Zobrazeno 1 - 10
of 62
pro vyhledávání: '"P P, Gimadiev"'
Publikováno v:
Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-12 (2023)
Abstract In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretraining of JT VAE on a large dataset a
Externí odkaz:
https://doaj.org/article/4697268f5b9747b0b552a45c1608cda7
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Igor V. Zhirov, Svetlana N. Nasonova, Ulia A. Khalilova, Yulia F. Osmolovskaya, Irina A. Zhirova, Rinat R. Gimadiev, Olga Ia. Chaikovskaia, Oleg M. Reitblat, Iuliia Sh. Prints, Anatoly G. Kochetov, Sergey N. Tereshchenko
Publikováno v:
Consilium Medicum, Vol 24, Iss 1, Pp 7-12 (2022)
In September 2021, the European Society of Cardiology issued new guidelines on the management of patients with heart failure (HF). In the current version, experts have focused on the 4 most common variants: acute HF decompensation, acute pulmonary ed
Externí odkaz:
https://doaj.org/article/1621f1fea5454b70a9404ff5cb1a0a79
Publikováno v:
Кардиоваскулярная терапия и профилактика, Vol 22, Iss 1 (2023)
Aim. To determine the characteristics of erythrocyte parameters, iron metabolism, erythropoiesis, inflammation markers in patients with heart failure (CHF) and anemia.Material and methods. The study included 68 patients with HF to describe the charac
Externí odkaz:
https://doaj.org/article/0d955b7c4d5c444d99c7cc4be41ddaf6
Autor:
Igor V. Zhirov, Svetlana N. Nasonova, Ulkiar A. Khalilova, Yulia F. Osmolovskaya, Olga Ia. Chaikovskaia, Irina A. Zhirova, Rinat R. Gimadiev, Anatolii G. Kochetov, Sergei N. Tereshchenko
Publikováno v:
Consilium Medicum, Vol 23, Iss 10, Pp 750-755 (2021)
Acute heart failure (HF) is a syndrome requiring urgent treatment. Due to drastic increase in the number of HF patients this problem is still of great importance for modern public health care and has a significant impact on morbidity and mortality. T
Externí odkaz:
https://doaj.org/article/978d682e2136458aa62765acdef170ec
Autor:
William Bort, Igor I. Baskin, Timur Gimadiev, Artem Mukanov, Ramil Nugmanov, Pavel Sidorov, Gilles Marcou, Dragos Horvath, Olga Klimchuk, Timur Madzhidov, Alexandre Varnek
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Abstract The “creativity” of Artificial Intelligence (AI) in terms of generating de novo molecular structures opened a novel paradigm in compound design, weaknesses (stability & feasibility issues of such structures) notwithstanding. Here we show
Externí odkaz:
https://doaj.org/article/3d91d19a63b3499ebdae5082b570347c
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Вестник Самарского университета: Аэрокосмическая техника, технологии и машиностроение, Vol 19, Iss 2, Pp 85-98 (2020)
It is necessary to ensure appropriate information content of the measuring instruments used for intelligent diagnosing systems of energy and technological complexes based on the measurement of dynamic parameters. Sensors and measuring equipment shoul
Externí odkaz:
https://doaj.org/article/b33f743cea674ca6a9772812b6afd5d3
Publikováno v:
Вестник Самарского университета: Аэрокосмическая техника, технологии и машиностроение, Vol 17, Iss 3, Pp 56-67 (2018)
Gas pressure pulsations are one of the main parameters taken into account in gas turbine engine development. Therefore, special attention is paid to the accuracy of measuring pressure fluctuations. A high temperature of gas flow, sensors size limitat
Externí odkaz:
https://doaj.org/article/b8cbac9133c9462b990e5e5239235430
Autor:
Albert Gareev, Vladimir Protsenko, Dmitriy Stadnik, Pavel Greshniakov, Yuriy Yuzifovich, Evgeniy Minaev, Asgat Gimadiev, Artem Nikonorov
Publikováno v:
Sensors, Vol 21, Iss 13, p 4410 (2021)
This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augment
Externí odkaz:
https://doaj.org/article/66ef6e6b89e94949a05d6bf7b1945da3