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%).
The main object of this article is to study and introduce a subclass of meromorphic univalent functions with fixed second positive defined by q-differed operator. Coefficient bounds, distortion and Growth theorems, and various are the obtained results.
The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimati
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