Atomistic Modelling of Size-Dependent Mechanical Properties and Fracture of Pristine and Defective Cove-Edged Graphene Nanoribbons

Cove-edged graphene nanoribbons (CGNR) are a class of nanoribbons with asymmetric edges composed of alternating hexagons and have remarkable electronic properties. Although CGNRs have attractive size-dependent electronic properties their mechanical properties have not been well understood. In practical applications, the mechanical properties such as tensile strength, ductility and fracture toughness play an important role, especially during device fabrication and operation. This work aims to fill a gap in the understanding of the mechanical behaviour of CGNRs by studying the edge and size effects on the mechanical response by using molecular dynamic simulations. Pristine graphene structures are rarely found in applications. Therefore, this study also examines the effects of topological defects on the mechanical behaviour of CGNR. Ductility and fracture patterns of CGNR with divacancy and topological defects are studied. The results reveal that the CGNR become stronger and slightly more ductile as the width increases in contrast to normal zigzag GNR. Furthermore, the mechanical response of defective CGNRs show complex dependency on the defect configuration and distribution, while the direction of the fracture propagation has a complex dependency on the defect configuration and position. The results also confirm the possibility of topological design of graphene to tailor properties through the manipulation of defect types, orientation, and density and defect networks.

Vine copulas structures modeling on Russian stock market

Pair-copula constructions have proven to be a useful tool in statistical modeling, particularly in the field of finance. The copula-based approach can be used to choose a model that describes the dependence structure and marginal behaviour of the data in efficient way, but is usually applied to pairs of securities. In contrast, vine copulas provide greater flexibility and permit the modeling of complex dependency patterns using the rich variety of bivariate copulas which may be arranged and analysed in a tree structure. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. So, to learn the best possible model, one has to identify the best possible structure, which necessitates identifying the connections between the variables and selecting between the multiple bivariate copulas for each pair in the structure. This paper features the use of regular vine copulas in analysis of the co-dependencies of four major Russian Stock Market securities such as Gazprom, Sberbank, Rosneft and FGC UES, represented by the RTS index. For these stocks the D-vine structures of bivariate copulas were constructed, which models are described by Gumbel, Student, BB1and BB7 copulas, and estimates of their parameters were obtained. Computer simulations showed a high accuracy of the approximation of the explored data by D-vine structure of bivariate copulas and the effectiveness of our approach in general.

Software packaging and distribution for LHCb using Nix

Software is an essential and rapidly evolving component of modern high energy physics research. The ability to be agile and take advantage of new and updated packages from the wider data science community is allowing physicists to efficiently utilise the data available to them. However, these packages often introduce complex dependency chains and evolve rapidly introducing specific, and sometimes conflicting, version requirements which can make managing environments challenging. Additionally, there is a need to replicate old environments when generating simulated data and to utilise pre-existing datasets. Nix is a “purely functional package manager” which allows for software to be built and distributed with fully specified dependencies, making packages independent from those available on the host. Builds are reproducible and multiple versions/configurations of each package can coexist with the build configuration of each perfectly preserved. Here we will give an overview of Nix followed by the work that has been done to use Nix in LHCb and the advantages and challenges that this brings.

Care, Disability, and Violence: Theorizing Complex Dependency in Eva Kittay and Judith Butler

How do we theorize the experiences of caregivers abused by their children with autism without intensifying stigma toward disability? Eva Kittay emphasizes examples of extreme vulnerability to overturn myths of independence, but she ignores the possibility that dependents with disabilities may be vulnerable and aggressive. Instead, her work over‐emphasizes caregivers' capabilities and the constancy of disabled dependents' vulnerability. I turn to Judith Butler's ethics and her conception of the self as opaque to rethink care amid conflict. Person‐centered planning approaches, pioneered by disability rights activists, merge Butler's analysis of opacity with Kittay's work on embodied care, while also inviting a broader network of people to both interpret needs and change communities. By expanding our conceptions of dependency, feminist disability studies can continue the aim of both Kittay and Butler: to humanize unintelligible lives.

An Improved Functional Dependency Network Model for SoS Operability Analysis

There are often complex dependency relationships between systems in a system of systems (SoS), which present great challenges for SoS operability analysis. Functional Dependency Network Analysis offers the capability to evaluate the effect of both topology and of the possible degraded functioning of one or more systems on the operability of each node in the SoS. But there are some insufficiencies in the method, so here we give exact definition to self-effectiveness, which includes absolute self-effectiveness and relative self-effectiveness. Then according to the new definitions we show the detail calculation equations. Finally we we take the application of Guariniello and DeLaurentis’s five-node aerospace network for example, and compare the results with theirs. Comparative results show that their results are larger than the exact outcome, and our version give the authentic outcomes.

Modulated Nanomagnetics

Thermodynamic properties of modulated magnetic ordering nanoparticles are studied by the Monte-Carlo methods. Temperature curves for main thermodynamic parameters are plotted. The availability of free surface is shown to lead to the displacement of phase transition temperatures and the complex dependency of thermodynamic parameters on a temperature. We determine the modulated ordering in nanoparicles is possible at sufficiently larger linear sizes.