Thi Qar University

This study analyses the IoT cybersecurity articles and provides a comprehensive view of research analysis of the collaborating networks, common keywords, and themes outlined. The objective of this study is to systematically assess, map, analyze, and synthesize the published literature on IoT cybersecurity in terms of quantities. OpenRefine and biblioMagika are used to clean data, while the R package and VosViewer are used for analysis on a sample of 1088 papers gathered from the Scopus. This study is distinctive because it used a pre-processing step to clean the data before starting a reliable bibliometric analysis. The analysis includes keyword co-occurrence data and publication trends, providing a detailed perspective on the trends, patterns, and gaps in the field. The results reveal a significant increase in publications in the areas of interest in Computer Science, Engineering, Decision Sciences, and Mathematics. In addition, India, the United States, China, the United Kingdom, and Saudi Arabia were the superior in total publications in this area, with 235, 211, 115, 62, and 56, respectively. Given the growing concern of IoT cybersecurity practices in computer science and other disciplines, this research aims at identifying the developmental process of IoT to chart the way forward for future research. Finally, the future research directions mentioned in the trending articles in the field were consistent with the key papers, and a possible way to conduct nonmyopic IoT cybersecurity research in the future is proposed.

The research aims to address the importance of the smart teaching system to guide and assist university students. By improving their learning experience. This system helps solve several problems related to the limited resources of universities and students, language barriers, and cultural differences. Uses technology to provide individual feedback, adaptive learning methods, and data analytics to improve student performance. Key features of the Smart Tutoring system include an easy-to-use interface, adaptive learning methods, and data analytics to monitor student achievement, identify areas for improvement, and help deliver personalized interventions. The system increases student engagement and motivation, improving the learning experience and promoting active participation in education.

The impact of solar ultraviolet (UV) radiation on public health is severe and can cause sunburn, skin aging and cancer, immunosuppression, and eye damage. Minimization of exposure to solar UV is required in order to reduce the risks of these illnesses to the public. Greater public awareness and the prediction of ultraviolet index (UVI) is considered an essential task for the minimization of solar UV exposures. This research has designed an artificial intelligence (AI) model to predict the multistep solar UVI. The proposed model was based on the integration of convolutional neural networks with long short-term memory network (CLSTM) as the primary model to predict solar UVI, tested for Brisbane (27.47°S, 153.02°E), the capital city in Queensland, Australia. Solar zenith angle (SZA) data were used together with UVI as inputs for the CLSTM of different scales (i.e., 10-min, 30-min, and 60-min) UVI prediction. The CLSTM model was benchmarked against well-established AI models e.g., long short-term memory network (LSTM), convolutional neural network (CNN), Deep Neural Network (DNN), multilayer perceptron (MLP), extreme learning machine (ELM), random forest regression (RFR), Extreme Gradient Boosting (XGB), and Pro6UV Deterministic models. The experimental results showed that the CLSTM model outperformed these models with Root Mean Square Error (RMSE = 0.3817), Mean Absolute Error (MAE = 0.1887), and Relative Root Mean Square Error (RRMSE = 8.0086%), for 10-min prediction. Whereas, for 30-min and 60-min prediction were RMSE = 0.4866/0.5146, MAE = 0.2763/0.3038, RRMSE = 10.4860%/11.5840%, respectively. The research finding confirmed the potential of the proposed data-intelligent model (i.e., CLSTM) can yield improved UVI prediction for both the public and the government agencies.

Lactic acid bacteria (LAB), known for their health benefits, exhibit antimicrobial and antibiofilm properties. This study investigated the cell-free supernatant (CFS) of Lactobacillus spp., particularly L. plantarum KR3, against the common foodborne pathogens S. aureus , E. coli and Salmonella spp. Lactobacillus strains were isolated from cheese, pickles and yoghurt. They were then identified by morphological, physiological and biochemical characteristics and confirmed by 16S rRNA gene sequencing. Culture supernatants from seven lactobacilli isolates showed varying inhibitory activities. Notably, L. plantarum KR3 and L. pentosus had the highest bacteriocin gene counts. L. plantarum KR3 CFS demonstrated significant antibacterial activity, with inhibition zones of 20 ± 0.34 mm for S. aureus , 23 ± 1.64 mm for E. coli , and 17.1 ± 1.70 mm for Salmonella spp. The CFS also exhibited substantial antibiofilm activity, with 59.12 ± 0.03% against S. aureus , 83.50 ± 0.01% against E. coli , and 60. ± 0.04% against Salmonella spp., which were enhanced at the minimum inhibitory concentration (MIC). These results highlighted the potential of L. plantarum KR3 in antimicrobial applications, however, further research is needed to evaluate its viability and functional properties for probiotic use. Additionally, the CFS demonstrated exceptional thermal stability, reinforcing its promise as an antimicrobial agent.

Gene therapy means introducing genetic information into a cell to treat or prevent disease. It can replace defective genes, suppress harmful genes, or enhance cellular functions; therefore, it is considered promising for various diseases such as genetic disorders, cancer, and viral infections. This review assembled evidence related to bacterial (Salmonella typhi, Escherichia coli, Listeria monocytogenes, and Lactococcus lactis) and viral (Retroviruses, Herpes simplex viruses, Lentiviruses, and Adenoviruses) vector-mediated gene therapeutics along with their efficacy, safety, and possible uses in gene therapy. The results demonstrated bacterial vectors can transfer their genes, especially in cancer treatment. Research has shown that live Salmonella strains can preferentially home into tumors and suppress their growth. E. coli has been modified to enhance the ability to transfer genetic material and minimize toxic impacts. Listeria monocytogenes bacterium has been considered for cancer treatment through immunotherapy, while Lactococcus lactis has the potential for use in inflammatory diseases because of its probiotic qualities. Surprisingly, viral vectors continue to dominate the field of gene therapy because they are effective in transferring genes. Both retroviruses and lentiviruses have been employed due to their capacity to integrate ad hoc within the host cell genome and maintain gene expression over long periods. Human herpes simplex viruses exhibit significant packaging capacity and neurotropism, while adenoviruses are utilized effectively in various cancer treatment applications.

Marburg disease (malignant multiple sclerosis, MS) is a rare, acute MS variant, predominantly occurring among young adults. Because it is characterized by rarity, high morbidity and mortality rates, the disease needs to be further characterized, and the experience of the physicians play a role in treatment regimens. We report the case of a 15-years-old female presenting with progressive weakness over the limbs, hyperreflexia and loss of sensation by physical examination, lab tests and radiological investigations (MRI). After treatment, nonimprovement was recognized by symptoms review after IV corticosteroids, Gabapentin and Clonazepam intake during a clinical visit. IV corticosteroids, Gabapentin and Clonazepam are not sufficient treatment for Marburg's variant of multiple sclerosis disease. Therefore, aggressive therapies can be used to further suppress the inflammatory process to prevent further neurological damage.

With the numerousness of political events and the competition among news media channels, news manufacturing becomes highly weighty to attract audience's attention aiming at changing their minds. As such, news reporters tend to pick out certain events that can be viewed as newsworthy. However, news manufacturing turns to be the reporters’ main interest and the various ways used to fulfill this purpose fall into their primary tasks. Among these ways, pragmatic mechanisms of language stand as the most appropriate means to create such newsworthiness. Thus, this study has set itself the task to be after these pragmatic mechanisms as employed by CNN reporters in their attempts to initiate, construct and maximize newsworthiness of the events in question. The findings attained at by this study fully verify some of its hypotheses and partially vindicate other ones.

Background The detection and classification of lung nodules are crucial in medical imaging, as they significantly impact patient outcomes related to lung cancer diagnosis and treatment. However, existing models often suffer from mode collapse and poor generalizability, as they fail to capture the complete diversity of the data distribution. This study addresses these challenges by proposing a novel generative adversarial network (GAN) architecture tailored for semi-supervised lung nodule classification. Methods The proposed DDDG-GAN model consists of dual generators and discriminators. Each generator specializes in benign or malignant nodules, generating diverse, high-fidelity synthetic images for each class. This dual-generator setup prevents mode collapse. The dual-discriminator framework enhances the model’s generalization capability, ensuring better performance on unseen data. Feature fusion techniques are incorporated to refine the model’s discriminatory power between benign and malignant nodules. The model is evaluated in two scenarios: (1) training and testing on the LIDC-IDRI dataset and (2) training on LIDC-IDRI, testing on the unseen LUNA16 dataset and the unseen LUNGx dataset. Results In Scenario 1, the DDDG-GAN achieved an accuracy of 92.56%, a precision of 90.12%, a recall of 95.87%, and an F1 score of 92.77%. In Scenario 2, the model demonstrated robust performance with an accuracy of 72.6%, a precision of 72.3%, a recall of 73.82%, and an F1 score of 73.39% when testing using Luna16 and an accuracy of 71.23%, a precision of 67.56%, a recall of 73.52%, and an F1 score of 70.42% when testing using LungX. The results indicate that the proposed model outperforms state-of-the-art semi-supervised learning approaches. Conclusions The DDDG-GAN model mitigates mode collapse and improves generalizability in lung nodule classification. It demonstrates superior performance on both the LIDC-IDRI and the unseen LUNA16 and LungX datasets, offering significant potential for improving diagnostic accuracy in clinical practice.

Desert soils present some issues that need improvement. Some of these are high permeability and collapsibility potential. These problems are due to the uniform particle size distribution and the lack of particle edges. Soil improvement is required to mitigate these issues. Cement is widely used for soil stabilization but has environmental issues since it is a significant source of CO2 emissions and requires high energy consumption. In this study, the calcined shale material is utilized as a partial replacement for cement to reduce the permeability and compressibility of soils more sustainably. The study considers three cement doses of 5%, 10%, and 15% and four calcined shale percentages of 10, 30, 50, and 70%. A series of falling head permeability and one-dimensional consolidation tests were conducted to examine the performance of cement and calcined shale as stabilizers. The results of the study indicate that the addition of 30% calcined shale as a partial replacement of cement has the most significant effect on the conductivity and compressibility of the soils. An increase in cement content decreases the permeability and compressibility of the soil due to the hydration of cement. Conversely, the conductivity and consolidation of the soil are initially decreased with an increase in the calcined shale up to 30% and then start to increase. In summary, this study reveals that the presence of CS and cement has a substantial effect on the conductivity and compressibility of the soils.

Background Diabetic cardiomyopathy (DCM) is a significant complication of diabetes mellitus (DM) and a major contributor to heart failure (HF). Despite its prevalence and impact, there is a notable lack of targeted therapies, highlighting the need for ongoing research into novel treatment strategies. Current management primarily involves blood sugar control, lifestyle modifications, and addressing risk factors. Conventional treatments, including Renin-angiotensin-aldosterone system (RAAS) inhibitors, angiotensin receptor/neprilysin inhibitor, beta-blockers, ivabradine, and vericiguat, are also employed. Methodology A comprehensive search was made using PubMed, Scopus, and Google Scholar for studies published. The search focused on DCM, therapeutic strategies, and emerging biomarkers. Articles were selected based on relevance, study quality, and inclusion criteria, which emphasized peer-reviewed studies on DCM management and biomarker identification. Results and discussion Our review reveals that targeting oxidative stress through these antioxidant therapies offers a promising approach for limiting DCM progression. Clinical trials provide evidence supporting the efficacy of these agents in reducing oxidative damage and improving cardiac function in diabetes-induced cardiomyopathy. Conclusion The current landscape of DCM management highlights the need for novel therapeutic strategies and early detection methods. Antioxidant therapies show potential for addressing the oxidative stress that underlies DCM, and ongoing research into emerging biomarkers may offer new avenues for early diagnosis and treatment.

One of the most dangerous issues in river engineering is erosion brought on by the flow around bridge piers, which causes failure. As a result, simulating scouring is a crucial technique for assessing the likelihood of scour-related bridge failure. The scour depth surrounding hydraulic structures (non-uniform piers) was investigated using various computational and laboratory models. This study examines the efficacy of the computational fluid dynamics (CFD) model in generating simulations of the scour depth along a bridge pier via the Flow3D software (version 10.1.1.3). k-ε model is built to more accurately consider the generation of turbulent kinetic energy and the anisotropic characteristics of the turbulence. The model’s calibration and verification are assessed using statistical metrics (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2)). Based on the statistical criteria, it may be concluded that the model’s results were promising because the discrepancy between the numerical and experimental models is negligible. The Froude number has been demonstrated to be a crucial factor that must be considered during the construction of piers, since reducing it will result in a decrease in longitudinal velocity and turbulent kinetic energy. Also, the Flow-3D program accurately reproduces the scour depth, flow, and velocity near the pier.

Geotechnical characteristics of soil are very important for civil applications and engineers because the structures almost construct and built on the ground such as high buildings, towers, bridges, tunnels and others on the soils. Electrical resistivity (ER) measurements are nowadays widely used to evaluate the geotechnical characterization of soil as they are fast, time-consuming and costly and non-destructive. This article provides the data of soil electrical resistivity measurements carried out on different zones and sites in Gharraf oil field area (northwestern part of the Thi-Qar governorate in the south east of Iraq). In this study the analyses of the obtained data were carried out to propose a new empirical quantitative correlation between soil resistivity and geotechnical characteristics. The improved empirical relationships reflect a very good correlation and it were found that the electrical resistivity of soil has a significant assessment and good fitting with the some of important geotechnical parameters. It was also concluded that the electrical resistivity method which has an advantage over other soil investigation methods that saving and reduce cost. Finally, the new generated empirical equation can be used to evaluate the ultimate bearing capacity of the soil rather than using the traditional widely used equations which require several inputs coefficients calculated using the extensive laboratory and field works. However, the results showed a good and more reliable correlation between soil electrical resistivity and some geotechnical characteristics for the studied region soil.

Impact of various types of fibres on the Mechanical propertes of Lightweght Concrete. This research aims to study the effect of adding fibers on the fresh, and hard propertes of light concrete. To achieving this goal, 13 mixtures were examined, containing different types and proportions of fiber, where the precipitation was examined, as well as the compressive strength, the splitting strength, the fracture modulus, in addition to the workability . The basic variables for this research. The type of fibre, where four types of fibre (steel hooked end, crimped steel, glass, and polypropylene,) were used, and the second variable is the percentage of fibre, as the ratios were as follows (0.3, 0.5, 0.6, 0.8, 1, and 1.3 ), As for the third variable, it was to combine the types of fibers with each other to obtain the hybrid fibre and study its effect on lightweghit concrete. The results showed that the mixing of steel fiber by 0.5 with the class by 0.5% with 0.3 polypropylene led to decrease the workability of mix, on other hand, Compressive strength and splitting tensile strength, in addition to other properties of concrete were improved, where the percentage of increase in the compressive and splitting strength was 52.17 and 45.36 %, respectively. Furthermore, the hooked end steel showed a better performance than crimped steel in improving proprieties of lightweight concrete.

Accurate spatial decision-making models are increasingly needed for wind energy planning as the globe rushes towards carbon-neutral energy. This research aims to improve existing decision-making approaches by proposing an ensemble weight-based model for mapping the spatial suitability of onshore wind systems. The model addressed three weighting scenarios: subjective weighting derived from the Analytical Hierarchy Process (AHP), objective weighting derived from the Entropy Weighting Method (EWM), and Artificial Intelligence (AI) weighting based on real-world experiences. The weight sources were harnessed in weighted and fuzzy overlays in a GIS context to create multiple suitability indices. The model was applied to the Wasit governorate in Iraq, considering 10 evaluation criteria and 6 restrictions. The results highlight the dominance of techno-economic considerations, with wind speed being an important factor in all weighting scenarios. Suitability indices suggest that the western, central, and southern areas of Wasit are most suitable for wind farms, with ideal sites identified south of Al-Hay, south of Sheikh Saad, and west of Al-Kut, covering an area of 756 km ² and potentially providing more than 3.5 GW of clean electricity. The findings could encourage wind energy investment in developing countries like Iraq.

In this paper, the problem of attack mitigation in an intelligent transportation network or vehicular network is considered as a game. The player’s perception of uncertainty and decision making is studied under a subjective prospect theoretic (PT) model and an objective expected utility theory (EUT) model. A game where each player chooses one of two strategies with certain probabilities is analysed. The case where subjective players bias their choices of the probabilities using the Prelec weighting function w(.) w(.) is considered and compared with the EUT based decisions and the effect of the framing effect function ν(.) $\nu (.)$ and w(.) w(.). The corresponding Nash equilibria (NE) are derived and found through the replicator dynamic equation. Under the Prelec function, the results agree with the previously published results that the defender is biased more into defending the more important road side units. However, under both the w(.) w(.) function and the framing effect, the players' behaviour does not depend on the loss penalty parameter, and the Prelec function dominates the framing effect. For small α $\alpha$ values, the players make conservative decisions compared to higher α $\alpha$ values regardless of the effect of the framing function. For high α $\alpha$ values the players are more certain in their decisions than the EUT players.

In this paper, an experimental and numerical investigation of the thermal behavior of a novel solar collector was performed. This last one is cylindrically shaped and comprises four layers: Pebble layer, phase change material (PCM) layer and two air layers separated by a plastic cover layer. The motivation for selecting four layers is to reduce the heat loss caused by the continuous convection heat transfer. The experimental solar collector was thermally insulated and covered with a plastic cover at the top. The experimental setup was built and operated in an arid area at the University of Thi-Qar in the city of Nasiriya in Iraq for 20 days (from 10 November 2022 to 30 November 2022). During this period, layer temperatures were measured using K-type thermocouples. A numerical model was developed based on the energy balance of each solar collector layer; the forward Euler method and MATLAB computational program were utilized to solve the formed equations. Comparisons between experimental and numerical results showed acceptable agreements with relative errors of 1.81%, 3.21% and 3.68% for the first air layer, second air layer and PCM layer, respectively. After the model validation, the numerical model was used to simulate the thermal behavior of the solar collector during the whole year of 2022. The results showed that the temperature varies from −2 °C to about 66 °C during the year, with a difference of 15.52 °C in July and around 6.84 °C in December. Additionally, the temperature of PCM reached a maximum of around 63 °C in June and over 54 °C in the same month for the pebble layer.

New Cd(II), Zn(II) and Cu(II)‐chelates with cetirizine.2HCl (CETZ.2HCl) in incidence of 1,10 phenanthroline monohydrate (Phen.H2O) were synthesized in search of new biologically active compounds. The ligands and their chelates were described by 1H NMR, FT‐IR, elemental analysis, UV‐vis. spectrophotometry, thermal‐analyses, molar conductance, X‐ray‐diffraction (XRD) and magnetic‐susceptibility measurements. FT‐IR demonstrated that CETZ.2HCl is bonded with metal ions, as a monodentate via carboxylate oxygen atom and Phen.H2O chelated via two nitrogen atoms. The molar conductivity data showed that the complexes were non‐electrolytes, while XRD data supported that the compounds were crystalline. Density functional theory‐(DFT) was utilized to gain insight into the compounds' optimized design. The effects of CETZ.2HCl and the complexes on the activity of Lipoprotein‐(L)‐lipase activity in mice were investigated. Unlike Cd(II) complex, all the other compounds exhibited significant increase in lipase activity, with reduction in triglycerides. Cu(II) and Zn(II) complexes showed robust hypolipidemic efficacy evidenced by lower levels of total cholesterol and LDL, concomitant with higher levels of HDL. Furthermore, Zn(II) complex was a safe alternative since it has a lower liver toxicity. Molecular docking demonstrated that Cu(II) and Zn(II) chelates exhibited greater affinities to lipase than the parent ligand. Finally, Cu(II) complex showed the highest antibacterial activity.

Surveillance video processing requires high efficiency, given its large datasets, demands significant resources for timely and effective analysis. This study aims to enhance surveillance systems by developing an automated method for extracting key events from outdoor surveillance videos. The proposed model comprises four phases: preprocessing and feature extraction, training and testing, and validation. Before utilizing a convolution neural networks approach to extract features from videos, the videos are pre-processed. Events classification uses gated recurrent units. In validation, motions and objects are extraction then feature extraction. Results show satisfactory performance, achieving 79% accuracy in events classification, highlighting the effectiveness of the methodology in identifying significant outdoor events.