A smart contract is known to be useful for automating business processes triggered by specific events caused by IoT sensors, data feeds, or other applications. A blockchain-based smart contract management system is an innovative technology that is foreseen to automate future business-to-business (B2B) processes. Blockchain is well-known to play a central role in business process re-engineering by optimizing business workflow operations, especially in multi-party arrangements. This paper presents a multi-organizational smart contract management system in which a user can create, deploy, and execute smart contracts. This paper consists of two parts; in the first part, we have compared existing smart contract management systems based on different characteristics that can play a vital role in selecting a particular system for a specific business need. In the second part, while utilizing and building upon the state-of-the-art techniques, we have built a framework for a blockchain-based smart contract and transaction management system. It is a unified architecture supporting DAO (Decentralized Autonomous Organizations) and organizational level blockchain-based smart contract execution. There are two types of separate blockchains utilized in the proposed framework, i.e., SBlockchain and TBlockchain. SBlockchain is used to store smart contracts, whereas all the data generated by the smart contracts is stored inside the TBlockchain. In addition, each smart contract has some terms and clauses necessary for some event execution. Various components of the framework and their implementation have been described in detail with the help of relevant use-cases.

Blockchain, smart contracts, and the Internet of Things (IoT) are essential technologies for the business process re-engineering of supply chains in the era of Industry 4.0. The agricultural food supply chain is one of the research areas where these disruptive technologies can play a crucial role in automating business processes, providing real-time goods monitoring, and securing transactions. With the help of blockchain, smart contracts, and the IoT, a product’s health and environment can be monitored throughout the supply chain. In this study, we have critically examined the relevance of these technologies through various activities of the agriculture supply chain using the approach of Business Process Modeling (BPM). The blockchain and smart contracts-based findings of the BPM were then incorporated along various layers of the Reference Architecture for Modeling Industry 4.0 (RAMI 4.0). This enabled us to introduce the IoT, blockchain, and smart contract based smart agriculture framework, Agri-4-All. Agri-4-All can be used to automate the intra-organizational and inter-organizational processes of the agricultural supply chain. We developed, deployed, and tested the intra-organizational and inter-organizational smart contracts written in the Solidity language for a typical scenario related to the agriculture supply chain. Our hybrid smart algorithms implemented using Ganache and Truffle suite reduce the gas cost in our proposed intra-organizational smart contracts by 13.89 times as compared to the traditional smart contract-based model.

Background: For some understudied populations, genotype data is minimal for genotype-phenotype prediction. However, we can use the data of some other large populations to learn about the disease-causing SNPs and use that knowledge for the genotype-phenotype prediction of small populations. This manuscript illustrated that transfer learning is applicable for genotype data and genotype-phenotype prediction. Results: Using HAPGEN2 and PhenotypeSimulator, we generated eight phenotypes for 500 cases/500 controls (CEU, large population) and 100 cases/100 controls (YRI, small populations). We considered 5 (4 phenotypes) and 10 (4 phenotypes) different risk SNPs for each phenotype to evaluate the proposed method. The improved accuracy with transfer learning for eight different phenotypes was between 2 and 14.2 percent. The two-tailed p-value between the classification accuracies for all phenotypes without transfer learning and with transfer learning was 0.0306 for five risk SNPs phenotypes and 0.0478 for ten risk SNPs phenotypes. Conclusion: The proposed pipeline is used to transfer knowledge for the case/control classification of the small population. In addition, we argue that this method can also be used in the realm of endangered species and personalized medicine. If the large population data is extensive compared to small population data, expect transfer learning results to improve significantly. We show that Transfer learning is capable to create powerful models for genotype-phenotype predictions in large, well-studied populations and fine-tune these models to populations were data is sparse.

Wind energy is one of the renewable energy sources like solar energy, and accurate wind power prediction can help countries deploy wind farms at particular locations yielding more electricity. For any prediction problem, determining the optimal time step (lookback) information is of primary importance, and using information from previous timesteps can improve the prediction scores. This article uses simulated annealing to find an optimal time step for wind power prediction. Finding an optimal timestep is computationally expensive and may require brute-forcing to evaluate the deep learning model at each time. This article uses simulated annealing to find an optimal time step for wind power prediction. The computation time was reduced from 166 hours to 3 hours to find an optimal time step for wind power prediction with a simulated annealing-based approach. We tested the proposed approach on three different wind farms with a training set of 50%, a validation set of 25%, and a test set of 25%, yielding MSE of 0.0059, 0.0074, and 0.010 for each wind farm. The article presents the results in detail, not just the mean square root error.

In smart cities of the future, data will be generated, integrated, processed and utilized from heterogeneous sources and at varying levels of complexity. For urban traffic planning in smart cities, one of the biggest challenges is traffic congestion prediction and its avoidance. Traffic congestion is a complex phenomenon and it is a manifestation of various contributing factors. In addition to vehicular mobility, properties of road network, weather, holidays and peak hours play a significant role in traffic congestion especially on arterial roads within a city. In this paper, we proposed a hybrid GRU-LSTM based deep learning model and applied it on city-wide novel traffic data integrated from heterogeneous sources. We have devised our indigenous data pipeline that is composed of a set of algorithms dealing with map matching, sparsity handling, outlier removal, zero speed adjustments, Open Street Map (OSM) and segment mapping etc. Extensive experimentations have been carried out to demonstrate the improved performance of the proposed method. The comparative analysis reveals that our methodology yields 95 % accuracy that outperforms other deep neural network models.