In this work, a simulated study was carried out for designing a novel spiral rectangular patch of microstrip antenna that is used in ultra-wideband applications by using a high frequency structure simulator software (HFSS). A substrate with dielectric constant of 4.4 and height 2.10 mm (commercial substrate height available is about 0.8-1.575 mm) has been used for the design of the proposed antenna. The design basis for enhancing bandwidth in the frequency range 6.63 - 10.93 GHz is based on increasing the edge areas that positively affect the antenna's efficiency. This design makes the designed antenna cost less by reducing the area of the patch. It has been noticed that the bandwidth of the antenna under this study is increasing to 4.30

Objective: The evaluation of serum osteocalcin (OSN) for Iraqi infertile patients to see the effect of osteocalcin insufficiency, which may lead to a decreased level of testosterone production in males that may cause infertility. Methods: Forty two newly diagnosed infertile males age range (24–47) years and thirty two apparently healthy males as controls age range (25–58) years. Serum levels of testosterone (TEST), stimulating follicle hormone (FSH) and luteinizing hormone (LH), prolactin (PROL), osteocalcin OSN, and fasting blood sugar (FBS) were performed in both patients and controls. Estimation of serum OSN by Immulite1000 auto-analyzer, TEST, FSH, LH, PROL, and FBS by Immulite2000 auto-analyzer. Results: Infertile patients

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has