University of Kufa
Recent publications
A strongly possible constraint is an intermediate concept between possible and certain constraints, based on the strongly possible world approach (a strongly possible world is obtained by replacing NULL’s by a value from the ones appearing in the corresponding attribute of the table). In the present paper, we introduce strongly possible versions of multivalued dependencies and cross joins, and we analyse the complexity of checking the validity of a given strongly possible cross joins. We also study two approximation measures, g3g_3 and g5g_5, of strongly possible keys (spKeys), functional dependencies (spFDs), multivalued dependencies (spMVDs) and cross joins (spCJs). For spKeys and spFDs, we show that the g3g_3 value is always an upper bound of the g5g_5 value for a given constraint in a table. However, there are tables of arbitrarily large number of tuples and a constant number of attributes that satisfy g3g5=pqg_3-g_5=\frac{p}{q} for any rational number 0pq<10\le \frac{p}{q}<1. On the other hand, we show that the values of measures g3g_3 and g5g_5 are independent of each other in the case of spMVDs and spCJs. We prove that checking whether a given strongly possible cross join holds in an incomplete table is NP-complete, in sharp contrast to the fact that checking a given cross join in a complete table is easily seen to be polynomially solvable. We also treat complexity questions of determination of the approximation values, namely we show that both, determining g3g_3 and g5g_5 for spCJs are NP-complete.
The research investigates the characteristics of polymers using density functional theory with the hybrid function technique and the basis set for quantum chemical computations. Polymers with a superior ionization potential and electron affinity, signifying enhanced stability in electron retention, polymers possessing a stronger electronic chemical potential, rendering it more chemically reactive, were detected using the above techniques. Spectral research, which underscored the adhesive qualities, was carried out. Furthermore, the polymers, which exhibit superior ultraviolet absorption in the studies region, hence augmenting their appropriateness for ultraviolet curable applications, were identified and thoroughly investigated.
The United States grappled with pressing challenges in healthcare cost management, prompting a search for innovative strategies. Healthcare expenses, reaching $4.5 trillion in 2022 with a 4.1% growth rate, pose substantial burdens on governments, employers, and individuals. National health expenditures (NHEs) are projected to increase by 5.4%, surpassing the GDP growth rate of 4.6% from 2022–2031. This trend suggests a rise in health spending’s GDP share from 18.3% in 2021 to 19.6% in 2031. This complex issue necessitates a comprehensive approach, exploring preventive measures, evidence-based guidelines, collaborative initiatives, and technology integration for sustainable healthcare cost containment. In the context of Value-Based Health Care (VBHC), wherein patient outcomes are assessed relative to costs, viewing sustainable cost management strategies through such a lens can offer great value. The seven primary areas of VBHC are Integrated Practice Units, Outcomes and Cost Measurement, Value-Based Reimbursement, Regional Systems Integration, Geography of Care, Information Technology and Artificial Intelligence, and Enabling Policy and Institutions. This paper uses these domains to facilitate the analysis of problems and approaches regarding cost containment in healthcare, prompting the exploration of innovative strategies. Preventive healthcare and collaborative initiatives prove crucial in reducing costly treatments and emergency visits involving multiple healthcare entities and have improved quality and cost reduction in addressing the gap in preventive services. Evidence-based therapeutic guidelines significantly affect cost containment by promoting efficient resource use and reducing unnecessary interventions. Health reforms and technology optimize cost efficiency by improving care quality and streamlining operations. The collective efforts of stakeholders are vital for navigating complex landscapes and ensuring a financially sustainable healthcare future in the United States.
Steel sections are usually manufactured with geometric defects and deviates from the original ideal shape during rolling process; these defects are called imperfection in international standards. This study is devoted towards investigating local geometric imperfections of steel columns under combined effect of axial load and lateral cyclic displacements which simulates steel columns under seismic events. Ten wide flange sections with two cases for each section: ideal and imperfect. A comprehensive nonlinear finite element model which is validated against available experimental data in literature was employed. A sustained axial load which ranges from 20-100 percent of the maximum axial capacity for the column was applied prior to application of the designated lateral cyclic displacement amplitude. It was found that web local imperfection has a substantial impact on the cyclic response of the columns. It was also revealed that higher axial load ratios impact column response than do lower load ratios. Moreover, drift angle was found more susceptible in stocky than in light sections. The study showed that there is a limiting zone within the slenderness ratios for both of flanges and web should be avoided when considering section design of columns under seismic events because it yields a big difference between ideal and imperfect section.
Cloud computing environments are increasingly popular due to their flexibility and scalability, but they also present significant security challenges, particularly in the form of malware attacks. These malicious attacks exploit weaknesses within cloud infrastructures, which can result in serious repercussions like data breaches, unauthorized system access, and identity theft. In this paper, we introduce an innovative malware detection classifier specifically designed to overcome the shortcomings of conventional machine learning algorithms, such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), in the unique context of cloud environments. Our proposed method relies on Log-spectral distance as a fundamental metric, which enables a more precise and effective approach to detecting malware. Through rigorous and extensive experimentation, our findings demonstrate that this novel classifier achieves an outstanding accuracy rate of 97% without the need for feature selection—surpassing the 95% accuracy attained when employing feature selection through the Mutual Information (MI) method. Additionally, our classifier outperforms both traditional machine learning (ML) and deep learning (DL) techniques, showcasing its robustness and dependability in identifying malware threats within cloud settings. The results of our study underscore the classifier's potential to serve as a crucial tool for enriching security in cloud environments. This advanced solution not only contributes to academic research but also offers practical applications for safeguarding cloud infrastructures against the continuously evolving landscape of malware threats.
The human factor is one of the most important factors that can affect soil salinity , as man plays an important role in influencing and changing it through various agricultural activities such as tillage, levelling, agriculture, irrigation, drainage , fertilization, and the whole of these processes affect soil salinity either negatively or positively, if used in nonscientific ways that lead to soil degradation, salinization, and low productivity, and thus the loss of this vital resource that we need constantly. In this research, these aforementioned factors were studied in detail and the extent of their impact on the salinity of the soil in Al-Qadisiyah, as it became clear that there are a group of human factors that led to the emergence of the problem of salinity within the soil of Al-Qadisiyah sub-district, and that this phenomenon is expanding at the expense of arable land.
Automatic facial expression recognition (AFER) has been shown to work well when restricted to subjects showing a limited range of 6-basic expressions (BE). Expression recognition in subjects showing a large range of 22-compound expressions (CE) is harder as it has been shown that CE and BE are partially similar which might lead to huge confusion in AFER. We present a discriminative system that predicts expression across a large range of emotions. We first build a fully automatic facial feature detector using Random Forest Regression Voting in a Constrained Local Models (RFRV-CLM) framework used to automatically detect facial points, and study the effect of CE on the accuracy of point localization task. Second, a set of expression recognizers is trained from the extracted features including shape, texture, and appearance, to analyze the effect of the CE on the facial features and subsequently on the performance of AFER. The performance was evaluated using the CE dataset of 22 emotions. The results show the system to be accurate and robust against a wide variety of expressions. Evaluation of point localization and expression recognition against ground truth data was obtained and compared with the existing results of alternative approaches tested on the same data. The quantitative results with 55.6 recognition rates, 2.1% error rates using manual points, and 51.8 recognition rates, 2.1% error rates using automatic points demonstrated that our system was encouraging in comparison with the state-of-the-art systems.
In the coming decade, a substantial rise in energy consumption within the buildings sector is predicted to lead to a 30% increase in greenhouse gas emissions. The choice of materials for building envelopes significantly influences the overall energy demand of HVAC systems, which contribute significantly to electricity usage. To enhance compatibility between grey clay and straw, a suggested approach involves using a composite material comprising rice water and grey clay, enriched with a high proportion of rice straw and soaked in rice water. This environmentally friendly technique yields a green construction material capable of reducing energy consumption in HVAC systems by up to 35.6% over a 24-h period. The potential energy-savings of this composite material are evaluated through numerical computations and real field measurements using ANSYS software. Experimental results reveal that the suggested grey clay bricks, compared to traditional materials, exhibit superior physical characteristics such as compressive strength and load stability. These bricks are up to 41.2 mass% lighter than regular bricks due to the incorporation of rice straw, which enhances their mass reduction. As a porous material, the suggested bricks can effectively absorb excess interior humidity, distinguishing them from traditional and fired bricks. The findings highlight the unique mechanical and thermal qualities of the suggested bricks.
Objective Esthesioneuroblastoma (ENB) or olfactory neuroblastoma, an infrequent neuroectodermal tumor primarily affecting the nasal cavity, manifests sporadically or in various clinical contexts. Bibliometric analyses have become a technique for evaluating the influence of papers in present-day clinical practice. Methods This article conducted a meticulous analysis of the top 100 cited articles concerning ENB, using Scopus and employing keywords (“Esthesioneuroblastoma” and “Olfactory Neuroblastoma”). Articles focusing on ENB were systematically evaluated. Data extraction encompassed comprehensive analyses of articles, authors, citation per year, total citations, and journals, categorizing publications into distinct domains: clinical features, histopathology, article types, or radiological aspects. Results The search yielded a substantial pool of 400 articles, from which the top 100 amassed a total of 10 900 citations, averaging 109 citations per article. These influential publications spanned a wide array of 47 journals, published between 1960 and 2019, involving contributions from diverse institutions across 14 countries. Notably, a significant proportion (45%) of contributions was made before the 2000, with the most prolific decade being 2000–2010, contributing 38 publications. Conclusion This bibliometric study summarizes the most-cited articles on esthesioneuroblastoma, providing light on the field and its seminal works that have shaped both present-day clinical treatment and the trajectory of future research.
Water pollution is a major issue nowadays, particularly with regard to heavy metal pollution. Laser Induced Breakdown Spectroscopy (LIBS) technology allows for the quick, in-situ, and real-time analyzing of water samples with little sample preparation and analysis without the need for additional chemical reagents. Here, with three water samples taken from contaminated surface water sources in Iraq, Calibration Free LIBS Analysis was used to diagnose the contaminants in the water both qualitatively and quantitatively. The results showed that the atomic lines of macronutrients (N, K, Ca, Mg), hazardous metals (Hg, Pb, Cd, Fe), and micronutrients (Ti, Cr, Co) are present in the recorded spectra. The discovered elements’ measured concentration in the river water displays the following trend: Mn ˃ S ˃ Fe ˃ N ˃ Co ˃ Ti ˃ Ni ˃ Cr ˃ Ca for S1, Cu ˃ S ˃ Al ˃ N ˃ Fe ˃ Co ˃ Mn ˃ Cr ˃ Ti ˃ Cd ˃ K ˃ Ca for S2, and N ˃ Hg ˃ S ˃ Fe ˃ Cu ˃ Co ˃ P ˃ Ca ˃ Cr ˃ Mn ˃ Ti ˃ Mg for S3. it is concluded that drinking such water could provide a long-term health risk.
Sentiment analysis in healthcare is critical for the precise identification and assessment of suicidal intentions within clinical notes, enabling timely intervention and improving patient outcomes. The proposed study introduces a novel approach, STFS_TF-IDF_HDSM, where Term Frequency-Inverse Document Frequency (TF-IDF) is employed for Semantic and Textual Feature Scaling with the integration of Hybrid Deep Sequential Model (HDSM) to enhanced sentiment analysis. The variations among features are captured in sentimentsby utilising TF-IDF on the corpus that scales both semantic and textual features effectively. Textual context focuses on surface-level interpretation while semantics focuses on more complex and deeper understanding and reasoning about the text Sequential deep learning produces more precise and refined sentiment predictions in sentiment analysis by effectively capturing the context and emotional flow throughout text sequences. By integrating these Uni-Directional and Bi-Directional techniques with deep sequential processing to refine sentiment analysis for predicting potential suicide intentions achieved accurate results of 98.46% and precision 96.71% with minimum modal loss of 0.3121. This innovative framework demonstrates significant improvements in capturing refined sentiment patterns, offering robust performance across diverse textual datasets
This research utilizes density functional theory to investigate the ground and excited-state properties of a new series of organic dyes with D–π–A configurations (D1–D6) for their potential application in dye-sensitized solar cells. The study focuses on modifying these dyes using various functional groups as π-bridges to optimize their electronic properties and improve their efficiency as sensitizers in DSSCs. The frontier molecular orbitals (HOMO and LUMO) were analysed to evaluate electron transfer properties. The energy gaps, ranging from 2.449 to 2.6979 eV, indicate favourable electron injection capabilities. Further analysis included molecular electrostatic potential, electron localization function, and localized orbital locator for all dyes. The maximum absorption wavelengths were found to range from 272.98 nm to 624.76 nm, covering both the UV and visible spectra. A significant redshift was observed with the addition of electron-withdrawing groups to the D–π–A structures, contributing to enhanced light-harvesting capabilities. The results indicate that all dyes exhibit improved open-circuit photovoltage, enhanced light-harvesting efficiency, and higher electron injection when compared to the reference dye (Dye1). Additionally, parameters such as oxidation potential, free energy change, redox potentials, electron transfer, and dye regeneration showed promising values, pointing to excellent photovoltaic efficiency. Electron injection from the dyes into the conduction band of TiO2, followed by efficient dye regeneration, was confirmed. The choice of the π-bridge group, in particular, plays a crucial role in optimizing dye performance. Based on the theoretical findings, all of the studied dyes demonstrate strong potential as effective photosensitizers for DSSCs applications.
The present research work focuses on the synthesis and evaluation of antibacterial activity of newly developed imidazopyridine analogues arising from 2,3-aminopyridine substructure. Three new imidazopyridine derivatives have been synthesized from the reaction of 2,3-aminopyridine with ibuprofen, naproxen and etodolac employing HCl as both the solvent and the catalyst. The resulting compounds were identified by using (FTIR) and (¹H-NMR). The antibacterial activity of these newly synthesized compounds was carried out against a series of bacteria: Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus & Streptococcus mutans by microdilution method of 96 well microplates. These findings revealed that all synthesized compounds possessed pronounced antimicrobial effects against all the bacterial strains studied. The highest antimicrobial activity was registered for compound 1-((-[(1H-imidazo [4,5-b]pyridin-2-yl)methyl)-]-1,8-diethyl-1,3,4,9-tetrahydropyrano[3,4-b]indole that was most effective toward Escherichia coli. Based on these discoveries, it can be assumed that the newly imidazopyridine derivatives synthesized might be of interest for further study, targeting potential antibacterial activity.
This article presents a theoretical study of the excitation of the terahertz field by the relativistic non-linear interaction between the laser beam and ripple plasma under the influence of the transverse magnetic field (a magnetic field perpendicular to the direction of propagation of the laser beam). The novelty in this study is the use of a non-Gaussian mode, which is a hollow Gaussian laser beam (HGLB) with different orders (n = 0, 1, 2), which self-focusing within the plasma and then this interaction generates terahertz waves. A mathematical model was derived to describe the Self-focusing process of HGLB and the generation of terahertz waves by this non-linear interaction. The MATLAB program was used to perform a theoretical simulation of the Self-focusing process of HGLB, the terahertz field excitation, the power spectrum of the terahertz wave, and the effect of some parameters on them. A terahertz beam was generated with a frequency of 7.12 THz and a conversion efficiency 0.07.
Background and Aim: Canine parvovirus 2 (CPV-2) is a highly contagious virus that infects wild and domestic canines. Despite the use of a routine vaccination protocol, it is endemic in Iraq. The genetic drift of CPV-2 is a major issue worldwide because it abrogates virus control. In Iraq, there is a knowledge gap regarding the genetic sequences of asymptomatic and symptomatic CPV-2 cases. Therefore, this study aimed to perform a genetic analysis of viral capsid protein 1 (VP1) and viral capsid protein 2 (VP2), two major capsid-encoding genes, to demonstrate the possible role of certain mutations in triggering infection. Materials and Methods: Symptomatic and asymptomatic cases (n = 100/each) were tested by a polymerase chain reaction targeting VP1 and VP2 genes. Results: The analysis revealed numerous synonymous and nonsynonymous mutations in VP1 and VP2 and in the intergenic sequence. Conclusion: The study identified significant genetic mutations in VP1, VP2, and the intergenic regions of CPV-2 in symptomatic and asymptomatic cases in Iraq. These mutations may contribute to the virus’s ability to evade control measures such as vaccination. These findings indicate that CPV-2 polymorphisms can influence the clinical state of the disease and/or trigger infection. Understanding these genetic variations provides critical insights into CPV-2 pathogenesis and could inform improved vaccination strategies to mitigate the virus’s impact in endemic regions. Keywords: canine parvovirus-2, capsid encoded genes, mutations.
Price-driven energy management (EM) systems and demand response (DR) programs are reviewed in this paper, considering different types of electricity prices, such as real-time prices, day-ahead prices, and time-of-use tariffs. This paper clarifies objectives, constraints and optimization techniques of price-driven EM systems and DR programs, while showing their relevant qualitative and quantitative modeling approaches. Price-driven EM systems and DR programs have capability to improve technical and financial aspects of distribution networks, when more renewable energy sources are integrated into smart grids. Environmental constraints, financial benefits, and global policies represent main incentives of price-driven EM systems and DR programs, whereas their barriers include social concerns (i.e., end-user privacy), technical complications (e.g., requirements of information and communication technologies), and revenue uncertainties. Moreover, this paper assesses strengths, weaknesses, opportunities and threats (SWOT) of implementing such energy management strategies in smart grids.
ABSTRACT Aim: The aim of this research is to clarify the potential effect of CDDO-EA against experimentally sepsis induced lung injury in mice. Materials and Methods: Mice have divided into four groups: Sham group CLP group, Vehicle-treatment group, CDDO-EA-treated group: mice in this group received CDDO-EA 2mg/kg intraperitoneally, 1hr before CLP, then the animals were sacrificed 24hr after CLP. After exsAngpuinations, tissue samples of lung were collected, followed by markers measurement including, TNF-α, IL-1β, VEGF, MPO, caspase11, Angp-1and Angp-2 by ELISA, gene expression of TIE2 and VE-cadherin by qRT-PCR, in addition to histopathological study. Results: A significant elevation (p<0.05) in TNF-α, IL-1β, MPO, ANGP-2, VEGF, CASPASE 11 in CLP and vehicle groups when compared with sham group. CDDO-EA group showed significantly lower levels p<0.05, level of ANGP-1 was significantly lower p<0.05 in the CLP and vehicle groups as compared with the sham group. Quantitative real-time PCR demonstrated a significant decrement in mRNA expression of TIE2&ve-cadherin genes p<0.05 in sepsis & vehicle. Conclusions: CDDO-EA has lung protective effects due to its anti-inflammatory and antiAngpiogenic activity, additionally, CDDO-EA showes a lung protective effect as they affect tissue mRNA expression of TIE2 and cadherin gene. Furthermore, CDDO-EA attenuate the histopathological changes that occur during polymicrobial sepsis thereby lung protection effect. KEY WORDS: CLP, sepsis, CDDO-EA, VEGF, cadherin, ANGP/TIE axis, endotoxaemia
Background Xanthones are dubbed as putative lead-like molecules for cancer drug design and discovery. This study was aimed at the synthesis, characterization, and in silico target fishing of novel xanthone derivatives. Methods The products of reactions of xanthydrol with urea, thiourea, and thiosemicarbazide reacted with α-haloketones to prepare the thiazolone compounds. Xanthydrol reacted sequentially with ethyl chloroacetate, hydrazine, carbon disulfide, and α-haloketones to prepare the dithiolane. The xanthydrol reacted with propargyl bromide and it submitted to click reaction with azide to prepare triazole ring. Results Finally, four novel xanthones derivatives including (E)-2-(2-(9H-xanthen-9-yl)hydrazono)-1,3-dithiolan-4-one (L3), 2-(2-(9H-xanthen-9-yl)hydrazinyl)thiazol-5(4H)-one (L5), 2-(9H-xanthen-9-ylamino)thiazol-5(4H)-one (L7), and 4-((9H-xanthen-9-yloxy)methyl)-1-(4-nitrophenyl)-1H-1,2,3-triazole (L9) were synthesized and characterized using thin layer chromatography, Fourier-transform infrared spectroscopy, and nuclear magnetic resonance (¹H and ¹³C). ADMET, Pfizer filter, adverse drug reaction, toxicity, antitarget interaction profiles, target fishing, kinase target screening, molecular docking validation, and protein and gene network analysis were computed for derivatives. Ligands obeyed Pfizer filter for drug-likeness, while all ligands were categorized as toxic chemicals. Major targets of all ligands were predicted to be kinases including Haspin, WEE2, and PIM3. Mitogen-activated protein kinase 1 was the hub gene of target kinase network of all derivatives. All the ligands were predicted to show hepatotoxic potentials, while L7 presented cardiac toxicity. Conclusion Acute leukemic T-cells were one of the top predicted tumor cell lines for these ligands. The possible antileukemic effects of synthesized xanthone derivatives are potentially very interesting and warrant further studies.
This study explores unusual magnetic coupling between two metal centers in dimers bridged by a tetrazine ring. Computational analysis was performed to understand how a radical perturbation on the bridging...
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2,973 members
Salam Hassan Mhesn Al-augby
  • Department of Computer Science, Faculty of Computer Science and Mathematics
Basim A. Almayahi
  • Department of Physics
Bahaa Qasim Musawi
  • Faculty of Engineering
Mohammed Sahib Mechee
  • Department of Mathematics
Akeel Yasseen
  • Department of Pathology and Forensic Medicine
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Najaf, Iraq
Head of institution
Prof.Dr.Yasir Lafta Hasson