Volume 10 | Issue 10 |

 Abstract

Cloud computing is a method for delivering a networked pool of configurable computing resources (such as servers, storage, networks, and services) that can be rapidly provided and transferred with low administration effort. Computing power, storage, platforms, and services are virtualized, dynamically scalable, managed, and given on-demand to cloud customers through the Internet. This is one sort of large-scale distributed computing paradigm. The cloud presents to people as a central hub from which they may access all their anticipated digital resources. With cloud computing, users are able to store data remotely and gain access to on-demand cloud applications without worrying about setting up any specialised hardware or software on their end. Concerns about cloud computing's security and dependability are widespread. The new technology was implemented to address the problems with security and data centre dependability. As a result of the benefits that cloud computing may provide to businesses, more and more of them are beginning to think about adopting cloud strategies and applying them in their operations. Because of the inherent risk of failure in cloud data centres and the critical importance of having constant access to cloud resources, it is imperative that various fault-tolerance strategies be tested and implemented. However, the ever-increasing number of people using cloud storage prompted us to look into fault-tolerance methods, as well as their advantages and disadvantages. After defining fault-tolerance and its application to cloud computing, this paper describes various fault-tolerant approaches and introduces related measures.

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 Keywords

Data Security, Cloud storage, fault tolerance.

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Creative Commons Attribution 4.0 and The Open Definition

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Transgender, disrespected, discriminated, backward classes, punishable, denies, enforcing.

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Creative Commons Attribution 4.0 and The Open Definition

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Madhumeha is one among the type of Vataja Prameha explained by Acharya Charaka. Madhumeha is a disease of Bastigata vikara. Nisha-Amalaki churna is mentioned in Ashtanga Hrudaya Chikitsa sthana and Vidangadi Loha is mentioned in Bhaisajya Ratnavali for Madhumeha. Thus, in the present study, two preparations Nisha-Amalaki churna and Vidangadi Loha are selected to evaluate their comparative efficacy on Madhumeha. The main objective of the present study was to compare the efficacy of Nisha Amalaki Churna and Vidangadi Loha in Madhumehi w.s.r to Type II Diabetes Mellitus. It was a comparative clinical study with pre and post-test design where in 40 patients of either sex diagnosed as Madhumeha w.s.r to type 2 Diabetes mellitus were selected and randomly assigned into two groups of 20 patients each. Group A were given Vidangadi Loha 250mg twice daily after food with Madhu for 90 days. Group B were given Nishaamalaki churna 1gm twice daily before food with Sukhoshnajala for 90 days. The statistical analysis after intervention showed statistically highly significant (p<0.001) Before Treatment to After Treatment and at after Follow up on all the parameters in both groups. On comparison, statistically there was no significant difference found between the two groups. However, the average improvement of group B 50.9% was slightly greater than group A 43.57%.

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Diabetes Mellitus, Madhumeha, Nisha-Amalaki, Jambu Beeja churna

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Creative Commons Attribution 4.0 and The Open Definition

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Neuropharmacology publishes excessive quality, unique studies in the subject of neuroscience. The emphasis of Neuropharmacology is at the have a look at and knowledge of the moves of acknowledged exogenous and endogenous chemical marketers on neurobiological approaches withinside the mammalian frightened system. Work with non- mammalian and invertebrate species can be taken into consideration in high-quality circumstances. The magazine does now no longer normally take delivery of medical studies, even though neuropharmacological research in human beings can be taken into consideration at the situation that they offer novel perception into both the moves of medication and/or neurobiological mechanisms.

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Creative Commons Attribution 4.0 and The Open Definition

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Having access to quality health care is crucial to surviving in life. However, getting an appointment with a doctor when you're sick can be difficult. The health care sector is expanding and has a profound impact on many facets of human existence. The healthcare sector is developing in tandem with technological innovation. Rapid technological advancements in the medical sciences promise improved methods of disease prevention, diagnosis, and treatment. Artificial intelligence (AI) is undergoing a sea change due to its numerous applications; for instance, it is reshaping the healthcare industry by allowing for better monitoring of patients with chronic diseases. Researchers have their work cut out for them as they try to streamline the architecture and management of intelligent delivery systems as a whole. The vast majority of these systems are driven by cutting-edge learning algorithms like Convolution Neural Network. The Convolutional Neural Network (CNN) algorithm is frequently used as an illustrative example of DL architecture. With the arrival of CNN hardware accelerators and a subsequent increase in the quantity of available annotated data, the field of CNN research and development has experienced a recent uptick, resulting in benchmarks being implemented on a wide range of different applications. For this meta-analysis, researchers looked for publications published between January 1, 2012 and June 6, 2022 in the databases Ovid-MEDLINE, Embase, Science Citation Index, and Conference Proceedings Citation Index. It was considered valid study to compare the diagnostic precision of deep learning models to that of healthcare professionals utilising medical imaging.

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Neural network, Healthcare, Deep Learning, CNN.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Rosuvastatin, Fimasartan, Validation, Stability indicating RP-HPLC

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Creative Commons Attribution 4.0 and The Open Definition

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A brain tumour forms when cells multiply rapidly and out of control. Death is a real possibility if treatment is delayed. Even with many significant efforts and promising successes in this sector, accurate segmentation and classification is still challenging to achieve. The detection of brain tumours is complicated by the wide variety of tumours that can occur in the brain and their varying sizes, shapes, and locations. The scientific community can benefit from this review because it provides a thorough literature on brain tumour detection by magnetic resonance imaging. In this study, we introduce a DL approach to brain tumour segmentation using FCNN and CRFs. The MR pictures are classified using a GoogleNet model trained with transfer learning techniques; furthermore, the images are pre-processed and postprocessed to improve the proposed model's performance. Segmentation and classification are the backbone of the proposed framework for detecting brain tumours, and both contribute to its efficacy and reliability. To train the model to produce these sophisticated and trustworthy outcomes, a substantial amount of data was required. Since the proposed model uses binary classification, it is trained and tested across three distinct data sets (BRATS2018, BRATS2019, and BRATS2020). Our suggested concept led to the development of interconnected modules that form the basis of GoogleNet's CNN architecture. The results show that the suggested model performs effectively even in low-contrast tumour locations.

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Brain Tumor, Deep Learning, CNN.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Mock meat, tofu, yam, plant based, replacement of meat

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Creative Commons Attribution 4.0 and The Open Definition

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It is unclear what causes Alzheimer's disease. However, genetic data support the amyloid hypothesis, which states that abnormal beta-amyloid protein aggregation initiates the illness process. There is a lengthy pre-clinical stage of Alzheimer's disease. The terrible consequences of amyloidogenic diseases like Alzheimer's. More than 7000 tiny molecules have been screened from ZINC subsets and exposed to 165 compounds and 16. From a research perspective, I have examined numerous databases like the ZINC Subset, Pubchem compound database, and Drug bank database. For docking investigations, these processed molecules were employed. The docking study enabled us to locate a small number of compounds with good inhibitory interactions with the protein, i.e., chemicals that impede the protein's function. These final compounds underwent drug-likeness testing and methods for ADME descriptor validation. These substances or molecules may help develop potent medications to treat Alzheimer's. To determine which protein had the lowest energy conformation and was, therefore, the most stable, a molecular dynamic of the protein was run.

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Bace-1 Enzyme, ZINC Subset, Beta-Amyloid peptide, Docking, Molecular Dynamic simulation.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition