ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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Computational Study of Some Urolithin Derivatives-based Biomass Corrosion Inhibitors on the Fe (110), Cu(111) and Al(111) Surface
Full text linkCorrosion poses a significant economic and environmental burden, highlighting the need for sustainable corrosion inhibitors. This study investigates the potential of urolithin derivatives (UroE, UroM5, UroM6, and UroM7) as eco-friendly corrosion inhibitors for Fe(110), Cu(111), and Al(111) surfaces. The research uses Density Functional Theory (DFT) calculations and Monte Carlo (MC) simulations to compute quantum chemical parameters, Fukui function, and noncovalent interactions. The results show that compounds with strong hydrogen bonding interactions form more robust bonds with the metal surface, potentially leading to enhanced corrosion protection. UroM5 demonstrates superior stability and lower reactivity due to its high band gap energy. MC simulations reveal that the adsorption energies of urolithin derivatives on metal surfaces follow a trend: UroM5 > UroM6 > UroE > UroM7, suggesting a stronger binding affinity for these metals. Thermal characteristics, particularly Gibbs free energy, were also investigated. The results suggest that a temperature increase from 825 to 1000 K may induce a transition from physisorption to chemisorption for all chemicals on the metal surface. These comprehensive analyses provide valuable insights into the mechanism and efficiency of urolithin derivatives as corrosion inhibitors, paving the way for the development of novel and eco-friendly anti-corrosion materials
Deep Learning for Cardiovascular Disease Detection: A Review Based on Cardiac Magnetic Resonance Imaging Data
Full text linkDespite improvements, cardiovascular diseases (CVD) remain the most significant killer globally, accounting for around 17.9 million lives annually. Advancement of cardiac imaging modalities has taken place with Magnetic Resonance Imaging (MRI) along with artificial intelligence (AI) for changing scenarios of early diagnosis and management in cardiovascular diseases. This work investigates the role and contribution of deep learning, especially Fully Convolutional Networks (FCNs) and Convolutional Neural Networks (CNNs), toward the improvement of accuracy and automation in cardiac MRI analysis. The integration of AI enables accurate segmentation, efficient clinical workflows, and scalable solutions for resource-limited environments. A review of publicly available datasets underlines challenges in data variability and generalizability and points to the need for standardized models and explainable AI approaches. This work, therefore, underlines the possibility of improved diagnostic efficiency and equity in healthcare delivery using AI-driven methodologies in cardiovascular diagnostics. Future directions will focus on refining model scalability, enhancing dataset diversity, and validating clinical applications to foster robust and adaptable solutions
Uric Acid Dynamics in Young Women: Associations with Radiation Exposure, Blood Groups, and Biochemical Markers
Full text linkA growing body of evidence implicates uric acid to play central roles in various disease complications. However, the delineation between causative and coincidental roles remains poorly elucidated. This study aimed to evaluating uric acid in young women considering variables such as disease types, body mass index, blood groups, radiation exposure, and to assess the relationship between uric acid levels and certain subclinical biomarkers. A cohort of 178 young women, between 18 and 39 years, (comprising 100 cases and 78 healthy controls) was included in this investigation. Diagnosed subjects with common diseases, renal, cardiovascular, blood, joints, diabetes, gastrointestinal diseases, were selected according to stringent criteria. Subjects were categorized based on disease types, blood groups, and exposure history to ionizing radiation including mammography and body or leg X-ray. Serum levels of uric acid and biochemical tests were analyzed by unpaired comparison tests and correlation coefficient tests. Subjects with hematological disorders exhibited lower uric acid concentrations when compared to healthy women. Uric acid concentrations in individuals with repeated exposure to X-ray radiation showed a high significant difference (p<0.01) compared to unexposed individuals. In Spearman correlation analyses, positive correlations (p < 0.001) are identified between uric acid and iron, calcium, prolactin, and body mass index. In healthy young women, uric acid levels fluctuated both within and beyond physiological ranges, independent of disease status, weight indexes, and blood types. Assessment of uric acid in young women, based on a history of radiation exposure, subclinical blood parameters might guide for better medical decision and treatment
The Optimum Sulfur Recovery Process From North Gas Company Sour Acid Gas: A Case Study and Simulation
Full text linkThe North Gas Company in Kirkuk, Iraq produces a sour gas stream that is loaded with considerable amounts of H2S and CO2, at concentrations of 2.95% and 2.54%, respectively. A previous study successfully treated this sour gas stream and produced a sweet gas stream by adopting a natural gas sweetening process using ProMax process simulation software. However, this process also produced an acid gas stream that was loaded with a considerable amount of H2S. The acid gas stream is processed in a sulfur recovery unit to protect the environment. The Claus process is the major technology used to produce elemental sulfur from H2S and SO2 gases. This study examines this process to treat the acid gas stream and recover the elemental sulfur, using ProMax simulation software developed by Bryan Research and Engineering, LLC. Moreover, the simulation model was successful in reducing the amount of H2S from 872.5 kg/h to 60.5 kg/h by adopting two Claus bed reactors to increase the process efficiency. Furthermore, process optimization was also adopted to find out the optimum Claus reactor bed operating temperature at 215°C
In-depth Analysis on Machine Learning Approaches: Techniques, Applications, and Trends
Full text linkMachine learning (ML) approaches cover several aspects of daily life tasks, including knowledge representation, data analysis, regression, classification, recognition, clustering, planning, reasoning, text recommendation, and perception. The ML approaches enable applications to learn and adapt with or without being directly programmed from previous data or experience. The ML techniques, coupled with current technologies, provide a range of solutions, starts from vision-based applications to text-generation applications. To this end, this article presents a comprehensive overview of the approaches of ML, including supervised, unsupervised, semi-supervised, reinforcement, and self-learning. This review critically examines the roles performed by these aforementioned approaches in terms of their weaknesses and strengths. Furthermore, within this study, a new comparative analysis is conducted by reviewing existing studies and evaluating ML techniques using metrics including data requirement, accuracy, complexity, interpretability, scalability, applications, and challenges. Thereafter, the implemented ML techniques are classified, and their key findings are examined. The comprehensive review demonstrates that neither standalone nor hybrid ML techniques can completely satisfy all of the evaluated metrics, the necessity of customized solutions based on the requirements of particular applications
Assessment of Corrosion Inhibition Efficiency of Some Amino Acids on Stainless Steel in Ethaline
Full text linkThis study evaluates simple amino acids as green corrosion inhibitors for AISI 302 stainless steel in Ethaline, a chloride-rich deep eutectic solvent (DES). General and localized corrosion behaviors were assessed using Tafel analysis and cyclic potentiodynamic polarization (CPP) in blank Ethaline and with 0.05 M, 0.075 M, and 0.1 M glycine, alanine, and leucine. Results show complex interactions between amino acid structure, concentration, and inhibition. The high viscosity of Ethaline shifts anodic control from charge transfer to mass transfer at higher potentials. All amino acids shift corrosion potential (Ecorr) nobly, with 0.05 M glycine giving the largest increase (~0.26 V) for general corrosion resistance, though it yields the narrowest passivation range (0.52 V), increasing pitting susceptibility. Alanine and leucine, with bulkier side chains, enhance localized corrosion resistance via steric hindrance to Cl⁻ ions. Low concentrations (0.05 M) optimize protection by lowering passivation current (ipass) and forming stable films, but higher levels (>0.05 M for glycine/leucine) reverse this, accelerating corrosion via acidity or complexation. A 0.05 M glycine/leucine binary mixture produces negative CPP hysteresis, indicating strong repassivation and self-healing. These findings reveal trade-offs between general and localized protection, emphasizing precise concentration control. The study advances tailored, synergistic amino acid inhibitors as sustainable, self-healing systems for DES environments
Impact of Cement Kiln Dust on Soil Geotechnical Properties: A Case Study of Soils Surrounding Cement Factories in Bazian Area, Sulaimani Governorate, Iraq
Full text linkAs cement demand rises daily; kiln dust from cement-producing facilities is being collected in large quantities. This tiny dust poses a risk to the environment when it is disposed of. This study investigates the potential reuse of CKD as a stabilizing agent for expansive soils. The amount of CKD added to intact soil was 0%, 10%, 20%, 30%, and 40% of the dry mass of the soil. The liquid limit (LL) of the intact soil decreased after adding CKD from 47.7% to 43.7%, and the plastic limit (PL) from 26.97% decreased to 21.36%. A sharp decrease was observed in the consistency index (Ic) and toughness index (It), while the flow index (If), liquidity index (Li) and shrinkage index (Is) are noticeably increased. The unconfined compressive strength (qu), modulus of elasticity (E), and Resilient Modulus (Ur) increased for the samples cured for 7 days and 28 days; the rate of increase in the values of these parameters was observed to be higher in 28 days during the period compared to 7 days. Also, the shear strength and cohesion sharply increased after the soil was treated with CKD. The UPV also increased for both sets of prepared cylindrical soil samples. The CKD additives decreased the hydraulic conductivity of the soil and successfully improved the geotechnical characteristics of fine-grained soil. Thus, CKD can be repurposed as a sustainable soil stabilizer for slopes, subgrades, and foundations, providing a viable solution for its disposal and reducing environmental impact
Synthesis, Characterization, and Antioxidant Studies of Novel Cu(II) and Ni(II) Homo Binuclear Complexes with N,N’-bis(Benzamidothiocarbonyl) Hydrazine Ligand Derivatives
Full text linkThis study presents the synthesis, spectral characterization, and antioxidant evaluation of Cu(II) and Ni(II) homo binuclear complexes derived from three bis(thiourea)hydrazine ligands: N, N′-bis(benzamidothiocarbonyl)hydrazine (L1), N, N′-bis(o-chlorobenzamidothiocarbonyl)-hydrazine (L2), and N, N′-bis(p-methylbenzamidothiocarbonyl)hydrazine (L3). The ligands were synthesized via condensation reactions and characterized using CHNS elemental analysis, FT-IR, UV-Vis, and ¹H NMR and ¹³C NMR spectroscopy. The metal complexes were prepared in a 2:1 metal-to-ligand molar ratio and characterized by melting point determination, magnetic susceptibility, molar conductivity, and spectral techniques. IR data confirmed coordination through the thiocarbonyl sulfur and amide oxygen atoms, forming neutral bidentate complexes. Magnetic moment values and electronic spectra were consistent with square planar geometries for both Cu(II) and Ni(II) complexes. Molar conductance measurements indicated non-electrolytic behavior in DMF. Antioxidant activity was assessed via the DPPH radical scavenging assay, revealing that the metal complexes exhibited enhanced radical scavenging capacity compared to the free ligands, likely due to increased delocalization and metal ion involvement in electron transfer. These findings highlight the potential of bis(thiourea)hydrazine-based metal complexes as antioxidant agents
Spatio-Temporal Evaluation of Physicochemical Parameters, Heavy Metals, and Pesticides in the Little Zab River
Full text linkWater quality is a fundamental determinant of human health, influencing the prevalence of waterborne diseases and the overall safety of potable water. Contaminated water sources expose populations to microbial pathogens and toxic chemical contaminants, presenting significant public health and environmental challenges. In this study, a comprehensive assessment of water quality was conducted on a segment of the Little Zab River, situated in the Kurdistan region of Iraq. Water samples were meticulously collected from five strategically selected sites along the river, spanning across all four seasons, to evaluate temporal variations in water quality. In situ measurements of critical physico-chemical parameters, including pH, electrical conductivity, total dissolved solids, salinity, dissolved oxygen, density, and turbidity, were performed to establish baseline water quality profiles. Concurrently, laboratory analyses were performed to quantify the concentrations of selected heavy metals (mercury, cadmium, arsenic, zinc, iron, lead, and copper) and pesticides (α-cypermethrin, acetamiprid, dichloro-diphenyl-trichloroethane, and p,p՛-DDD) using standardized methods. Descriptive statistical analyses, conducted using Excel and Statistical Package for Social Sciences, revealed significant spatial and seasonal fluctuations in both the physicochemical parameters and contaminant levels, with certain sites exhibiting concentrations that raise potential health and ecological concerns. The findings underscore the critical need for continuous monitoring of water used in agriculture and the implementation of targeted management strategies to mitigate contamination and protect public health in the region. This work contributes valuable insights into the interplay between natural processes and anthropogenic impacts on riverine water quality in arid and semi-arid environments
Identification of OPN1LW Exon 3 Variants Impairing Red-Cone Function in Color Vision Deficiency
Full text linkThe most common form of inherited color blindness is red-green color vision deficiency (CVD), which is frequently caused by mutations in the X-linked OPN1LW gene. Red cone malfunction is linked to mutations in exon 3 of this gene. In this study, the Ishihara test was used to evaluate the color vision of 1500 Kurdish students, ages 13–18. Polymerase chain reaction amplification and Sanger sequencing of OPN1LW exon 3 were performed on 50 students who had been diagnosed with protanopia or protanomaly. Variants (nucleotide changes) were analyzed using Geneious Prime® software. Functional impact of variants was predicted using PolyPhen-2 and SIFT. The study found 30 different nucleotide variations, comprising 63.3% missense mutations, 23.3% silent mutations, and 13.3% frameshift mutations. The most common variants were found c.30G>A (p. Arg10Arg), c.106T>C (p. His35Pro), and c.161_162insG (p. Asp54Gly). SIFT found (57.8%) of variations as deleterious (scoring ≤0.05), but PolyPhen-2 assessed (63.1%) as potentially damaging (score >0.9). ABO blood type was unrelated to CVD risk, although consanguinity and family history were strongly linked to CVD risk. Our study revealed that people with red-green CVD have frequent and possibly harmful mutations in exon 3 of OPN1LW. These results may aid in the molecular characterization of CVD in the Kurdish population and could help develop future diagnostic and treatment approaches