106 research outputs found
DEALed : A tool suite for distributed real-time systems development
No full textDEALed is a tool suite for development of distributed systems using DEAL language. DEAL is being developed at Eindhoven University of Technology as a part of DEDOS project. Area of application of the DEALed is the development of the distributed real- time safety-critical control systems
Chemical lattice strain in nonstoichiometric oxides: an overview
No full textStrong coupling between the chemical composition and crystal lattice dimensions resulting in the contraction or expansion of a material upon change of its chemical composition is known as chemical expansion or chemical strain. This phenomenon significantly influences the performance of oxide materials in different energy conversion and storage devices. In many such applications, e.g., in oxygen-permeating membranes or solid oxide fuel cells (SOFCs), the materials are subject to significant in situ variation of their chemical composition, which is accompanied by large volume changes. This may often be detrimental to the operation of the electrochemical device. Not only is chemo-mechanical coupling crucial for various practical applications, but also, when measured accurately and discussed appropriately, the chemical strain of an oxide material allows better understanding of its local electronic and defect structure. Chemical strain is also strongly correlated with diffusion phenomena in high-temperature electrochemical devices - the subject that for many years has been of particular scientific interest for John Kilner and where he holds a lot of pioneering achievements. This article reviews the state of the art in the field of chemical strain of various oxide materials, primarily those intended to operate at elevated temperatures, and aims at summarizing the available experimental, theoretical and computational insights into its origins, factors impacting its magnitude, and the available means for its a priori quantitative estimation. © 2022 The Royal Society of ChemistryMinistry of Education and Science of the Russian Federation, Minobrnauka: 075-03-2021-051/5; Council on grants of the President of the Russian FederationDmitry Malyshkin acknowledges the project MK-800.2020.3 supported by the Council on grants of the President of the Russian Federation. The authors are grateful for the financial aid of the Ministry of Science and Higher Education of the Russian Federation (State Assignment No. 075-03-2021-051/5)
Natural Phenomena in the Mechanism of Legal Regulation
No full textThe article examines the interaction of nature phenomena and law in the regulation of social relations. The author proves that natural phenomena should be considered when legal acts are worked out. The analysis of the place and role of natural phenomena in the mechanism of legal regulation is analyse
First-order logic of uniform attachment random graphs with a given degree
No full textIn this paper, we prove the first-order convergence law for the uniform
attachment random graph with almost all vertices having the same degree. In the
considered model, vertices and edges are introduced recursively: at time
we start with a complete graph on vertices. At step the vertex
is introduced together with edges joining the new vertex with
vertices chosen uniformly from those vertices of , whom degree is
less then . To prove the law, we describe the dynamics of the logical
equivalence class of the random graph using Markov chains. The convergence law
follows from the existence of a limit distribution of the considered Markov
chain
Logical convergence laws via stochastic approximation and Markov processes
No full textSince the paper of Kleinberg and Kleinberg, SODA’05, where it was proven that the preferential attachment random graph with degeneracy at least 3 does not obey the first order 0-1 law, no general methods were developed to study logical limit laws for recursive random graph models with arbitrary degeneracy. Even in the (possibly) simplest case of the uniform attachment, it is still not known whether the first order convergence law holds in this model. We prove that the uniform attachment random graph with bounded degrees obeys the first order convergence law. To prove the law, we describe dynamics of first order equivalence classes of the random graph using Markov chains. The convergence law follows from the existence of a limit distribution of the considered Markov chain. To show the latter convergence, we use stochastic approximations
Integration of artificial intelligence into public life: some ethical and legal problems
No full textThe spread of artificial intelligence systems raises a number of technical, philosophical, legal
and ethical issues related to the admissibility of using artificial intelligence in various fields,
and the need to comply with ethical standards in the creation of artificial intelligence systems,
as well as the possibility of introducing ethical standards in the decision-making process of artificial intelligence. Since for many people religion is the basis of the worldview, present in
public life as ethics, not dogmatics, the study of various aspects of the relationship between
religion and artificial intelligence is also extremely important. The author analyzes the ethical
and religious problems associated with the creation and distribution of artificial intelligence
systems and proposes ways of legal regulation of new social relations associated with the use
of artificial intelligence. The author explores the following problems: the ability of artificial
intelligence to be a subject of law; liability of artificial intelligence; sacralization of artificial intelligence;
the responsibility for harm caused by artificial intelligence systems; the adoption of
artificial intelligence decisions concerning the rights and duties of people; increasing stratification
and inequality; mass unemployment; intellectual superiority of the carriers of artificial
intelligence over man; alienation of people from each other, the loneliness of man; the ability
to follow ethical standards when artificial intelligence makes decisions
Mixing Enthalpy Estimation for CsX–PbX2 Melts (X = Cl, Br) by Differential Scanning Calorimetry
No full textAbstract: A comparatively simple method for estimating the mixing enthalpy of melts by differential scanning calorimetry using standard equipment is proposed. The enthalpies of mixing of CsX–PbX2 (X = Cl, Br) melts are determined by this method. The measured values of mixing enthalpy in the CsCl–PbCl2 system are in good agreement with those obtained by means of independent measurements. For the CsBr–PbBr2 system, the enthalpy of mixing was measured for the first time. The similar values of mixing enthalpy were found for both studied systems. © The Author(s) 2024. ISSN 0036-0244, Russian Journal of Physical Chemistry A, 2024, Vol. 98, No. 12, pp. 2675–2680. The Author(s), 2024. This article is an open access publication.Russian Science Foundation, RSF, (24-23-00492); Russian Science Foundation, RSFThe work was supported by the Russian Science Foundation (project no. 24-23-00492)
Modeling spallation reactions in tungsten and uranium targets with the Geant4 toolkit*
No full textWe study primary and secondary reactions induced by 600 MeV proton beams in monolithic cylindrical targets made of natural tungsten and uranium by using Monte Carlo simulations with the Geant4 toolkit [1–3]. Bertini intranuclear cascade model, Binary cascade model and IntraNuclear Cascade Liège (INCL) with ABLA model [4] were used as calculational options to describe nuclear reactions. Fission cross sections, neutron multiplicity and mass distributions of fragments for 238U fission induced by 25.6 and 62.9 MeV protons are calculated and compared to recent experimental data [5]. Time distributions of neutron leakage from the targets and heat depositions are calculated
Vertex and energy reconstruction in JUNO with machine learning methods
No full textThe Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters sin2θ12, Δm212 and Δm312 are the main goals of the experiment. A rich physical program beyond the oscillation analysis is also foreseen. The ability to accurately reconstruct particle interaction events in JUNO is of great importance for the success of the experiment. In this work we present several machine learning approaches applied to the vertex and the energy reconstruction. Multiple models and architectures were compared and studied, including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph Neural Network based on DeepSphere. The models of BDT and DNN are trained with aggregated information, pre-calculated from PMT signal, while the others are trained with PMT-wise measured information from 17600 PMTs. Based on a study, carried out using the dataset, generated by the official JUNO software, we demonstrate that machine learning approaches achieve the necessary level of accuracy for reaching the physical goals of JUNO: σE=3% at Evis=1MeV for the energy and σx,y,z=10cm at Evis=1MeV for the position
DEALed : A tool suite for distributed real-time systems development
No full textDEALed is a tool suite for development of distributed systems using DEAL language. DEAL is being developed at Eindhoven University of Technology as a part of DEDOS project. Area of application of the DEALed is the development of the distributed real- time safety-critical control systems
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