Guar gum and its derivatives are highly important industrial hydrocolloids as they find applications in various industrial sectors. Guar is a polymer of high molecular weight and its aqueous solutions exhibit unique rheological properties, which has led to its wide acceptance by the industry. In certain industrial applications low molecular weight guar and its derivatives are needed, and conventionally chemical depolymerisation of guar is carried out for this purpose. Radiation processing is a novel and green technology for carrying out depolymerization and can be an ideal substitute for chemical depolymerisation technique. In order to study the effect of radiation on guar derivatives, three types of derivatives have been taken in the present study: carboxymethyl, hydroxyethyl, and methyl guar. The effect of 1–50 KGy radiation dose on the rheological behavior of these derivatives has been studied, and the results have been described in the present paper. The effect on storage and loss modulus with respect to frequency and effect on viscosity with respect to shear rate have been discussed in detail.
Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results.
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