Power contamination and domination on the grid

The contamination game of a grid graph G(n,m) is a dynamic variant of the domination, similar to the power domination. This standard is introduced by Haynes, Hedetniemi and Henning in 2002, which is initially defined as a basic domination for a set of vertices S in a graph G, and then a propagation of this domination in all vertices of G, while starting with S. On the other hand, the contamination phenomena in G(n,m) is interpreted by an evolutionary automaton cellular, which aims to propagate viruses according to a given propagation rules. In this paper, we define a mathematical self-playing game called a contamination game based on the power domination, in which, we identify the minimum number of contaminant cells for G(n,m), called the contamination number and denoted γ(G(n,m)).

Comparison of CSES ionospheric RO data with COSMIC measurements

CSES is a newly launched electric-magnetic satellite in China; its main scientific objective is to monitor earthquake related disturbances in ionosphere. A GNSS occultation receiver (GOR) is installed on the satellite to inverse electron density related parameters. In order to validate the radio occultation (RO) data from GOR onboard CSES, a comparison between CSES RO and the co-located COSMIC RO data is conducted to check the consistency and reliability of the CSES RO data using measurements from February 12, 2018 to March 31, 2019. CSES RO peak values (NmF2), peak heights (hmF2), and electron density profiles (EPDs) are compared with corresponding COSMIC measurements in this study. The results show that: (1) NmF2s between CSES and COSMIC are in extremely good agreement with a correlation coefficient of 0.9891. The near zero bias between the two sets is 0.01235 × 105/cm3 with a RMSE of 0.3680 × 105/cm3; and the relative bias is 2.14 % with a relative RMSE of 16.40%, which are in accordance with previous studies according to error propagation rules. (2) hmF2s between the two missions are also in very good agreement with a correlation coefficient of 0.9379; the mean difference between the two sets is 0.73 km with a RMSE of 13.02 km, which is within the error limits of previous studies; (3) Co-located EDPs between the two sets are generally in good agreements, but with a better agreement for data above 200 km than that below this altitude. Data at the peak height ranges show the best agreement, and then data above the peak regions; data below the peak regions, especially at the altitude of about the E layer, show relatively large fluctuations. It is concluded that CSES RO data are in good agreement with COSMIC measurements, and the CSES RO data are applicable for most ionospheric-related studies. However, particular attention should be paid to EDP data below peak regions in application.

Propagation of the Measurement Error and the Measured Mean of a Physical Quantity for Elementary Functions ax and loga x

Rules have been obtained for the propagation of the error and the mean value for a measured physical quantity onto another one with a functional relation of the type ax or loga x between them. In essence, these rules are inherently based on the Gaussian weight scheme. Therefore, they should be valid in the framework of a real Gaussian weight scheme applied to discrete data of a real physical experiment (a sample). An analytical form that was used to present the rules concerned (“analytical propagation rules”) and their character allow the processing and the analysis of experimental data to be simplified and accelerated.

A logic-based framework for collection/item metadata relationships

Purpose The purpose of this paper is to present a framework for the articulation of relationships between collection-level and item-level metadata as logical inference rules. The framework is intended to allow the systematic generation of relevant propagation rules and to enable the assessment of those rules for particular contexts and the translation of rules into algorithmic processes. Design/methodology/approach The framework was developed using first order predicate logic. Relationships between collection-level and item-level description are expressed as propagation rules – inference rules where the properties of one entity entail conclusions about another entity in virtue of a particular relationship those individuals bear to each other. Propagation rules for reasoning between the collection and item level are grouped together in the framework according to their logical form as determined by the nature of the propagation action and the attributes involved in the rule. Findings The primary findings are the analysis of relationships between collection-level and item-level metadata, and the framework of categories of propagation rules. In order to fully develop the framework, the paper includes an analysis of colloquial metadata records and the collection membership relation that provides a general method for the translation of metadata records into formal knowledge representation languages. Originality/value The method for formalizing metadata records described in the paper represents significant progress in the application of knowledge representation techniques to problems of metadata creation and management, providing a flexible technique for encoding colloquial metadata as a set of statements in first-order logic. The framework of rules for collection/item metadata relationships has a range of potential applications for the enhancement or metadata systems and vocabularies.