There is substantial data supporting the importance of both endogenous and exogenous estrogen in maintaining reproductive health and preventing chronic disease, androgens in women's health are rarely discussed. This is one of the first researches to investigate correlates of blood testosterone concentrations in women with osteopenia, in anticipation of the growing interest in the role of androgens in women's health. A 65 volunteer women were enrolled in the current study, they were divided into two groups, 35 postmenopausal women with osteopenia were in the first group, and the second group contained 30 postmenopausal women without osteopenia as a control. Blood samples were collected from all participants and analyzed for testosterone level, also demographic data were collected. The results showed that women with osteopenia have significantly low levels of testosterone as compared to control, the correlation analysis using postmenopausal women with osteopenia as a model showed a significant reversed correlation between testosterone and T score. Cluster analysis results illustrated that T-Score, testosterone and, duration of the postmenopausal were organized in one cluster, which means the three variables were associated with each other in most of the studied cases. The second cluster included t-score, testosterone and, BMI. Whereas the age factor contributed to the third cluster. Testosterone levels were significantly associated with osteopenia, which could indicate the development of osteoporosis in post-menopause women. Testosterone results were organized in one cluster with T-score and, duration of the postmenopausal. So the three variables were associated with each other in most studied cases.
In this study, manganese dioxide (MnO₂) nanoparticles (NPs) were synthesized via the hydrothermal method and utilized for the adsorption of Janus green dye (JG) from aqueous solutions. The effects of MnO₂ NPs on kinetics and diffusion were also analyzed. The synthesized NPs were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray analysis (EDX), and Fourier-transform infrared spectroscopy (FT-IR), with XRD confirming the nanoparticle size of 6.23 nm. The adsorption kinetics were investigated using three models: pseudo-first-order (PFO), pseudo-second-order (PSO), and the intraparticle diffusion model. The PSO model provided the best fit (R² = 0.999), indicating that the adsorpti
... Show MoreIndustrial development has recently increased, including that of plastic industries. Since plastic has a very long analytical life, it will cause environmental pollution, so studies have resorted to reusing recycled waste plastic (sustainable plastic) to produce environmentally friendly concrete (green concrete). In this research, producing environmentally friendly load-bearing concrete masonry units (blocks) was considered where five concrete mixtures were compressed at the blocks producing machine. The cement content reduced from 400 kg/m3 (B-400) to 300 kg/m3 (B-300) then to 200 kg/m3 (B-200). While (B-380) was produced using 380 kg/m3 cement and 20 kg/m3 nano-sil
... Show MoreWe aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure
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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