Model for revelation of unfriendly information impacts in mass-media which are directed on change of public opinion

In this article we proposes the mathematical model for revelation of deliberate unfriendly information impacts which are fulfilled by means of specially prepared information messages (news, reviews and others) in mass-media. The model calculates the quantitative measure for fact determination of purposeful information impact and evaluation of potential damage to interests of state (party, corporation) from impact fulfilment. The model use the following data: intensity and direction of information streams (publication frequency and themes of news), structure of important state and public problems, structure of social groups of a society, priorities of these social groups, mass-media popularity in social groups, priorities of a state policy. The model is the semantic network in which the relations between concepts we formalize by use of fuzzy measures by Sugeno. We have used this model for revelation of information impacts on public opinion of Russian-speaking national minority of Crimea (Ukraine) during 01.2002 - 02.2005 (final stage of presidential elections). The model also can has important implications for evaluation of election cleanness, for neutralization of dirty voting technologies, for facts determination of unfair competition, when corporations involve a public opinion into own competitive activity.Information impact; public opinion; fuzzy measures; preferences; social groups

MODEL FOR REVELATION OF UNFRIENDLY INFORMATION IMPACTS IN MASS-MEDIA WHICH ARE DIRECTED ON CHANGE OF PUBLIC OPINION

In this article we propose the mathematical model for revelation of deliberate unfriendly information impacts which are fulfilled by means of specially prepared information messages (news, reviews and others) in mass-media. The model calculates the quantitative measure for fact determination of purposeful information impact and evaluation of potential damage to interests of state (party, corporation) from impact fulfilment. The model use the following data: intensity and direction of information streams (publication frequency and themes of news), structure of important state and public problems, structure of social groups of a society, priorities of these social groups, mass-media popularity in social groups, priorities of a state policy. The model is the semantic network in which the relations between concepts we formalize by use of fuzzy measures by Sugeno. We have used this model for revelation of information impacts on public opinion of Russian-speaking national minority of Crimea (Ukraine) during 01.2002 - 02.2005 (final stage of presidential elections). The model also can has important implications for evaluation of election cleanness, for neutralization of dirty voting technologies, for facts determination of unfair competition, when corporations involve a public opinion into own competitive activity.public opinion; information impact; fuzzy measures; mathematical model

Model for revelation of unfriendly information impacts in mass-media which are directed on change of public opinion

In this article we proposes the mathematical model for revelation of deliberate unfriendly information impacts which are fulfilled by means of specially prepared information messages (news, reviews and others) in mass-media. The model calculates the quantitative measure for fact determination of purposeful information impact and evaluation of potential damage to interests of state (party, corporation) from impact fulfilment. The model use the following data: intensity and direction of information streams (publication frequency and themes of news), structure of important state and public problems, structure of social groups of a society, priorities of these social groups, mass-media popularity in social groups, priorities of a state policy. The model is the semantic network in which the relations between concepts we formalize by use of fuzzy measures by Sugeno. We have used this model for revelation of information impacts on public opinion of Russian-speaking national minority of Crimea (Ukraine) during 01.2002 - 02.2005 (final stage of presidential elections). The model also can has important implications for evaluation of election cleanness, for neutralization of dirty voting technologies, for facts determination of unfair competition, when corporations involve a public opinion into own competitive activity

Model for revelation of unfriendly information impacts in mass-media which are directed on change of public opinion

In this article we proposes the mathematical model for revelation of deliberate unfriendly information impacts which are fulfilled by means of specially prepared information messages (news, reviews and others) in mass-media. The model calculates the quantitative measure for fact determination of purposeful information impact and evaluation of potential damage to interests of state (party, corporation) from impact fulfilment. The model use the following data: intensity and direction of information streams (publication frequency and themes of news), structure of important state and public problems, structure of social groups of a society, priorities of these social groups, mass-media popularity in social groups, priorities of a state policy. The model is the semantic network in which the relations between concepts we formalize by use of fuzzy measures by Sugeno. We have used this model for revelation of information impacts on public opinion of Russian-speaking national minority of Crimea (Ukraine) during 01.2002 - 02.2005 (final stage of presidential elections). The model also can has important implications for evaluation of election cleanness, for neutralization of dirty voting technologies, for facts determination of unfair competition, when corporations involve a public opinion into own competitive activity

Exploring the structure of hadronic showers and the hadronic energy reconstruction with highly granular calorimeters

Prototypes of electromagnetic and hadronic imaging calorimeters developed and operated by the CALICE collaboration provide an unprecedented wealth of highly granular data of hadronic showers for a variety of active sensor elements and different absorber materials. In this presentation, we discuss detailed measurements of the spatial and the time structure of hadronic showers to characterise the different stages of hadronic cascades in the calorimeters, which are then confronted with GEANT4-based simulations using different hadronic physics models. These studies also extend to the two different absorber materials, steel and tungsten, used in the prototypes. The high granularity of the detectors is exploited in the reconstruction of hadronic energy, both in individual detectors and combined electromagnetic and hadronic systems, making use of software compensation and semi-digital energy reconstruction. The results include new simulation studies that predict the reliable operation of granular calorimeters. Further we show how granularity and the application of multivariate analysis algorithms enable the separation of close-by particles. We will report on the performance of these reconstruction techniques for different electromagnetic and hadronic calorimeters, with silicon, scintillator and gaseous active elements

Particle identification using boosted decision trees for the CALICE highly granular SiPM-on tile calorimeter

The Analog Hadron Calorimeter (AHCAL) is a highly granular SiPM-on-tile sampling calorimeter developed by the CALICE collaboration for future e+e− colliders such as the International Linear Collider (ILC) or the Compact Linear Collider (CLIC). The AHCAL technological prototype consists of 39 active layers alternating with 1.72 cm steel absorber plates. Each active layer is equipped with 576 3×3 cm2 scintillator tiles with individual readout by silicon photomultipliers. The prototype was tested with muon, electron and pion beams at the CERN SPS facilities in 2018. The high granularity provides detailed spatial information about energy depositions of particles in the detector material that can be used for the event characterisation. We perform a gradient boosted decision tree method to classify events according to incoming particle type. Monte-Carlo simulations were used to train and test the classification model. In this contribution, the particle identification method, its efficiency in simulations and the results of data purification will be discussed