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Site diversity performance in Ka band using a 7.3 m antenna diameter at tropical climate: a comparison of prediction models

Samat Fazdliana, Singh Mandeep Jit

Acta Geophysica

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2020

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Vol. 68, no. 4

1213--1221

EN

Site diversity gain prediction models were created to estimate mathematically the acquired benefts from the implementation of site diversity at place of choice. This work contributes to the comparison of existing gain prediction model to the gain of measured attenuation at Cyberjaya and Rawang, Malaysia. The experiment has been conducted for 4 years from 2014 to 2017, in Ka band using a large 7.3-m diameter antenna and a high elevation angle of 68.8°, together with the rain analysis at both places for the same duration. The average monthly rainfall and attenuation for 4 years were presented. The results revealed that prediction model Hodge performs better than other models, while X. Yeo and Panagopoulos models appear to exhibit very similar graph shape to the measured gain data. More research on gain development in tropical region should be conducted, as the existing prediction model appears to be less consistent with the current data.

2

Reaction System Models for the Heat Shock Response

Azimi S., Iancu B., Petre I.

Fundamenta Informaticae

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2014

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Vol. 131, nr 3-4

299--312

EN

Reaction systems are a formal framework for modeling processes driven by biochemical reactions. They are based on the mechanisms of facilitation and inhibition. A main assumption is that if a resource is available, then it is present in sufficient amounts and as such, several reactions using the same resource will not compete concurrently against each other; this makes reaction systems very different as a modeling framework than traditional frameworks such as ODEs or continuous time Markov chains. We demonstrate in this paper that reaction systems are rich enough to capture the essential characteristics of ODE-based models. We construct a reaction system model for the heat shock response in such a way that its qualitative behavior correlates well with the quantitative behavior of the corresponding ODE model. We construct our reaction system model based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We conclude with a discussion on the expressivity of reaction systems as compared to that of ODE-based models.

3

A Boolean Approach for Disentangling the Roles of Submodules to the Global Properties of a Biomodel

Czeizler E., Mizera A., Petre I.

Fundamenta Informaticae

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2012

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Vol. 116, nr 1-4

51-63

EN

To disentangle the numerical contribution of modules to the system-level behavior of a given biomodel, one often considers knock-out mutant models, investigating the change in the model behavior when modules are systematically included and excluded from the model architecture in all possible ways. We propose in this paper a Boolean approach for extracting conclusions about the role of each module from the systematic comparison of the numerical behavior of all knock-out mutants. We associate a Boolean variable to each module, expressing when the module is included in the architecture and when it is not. We can then express the satisfiability of system-level properties of the full model, such as efficiency, or economical use of resources, in terms of a Boolean formula expressing in a compact way which model architectures, i.e., which combinations of modules, give rise to the desired property. We demonstrate this method on a recently proposed computational model for the heat shock response in eukaryotes. We describe the contribution of each of its three feedback loops towards achieving an economical and effective heat shock response.

4

Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks

Klęsk P.

International Journal of Applied Mathematics and Computer Science

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2010

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Vol. 20, no 3

525-544

EN

Two known approaches to complexity selection are taken under consideration: n-fold cross-validation and structural risk minimization. Obviously, in either approach, a discrepancy between the indicated optimal complexity (indicated as the minimum of a generalization error estimate or a bound) and the genuine minimum of unknown true risks is possible. In the paper, this problem is posed in a novel quantitative way. We state and prove theorems demonstrating how one can calculate pessimistic probabilities of discrepancy between these minima for given for given conditions of an experiment. The probabilities are calculated in terms of all relevant constants: the sample size, the number of cross-validation folds, the capacity of the set of approximating functions and bounds on this set. We report experiments carried out to validate the results.