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bsj-8819
Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods
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Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of the study is the generated data sets obtained on the basis of theoretical stress relaxation curves. Tables of initial data for training models for all samples are presented, a statistical analysis of the characteristics of the initial data sets is carried out. The total number of numerical experiments for all samples was 346020 variations. When developing the models, CatBoost artificial intelligence methods were used, regularization methods (Weight Decay, Decoupled Weight Decay Regularization, Augmentation) were used to improve the accuracy of the model, and the Z-Score method was used to normalize the data. As a result of the study, intelligent models were developed to determine the rheological parameters of polymers included in the generalized non-linear Maxwell-Gurevich equation (initial relaxation viscosity, velocity modulus) using generated data sets for the EDT-10 epoxy binder as an example. Based on the results of testing the models, the quality of the models was assessed, graphs of forecasts for trainees and test samples, graphs of forecast errors were plotted. Intelligent models are based on the CatBoost algorithm and implemented in the Jupyter Notebook environment in Python. The constructed models have passed the quality assessment according to the following metrics: MAE, MSE, RMSE, MAPE. The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. Model performance estimates obtained during testing are valid.

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Medicinal And Pharmaceutical Chemistry Research
The effect of selenium nanoparticles with fenugreek extract on oxidative stress related to polycystic ovary syndrome
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In recent years, there has been growing interest in using Nanosystems in different biomedical applications. Among all metal nanoparticles, selenium nanoparticles have attracted the attention of many researchers due to its low toxicity and nutritional supplementation value. The purpose of the current study was designed to examine the possible effect of selenium nanoparticles in combination with fenugreek leaves extract (an edible herb with good medicinal properties) in the treatment of oxidative stress status-related to polycystic ovary syndrome in letrozole-induced PCOS (an imbalance of reproductive hormones that causes infertility) in adult female rats. Cold plasma was used in the preparation of selenium nanoparticles subsequently the prod

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Publication Date
Tue Feb 18 2020
Journal Name
Modelling And Simulation In Engineering
Temperature and Stress Evaluation during Three Different Phases of Friction Stir Welding of AA 7075-T651 Alloy
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The current study performs an explicit nonlinear finite element simulation to predict temperature distribution and consequent stresses during the friction stir welding (FSW) of AA 7075-T651 alloy. The ABAQUS® finite element software was used to model and analyze the process steps that involve plunging, dwelling, and traverse stages. Techniques such as Arbitrary Lagrangian–Eulerian (ALE) formulation, adaptive meshing, and computational feature of mass scaling were utilized to simulate sequence events during the friction stir welding process. The contact between the welding tool and workpiece was modelled through applying Coulomb’s friction model with a nonlinear friction coefficient value. Also, the model considered the effect of nonlin

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Publication Date
Fri Sep 29 2023
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Evaluation of The Effect of Fisetin against Cyclophosphamide-Induced Myelosuppression and Oxidative Stress in Male Albino Rats
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Myelosuppression is a serious disease that is related to the malfunction of blood cells production that leads to cytopenia which is the most serious hematologic toxicity of cancer chemotherapies including cyclophosphamide, which is a strong oxazaphosphorine [a nitrogen mustard alkylating agent] that can be used alone or combined with other chemotherapeutic agents for the treatment of different malignant diseases. It induces severe bone marrow suppression by damaging hematopoietic stem cells through the generation of oxidative stress. Fisetin is a hydrophobic polyphenolic compound with a wide range of pharmacological properties such as antioxidant, anti-inflammatory, antimicrobial, osteoprotective, antidiabetic, and anti-carcinogenic

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Publication Date
Wed Jan 01 2020
Journal Name
Biochemical And Cellular Archives
EVALUATION OF WATER STRESS EFFECT ON SOME CHEMICAL PROPERTIES OF SEVERAL SUNFLOWER VARIETIES CALLUS TREATED WITH ULTRAVIOLET
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The study was carried out in plant tissue culture laboratory, University of Baghdad during the period 2017-2019, as factorial experiment in complete randomized design, to study the effect of PEG at (0, 2, 4, 6 and 8%) on physiological and chemical changes in callus of three sunflower (Ishaqi 1, Aqmar and Al-haga) induced by the cultivation of the young stem in vitro under water stress. The content of callus cells of SOD, POD, CAT and APX enzymes as well as content of hydrogen peroxide were determined as indicators to determine the effect of PEG in callus tissue cells cultivated on medium equipped with the PEG concentrations. The results showed that cultivars were differs significantly and Al-hajavariety was superior in increasing SOD to 24.

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Publication Date
Sun Jan 25 2026
Journal Name
Journal Of Kufa For Chemical Sciences
Evaluation of Oxidative Stress and Liver Function Parameters in the Sera Samples of Kufa Cement Factory Workers
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AbstractThe objective of the present study was measured of several oxidative stresses and liver function parameters in workers occupationally exposed to cement dust in Kufa Cement Factory, in order to test the hypothesis that cement dust exposure may perturb these parameters. Assessment of oxidative stress and liver function parameters were performed in 63 workers occupationally, in different departments of Kufa Cement Factory, exposed to cement dust (range of the exposure time was 5-38 years) and 36 matched unexposed controls. The study results illustrated an increasing in the oxidative stress parameters, moreover; liver function parameters showed abnormal results in the exposed workers compared to the unexposed. An increase in theses para

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Publication Date
Fri Sep 29 2023
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Evaluation of The Effect of Fisetin against Cyclophosphamide-Induced Myelosuppression and Oxidative Stress in Male Albino Rats
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Myelosuppression is a serious disease that is related to the malfunction of blood cells production that leads to cytopenia which is the most serious hematologic toxicity of cancer chemotherapies including cyclophosphamide, which is a strong oxazaphosphorine [a nitrogen mustard alkylating agent] that can be used alone or combined with other chemotherapeutic agents for the treatment of different malignant diseases. It induces severe bone marrow suppression by damaging hematopoietic stem cells through the generation of oxidative stress. Fisetin is a hydrophobic polyphenolic compound with a wide range of pharmacological properties such as antioxidant, anti-inflammatory, antimicrobial, osteoprotective, antidiabetic, and anti-carcinogenic activit

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Publication Date
Thu May 31 2012
Journal Name
Al-khwarizmi Engineering Journal
Effect of Journal Misalignment on the Static Characteristics of Porous Journal Bearings Lubricated with Couple Stress Fluid
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In this paper, a theoretical study to the effect of journal misalignment on the static characteristics of oil filled porous journal bearing when lubricated with couple stress fluid has been carried out.

The analytical model used through this work is for a bearing with isotropic permeability. Considering isotropic permeability the Reynolds' equation for the oil film is modified to include a so – called filter term and the effect of fluid coupled stress. The pressure equation for the porous medium is obtained from Darcy's law and continuity equation. The equation which was used to evaluate the oil film thickness was modified to include the effect of possible misalignment in longitudinal and transverse directions. The governing eq

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Publication Date
Mon May 01 2023
Journal Name
Complementary Therapies In Medicine
Effects of almond intake on oxidative stress parameters: A systematic review and meta-analysis of clinical trials
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Publication Date
Tue Mar 01 2016
Journal Name
International Journal Of Engineering Research And Advanced Technology (ijerat)
Speeding Up Back-Propagation Learning (SUBPL) Algorithm: A New Modified Back_Propagation Algorithm
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The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.

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Publication Date
Mon Nov 21 2022
Journal Name
Sensors
Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets
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Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

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