the CD19. In addition, a lot of research goes on the discovery of novel target antigens with therapeutic efficacy and utilizable for generating CAR T cells against various types of cancers. In recent years, many pieces of research on screening and identification of novel AML antigen targets with the goal of generation of effective anti-cancer CAR T cells have led to new therapies with strong cytotoxicity against cancerous cells and impressive clinical outcomes. Also, more recently, an improved version of CAR T cells which were called modified or smartly reprogrammed CAR T cells has been designed with less unwelcome effects, less toxicity against normal cells, more safety, more specificity, longer persistence, and proliferation capability. The purpose of this review is to discuss and explain the most recent advances in CAR T cell-based therapies targeting AML antigens and review the results of preclinical and clinical trials. Moreover, we will criticize the clinical challenges, side effects, and the different strategies for CAR T cell therapy.

Vinoth S., Vemula H.L., Haralayya B., Mamgain P., Hasan M.F., Naved M.

Cloud computing is one of the most powerful inventions that has grabbed the curiosity of technologists all around the world. Cloud computing has many advantages, but it also has a slew of security risks that no organization can afford to ignore. For a successful Cloud Computing adoption in a corporation, proper planning and awareness of emerging risks, threats, vulnerabilities, and potential solutions are necessary. As a result, determining the most effective solution instructions to increase cloud security has become important for all cloud operations. In this research, we are investigating and assessing the most noteworthy network security and data security risks on cloud systems based on a literature review. Because many businesses have promoted and marketed virtual environments as the solution to current security concerns, a deeper look finds that virtualization adds extra software to the network system, which may have a negative influence on security if built and deployed poorly. Furthermore, data center hubs link their servers through software, which means that if something goes wrong, the final effect might be damaging to security. Consumers must rely on trust mechanisms since they have no control over the cloud's resources. This article examines several cloud computing applications in banking and e-commerce, as well as the security issues associated with them.

Majdi H.S., Shubbar A.A., Nasr M.S., Al-Khafaji Z.S., Jafer H., Abdulredha M., Masoodi Z.A., Sadique M., Hashim K.

The development in the construction sector and population growth requires an increase in the consumption of construction materials, mainly concrete. Cement is the binder in concrete, so increasing cement production will increase the energy consumed, as well as in the emission of carbon dioxide. This harmful effect of the environment led to the search for alternative materials for cement, as the waste or by-products of other industries is a promising solution in this case. Among these common materials are ground granulated blast furnace slag (GGBS) and cement kiln dust (CKD). This dataset describes the compressive strength and ultrasonic pulse velocity of mortar consisted of high content of GGBS and CKD combinations as a partial substitute for cement (up to 80%) at the ages of 1, 2, 3, 7, 14, 21, 28, 56, 90 and 550 days. This dataset can help the researchers to understand the behaviour of GGBS and CKD in high replacement levels for cement during early (1 day) and later ages (550 days). According to this understanding, the authors believe that the data available here can be used to produce more environmentally friendly mortar or concrete mixtures by significantly reducing the amount of cement used by replacing it with waste or by-products of other industries.