This study aimed to investigate the impacts of the Trigonella foenum-graecum (T. foenum-graecum) seeds on the female gonad. A total of twenty local rabbits were used in this study; were divided into four groups (5 each): first group (G1) was considered as the control group. The second group (G2), third group (G3) and fourth group (G4) were fed daily1.5%, 3%, and 4.5% of T. foenum-graecum seeds respectively for 60 days (twice daily). At the end of the experiment, the animals were euthanized by diethyl ether (C2H52O). Then the abdomen was incised, and the samples of ovaries were collected and fixed by 10% neutral buffered formalin. The histological assessment was done with a paraffin embedding technique and the histological sections were stained with Hematoxylin and Eosin stain. The result showed that the numbers of primary and secondary follicles were significantly P< 0.05 decreased in G3and G4 compared with the control (G1) and G2. The numbers of Graafian follicles were significantly P<0.05 decreased G4 compared with other groups. The diameters of the primary, secondary, and Graafian follicles were significantly lower than the other groups. The thickness of the granulosa cell layer in G3and G4 were significantly lower than the other groups. The histological figures declared that the ovary of G2 was similar to that in G1. The histological sections of G3 and G4 were revealed marked cortical and medullary vascular congestion and focal hemorrhage; there were also marked follicular degeneration and cystic necrosis. The study concluded that the low concentration of T. foenum-graecum (fenugreek) seeds do not have any positive effect in terms of ovarian stimulation
This paper presents the enhancement of the output performance of a non-linear fuel cell (FC) system using a new design that comprises an adaptive SIMO-PID neural controller with different types of online swarm optimization algorithms. The work focuses on improving the use of single-input multi-output (SIMO) PID neural networks to control the non-linear FC system. The goal of the proposed adaptive SIMO-PID neural voltage-tracking controller is to rapidly and precisely identify the optimal hydrogen flow rate and oxygen flow rate control actions that are used to control the (FC) stack terminal output voltage. Three swarm optimization algorithms are used to find and tune the weights of the SIMO-PID neural controller: the Firefly algorithm, chaotic particle swarm optimization algorithm, and proposed hybrid Firefly-chaotic particle swarm optimization (F-CPSO) algorithm. Numerical simulation results show that the proposed controller using the (F-CPSO) algorithm is more accurate than with the FA or CPSO; the proposed SIMO-PID neural controller parameters are obtained more rapidly there is a high reduction in the number of function evolutions. Furthermore, the proposed controller’s ability with the F-CPSO algorithm to generate a smooth flow rate control response for the non-linear (PEMFC) system without voltage oscillation in the output is determined by investigations under load variations.
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