Rajendran Karthik

This comprehensive study investigates the advanced application of green technology-based, non-toxic, metal oxide-derived edible nano preparations, especially edible nanoparticles (ENPs) and nanoemulsions, for crop and food preservation. The study is designed to address critical food security problems, these nanoformulations can be integrated into preservative films, packets, and coatings to extend the shelf life of food products. The gold, copper and chitosan will serve as core materials, integrated with bioactive substances to create sustainable nanostructures. The physicochemical and biological properties will be categorized using SEM, TEM, XRD, and FTIR, while antioxidant, anticoagulant, and cytotoxicity assays will estimate safety and functional performance. The aim is to enhance formulations that promote sustainable agriculture by reducing the need for chemical inputs and increasing crop yield. Nano-based edible films present an environmentally friendly alternative to conventional preservation techniques. In addition, their biodegradability and consumer safety statement pressing concerns regarding the adverse health and ecological impacts of plastic packaging. The green synthesis approach ensures eco-friendly, scalable, and sustainable production methods that hold potential applications across agriculture, food systems, healthcare, and medicine. The conclusion of this review is to demonstrate that edible nanoparticles and nanoemulsions can effectively improve food preservation, enhance sustainability, and replace harmful plastic packaging. The outcomes will contribute to advancing food security, protecting human health, and mitigating environmental challenges.

Autonomous mobile robots are increasingly used across various applications, relying on multiple sensors for environmental awareness and efficient task execution. Given the unpredictability of human environments, versatility is crucial for these robots. Their performance is largely determined by how they perceive their surroundings. This paper introduces a machine learning (ML) approach focusing on land conditions to enhance a robot’s locomotion. The authors propose a method to classify terrains for data collection, involving the design of an apparatus to gather field data. This design is validated by correlating collected data with the output of a standard ML model for terrain classification. Experiments show that the data from this apparatus improves the accuracy of the ML classifier, highlighting the importance of including such data in the dataset.

The rhizosphere, a narrow region of soil surrounding plant roots, is an environment rich in microbial diversity that profoundly influences plants’ health, growth, and agricultural productivity. This microbial community, known as the rhizosphere microbiome, consists of a complex array of bacteria, fungi, archaea, and other microorganisms that engage in complex interactions with plant roots. These microorganisms contribute to nutrient cycling, mineral uptake facilitation, and protection against soil-borne pathogens, thereby promoting plant growth and resilience towards biotic and abiotic stresses. Additionally, microbial signaling molecules, including phytohormones such as auxins, cytokinin, gibberellins, ethylene, and abscisic acid, play a pivotal role in regulating these interactions by modulating plants’ responses to environmental stressors. Recent advancements in microbiomics have enabled a deeper understanding of the rhizosphere’s diversity, composition, and functions, paving the way for more sustainable agricultural practices. By harnessing the potential of the rhizosphere microbiome, innovative strategies can be developed to reduce dependency on synthetic agrochemicals, enhance soil fertility, and increase crop yields. This review discusses the diversity and mechanisms of plant–microbe interactions, focusing on the role of microbial signaling molecules, and explores their applications in promoting agricultural sustainability. The insights gained from microbiomics studies can revolutionize farming practices by reducing dependency on chemical inputs, enhancing crop productivity, and nurturing soil health and environmental sustainability.

Animal locomotion is the process through which animals move from one location to another. Self-propelled locomotion is based on the animal performing a series of actions to move towards a predetermined target. All of these motions occur sequentially and repeat themselves during a gait cycle. A gait cycle may be simulated by duplicating each motion in the cycle sequentially. To achieve this goal, a problem known as the gait planning issue was formulated, in which various systems were created to provide suitable signals for the execution of distinct gaits (patterns of steps of an animal at a specified speed). This research approaches the problem using Horner's polynomials for quadruped robots. The approach entails first creating a sequence table for each gait and fit two polynomial equations. In this study, an attempt is made to combine several gaits using Horner's polynomials. An algorithm uses elaborated polynomials to generate the desired gaits signals.

Mobile robots are used in a variety of applications including research, education, healthcare, customer service, security and so on. Based upon the application, the robots employ different locomotion systems for their mobility. When it comes to rolling locomotion, the wheels used to provide mobility to robots can be categorized as: tracks, omnidirectional wheels, and unidirectional wheels with a steering system. The ability of omnidirectional wheels to drive machines in small spaces makes them interesting to use. Among the types of omnidirectional wheels, mecanum wheels are widely used due to their inherent benefits. With the right control strategy, robots equipped with mecanum wheels can move freely, in all possible directions. In this study, a triangle decomposition approach is employed for controlling omnidirectional mecanum wheel-based robots. The method consists of breaking down any path into a set of linear motions that can be horizontal, vertical, or oblique. Furthermore, the oblique paths are divided into smaller segments that can be resolved into a horizontal and vertical component in a right-angle triangle. The suggested control method is tested and proved on a simple scenario using Webots simulation software.

The authors have selected a very good topic and have done good analysis. I have few suggestions for the improvement of the paper technically, 1.The limitations of the work proposed and their future improved should be given in more detail. 2.The Int...