A total of 116 males with primary infertility and 32 fertile males (normozoospermia;
NOR) were studied to evaluate parameters of seminal fluid analysis. Based on WHO criteria
of 2010 for general seminal fluid analysis, the patients were distributed into three groups: 32
azoospermic (AZO), 40 oligozoospermic (OLI) and 44 asthenozoospermic (AST) patients.
AZO and OLI patients and NOR shared an approximated mean of seminal fluid volume (2.25,
2.75 and 2.50 ml, respectively), while it was significantly increased (3.58 ml) in AST patients.
In NOR men, the spermatozoa concentration was 65.13 x 10
6
spermatozoa/ml, while it was
significantly decreased in AST (51.42 x 10
6
spermatozoa/ml) and OLI (5.58 x 10
6
spermatozoa /ml) patients. The percentage frequency of progressive motility was significantly
decreased in OLI and AST patients as compared to NOR (9.6 and 16.3, respectively vs.
50.6%). In contrast, the non-progressive motile (45.4 and 30.4, respectively vs. 21.9%) or
immotile (45.0 and 52.5, respectively vs. 27.5%) spermatozoa were significantly increased in
the patients. OLI and AST patients shared an approximated mean of abnormal spermatozoa
frequency (56.7 and 58.3%, respectively), but both frequencies were significantly higher than
the observed frequency in NOR (23.1%).
(165)
(106)
(6)
(3)
The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,
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This study shows that it is possible to fabricate and characterize green bimetallic nanoparticles using eco-friendly reduction and a capping agent, which is then used for removing the orange G dye (OG) from an aqueous solution. Characterization techniques such as scanning electron microscopy (SEM), Energy Dispersive Spectroscopy (EDAX), X-Ray diffraction (XRD), and Brunauer-Emmett-Teller (BET) were applied on the resultant bimetallic nanoparticles to ensure the size, and surface area of particles nanoparticles. The results found that the removal efficiency of OG depends on the G‑Fe/Cu‑NPs concentration (0.5-2.0 g.L-1), initial pH (2‑9), OG concentration (10-50 mg.L-1), and temperature (30-50 °C). The batch experiments showed
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(5)
The δ-mixing of γ-transitions in 70As populated in the 32 70 70 33 ( , ) Ge p n As γ
reaction is
calculated in the present work by using the a2-ratio methods. In one work we applied this method for two cases, the first one is for pure transition and the sacend one is for non pure transition, We take into account the experimental a2-coefficient for previous works and δ -values for one transition only.The results obtained are, in general, in a good agreement within associated errors, with those reported previously , the discrepancies that occur are due to inaccuracies existing in the experimental data of the previous works.