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    Chuvash State University

    院校EST. 1967
    3,675论文总数
    8,250引用总数

    I. N. Ulianov Chuvash State University is a public university located in Cheboksary, the capital of the Chuvash Republic, Russia. The university is one of the leading institutions of higher learning in Russia and is the scientific, educational and cultural center of the Privolsky Federal Region of the Russian Federation, which is highly regarded among national universities.Chuvash State University has over 10,000 students and 3,865 faculty, including 77 Doctors of Sciences (equivalent to full professors) and 446 Candidates of Sciences (equivalent to PhDs).The rector is Andrey Aleksandrov..

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    机构学者

    论文(3676)

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    1Synthesis of Pyrrolo[3,4-c]pyridine Derivatives with an Aromatic Substituent Affecting the Sensitivity of Fluorescence Detection of Mercury(II) Ions
    E. N. Ramskaya,Ya. S. Kayukov, I. N. Bardasov

    Ethoxy derivatives of pyrrolo[3,4-c]pyridine have been synthesized by dehydroxyalkoxylation of the corresponding hydroxy derivatives on heating in boiling ethanol in the presence of sulfuric acid. The synthesized compounds have been studied as reagents for the fluorometric determination of mercury(II) ions. Leading compounds containing a 4-chloro- or 4-bromophenyl substituent showed Hg(II) detection limits of 0.43×10–6 or 0.92×10–6 M, respectively, these values are comparable with those of existing small molecule-based reagents.

    2026Russian Journal of Organic Chemistry(2026)引用:17
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    2Metrological Assurance of Wastewater Monitoring: Application of Neural Networks in Measurement Data Processing Methods
    Oleg M. Rozental, Vladislav Kh. Fedotov

    The article examines the metrological assurance of wastewater monitoring, specifically, at chemical industry facilities. Automated monitoring, which involves assessing the initial quality of effluents and the quality of their treatment prior to discharge into natural water bodies, helps to reduce water pollution. In order to improve the monitoring efficiency, it is necessary to address a priority innovation issue faced by water-intensive chemical industry facilities, i.e., to ensure reliable comparability of measurement results for various parameters of water composition and properties, as well as subsequent selective reduction in the concentration of particularly hazardous toxicants. It is shown that a need exists to improve the procedure for identifying pollutants (with their subsequent removal) that cause a hazardous increase in the informative indicators of anthropogenic water pollution. In hygiene, hydrochemistry, and ecology, such indicators include chemical and biochemical oxygen demand. The authors conducted a comparative analysis of methods for processing data (the results of measuring wastewater parameters at industrial facilities) since by selecting the most effective treatment method, the identification of particularly hazardous toxicants and a selective reduction in their concentration can be improved. On the example of studying the sewage wastewater of Kemerovo AZOT enterprise, the following processing methods were analyzed: predictive mathematics, conventional regression analysis, and neural network simulation. Neural networks proved to be highly effective, as they helped to identify the largest (maximum) number of causal relationships, as compared to other methods, including nonlinear relationships between pollutants and chemical and biochemical oxygen demand. According to the results of comparing the data processing methods for analysis of the causal relationships between the measured parameters of water composition and properties, it is recommended to use neural networks. The obtained results can be used to provide metrological support for environmental management, monitoring, and emission/discharge control (accounting) systems at nitrogen fertilizer facilities.

    2026Measurement Techniques(2026)引用:7
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    3Understanding the Training Dynamics of CoLaNET by Its Simplified Model
    O. A. Goryunov, O. V. Maslennikov, M. V. Kiselev,Igor Franovic, V. V. Klinshov

    Training complex, biologically plausible Spiking Neural Networks (SNNs) with local learning rules is a significant challenge for theoretical analysis. Here we address this problem by developing a comprehensive analytical theory for the learning dynamics of CoLaNET, a recently proposed columnar SNN. In particular, we consider a simplified model that captures the core algorithmic logic of CoLaNET’s training process. We derive closed-form expressions that accurately predict the number of emergent microcolumns, the complete temporal evolution of synaptic weights, and the model’s learning curve. Our theoretical predictions show excellent quantitative agreement with numerical simulations of the simplified model and successfully capture the qualitative behavior of the full spiking CoLaNET. Furthermore, our analysis reveals that the spike-based, asynchronous competition in the full model delays neuronal specialization and slows down the training process relative to its simplified counterpart. This work provides a scalable theoretical foundation for understanding and configuring biologically inspired SNNs and highlights a key difference introduced by spike-based dynamics.

    2026CHAOS SOLITONS & FRACTALS(2026)引用:2
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    4Effect of Fillers on Physical, Mechanical, Performance, and Dynamic Properties of Rubber for Pads of Rail Fastenings
    E. N. Egorov,N. I. Kol’tsov

    The influence of various dispersed fillers (RG-96P-10 product, NDP-D-400 diatomite, natural chalk, and TS-D taurite) on the rubber compound vulcanization properties, as well as the physical, mechanical, performance, and dynamic (damping) properties of rubber used for rail pads, was studied. It was found that, with the content of the RG-96P-10 product in the amount of 20.0 parts per hundred parts of rubber (phr), the rubber possesses satisfactory elastic-strength properties and the least changes in them after daily thermo-oxidative aging in air and thermal action of standard petroleum fluid SZhR-1, changes in weight after exposure to aggressive environments, and increased dynamic properties, which allows recommending it for the manufacture of rail fastening pads for railway tracks.

    2026Inorganic Materials Applied Research(2026)引用:1
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    5Tax Administration: Directions of Improvement
    N. Z. Zotikov

    Tax administration is a system of state management of the tax process with the purpose of building certain relations between the state, represented by tax authorities, and taxpayers in order to improve the efficiency of the tax system. The subject of tax administration is tax production, the object is the process of managing tax production, the subject is tax administrations. The mains task of tax administration are to regulate economic processes and legal relationships in the field of taxes and fees in order to achieve an optimal ratio of fiscal and distribution functions of taxes at minimal costs, to meet the state’s financial needs with a reasonable burden on the taxpayer, to stimulate honest payment of taxes than tax evasion. This is the relevance of the research topic. The purpose of the research is to study the mechanism and methods of tax administration used by tax authorities in their activities. The objectives of the study are to analyse the effectiveness of tax administration methods, their compliance with current legislation, and to develop proposals for their improvement. The following methods are used: analysis and synthesis, grouping, generalisation, the tabular method, etc. The information base of the study is the tax and budget legislation, official data of the Federal State Statistics Service, reports of the Federal Tax Service of the Russian Federation. In order to ensure the validity of the conclusions and proposals, we provide arbitration practice of dispute resolution. The practical significance of the study is that its results can be used in the practical activities of regulatory authorities as well as in further research on the topic under study.

    2026Вестник университета(2026)引用:1
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    合作机构(100)

    俄罗斯科学院合作论文 68
    Chuvash State Pedagogical University合作论文 67
    Chuvash State Agricultural Academy合作论文 65
    莫斯科罗蒙诺索夫国立大学合作论文 52
    喀山联邦大学合作论文 26
    Russian University of Cooperation合作论文 25
    Kazan State Medical University合作论文 25
    俄罗斯人民友谊大学合作论文 20
    Ulyanovsk State University合作论文 17
    莫斯科国立大学合作论文 17

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