The current research aims to identify the effect of the Cornell note-taking strategy on the achievement of chemistry among second-grade female students in government daytime middle schools. Al-Fadhila School was chosen intentionally as its student sample, affiliated with the First Karkh Directorate for the academic year (2024-2025). One of the two classes was then chosen to represent the experimental group, taught according to the Cornell note-taking strategy, and the other class, the control group, was taught according to the traditional method. The equivalence of the two research groups was verified using a set of variables, including chronological age calculated in months, the Raven Intelligence Test, previous achievement in chemistry, and a post-achievement test. The equivalence of the two research groups was confirmed. The researchers prepared the research tool, an achievement test consisting of 39 multiple-choice items and one essay item. This was administered to the research sample after being reviewed by arbitrators to ensure its validity. The reliability coefficient was extracted using the Cronbach's alpha equation. The research found a statistically significant difference at the 0.05 level between the average scores of the experimental group students who studied using the Cornell note-taking strategy and the average scores of the control group students who studied using the traditional method, in favor of the experimental group. In light of this, the research confirmed that the Cornell note-taking strategy contributes to increasing chemistry achievement among second-year middle school students more than teaching using the traditional method. It also emphasized the need to encourage chemistry teachers to use the Cornell note-taking strategy, given its importance in organizing and analyzing information and identifying key ideas, which facilitates retrieval.
Copper (Cu) is an essential trace element for the efficient functioning of living organisms. Cu can enter the body in different ways, and when it surpasses the range of biological tolerance, it can have negative consequences. The use of different nanoparticles, especially metal oxide nanoparticles, is increasingly being expanded in the fields of industry and biomedical materials. However, the impact of these nanoparticles on human health is still not completely elucidated. This comparative study was conducted to evaluate the impacts of copper oxide nanoparticles (CuO NPs) and copper sulphate (CuSO4 0.5 (H2O)) on infertility and reproductive function in male albino mice BALB/c. Body weight, the weight of male reproductive organs, mal
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The purpose of this study was to measure serum levels of insulin-like growth factor-binding protein (IGFBP7), Insulin-like Growth Factor 1 (IGF-1), Growth Hormone (GH), Interleukin 6 (IL-6) and insulin in acromegaly patients and healthy controls. The acromegaly group had 60 patients, while the population group had 30 people who had never had acromegaly before. The concentration of IGFBP7, IGF-1, GH, IL-6, and insulin were determined. The results of the present study indicate that IGFBP7 level in the acromegaly group was significantly lower (1.690.07 ng/mL vs. 2.740.12 ng/mL, respectively, p = 0.001). IGF-1, GH, IL-6, and insulin concentrations were also significantly higher in acromegaly patients. The diagnostic accuracy (2.194) was exce
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Background: Inflammatory bowel disease (IBD) is a collection of chronic, recurrent inflammatory illnesses of the gastrointestinal system, including Crohn's disease (CD). Infliximab is one of the biological medications used to treat CD. Therapeutic drug monitoring has evolved as a treatment in IBD, aiming to optimize benefit while meeting more demanding, objective end criteria. Objective: To determine the achievement of target trough level (TL), develop anti-drug antibodies (ADAs) to infliximab, assess response to therapy, and study TL relations with different variables. Methods: The present study was cross-sectional and conducted from May 2022 to November 2022. It included 40 CD patients allotted into 2 groups: group 1 patients ach
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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