Vivek Kumar

TerraSwarm applications, or swarmlets, are characterized by their ability to dynamically recruit resources such as sensors, communication networks, computation, and information from the cloud; to aggregate and use that information to make or aid decisions; and then to dynamically recruit actuation resources. The TerraSwarm vision cannot be achieved by a single vendor providing the components as an integrated system. What is needed instead is the swarm equivalent of the common, general framework that has recently enabled smartphones and similar devices to rapidly deploy and serve a vast range of often unanticipated applications by recruiting resources and composing services. The SwarmOS must support continual reconfiguration of applications and of its own service definitions without ever having the luxury of a clean restart. It must also support richly heterogeneous computing. TerraSwarm protocols will need to detect compromises, distinguish trusted from untrusted data and resources, and be robust to the presence of a certain number of malicious nodes.

Facial Expression conveys non-verbal cues, which plays an important role in interpersonal relations. The Cognitive Emotion AI system is the process of identifying the emotional state of a person. The main aim of our study is to develop a robust system which can detect as well as recognize human emotion from live feed. There are some emotions which are universal to all human beings like angry, sad, happy, surprise, fear, disgust and neutral. The methodology of this system is based on two stages- facial detection is done by extraction of Haar Cascade features of a face using Viola Jones algorithm and then the emotion is verified and recognized using Artificial Intelligence Techniques. The system will take image or frame as an input and by providing the image to the model the model will perform the preprocessing and feature selection after that it will be predict the emotional state.

The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network and long short-term memory with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily.

Today the whole world is facing a very difficult time due to corona virus which initially originated in Wuhan city of China. In China an unusual pneumonia was noticed earlier which later recognized as a pandemic. There have been two events in the past wherein crossover of animal corona viruses to humans has resulted in severe disease, one was SARS-CoV and the other was MERS-CoV. The genetic sequence of the COVID19 showed more than 80% similarities to SARS-CoV and 50% to the MERSCoV. Corona viruses are enveloped positive sense single-stranded RNA virus belongs to a large family in which all strains of the family do not have virulent capacity. World Health Organisation has classified COVID-19 as a β CoV of group 2B. Structurally, SARS-CoV-2 has four main structural proteins and several accessory proteins through which it enters into the cell. This virus enters the through ACE2 receptors, which are found on various organs in human body. After entering into one body, it enters to another body via transmission through sneezing, coughing etc. People who have had diabetes, chronic respiratory disease, cardiovascular disease, or even high blood pressure and cancer are at higher risk of coronavirus.

© 2015 The Authors. Through the use of bulk measurements in metabolic organs, the circadian clock was shown to play roles in organismal energy homeostasis. However, the relationship between metabolic and circadian oscillations has not been studied invivo at a single-cell level. Also, it is unknown whether the circadian clock controls metabolism in stem cells. We used a sensitive, noninvasive method to detect metabolic oscillations and circadian phase within epidermal stem cells in live mice at the single-cell level. We observe a higher NADH/NAD+ ratio, reflecting an increased glycolysis/oxidative phosphorylation ratio during the night compared to the day. Furthermore, we demonstrate that single-cell metabolic heterogeneity within the basal cell layer correlates with the circadian clock and that diurnal fluctuations in NADH/NAD+ ratio are Bmal1 dependent. Our data show that, in proliferating stem cells, the circadian clock coordinates activities of oxidative phosphorylation and glycolysis with DNA synthesis, perhaps as a protective mechanism against genotoxicity.

Purpose The purpose of this study is to examine how the adoption of blockchain technology can improve organizational sustainability and what are the contributions of morality, ethics and governance in this scenario. Design/methodology/approach This study has used different literature and theories to build a successful theoretical model and then validated it using the partial least squares structural equation modeling approach. Various statistical modeling analyses have been performed to test the robustness of the proposed model, which is found to be effective and unique as it has a high explanatory power. Findings This study has found that adoption of blockchain technology can improve the sustainability performance of organizations. It has also highlighted that organizational sustainability will be further enhanced if the underlying algorithms of blockchain technology are transparent to make the applications moral and ethical. This study has further demonstrated that appropriate governance of blockchain technology can support the organizations to enhance their sustainability performance. Research limitations/implications This study demonstrates the importance of an organization’s governance over blockchain technology and its impact on sustainability. The study provides valuable input to practitioners and policy makers regarding blockchain technology and its governance models. Also, the study provides valuable recommendations to leaders and managers of organizations on how to successfully adopt blockchain technology. Originality/value This study presents a unique theoretical model to explain the influence of the adoption of blockchain technology and an organization’s sustainability. The study also adds value to the overall body of literature in the areas of ethics, governance and blockchain technology along with sustainability.

Monitoring of clinical trials is important to ensure adherence to protocol, to safeguard the rights of research participants and to achieve compliance with principles of good clinical practice. Recent regulatory changes in India require Ethics Committees to keep an oversight of ongoing clinical trials including on-site monitoring. In this article, we share the experience of on-site monitoring of clinical trials by the Ethics Committee of a tertiary care, academic and research centre in India. We found a large number of shortcomings in the areas of informed consent, adverse events, insurance and reimbursement, which would not have been detected by off-site document review. Interestingly, many shortcomings were also not detected by on-site monitoring arranged by clinical trial sponsors. We therefore conclude that on-site monitoring of ongoing clinical trials is a highly important activity for Indian Ethics Committees.