Rift: a high-performance consensus algorithm for Consortium Blockchain

The emergence of Blockchain have revolutionize the decentralization in distributed architecture. The advances in the consensus mechanism techniques and the development of different variants of consensus algorithms gives a huge impact on its progress. These technologies allow to have a distributed peer-to-peer network in which each external entity can be able to interact with other entities without any trusted intermediary in a verifiable manner. The existing consensus algorithms are mostly concerned with public blockchain having focused on public ledgers in general. The consortium blockchain is least focused as compared with other variants of blockchain (public and private) showing the need to address this vacuum. In this paper, we proposed a consensus algorithm named Rift for consortium blockchain which works on the principle of trust mechanism for achieving consensus in a blockchain. The consensus is achieved by distributed nodes in a consortium blockchain which were controlled by consortium members to decentralize the arbitration by voting and trust metrics. In this paper, we elaborate the comprehensive idea of Rift and discuss the working model for this algorithm. We also perform simulation on the proposed algorithm and determine the performance variables to evaluate the effectiveness of Rift. The evaluated results show the improvement in the performance which is the objective requirement for the evaluation

Cotton crop cultivation oriented semantic framework based on IoT smart farming application

The fact that each technological concept comes from the advances in the research and development, Internet of Things (IoT) grows and touches virtually every area of human activities. This has yielded the possibility of analyzing various types of sensors-environment from any kind of IoT platform. The existing IoT platforms focuses more on the area related to urban infrastructure, smart cities, healthcare, smart industry, smart mobility and much more. In this paper, we are focusing on the architecture of designing the application of IoT based solution in agriculture with more specific to Cotton farming. Our specific approach on farming is relevant to cotton crops cultivation, irrigation and harvesting of yields. In the context of cotton crops cultivation, there are many factors that should be concerned which includes weather, legal regulation, market conditions and resource availability. As a result, this paper presents a cotton crops cultivation oriented semantic framework based on IoT smart farming application which supports smart reasoning over multiple heterogenous data streams associated with the sensors providing a comprehensive semantic pipeline. This framework will support large scale data analytic solution, rapid event recognition, seamless interoperability, operations, sensors and other relevant features covering online web based semantic ontological solution in an agriculture context

Non-fungible token based smart manufacturing to scale Industry 4.0 by using augmented reality, deep learning and industrial Internet of Things

The recent revolution in Industry 4.0 (IR 4.0) has characterized the integration of advance technologies to bring the fourth industrial revolution to scale the manufacturing landscape. There are different key drivers for this revolution, in this research we have explored the following among them such as, Industrial Internet of Things (IIoT), Deep Learning, Blockchain and Augmented Reality. The emerging concept from blockchain namely “Non-Fungible Token” (NFT) relating to the uniqueness of digital assets has vast potential to be considered for physical assets identification and authentication in the IR 4.0 scenario. Similarly, the data acquired through the deployment of IIoT devices and sensors into smart industry spectrum can be transformed to generated robust analytics for different industry use-cases. The predictive maintenance is a major scenario in which early equipment failure detection using deep learning model on acquired data from IIoT devices has major potential for it. Similarly, the augmented reality can be able to provide real-time visualization within the factory environment to gather real-time insight and analytics from the physical equipment for different purposes. This research initially conducted a survey to analyse the existing developments in these domains of technologies to further widen its horizon for this research. This research developed and deployed a smart contract into an ethereum blockchain environment to simulate the use-case for NFT for physical assets and processes synchronization. The next phase was deploying deep learning algorithms on a dataset having data generated from IIoT devices and sensors. The Feedforward and Convolutional Neural Network were used to classify the target variables in relation with predictive maintenance failure analysis. Lastly, the research also proposed an AR based framework for the visualization ecosystem within the industry environment to effectively visualize and monitory IIoT based equipment’s for different industrial use-cases i.e., monitoring, inspection, quality assurance

An Unusual Cause of Elbow Pain – A Case Report

Giant cell tumours are common bone tumours usually benign which arise at the metaphysis and extend towards the epiphysis of bone. A case of giant cell tumour in the distal humerus which is a rare site is presented here. Radiological investigations and biopsy in a 20 year old male who presented to our orthopedic department with elbow pain, led to a diagnosis of giant cell tumour at the medial epicondyle of humerus. Literature is reviewed regarding the common sites of giant cell tumours along with the treatment modalities currently followed. Giant cell tumour should be kept in a mind as a rare cause of elbow pain

Environmental monitoring and disease detection of plants in smart greenhouse using Internet of Things

This research implements the idea of automation using Internet of Things (IoT) in a greenhouse environment. The development is focuses on deployment of agicultural greenhouses into small-scale level transforming it into a smart greenhouse. They are to help in monitoring the greenhouse environment conditions, water irrigation management, image collection using installed cameras as well as predicting diseases in the plants on collected leaf datasets. This research focus on development for the purpose of validating a proposed system design and architecture for a suitable IoT based monitoring for environment conditions, managing water irrigation system and a effective method for detecting leaf diseases on the plants inside a greenhouse environment

BSCL: Blockchain-Oriented SDN Controlled cloud based Li-Fi communication architecture for smart city network

The Internet of Things (IoT) smart city initiative has transformed technology spectrum into its new era of development. The increasing amount of data generated by millions of IoT devices and the rapid flow of data across distributed IoT devices are transmitting to remotely located cloud infrastructure over the Internet. Unfortunately, these large amounts of data and its flow based on the traditional energy-intensive network infrastructure is neither efficient nor substantially scalable. It is essential to design a comprehensive network infra-structure to handle large amount of high-speed data-processing in an IoT spectrum. Apparently, Blockchain and Software-Defined Net-working (SDN) approaches can leveraged the scalability of the environment for IoT spectrum. In addition, the emergence of distributed cloud technology and Li-Fi spectrum can transform the capability of data-processing for IoT devices. The challenge lies in efficiently blend the integration of Li-Fi, Blockchain, SDN and Cloud technologies for IoT environment. To address this challenge, we design a multiaccess communication modulation model for efficient optimization of distributed network with an SDN based controller and inte-gration of robust cloud infrastructure for high-speed data-processing. The proposed model is based on Li-Fi communication architecture which significantly reduced in the utilization of energy for managing large-scale infrastructure. We performed simulation and analysis across multiple dimensions to evaluate the performance and effectiveness of our proposed model. The evaluated output shows that our model significantly improved the overall performance and efficiency of the communication infrastructure as compared with other ultra-modern models

Data_Sheet_1_MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.PDF

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.</p

Data_Sheet_2_MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.PDF

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.</p