Managing the integrity of products and processes in a multi-stakeholder supply chain environment is a significant challenge. Many current solutions suffer from data fragmentation, lack of reliable provenance, and diverse protocol regulations across multiple distributions and processes. Amongst other solutions, Blockchain has emerged as a leading technology, since it provides secure traceability and control, immutability, and trust creation among stakeholders in a low cost IT solution. Although Blockchain is making a significant impact in many areas, there are many impediments to its widespread adoption in supply chains. This article is the first survey of its kind, with detailed analysis of the challenges and future directions in Blockchain-enabled supply chains. We review the existing digitalization of the supply chain including the role of GS1 standards and technologies. Current use cases and startups in the field of Blockchain-enabled supply chains are reviewed and presented in tabulated form. Technical and non-technical challenges in the adoption of Blockchain for supply chain applications are critically analyzed, along with the suitability of various consensus algorithms for applications in the supply chain. The tools and technologies in the Blockchain ecosystem are depicted and analyzed. Some key areas as future research directions are also identified which must be addressed to realize mass adoption of Blockchain-based in supply chain traceability. Finally, we propose MOHBSChain, a novel framework for Blockchain-enabled supply chains.

Astronomy & Astrophysics (A&A) is an international journal which publishes papers on all aspects of astronomy and astrophysics

We present the results of 2.5D hydrodynamic calculations of the effects of the underlying binary system on shaping the ejecta in a classical nova outburst. In the model, the outburst takes place in the form of a wind with secularly increasing velocity. This wind flows past a binary companion, which experiences a frictional drag force and transfers energy and angular momentum from its orbit into the expanding envelope. We find that many of the features seen in classical novaremnants can be reproduced, including polar blobs, polar rings and shells with tropical and equatorial bands. The features seen in the shells correlate with speed class in the observed manner — fast novae produce spherical shells, whereas slower novae produce banded shells and polar blobs. The effects of radiative cooling andthe Rayleigh-Taylor instability on the shells are discussed. It is found that, as observations appear to indicate, the shells of fast novae should comprise a few, large clumps, whereas the shells of slower novae will be clumped on scales which are small compared to the shell radius.

Many recent studies have addressed the detection of negative affective states such as stress and anxiety from physio-logical signals taken from body-worn sensors. Typically, machine learning classifiers are applied to features derived from sensor signals, and several authors have reported high accuracy results from a range of signals including cardiac, skin conductance and skin temperature. However, the issue of how robust these models are for deployment in the field is rarely addressed. In this paper, we use open data from two large experimental studies to evaluate the generalizability of models derived from cardiac signals, focusing on detection of stress and anxiety. We choose the cardiac signal since the commonly used heart rate variability features can be derived frommultiple sensor modalities, allowing us to evaluate the robustness of models within, as well as between, experimental settings. We show that consistent classification outside the original experimental setting relies on high-quality training data with minimal artefacts, and that models may often train on proxies within the noise of lower quality data. Our results also underline the importance of including a wide range of emotional states in the training data to minimize erroneous classification from unseen regions of feature space.