Bio-composites of oil palm empty fruit bunch (OPEFB) fibres and polycaprolactones (PCL) with a thickness of 1 mm were prepared and characterized. The composites produced from these materials are low in density, inexpensive, environmentally friendly, and possess good dielectric characteristics. The magnitudes of the reflection and transmission coefficients of OPEFB fibre-reinforced PCL composites with different percentages of filler were measured using a rectangular waveguide in conjunction with a microwave vector network analyzer (VNA) in the X-band frequency range. In contrast to the effective medium theory, which states that polymer-based composites with a high dielectric constant can be obtained by doping a filler with a high dielectric constant into a host material with a low dielectric constant, this paper demonstrates that the use of a low filler percentage (12.2%OPEFB) and a high matrix percentage (87.8%PCL) provides excellent results for the dielectric constant and loss factor, whereas 63.8% filler material with 36.2% host material results in lower values for both the dielectric constant and loss factor. The open-ended probe technique (OEC), connected with the Agilent vector network analyzer (VNA), is used to determine the dielectric properties of the materials under investigation. The comparative approach indicates that the mean relative error of FEM is smaller than that of NRW in terms of the corresponding S21 magnitude. The present calculation of the matrix/filler percentages endorses the exact amounts of substrate utilized in various physics applications.
Brain tumor detection at early stages can increase the chances of the patient's recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP-DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.
In this study, a nanocomposite of reduced graphene oxide (RGO) nanofiller-reinforcement poly(lactic acid) (PLA)/poly(ethylene glycol) (PEG) matrix was prepared via the melt blending method. The flexibility of PLA was improved by blending the polymer with a PEG plasticizer as a second polymer. To enhance the electromagnetic interference shielding properties of the nanocomposite, different RGO wt % were combined with the PLA/PEG blend. Using Fourier-transform infrared (FT-IR) spectroscopy, field emission scanning electron microscopy (FE-SEM) and X-ray diffraction, the structural, microstructure, and morphological properties of the polymer and the RGO/PLA/PEG nanocomposites were examined. These studies showed that the RGO addition did not considerably affect the crystallinity of the resulting nanomaterials. Thermal analysis (TGA) reveals that the addition of RGO highly improved the thermal stability of PLA/PEG nanocomposites. The dielectric properties and electromagnetic interference shielding effectiveness of the synthesized nanocomposites were calculated and showed a higher SE total value than the target value (20 dB). On the other hand, the results showed an increased power loss by increasing the frequency and conversely decreased with an increased percentage of filler.