Data workflow to incorporate thermodynamic energies from Calphad databases into grand-potential-based phase-field models

In order to approximate Gibbs energy functions, a semi-automated framework is introduced for binary and ternary material systems, using CALPHAD databases. To generate Gibbs energy formulations by means of second-order polynomials, the framework includes a precise approach. Furthermore, an optional extensional step enables the modeling of systems in which a direct generation leads to the unsatisfactory results in the representation of the thermodynamics. Furthermore, an optional extensional step enables the modeling of systems, in which a direct generation leads to the unsatisfactory results, when representing the thermodynamics. Within this extension, the commonly generated functions are modified to satisfy the equilibrium conditions in the observed material systems, leading to a better correlation with thermodynamic databases. The generated Gibbs energy formulations are verified by recalculating the equilibrium concentrations of the phases and rebuilding the phase diagrams in the considered concentration and temperature ranges, prior to the simulation studies. For all comparisons, a close match is achieved between the results and the CALPHAD databases. As practical examples of the method, phase-field simulation studies for the directional solidification of the binary Ni–35Mo and the ternary NiAl–10Mo eutectic systems are performed. Good agreements between the simulation results and the reported theoretical and experimental studies from literature are found, which indicates the applicability of the presented approaches

Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips

The commercial adoption of Autonomous Vehicles (AVs) and the positive impact they are expected to have on traffic safety depends on appropriate insurance products due to the high potential losses. A significant proportion of these losses are expected to occur from the out-of-distribution risks which arise from situations outside the AV’s training experience. Traditional vehicle insurance products (for human-driven vehicles) rely on large data sets of drivers’ background and historical incidents. However, the lack of such datasets for AVs makes it imperative to exploit the ability to deploy AVs in simulated environments. In this paper, the data collected by deploying Autonomous Driving Systems (ADSs) in simulated environments is used to develop models to answer two questions: (1) how risky a road Section is for an AV to drive? and (2) how does the risk profile vary with different (SAE levels) of ADSs? A simulation pipeline was built on the CARLA (Car Learning to Act): an open-source simulator for autonomous driving research. The environment was specified using parameters such as weather, lighting, traffic density, traffic flow, no. of lanes, etc. A metric - risk factor was defined as a combination of harsh accelerations/braking, inverse Time to Collision, and inverse Time Headway to capture the crashes and near-crashes. To assess the difference between ADSs, two ADSs: OpenPilot (Level 2/3) and Pylot (Level 4) were implemented in the simulator. The results (from data and model predictions) show that the trends in the relation between the environment features and risk factor for an AV are similar to those observed for human drivers (e.g., risk increases with traffic flow). The models also showed that junctions were a risk hot-spot for both ADSs. The feature importance of the model revealed that the Level 2/3 ADS is more sensitive to no. of lanes and the Level 4 ADS is sensitive to traffic flow. Such differences in feature importance provide valuable insights into the risk characteristics of different ADSs. In the future, this base model will be extended to include other features (other than the environment), e.g., take over requests, and also address the deficiencies of the current simulation data in terms of insensitivity to weather and lighting

A hierarchical key pre-distribution scheme for fog networks

Security in fog computing is multi-faceted, and one particular challenge is establishing a secure communication channel between fog nodes and end devices. This emphasizes the importance of designing efficient and secret key distribution scheme to facilitate fog nodes and end devices to establish secure communication channels. Existing secure key distribution schemes designed for hierarchical networks may be deployable in fog computing, but they incur high computational and communication overheads and thus consume significant memory. In this paper, we propose a novel hierarchical key pre-distribution scheme based on “Residual Design” for fog networks. The proposed key distribution scheme is designed to minimize storage overhead and memory consumption, while increasing network scalability. The scheme is also designed to be secure against node capture attacks. We demonstrate that in an equal-size network, our scheme achieves around 84% improvement in terms of node storage overhead, and around 96% improvement in terms of network scalability. Our research paves the way for building an efficient key management framework for secure communication within the hierarchical network of fog nodes and end devices. KEYWORDS: Fog Computing, Key distribution, Hierarchical Networks

Ultra-soft 100 nm Thick Zero Poisson’s Ratio Film with 60% Reversible Compressibility

About a 100 nm thick multilayer film of nanoparticle monolayers and polymer layers is shown to behave like cellular-foam with a modulus below 100 KPa. The 1.25 cm radius film adhered to a rigid surface can be compressed reversibly to 60% strain. The more than four orders of magnitude lower modulus compared to its constituents is explained by considering local bending in the (nano)cellular structure, similar to cork and wings of beetles. As the rigidity of the polymer backbone is increased in just four monolayers the modulus of the composite increases by over 70%. Electro-optical map of the strain distribution over the area of compression and increase in modulus with thickness indicates the films have zero Poisson’s ratio

The beneficial effects of chick embryo extract preconditioning on hair follicle stem cells: A promising strategy to generate Schwann cells

The beneficial effects of hair follicle stem cells in different animal models of nervous system conditions have been extensively studied. While chick embryo extract (CEE) has been used as a growth medium supplement for these stem cells, this is the first study to show the effect of CEE on them. The rat hair follicle stem cells were isolated and supplemented with 10% fetal bovine serum plus 10% CEE. The migration rate, proliferative capacity and multipotency were evaluated along with morphometric alteration and differentiation direction. The proteome analysis of CEE content identified effective factors of CEE that probably regulate fate and function of stem cells. The CEE enhances the migration rate of stem cells from explanted bulges as well as their proliferation, likely due to activation of AP-1 and translationally controlled tumour protein (TCTP) by thioredoxin found in CEE. The increased length of outgrowth may be the result of cyclic AMP response element binding protein (CREB) phosphorylation triggered by active CamKII contained in CEE. Further, CEE supplementation upregulates the expression of vascular endothelial growth factor (VEGF), brain-derived neurotrophic factor and glial cell line-derived neurotrophic factor. The elevated expression of target genes and proteins may be due to CREB, AP-1 and c-Myc activation in these stem cells. Given the increased transcript levels of neurotrophins, VEGF, and the expression of PDGFR-α, S100B, MBP and SOX-10 protein, it is possible that CEE promotes the fate of these stem cells towards Schwann cells

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

© The Author(s) 2017 This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/ genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.Peer reviewedFinal Published versio