Selecting the foremost big data tool to optimize YouTube data in dynamic Fermatean fuzzy knowledge.

Big data pertains to extensive and intricate compilations of information that necessitate the implementation of proficient and cost-effective evaluation and analysis tools to derive insights and support decision making. The Fermatean fuzzy set theory possesses remarkable capability in capturing imprecision due to its capacity to accommodate complex and ambiguous problem descriptions. This paper presents the study of the concepts of dynamic ordered weighted aggregation operators in the context of Fermatean fuzzy environment. In numerous practical decision making scenarios, the term "dynamic" frequently denotes the capability of obtaining decision-relevant data at various time intervals. In this study, we introduce two novel aggregation operators: Fermatean fuzzy dynamic ordered weighted averaging and geometric operators. We investigate the attributes of these operators in detail, offering a comprehensive description of their salient features. We present a step-by-step mathematical algorithm for decision making scenarios in the context of proposed methodologies. In addition, we highlight the significance of these approaches by presenting the solution to the decision making problem and determining the most effective big data analytics platform for YouTube data analysis. Finally, we perform a thorough comparative analysis to assess the effectiveness of the suggested approaches in comparison to a variety of existing techniques.

A novel perspective on the selection of an effective approach to reduce road traffic accidents under Fermatean fuzzy settings.

Road traffic accidents (RTAs) pose a significant hazard to the security of the general public, especially in developing nations. A daily average of more than three thousand fatalities is recorded worldwide, rating it as the second most prevalent cause of death among people aged 5-29. Precise and reliable decisionmaking techniques are essential for identifying the most effective approach to mitigate road traffic incidents. This research endeavors to investigate this specific concern. The Fermatean fuzzy set (FFS) is a strong and efficient method for addressing ambiguity, particularly when the concept of Pythagorean fuzzy set fails to provide a solution. This research presents two innovative aggregation operators: the Fermatean fuzzy ordered weighted averaging (FFOWA) operator and the Fermatean fuzzy dynamic ordered weighted geometric (FFOWG) operator. The salient characteristics of these operators are discussed and important exceptional scenarios are thoroughly delineated. Furthermore, by implementing the suggested operators, we develop a systematic approach to handle multiple attribute decisionmaking (MADM) scenarios that involve Fermatean fuzzy (FF) data. In order to show the viability of the developed method, we provide a numerical illustration encompassing the determination of the most effective approach to alleviate road traffic accidents. Lastly, we conduct a comparative evaluation of the proposed approach in relation to a number of established methodologies.

A comprehensive study for selecting optimal treatment modalities for blood cancer in a Fermatean fuzzy dynamic environment.

Cancer is characterized by uncontrolled cell proliferation, leading to cellular damage or death. Acute lymphoblastic leukemia (ALL), a kind of blood cancer, that affects lymphoid cells and is a challenging malignancy to treat. The Fermatean fuzzy set (FFS) theory is highly effective at capturing imprecision due to its capacity to incorporate extensive problem descriptions that are unclear and periodic. Within the framework of this study, two innovative aggregation operators: The Fermatean fuzzy Dynamic Weighted Averaging (FFDWA) operator and the Fermatean fuzzy Dynamic Weighted Geometric (FFDWG) operator are presented. The important attributes of these operators, providing a comprehensive elucidation of their significant special cases has been discussed in details. Moreover, these operators are utilized in the development of a systematic approach for addressing scenarios involving multiple attribute decision-making (MADM) problems with Fermatean fuzzy (FF) data. A numerical example concerning on finding the optimal treatment approach for ALL using the proposed operators, is provided. At the end, the validity and merits of the new method to illustrate by comparing it with the existing methods.

Protective effects of niacin following high fat rich diet: an in-vivo and in-silico study.

Niacin had long been understood as an antioxidant. There were reports that high fat diet (HFD) may cause psychological and physical impairments. The present study was aimed to experience the effect of Niacin on % growth rate, cumulative food intake, motor activity and anxiety profile, redox status, 5-HT metabolism and brain histopathology in rats. Rats were administered with Niacin at a dose of 50 mg/ml/kg body weight for 4 weeks following normal diet (ND) and HFD. Behavioral tests were performed after 4 weeks. Animals were sacrificed to collect brain samples. Biochemical, neurochemical and histopathological studies were performed. HFD increased food intake and body weight. The exploratory activity was reduced and anxiety like behavior was observed in HFD treated animals. Activity of antioxidant enzymes was decreased while oxidative stress marker and serotonin metabolism in the brain of rat were increased in HFD treated animals than ND fed rats. Morphology of the brain was also altered by HFD administration. Conversely, Niacin treated animals decreased food intake and % growth rate, increased exploratory activity, produced anxiolytic effects, decreased oxidative stress and increased antioxidant enzyme and 5-HT levels following HFD. Morphology of brain is also normalized by the treatment of Niacin following HFD. In-silico studies showed that Niacin has a potential binding affinity with degradative enzyme of 5-HT i.e. monoamine oxidase (MAO) A and B with an energy of ~ - 4.5 and - 5.0 kcal/mol respectively. In conclusion, the present study showed that Niacin enhanced motor activity, produced anxiolytic effect, and reduced oxidative stress, appetite, growth rate, increased antioxidant enzymes and normalized serotonin system and brain morphology following HFD intake. In-silico studies suggested that increase 5-HT was associated with the binding of MAO with Niacin subsequentially an inhibition of the degradation of monoamine. It is suggested that Niacin has a great antioxidant potential and could be a good therapy for the treatment of HFD induced obesity.