Al-Mustansiriyah Journal of Science

Background:Finding an analytical solution to Volterraintegro-differential equations (VIDEs), especially nonlinear types, often posesserious difficulties and is many times impossible, thus the need to provide asemi-analytical solution.Objective:This research focuses on the solutionsof systems of linear and nonlinear fractional-order integro-differential equa-tions with difference kernels. To achieve this, we exploited the advantage ofintegral transforms and one of the existing semi-analytical methods to developthe desired method of solution.Methods:One of the recently developed inte-gral transforms, the Shehu transform, which generalizes Laplace and Sumudutransforms, is systematically integrated into the well-known Adomian Decom-position Method (ADM) to obtain a simplified approach to solving the classof problems considered. The Shehu transform is first applied to both sidesof the given VIDEs with difference kernels, followed by the application of theconvolution theorem. The ADM is then employed to handle the nonlinearitiesencountered.Results:The proposed method, the Modified Semi-analyticalMethod (MSM), is applied to selected problems in the literature and producescomparatively good results. The method also produces the exact solution when-ever the solution is in closed form. The results are presented in tabular and2D or 3D graphical forms for easy comparison. All computations are carriedout using Mathematica 13.3, with the fractional-order derivative interpretedin the Caputo sense.Conclusions:Since MSM has been successfully usedto solve linear and nonlinear VIDEs with difference kernels, the scope of themethod can be expanded to cover Volterra-Fredholm integro-differential equa-tions (VFIDEs) in future studies

Background:CR-39 nuclear track detectors are widely usedin various fields, including science, technology, astronomy, and environmentalpreservation, to detect and register heavy ions, neutrons, and alpha parti-cles.Objective:This study aims to determine the optimal etching durationfor CR-39 nuclear track detectors by comparing etching methods: The wetetching (using a water bath) and the dry etching technique (using dielectricbarrier discharge (DBD) plasma).Methods:The traditional etching methodinvolved immersing CR-39 detectors in a sodium hydroxide (NaOH) solutionat 70 °C for varying durations. The dry etching method employed employsa non-thermal DBD plasma system to etch solid-state nuclear track detectors(SSNTDs) without the use of chemical solutions.Results:Photomicrographsrevealed that tracks from chemical etching became clearly visible after 1 hourand developed at 5 hours. In contrast, the dry etching technique was signifi-cantly faster; tracks began to appear after just 1 minute of DBD plasma etchingand were fully developed at 5 minutes. The optimum track density for DBDplasma was 20340±56 tracks/mm2at 3 minutes, whereas the traditional waterbath method achieved an optimum track density of 5238.0±5.7 tracks/mm2at2 hours. The shorter etching duration in the dry method likely facilitates theemergence of latent tracks without overlap.Conclusions:The bulk etch rate(VB) for CR-39 detectors using the dry DBD plasma etching technique wasfaster compared to the traditional chemical etching method. This demonstratesthe efficacy of the dry etching process in significantly reducing etching timerequired for etching SSNDs.

Background:Chalcone derivatives are well-known for theirversatile pharmacological properties, particularly their antimicrobial and an-tioxidant activities. Their simple chemical structure and ease of synthesis makethem attractive candidates for drug discovery, especially in combating micro-bial resistance and oxidative stress-related disorders.Objective:The studyaimed to synthesize and characterize a series of new chalcone derivatives andevaluate their antimicrobial and antioxidant activities, alongside moleculardocking studies to assess their potential as therapeutic agents.Methods:Thechalcone derivatives were synthesized via Claisen–Schmidt condensation of twomethyl ketones (p-aminoacetophenone and cyclopropyl methyl ketone) with p-substitutedphenyl aldehydes under mild conditions. Structural elucidation wasperformed using FT-IR,1H-NMR,13C-NMR, and GC-MS spectroscopy. An-timicrobial activities were tested against five microbial strains: Staphylococcusaureus, Staphylococcus epidermidis, Escherichia coli, Klebsiella pneumoniae,and Candida albicans, using the agar well-diffusion method. Antioxidant activ-ity was evaluated using the standard DPPH radical scavenging assay. Moleculardocking studies were conducted using PyRx software to investigate the bindinginteractions of the synthesized chalcones with the active site of glucosamine-6-phosphate (GlcN-6-P) synthase, a target enzyme for antimicrobial agents.Results:The synthesized chalcone derivatives were successfully characterizedand exhibited varying levels of antimicrobial activity against the tested strains,with some compounds demonstrating significant inhibitory effects. Antioxidantassays revealed promising radical scavenging capabilities for several synthe-sized chalcones. Docking studies showed favorable binding affinities betweenthe identified compounds and the GlcN-6-P synthase active site, supportingthe experimental bioactivity results.Conclusions:This study demonstratesthat the newly synthesized chalcone derivatives possess both antimicrobial andantioxidant activities. Their effective interaction with GlcN-6-P synthase sug-gests potential for further development as lead compounds in antimicrobial drugresearch.

Background:Anemia is a widespread global health issuethat affects millions of individuals worldwide. Early and accurate diagnosis iscritical for effective treatment. Traditional diagnostic approaches rely on com-plete blood count (CBC) parameters, which provide valuable clinical insights butmay require advanced tools to enhance diagnostic accuracy.Objective:Thisstudy aims to develop and evaluate machine learning models for the classifica-tion of different anemia subtypes using CBC data. The objective is to assess theperformance of individual models and ensemble methods in improving diagnos-tic accuracy.Methods:Five machine learning algorithms were implementedfor the classification task: Decision tree, random forest, XGBoost, gradientboosting, and neural networks. In addition to individual model evaluation,ensemble techniques including hard voting, soft voting, and stacking were ap-plied to enhance model performance.Results:Experimental results showedthat ensemble methods significantly outperformed individual models in classi-fication accuracy. Among them, the stacking ensemble achieved the highestaccuracy of 98.44%, indicating superior performance in distinguishing ane-mia subtypes.Conclusions:The study demonstrates that ensemble learningmethods, particularly stacking, can substantially improve the performance ofmachine learning models in anemia classification based on CBC data. Thesefindings suggest the potential integration of such ensemble techniques into clin-ical decision-support systems to assist healthcare providers in making efficientand timely diagnoses.

Background:FGFBP-3 plays a role in metabolic syndromein mice by regulating fat and glucose metabolism. The FGFBP3 proteinis secreted by adipose tissue and also functions in the central nervous sys-tem, similar to thyroid hormone. Thyroid dysfunction is the second mostcommon endocrine disorder after diabetes mellitus (DM).Objective:Thisstudy evaluates FGFBP-3 levels in patients with hyperthyroidism, both withand without DM.Methods:The study included 90 participants divided intothree groups: 30 patients with hyperthyroidism and DM, 30 patients with hy-perthyroidism without DM, and 30 healthy individuals as controls. Thyroidhormones (TSH, total T3, and total T4) were measured using the Minividasdevice. FGFBP-3 levels were estimated by the enzyme-linked immunosorbentassay (ELISA) sandwich method. Additionally, lipid profile parameters (to-tal cholesterol, triglycerides, HDL, LDL, and VLDL) were determined usingenzymatic colorimetric methods.Results:The study found that TSH andHDL levels were significantly lower in both hyperthyroidism groups comparedto controls. Conversely, FGFBP3, HbA1c, free T4, free T3, total cholesterol,triglycerides, LDL, and VLDL levels were significantly elevated in both hy-perthyroidism groups relative to the healthy group. There were no significantdifferences in lipid profile levels between the hyperthyroidism with DM groupand the hyperthyroidism without DM group. High serum FGFBP3 levels wereobserved in hyperthyroid patients regardless of DM status, whereas the controlgroup exhibited lower FGFBP3 levels. Receiver operating characteristic (ROC)analysis indicated that FGFBP3 could serve as a biomarker for monitoring hy-perthyroidism with and without DM.Conclusions:This study demonstratesa strong association between FGFBP3 levels and hyperthyroidism, irrespectiveof the presence of diabetes mellitus.