Preparation and Characterization of Jute Cellulose Crystals-Reinforced Poly(L-lactic acid) Biocomposite for Biomedical Applications

Crystalline cellulose was extracted from jute by hydrolysis with 40% H2SO4 to get mixture of micro/nanocrystals. Scanning electron microscope (SEM) showed the microcrystalline structure of cellulose and XRD indicated the Iβ polymorph of cellulose. Biodegradable composites were prepared using crystalline cellulose (CC) of jute as the reinforcement (3–15%) and poly(lactic acid) (PLA) as a matrix by extrusion and hot press method. CC was cellulose derived from mercerized and bleached jute fiber by acid hydrolysis to remove the amorphous regions. FT-IR studies showed hydrogen bonding between the CC and the PLA matrix. The X-ray diffraction (XRD) and differential scanning calorimetry (DSC) studies showed that the percentage crystallinity of PLA in composites was found to be higher than that of neat PLA as a result of the nucleating ability of the crystalline cellulose. Furthermore, Vicker hardness and yield strength were found to increase with increasing cellulose content in the composite. The SEM images of the fracture surfaces of the composites were indicative of poor adhesion between the CC and the PLA matrix. The composite with 15% CC showed antibacterial effect though pure films but had no antimicrobial effect; on the other hand its cytotoxicity in biological medium was found to be medium which might be suitable for its potential biomedical applications

An Intelligent Walking Stick for the Visually-Impaired People

Blind stick or white cane is introduced to blind people after the First World War as a mobility tool to detect the obstacles in the path of the user. This paper proposes an Arduino Nano based obstacle finding stick for visually-impaired people, which helps a blind person by detecting the obstacles using Ultrasonic sensors and android mobile application. It is able to inform the blind person about the circumstances & present condition of the path where he/she is walking. The main objective of this paper is to help a blind people to move more freely by using a reliable stick. The device consists of arduino nano, HC SR-04 ultrasonic sensor, HC-05 Bluetooth module, push buttons, 100nF ceramic capacitors and a 9V battery as the power source. And the android app is developed using MIT App Inventor 2

EXTENDS LIFE TO THE MOBILE AD-HOC NETWORK BY DETECTING MALICIOUS NODES AND REDUCING ENERGY CONSUMPTION USING ROUTING PROTOCOL

The incidence of malicious nodes in an ad-hoc network worsens the performance of the network. A novel approach to detect increasing intrusion of malicious nodes is proposed to protect against attack on MANET vector route protocol. These malicious nodes can reduce overall data access in the network, with the increase in query delays. However some nodes can only decide to support partially or not at all with other nodes. Those mobile nodes that take their own packets but do not send neighbors to packets are known as the unfaithfully nodes. This type of malicious nodes can reduce overall data access in the network. Due to this kind of problem, the overall process of MANET was affected. The proposed approach employs a technique for determining the conditions less than which the first stage is based on the secure malicious nodes detecting method (SMND) to the network and the second step is based on the smallest route to reducing energy consumption using AODV (REC-AODV). In accumulation to identifying malicious nodes, it have been experiential that in this approach, ad- hoc routing has less security and less communication breaks. MANET has many issues that attract researchers to work in these areas. Experimental outcome show that the proposed methodology is to successfully detect malicious nodes, and with the help of routing protocol, the shortest route reduces the energy consumption of the packets in the network, which will be for long life in the network. By keeping these parameters in mind we calculate the packet delivery ratio, throughput, energy usage, congestion control and routing overhead

BDMediLeaves: A leaf images dataset for Bangladeshi medicinal plants identification

This paper introduces a newly curated dataset named “BDMediLeaves” that includes a diverse collection of leaf images of ten distinct medicinal plants from various regions in Dhaka, Bangladesh. The ten distinct categories are Phyllanthus emblica, Terminalia arjuna, Kalanchoe pinnata, Centella asiatica, Justicia adhatoda, Mikania micrantha, Azadirachta indica, Hibiscus rosa-sinensis, Ocimum tenuiflorum, and Calotropis gigantea. The dataset contains a total of 2,029 original leaf images, along with an additional 38,606 augmented images. Each original image was meticulously captured under natural lighting conditions with an appropriate background. Experts provided accurate labeling for each image, ensuring its seamless integration into various machine learning (ML) and deep learning (DL) models. This comprehensive dataset holds immense potential for researchers in utilizing various ML and DL methods to make significant advancements in the healthcare and pharmaceutical sectors. It serves as a valuable resource for future investigations, laying the foundation for crucial developments in these domains