In this study, we introduce new a nanocomposite of functionalize graphene oxide FGO and functionalize multi wall carbon nanotube (F-MWCNT-FGO).The formation of nanocomposite was confirmed by FT-IR ,XRD and SEM. The magnitude of the dielectric permittivity of the (F-MWCNT-FGO) nanocomposite appears to be very high in the low frequency range and show a unique negative permittivity at frequencies range from 400 Hz to 4000Hz. The ac conductivity of nanocomposite reaches 23.8 S.m-1 at 100Hz.

Background: The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor historically recognized for its role in the regulation of toxicity mediated by environmental chemicals. Recent research points to AhR's critical participation in male reproductive physiology, particularly in spermatogenesis, hormone signaling, and the maintenance of sperm quality. Both endogenous ligands (e.g., dietary and gut microbiota-derived metabolites) and exogenous pollutants (e.g., dioxins and benzo-α-pyrene) influence AhR-mediated pathways, making it a key link between environmental exposures and male fertility. Results: This review highlights AhR's influence on the male reproductive system, emphasizing the role of endogenous AhR ligands an

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

Recently, numerous the generalizations of Hurwitz-Lerch zeta functions are investigated and introduced. In this paper, by using the extended generalized Hurwitz-Lerch zeta function, a new Salagean’s differential operator is studied. Based on this new operator, a new geometric class and yielded coefficient bounds, growth and distortion result, radii of convexity, star-likeness, close-to-convexity, as well as extreme points are discussed.

In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

As one type of heating furnaces, the electric heating furnace (EHF) typically suffers from time delay, non-linearity, time-varying parameters, system uncertainties, and harsh en-vironment of the furnace, which significantly deteriorate the temperature control process of the EHF system. In order to achieve accurate and robust temperature tracking performance, an integration of robust state feedback control (RSFC) and a novel sliding mode-based disturbance observer (SMDO) is proposed in this paper, where modeling errors and external disturbances are lumped as a lumped disturbance. To describe the characteristics of the EHF, by using convection laws, an integrated dynamic model is established and identified as an uncertain nonlinear second ord

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class