University of Kerbala

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.

The multipotency property of mesenchymal stem cells (MSCs) has attained worldwide consideration because of their immense potential for immunomodulation and their therapeutic function in tissue regeneration. MSCs can migrate to tissue injury areas to contribute to immune modulation, secrete anti-inflammatory cytokines and hide themselves from the immune system. Certainly, various investigations have revealed anti-inflammatory, anti-aging, reconstruction, and wound healing potentials of MSCs in many in vitro and in vivo models. Moreover, current progresses in the field of MSCs biology have facilitated the progress of particular guidelines and quality control approaches, which eventually lead to clinical application of MSCs. In this literature, we provided a brief overview of immunoregulatory characteristics and immunosuppressive activities of MSCs. In addition, we discussed the enhancement, utilization, and therapeutic responses of MSCs in neural, liver, kidney, bone, heart diseases, and wound healing.

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.

The power conversion efficiency (PCE) of cesium lead halide (CsPbX 3, X = l, Br, and Cl)-based all-inorganic perovskite solar cells (PSCs) is still struggling to compete with conventional organic–inorganic halide perovskites. A combined material and device-related analysis is much needed to understand the working principle to explore the efficiency potential of CsPbX 3 -based PSCs. Therefore, here, density functional theory (DFT) and SCAPS-1D-based studies were reported to evaluate the photovoltaic (PV) performance of CsPbBr 3 -based PSCs. DFT is first applied to assess and extract structural and optoelectronic properties (band structure, density of states, Fermi surface, and absorption coefficient) of the considered absorber layer. The calculated electronic band gap ( E g ) of the CsPbBr 3 absorber was 1.793 eV, which matched well with the earlier computed theoretical value. Additionally, the Pb 6p orbital contributed largely to the calculated density of states (DOS), and the electronic charge density map showed that the Pb atom acquired the majority of charges. In order to examine the optical response of CsPbBr 3, optical characteristics were computed and correlated with electronic properties for its probable photovoltaic applications. Fermi surface computation showed multiband characters. Furthermore, to look for a suitable combination of the charge transport layer, a total of nine HTLs (Cu 2 O, CuSCN, P3HT, PEDOT:PSS, Spiro-MeOTAD, CuI, V 2 O 5, CBTS, and CFTS) and six ETLs (TiO 2, PCBM, ZnO, C 60, IGZO, and WS 2 ) are used considering the experimental E g (2.3 eV). The best power conversion efficiency (PCE) of 13.86% is reported for TiO 2 and CFTS in combination with the CsPbBr 3 absorber. The effects of operating temperature, series and shunt resistances, Mott–Schottky, capacitance, generation and recombination rates, quantum efficiency, and current–voltage density were also examined. The resulting PV properties were also compared with previously published data. Results reported in this study will pave the way for the development of high-efficiency all-inorganic CsPbBr 3 -based solar cells in the future.

Biogas is obtained from the breakdown of biomass by microorganisms and bacteria in the absence of oxygen. Biogas is considered a renewable source of energy, similar to solar energy and wind energy. Biogas can be produced from biomass or bio-waste; thus, it is environmentally friendly. Biogas is obtained in a suspended monoxide decomposition process by anaerobic bacteria or in a fermentation process of decomposable materials such as agricultural manure, sewage, municipal waste, green waste (gardens and parks), plant material and agricultural products. Biogas is a renewable natural energy source that leaves effective effects on nature and industries. This gas is produced from the decomposition of organic materials, including animal manure, food waste and sewage. Fertilizers and waste produce biogas through anaerobic digestion (ie without the presence of oxygen). Biogas is a mixture of gases generated by decaying biodegradable material without the presence of oxygen. Its main contents are 50–70 % of methane (CH4) by volume, 30–50 % of carbon dioxide (CO2), and traces of other gases, like hydrogen sulfide (H2S) and water vapor (H2O). CO2, H2S, and water vapor content in biogas may affect the performance and life of the energy conversion devices; consequently, their removal before end-use is essential for improving the quality of biogas. This combination is an ideal option for making renewable energy. The most important advantages of biogas (production of energy, reduction of the amount of discarded waste, reduction of pathogens, conversion of waste containing organic matter into high quality fertilizer, protection of vegetation, soil, water, increasing productivity in the field of livestock and agriculture) and It is also one of the disadvantages of biogas (incomplete and small technologies, containing impurities, the effect of temperature on biogas production, unsuitable for urban and dense areas, not affordable). For economical use of biogas, the fermentation process can be carried out under controlled conditions in a relatively simple device called a digestion reservoir. This review summarizes the current state-of-the-art and presents future perspectives related to the anaerobic digestion process for biogas production. Moreover, a historical retrospective of biogas sector from the early years of its development till its recent advancements give an outlook of the opportunities that are opening up for process optimization.

Milk thistle (Silybum marianum) is a medicinal plant from the Asteraceae family. Silymarin is the major constituent of milk thistle extract and is a mixture of some flavonolignans such as silybin, which is the most active component of silymarin. It is most commonly known for its hepatoprotective effect. Also, studies have shown other therapeutic effects such as anticancer, anti-Alzheimer, anti-Parkinson, and anti-diabetic, so its safety is very important. It has no major toxicity in animals. Silymarin was mutagen in Salmonella typhimurium strains in the presence of metabolic enzymes. Silybin, silydianin, and silychristin were not cytotoxic and genotoxic at concentration of 100 μM. Silymarin is safe in humans at therapeutic doses and is well tolerated even at a high dose of 700 mg three times a day for 24 weeks. Some gastrointestinal discomforts occurred like nausea and diarrhea. One clinical trial showed silymarin is safe in pregnancy, and there were no anomalies. Consequently, caution should be exercised during pregnancy, and more studies are needed especially in humans. Silymarin has low-drug interactions, and it does not have major effects on cytochromes P-450. Some studies demonstrated that the use of silymarin must be with caution when co-administered with narrow therapeutic window drugs.

In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increasethe visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectively.

Spirulina is a kind of blue-green algae (BGA) that is multicellular, filamentous, and prokaryotic. It is also known as a cyanobacterium. It is classified within the phylum known as blue-green algae. Despite the fact that it includes a high concentration of nutrients, such as proteins, vitamins, minerals, and fatty acids-in particular, the necessary omega-3 fatty acids and omega-6 fatty acids-the percentage of total fat and cholesterol that can be found in these algae is substantially lower when compared to other food sources. This is the case even if the percentage of total fat that can be found in these algae is also significantly lower. In addition to this, spirulina has a high concentration of bioactive compounds, such as phenols, phycocyanin pigment, and polysaccharides, which all take part in a number of biological activities, such as antioxidant and anti-inflammatory activity. As a result of this, spirulina has found its way into the formulation of a great number of medicinal foods, functional foods, and nutritional supplements. Therefore, this article makes an effort to shed light on spirulina, its nutritional value as a result of its chemical composition, and its applications to some food product formulations, such as dairy products, snacks, cookies, and pasta, that are necessary at an industrial level in the food industry all over the world. In addition, this article supports the idea of incorporating it into the food sector, both from a nutritional and health perspective, as it offers numerous advantages.

Bioactive plant chemicals are considered to be rich and useful for creating nanomaterials. The current work investigated the biosynthesis of silver nanoparticles (AgNPs) using ethanolic flaxseed extract as an efficient reducing factor. The production of AgNPs was verified by color-shifting observation of the mixture of silver nitrate (AgNO3) from yellow to a reddish suspension after the addition of the extract and by evaluating it by UV–visible inspection. Additionally, FTIR spectrum was used to support the identification of functional groups. The morphology and structure of AgNPs were assessed using scanning electron microscopy (SEM), and X-ray diffraction (XRD) examinations, which revealed spherical AgNPs with a diameter of 46.98 ± 12.45 nm and a crystalline structure. The zeta potential (ZP) and dynamic light scattering (DLS) measurements of AgNPs revealed values of −44.5 mV and 231.8 nm, respectively, suggesting appropriate physical stability. The antibacterial activity of AgNPs was investigated against Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Streptococcus pyogenes, while the antioxidant effect was investigated using the DPPH technique. These obtained AgNPs could potentially be used as efficient antibacterial and antioxidant nanomaterials.

This study employs theoretical simulations to identify ways to improve the efficiency of CsSnI 3 -based perovskite solar cells with PCBM ETL. The optimized device with CFTS HTL with a structure of ITO/PCBM/CsSnI 3 /CFTS/Se shows the highest PCE of 24.73%.

Hydrogen geo-storage is considered as an option for large scale hydrogen storage in a full-scale hydrogen economy. Among different types of subsurface formations, coal seams look to be one of the best suitable options as coal’s micro/nano pore structure can adsorb a huge amount of gas (e.g. hydrogen) which can be withdrawn again once needed. However, literature lacks fundamental data regarding H2 diffusion in coal. In this study, we measured H2 adsorption rate in an Australian anthracite coal sample at isothermal conditions for four different temperatures (20 °C, 30 °C, 45 °C and 60 °C), at equilibrium pressure ∼ 13 bar, and calculated H2 diffusion coefficient (DH2) at each temperature. CO2 adsorption rates were measured for the same sample at similar temperatures and equilibrium pressure for comparison. Results show that H2 adsorption rate, and consequently DH2, increases by temperature. DH2 values are one order of magnitude larger than the equivalent DCO2 values for the whole studied temperature range 20–60 °C. DH2 / DCO2 also shows an increasing trend versus temperature. CO2 adsorption capacity at equilibrium pressure is about 5 times higher than that of H2 in all studied temperatures. Both H2 and CO2 adsorption capacities, at equilibrium pressure, slightly decrease as temperature rises.

A new aromatic Schiff base with azo linkage has been synthesized and characterized by FT-IR, 1H-NMR, and 13C-NMR spectroscopy. The new compound 2-(((5-mercapto-1,3,4-thiadiazol-2-yl)imino)methyl)-4-(p-tolyldiazenyl)phenol (5, denoted as AT) was tested as a carbon steel corrosion inhibitor in 1 M H2SO4. The presence of AT in 0.04 mM concentration at 303 K achieved excellent inhibition efficiency values, 96.6 and 97.4% by potentiodynamic polarization and weight loss measurements, respectively. The adsorption process of AT on carbon steel surface was found to obey Langmuir adsorption isotherm with the highest Kads value 476,190 M−1 at 313 K, and ΔG values −25.53, −26.49, −25.97, and −25.78) kJ mol−1 over the studied range of temperatures 303–333 K, indicating the spontaneous formation of stable protection film through a strong adsorption process. Density function theory (DFT) studies were employed for further investigations about the nature of the interaction between the molecules of AT (both of its tautomers) and metal surface. SEM and AFM analysis were used to confirm the inhibition by comparing the morphology of the corroded surface with the inhibited one.

Abstract Solar energy has emerged as a pivotal player in the transition towards sustainable and renewable power sources. However, the efficiency and longevity of solar cells, the cornerstone of harnessing this abundant energy source, are intrinsically linked to their operating temperatures. This comprehensive review delves into the intricate relationship between thermal effects and solar cell performance, elucidating the critical role that temperature plays in the overall efficacy of photovoltaic systems. The primary objective of this review is to provide a comprehensive examination of how temperature influences solar cells, with a focus on its impact on efficiency, voltage, current output, and overall stability. By synthesizing existing knowledge and exploring recent advances in the field, we aim to elucidate the underlying mechanisms of thermal effects and offer insights into mitigating their adverse consequences. Our review encompasses a thorough discussion of the fundamentals of solar cells, including their operation and various types, before delving into the intricacies of thermal effects. We present an overview of experimental techniques for thermal analysis, factors influencing temperature variations, and strategies to alleviate thermal stresses. Additionally, we offer real-world case studies and discuss future trends and research directions, providing a comprehensive roadmap for advancing solar cell technology. In an era where the harnessing of solar energy has become increasingly vital, understanding and addressing thermal effects are imperative to maximize the efficiency and longevity of solar cells. This review article serves as a valuable resource for researchers, engineers, and policymakers by shedding light on the significance of thermal effects on solar cell performance and guiding the pursuit of innovative solutions in the quest for more efficient and sustainable photovoltaic systems.

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%.

The global production and consumption of plastics, as well as their deposition in the environment, are experiencing exponential growth. In addition, mismanaged plastic waste (PW) losses into drainage channels are a growing source of microplastic (MP) pollution concern. However, the complete understanding of their environmental implications throughout their life cycle is yet to be fully understood. Determining the potential extent to which MPs contribute to overall ecotoxicity is possible through the monitoring of PW release and MP removal during remediation. Life cycle assessments (LCAs) have been extensively utilized in many comparative analyses, such as comparing petroleum-based plastics with biomass and single-use plastics with multi-use alternatives. These assessments typically yield unexpected or paradoxical results. Nevertheless, there is still a paucity of reliable data and tools for conducting LCAs on plastics. On the other hand, the release and impact of MP have so far not been considered in LCA studies. This is due to the absence of inventory-related data regarding MP releases and the characterization factors necessary to quantify the effects of MP. Therefore, this review paper conducts a comprehensive literature review in order to assess the current state of knowledge and data regarding the environmental impacts that occur throughout the life cycle of plastics, along with strategies for plastic management through LCA.

Abstract This study investigated for the first time a simple bio-synthesis approach for the synthesis of copper oxide nanoparticles (CuO NPs) using Annona muricata L ( A. muricata ) plant extract to test their anti-cancer effects. The presence of CuONPs was confirmed by UV–visible spectroscopy, Scanning electron microscope (SEM), and Transmission electron microscope (TEM). The antiproliferative properties of the synthesized nanoparticles were evaluated against (AMJ-13), (MCF-7) breast cancer cell lines, and the human breast epithelial cell line (HBL-100) as healthy cells. This study indicates that CuONPs reduced cell proliferation for AMJ-13 and MCF-7. HBL-100 cells were not significantly inhibited for several concentration levels or test periods. The outcomes suggest that the prepared copper oxide nanoparticles acted against the growth of specific cell lines observed in breast cancer. It was observed that cancer cells had minor colony creation after 24 h sustained CuONPs exposure using (IC 50 ) concentration for AMJ-13 was (17.04 µg mL −1 ). While for MCF-7 cells was (18.92 µg mL −1 ). It indicates the uptake of CuONPs by cancer cells, triggering apoptosis. Moreover, treatment with CuONPs enhanced Lactate dehydrogenase (LDH) production, probably caused by cell membrane damage, creating leaks comprising cellular substances like lactate dehydrogenase. Hence, research results suggested that the synthesized CuONPs precipitated anti-proliferative effects by triggering cell death through apoptosis.

In recent decades, the advent of immune-based therapies, most notably Chimeric antigen receptor (CAR)-T cell therapy has revolutionized cancer treatment. The promising results of numerous studies indicate that CAR-T cell therapy has had a remarkable ability and successful performance in treating blood cancers. However, the heterogeneity and immunosuppressive tumor microenvironment (TME) of solid tumors have challenged the effectiveness of these anti-tumor fighters by creating various barriers. Despite the promising results of this therapeutic approach, including tumor degradation and patient improvement, there are some concerns about the efficacy and safety of the widespread use of this treatment in the clinic. Complex and suppressing tumor microenvironment, tumor antigen heterogeneity, the difficulty of cell trafficking, CAR-T cell exhaustion, and reduced cytotoxicity in the tumor site limit the applicability of CAR-T cell therapy and highlights the requiring to improve the performance of this treatment. With this in mind, in the last decade, many efforts have been made to use other treatments for cancer in combination with tuberculosis to increase the effectiveness of CAR-T cell therapy, especially in solid tumors. The combination therapy results have promising consequences for tumor regression and better cancer control compared to single therapies. Therefore, this study aimed to comprehensively discuss different cancer treatment methods in combination with CAR-T cell therapy and their therapeutic outcomes, which can be a helpful perspective for improving cancer treatment in the near future.

Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical. Fortunately, there are many artificial intelligence techniques that can be used to detect malware and distinguish it from normal activities. However, the problem of dealing with large and high-dimensional data has not been addressed enough. In this paper, a high-performance malware detection system using deep learning and feature selection methodologies is introduced. Two different malware datasets are used to detect malware and differentiate it from benign activities. The datasets are preprocessed, and then correlation-based feature selection is applied to produce different feature-selected datasets. The dense and LSTM-based deep learning models are then trained using these different versions of feature-selected datasets. The trained models are then evaluated using many performance metrics (accuracy, precision, recall, and F1-score). The results indicate that some feature-selected scenarios preserve almost the same original dataset performance. The different nature of the used datasets shows different levels of performance changes. For the first dataset, the feature reduction ratios range from 18.18% to 42.42%, with performance degradation of 0.07% to 5.84%, respectively. The second dataset reduction rate is between 81.77% and 93.5%, with performance degradation of 3.79% and 9.44%, respectively.

Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.

Numerous studies have examined the manner in which minority groups, including refugees, are depicted in the media discourse of the host countries or the dominant majority groups. The results of such studies indicate that media systematically discriminate these minority groups and deem them as a security, economic and hygiene threat to the majority groups. Through the use of Lakoff and Jonson’s conceptual metaphor theory, this study compares and contrasts the representation of Syrian refugees in the online media discourse of not only host countries but also non-host countries, which, in this study, refers to nations that do not host Syrian refugees. The results show that statistical differences between the metaphors used by host and non-host countries only occur when using the metaphors that describe the entry of refugees and the burden they are inflicting on the host countries. This is clearly indicated by the p-values of the log-likelihood test.