N. Bharatha Devi

This study aimed to examine the impact of fiber orientation on the tensile and flexural strengths of composites composed of Flax and sisal fibers, particularly at orientations of 0, 45, and 90 degrees. Furthermore, the research sought to determine the most effective parameters for the treatment process. The researchers utilized Response Surface Methodology (RSM) in conjunction with Central Composite Designs (CCD) to develop and analyze their studies. The main objective was to enhance crucial variables such as fiber alignment and the proportions of flax and sisal fibers. Quadratic models were employed in this study to forecast the tensile and bending characteristics of the materials. The best tensile strength (TS) was reached by meticulously experimenting with the fiber orientation and altering the amounts of flax and sisal fibers. The most effective parameter was a fiber orientation of 0 degrees, with a combination of flax and sisal fibers at a concentration of 60 percent each. Under these circumstances, the composite exhibited a significant improvement of 35% in tensile strength and 26% in bending strength when compared to the lowest values achieved with RSM optimization. The notable enhancements demonstrate that aligning the fibers can effectively increase the tensile and flexural strength (FS) and strengthen the bonding between natural fibers and polymer matrix structures.

A recently published study describes the use of an electrochemical biosensor that employs a nanohybrid of MoO3. This hybrid material is synthesized using a one-pot hydrothermal process. NH2-functionalized multi-walled carbon nanotubes (MWCNTs) are renowned for their exceptional electrical characteristics, modest surface area, and fast electron transport capacities. The combination of MoO3 and NH2-MWCNTs produces an advanced immune sensing platform that improves the electrochemical performance and sensitivity in detecting HER-2. The Scanning Electron Microscopy (SEM) analysis of the synthesized nanocomposite demonstrates the presence of nanorods enveloped by slender MWCNT fibers, resulting in the creation of a compact network. The addition of NH2-MWCNTs greatly enhances electron transfer, resulting in a tenfold increase compared to pristine MoO₃, which has an average surface area of 63 m² g⁻¹. The biosensor has exceptional sensitivity, approximately 26 A ng⁻¹ cm⁻² per decade, over a dynamic linear range of 10⁻⁶ to 10³ ng mL⁻¹. Additionally, it retains its effectiveness for around five weeks when maintained at a temperature of 4 °C. This immunological sensing platform utilizes anti-HER-2 antibodies to identify the presence of HER-2. Therefore, the MoO3 composite exhibits outstanding electrochemical performance, similar to APTES/MoO3 and APTES/MoO₃@RGO (Reduced Graphene Oxide) electrodes. It has the potential to be used as a matrix for immunological sensing to identify various cancer biomarkers.

Objective: To effectively analyze diverse satellite images and derive valuable insights, it's crucial to employ efficient methods for classification and image processing. However, due to imperfections in image formats and sensor data, satellite imagery often contains flaws and inaccuracies, requiring extra steps to enhance its quality. The proposed solution involves two key techniques: segmenting the input image using a Hybrid HRSVM-CNN and classifying the resulting high-resolution remote sensing image using a Convolutional Neural Network. This combined approach addresses the challenges posed by image inconsistencies and aims to improve the accuracy and efficiency of current methods for satellite image analysis. Methods: In this research, a high-resolution Support Vector Machine-Convolutional Neural Network (Hybrid HRSVM-CNN) and texture characteristics are used to create an automated land identification method for satellite Remote sensing (RS) images. This approach's main focus is segmentation using the Bendlet Transform and Improved Chan-Vese, and it also does classification using a Hybrid HRSVM-CNN based on feature extraction and gray-level co-occurrence matrix algorithm. Findings: The proposed classification method's accuracy was evaluated against several other classification algorithms, including Semi-Supervised Graph Based Method (SSG), Conditional Random Fields (CRF), k-Nearest Neighbor (KNN), and Bi-layer Graph-based Learning (BLGL), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). When compared with existing methods, the findings of the proposed method display excellent accuracy of 98.83%. Novelty: To start, an adaptive median filter is used to pre-process the satellite Remote sensing images, removing unwanted noises and other impacts. Following pre-processing, the image is segmented using the Bendlet Transform and Improved Chan-Vese algorithms. Gray-level co-occurrence matrix is utilized to extract texture information, and the Hybrid HRSVM-CNN is then used to categorize the various types of land. These applications frequently have a variety of issues that affect the categorization accuracy. A few significant factors, like location, irregularity, form, and diameter, reduce the process's overall accuracy. This research focuses on presenting a unique Land-Cover classification model in order to address such problems. The UC Merced Land Use dataset was considered in this research. Keywords: Bendlet Transform and Improved Chan­Vese Segmentation, Hybrid HRSVM­CNN, Land detection system, Satellite remote sensing images, Adaptive median filter, Gray­ level co­occurrence matrix