Femtosecond laser pulse propagation in monomode optical fibers is demonstrated and investigated numerically (by simulations) and experimentally in this paper. A passively mode locked Nd:glass laser giving a pulse duration of about 200 fsec at 1053 nm wavelength and 120 mW average optical power with 100 MHz repetition rate is used in the experimental work. Numerical simulations are done by solving the nonlinear Schrödinger equation with the aid of Matlab program. The results show that self phase modulation (SPM) leads to compression of the spectral width from 5 nm to 2.1 nm after propagation of different optical powers (34, 43, 86 and 120 mW) in fibers of different length (5, 15, 35 m). The varying optical powers produced a varying

Two of the main advantages of segmental construction are economics, as well as the rapid construction technique. One of the forms of segmental construction, for structural elements, is the segmental beams that built-in short sections, which referred to segments. This research aims to exhibit a new technique for the fabrication of short-span segmental beams from wedge-shaped concrete segments and carbon fiber reinforced polymers (CFRP) in laminate form. The experimental campaign included eight short-span segmental beams. In this study, two selected parameters were considered. These parameters are; the number of layers of CFRP laminates and the adhesive material that used to bond segments to each other, forming short-span segmental be

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

In cognitive radio system, the spectrum sensing has a major challenge in needing a sensing method, which has a high detection capability with reduced complexity. In this paper, a low-cost hybrid spectrum sensing method with an optimized detection performance based on energy and cyclostationary detectors is proposed. The method is designed such that at high signal-to-noise ratio SNR values, energy detector is used alone to perform the detection. At low SNR values, cyclostationary detector with reduced complexity may be employed to support the accurate detection. The complexity reduction is done in two ways: through reducing the number of sensing samples used in the autocorrelation process in the time domain and through using the Slid

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an