Blockchain promises to provide a distributed and decentralized means of trust among untrusted users. However, in recent years, a shift from decentrality to centrality has been observed in the most accepted Blockchain system, i.e., Bitcoin. This shift has motivated researchers to identify the cause of decentrality, quantify decentrality and analyze the impact of decentrality. In this work, we take a holistic approach to identify and quantify decentrality in Blockchain based systems. First, we identify the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer. Then, we quantify decentrality in these layers using various metrics. At the governance layer, we measure decentrality in terms of fairness, entropy, Gini coefficient, Kullback–Leibler divergence, etc. Similarly, in the network layer, we measure decentrality by using degree centrality, betweenness centrality and closeness centrality. At the storage layer, we apply a distribution index to define centrality. Subsequently, we evaluate the decentrality in Bitcoin and Ethereum networks and discuss our observations. We noticed that, with time, both Bitcoin and Ethereum networks tend to behave like centralized systems where a few nodes govern the whole network.

Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development.

Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). We compared our one-step CNN–LSTM model with other LSTM models, the CNN model, support vector machine (SVM), and the autoregressive integrated moving average (ARIMA) model. Also, we compared our multi-step CNN–LSTM model with LSTM, CNN, and the time series encoder–decoder model. Extensive experiments were conducted using short-, medium-, and long-term price data of one, five, and ten years, respectively. In terms of accuracy, the proposed model outperformed existing models in both one-step and multi-step predictions.

Inspired by the success of conventional cloud services, researchers have recently introduced the concept of a vehicular cloud. In this work, we envision a vehicular cloud involving cars in the parking lot of a major airport. The patrons of the parking lot are typically on travel for several days, providing a pool of cars that can serve as the basis for a datacenter. We investigate the effect of a redundancy-based job assignment strategy on job completion time in vehicular clouds. We offer a tight theoretical analysis of the expected job completion time under this strategy. We also discuss various approximations of the expected completion time. A comprehensive set of simulations have confirmed the accuracy of our theoretical predictions.