Journal of Experimental and Theoretical Physics
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SCImago
Q3
WOS
Q3
Impact factor
1
SJR
0.278
CiteScore
1.9
Categories
Physics and Astronomy (miscellaneous)
Areas
Physics and Astronomy
Years of issue
1980, 1983, 1992, 1997-2023
journal names
Journal of Experimental and Theoretical Physics
J EXP THEOR PHYS+
Top-3 citing journals

Journal of Experimental and Theoretical Physics
(3184 citations)

Physical Review B
(3079 citations)

JETP Letters
(1821 citations)
Top-3 organizations

Lomonosov Moscow State University
(566 publications)

P.N. Lebedev Physical Institute of the Russian Academy of Sciences
(372 publications)

National Research Centre "Kurchatov Institute"
(358 publications)

Lomonosov Moscow State University
(96 publications)

Moscow Institute of Physics and Technology
(62 publications)

P.N. Lebedev Physical Institute of the Russian Academy of Sciences
(50 publications)
Most cited in 5 years
Found
Publications found: 2850

Analysis of The Role of Deep Learning Models in Image Classification Applications
Li X.
Image classification is a fundamental task in computer science, underpinning various applications such as object detection, face recognition, and object interaction analysis. The concept holds significant value due to its wide-ranging applications across multiple fields. Traditional methods for image classification, however, have been limited by their slow processing speed, rigidity, and high costs. The integration of deep learning models, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), has revolutionized this process, enabling the development of automated, fast, and practical systems. These advanced models are now employed in diverse areas, including biomedical science, remote sensing, and business management, thanks to their ability to achieve high accuracy across a broad spectrum of scenarios. Training these models involves the use of well-known datasets like Canadian Institute for Advanced Research (CIFAR) and Modified National Institute of Standards and Technology (MNIST), which provide the necessary data for optimization and validation. The paper examines the structure, functionality, advantages, and limitations of CNNs and SVMs in the context of image classification, demonstrating that deep learning-driven classification is now a mainstream research focus. This study highlights the transformative impact of these models and provides insights into their future potential.

Comparative Analysis of YOLO Variants Based on Performance Evaluation for Object Detection
Chen A.
This study focuses on analysing and exploring the You Only Look Once (YOLO) algorithm. Specifically, this article analyses the evolution and performance of three versions (YOLOv1, YOLOv5, and YOLOv8) in object detection. The research begins by detailing the fundamental concepts of object detection and the datasets commonly used in this field. It then delves into the specific architectures and experimental outcomes associated with each YOLO version. The analysis reveals that while YOLOv8 introduces advanced features and improvements, earlier versions like YOLOv5 may offer superior stability and performance under certain conditions, particularly in specific tasks such as car detection. The discussion emphasizes the significant impact of factors such as batch size on model performance, suggesting that fine-tuning these parameters can optimize the algorithm for particular applications. The study concludes that the future of YOLO development lies in exploring and refining different variants, particularly those of YOLOv8, to better meet diverse requirements. By focusing on five distinct YOLOv8 variants, the research aims to enhance the adaptability and effectiveness of the YOLO framework across a wide range of object detection challenges, thereby contributing valuable insights into the ongoing advancement of this technology.

Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation
Jin Z.
Glaucoma, the leading cause of irreversible blindness, must be diagnosed early and thus treated in time. However, it has no noticeable symptoms in its early stages and may not be detected easily. This paper aims to compare two well-known convolutional neural network (CNN) structures, namely Fully Convolutional Networks (FCNs) and U-Net for the segmentation of the optic disc (OD) and optic cup (OC) from retinal fundus images which play an important role in glaucoma diagnosis. The performance of both models is assessed using qualitative parameters such as the Dice coefficient, Jaccard index, and cup-to-disc ratio (CDR) error. In our experiment, the U-Net model yields more accurate segmentation results with 0.9601 average pixel accuracy and 0.9255 dice score for OD segmentation, outperforming the FCNs model with 0.9560 average pixel accuracy and 0.9132 dice score for OD segmentation. However, FCNs have a shorter inference time of 0. 0043 seconds against U-net’s 0. 0062 seconds making FCNs more suitable for real-time applications. The restrictions related to this study include biases from using only one dataset acquired from particular imaging devices, dependency on mask-based cropping techniques, and comparison being restricted to two fundamental architectures. This work presents the contribution of the deep learning models in improving glaucoma screening and therefore helping in avoiding blindness.

Sentiment Analysis of Mobile Phone Reviews Using XGBoost and Word Vectors
Wang Z.
Consumer reviews are an important source of data used to judge and examine consumer sentiment, and data mining for reviews of electronic products is an important way to help improve the design of electronic products. The research is based on the consumer reviews of online cell phone e-commerce, The paper constructs a sentiment dictionary in this field based on the Sentiment Oriented Point Mutual Information (SO-PMI) algorithm, and the sentiment weight of the review word vectors. An extreme Gradient Boosting Tree (XGBoost) is used to integrate word vectors and a Large Language Model (LLM) to construct a sentiment recognition model, and finally, a review sentiment index is derived, which unfolds from multiple dimensions to analyze the sentiment tendency in consumer reviews. The empirical analysis shows that the accuracy, recall, area under the curve (AUC), and other validation indexes of the constructed sentiment recognition model are further improved compared with the LLM model, which has a certain application value. When applying the weighted word vector method, the model has been significantly improved compared with the LLM model, the accuracy is increased by 5%, the accuracy is increased by 10%, and the comprehensive accuracy is increased by 2% after the comprehensive application of the two.

A Hybrid Machine Learning Framework for Soccer Match Outcome Prediction: Incorporating Bivariate Poisson Distribution
Chen Z.A.
The 2022 FIFA World Cup final attracted 1.5 billion viewers, while billions of dollars are wagered on soccer matches every year. The increasing demand for accurate predictions, both for academic research and betting purposes, has driven the development of advanced forecasting models. This study explores the application of mathematical and machine learning models to predict results of soccer matches, with the dual aim of academic advancement and profitable betting. The author utilizes a comprehensive dataset from top European leagues (2014-2022) and employ models including Bivariate Poisson Distribution, Naive Bayes, Neural Networks, Support Vector Machines, Random Forests, and Gradient Boosting. The paper’s feature engineering combines historical match statistics, FIFA ratings, and betting odds. While Random Forests achieved the highest accuracy (56.25%), predicting draws remains challenging. The study highlights the potential for improved prediction systems and suggests future research in advanced draw prediction techniques and profitability analysis, the paper provides research directions for researchers in related fields.

The Applications and Prospects of Large Language Models in Traffic Flow Prediction
Liu Y.
Predicting traffic flow is crucial for the functionality of intelligent transportation systems. It is of critical importance to relieve traffic pressure, reduce accident rates, and alleviate environmental pollution. It is an important part of the construction of modern intelligent road networks. With advancements in deep learning (DL), DL models have made notable strides in prediction. However, due to the complexity and non-transparency of DL models themselves, there are still problems of low accuracy and interpretability in traffic flow prediction (TFP). Leveraging large language models (LLM) helps to improve the negative conditions caused by other DL models in prediction. This paper first briefly summarizes the basic characteristics of LLM and their advantages in TFP; then conducts relevant research and analysis in the order of experimental design steps comparison and results and conclusions comparison; then analyzes and discusses the current problems and challenges faced by LLM; finally, it looks forward to future research directions and development trends, and summarizes this paper.

Hierarchical Learning: A Hybrid of Federated Learning and Personalization Fine-Tuning
Li S., Zhang B.
Hierarchical Federated Learning (FL) presents a novel approach that combines global model training with localized personalization fine-tuning to enhance the predictive accuracy of decentralized machine learning systems. Traditional FL methods, which allow multiple clients to collaboratively train a global model without sharing raw data, are hindered by issues such as non-independent and identically distributed (non-IID) data, communication overhead, and limited generalization across diverse client datasets. This study proposes a hierarchical model that mitigates these challenges by incorporating a global model, trained using the Federated Averaging (FedAvg) algorithm, and applying client-specific fine-tuning to improve local model performance. The experiment conducted on a movie recommendation system demonstrates that this hierarchical approach significantly reduces the global model’s error while offering personalized improvements on client-specific datasets. Results show an average Root Mean Squared Error (RMSE) reduction of 0.0460 following local personalization. This hybrid approach not only enhances model accuracy but also preserves data privacy and increases scalability, making it a promising solution for decentralized recommendation systems.

Research and Application of Heart Disease Prediction Model Based on Machine Learning
Bao Y.
As heart disease has become the leading cause of death worldwide, early and accurate prediction is crucial to help doctors make initial judgments about patients and improve their survival rates. This study aims to improve the accuracy and efficiency of heart disease prediction through Machine learning (ML) methods to help medical diagnosis. A heart disease dataset was used in the study, and multiple ML models were used to analyze multiple key health features, and the model performance was verified through a test set. This paper concludes that Logistic regression and random forests perform well in this task and have high practical value. Future research can stack models and optimize data sources to improve the practical performance of the model. This study provides a basic framework for building an intelligent medical auxiliary diagnosis system, which helps to achieve early prevention and timely judgment of heart disease, thereby improving the overall efficiency of medical services.

Advances in Image Generation Technology: Exploring GANs and MirrorGANs
Shi L.
This paper is an in-depth study by delving into the latest in image generation technology, where thesis is focusing on the Generative Adversarial Networks (GANs) and MirrorGANs possibilities. Image Generation is the backbone of visual computing, mostly utilized in intelligent designs. It is for this reason that this research aims at unravelling the theoretical basis and consolidated practices of GANs when it conies to generating both high-quality and semantically consistent imagery. The study will investigate the whole of the image generation process, starting from data preprocessing to the use of GANs to generate images from textual descriptions. The work discussed the relevance as well as the limitations of these technologies from the artistic point of view, medical imaging, and virtual reality. Tire article concludes that the paper sketches the data and experiments that show that the realism and richness hi picture quality are accentuated when GANs and MirrorGANs are incorporated. This suggests the scope of image-generation technology to enhance human-machine collaboration and allow for innovating hi smart tech. Further studies will be geared to enhancing these methods and consequently drawing humanity and machines closer, which hi nun will fuel the ongoing progress in this fast-paced sphere.

Effectiveness Evaluation of Random Forest, Naive Bayes, and Support Vector Machine Models for KDDCUP99 Anomaly Detection Based on K-means Clustering
Zhang M.
Security in the World Wide Web has recently seen an enormous upgrade in almost every aspect. Identifying malicious activities hi a network such as network attacks and malicious users plays a significant role hi these upgraded security directions. This research utilizes the KDDCUP99 dataset to incorporate K-means clustering with three classifiers: Random Forest (RF). Naïve Bayes (NB). and Support Vector Machine (SVM) with the goal to boost the accuracy of predicting network intrusions. In tins paper. K-means clustering technique is applied as a preprocessing step to enhance the overall quality of network intrusion detection and maximize the accuracy of the network security measures. The goal is to identify anomalies with high accuracy. Experimental results hidicate that the optimal combination is K-means + RF. which outperformed the others hi precision, recall, and Fl-score. Although K-means + NB demonstrated superior recall for certahi smaller anomalies, it underperformed compared to the RF model. The paper concludes by highlighting the value of ensemble approaches, in particular Random Forest, for tackling anomaly detection and network security issues, particularly hi light of the expanding significance of social networks and the internet.

Time Series Analysis: Application of LSTM model in predicting PM 2.5 concentration in Beijing
Yang R.
Air pollution forecasting for public health and policy-making has a critical importance, this paper employs a Long Short-Term Memory (LSTM) model to perform in-depth prediction of PM2.5 concentrations measured at the U.S. Embassy in Beijing, outperforming regular forecasting approaches. In the LSTM model, the research examines a very detailed hourly dataset and beats regular forecasting approaches. A key finding is the model’s ability to effectively generalize from historical data to predict future air quality trends, with its adeptness at handling time-dependent relationships. This research emphasizes the importance of LSTM in air pollution prediction and management in environmental science as it provides an effective means for planning and making decisions on air quality management. This research is of great importance in providing a groundwork for further enhancement of prediction modeling. By offering a more reliable and sophisticated picture of air quality variations, this study addresses the current problem about how urban air pollution control could be improved in the city.

Research on Analyzing the Emotional Polarity of Malicious Swipe Comments on E-commerce Platforms Based on NPL
Ren C.
In the era of rapid advancements in natural language processing (NLP) models, these technologies have immense potential to detect and address societal issues, enhancing the functioning of the digital society. Online shopping platforms rely heavily on user reviews to influence buyer decisions, yet malicious reviews can significantly degrade user experience. This study focuses on analyzing the emotional polarity of malicious brushorder (falsely generated) reviews in e-commerce product comments, utilizing the Jingdong product review dataset. The methodology involves utilizing the Word2Vec model to vectorize the text data, followed by principal component analysis (PCA) for outlier detection to identify potential malicious reviews based on their unique characteristics. The PCA results are further leveraged for dimensionality reduction, simplifying the dataset. Subsequently, the BERT model is employed to perform semantic similarity analysis, allowing for the screening and expansion of the experimental dataset with similar malicious comments. This enriched dataset is then subjected to sentiment polarity analysis, enabling a deeper tinderstanding of the nature and impact of these malicious reviews. By facilitating buyers in making informed decisions based on genuine reviews, this research underscores the practical value of NLP hi addressing real-world challenges in e-commerce.

The Use of Natural Language Processing Model in Literary Style Analysis of Chinese Text
Ye J.
In recent years, research on Natural Language Processing (NLP) has made consistent progress and has become a popular topic. As a promising branch of Machine Learning, NLP focuses on the understanding, generating and analysing of human languages. The applications of NLP include chatbots and language translation. This paper represents a HanLP based NLP model. The model is capable of analysing the literary style of given Chinese text by quantifying the literary style of the text on the basis of five fundamental elements, namely literary grace, sentiments, momentum, climate and lingering charm. This paper presents the input and output data of the research and conducts analyses on these data. Moreover, this paper draws a conclusion on the deviation rate and robustness of the model. It is reckoned that this model initially possesses the function of literary style analysis of Chinese text. The research, per se, along with its data, is capable of being reference for research in NLP and related fields.

Sql injection detection using Naïve Bayes classifier: A probabilistic approach for web application security
Lu Z.
A pervasive security issue in web applications is database injection, enabling attackers to alter SQL queries in order to get unauthorized access to confidential information. Using the Naive Bayes classifier, a probabilistic model specifically developed for text classification tasks, this work introduces a novel method for detecting SQL injection vulnerabilities.The process begins by collecting and organizing a comprehensive dataset, which includes both harmful and non-malicious SQL queries. Feature extraction is later employed to identify patterns and characteristics commonly associated with SQL injection, such as certain SQL clauses and logical operators. This collection of attributes is employed to generate a feature vector that serves as the input for the Naive Bayes classification algorithms. The classifier is trained using a labeled dataset and then learns to distinguish between benign and malicious requests by assessing their computed probabilities. Conventional measures such as accuracy, precision, recall, and F1-score are employed to assess the model’s ability in correctly identifying SQL while reducing false positive classifications.The present study demonstrates the potential of Naive Bayes in enhancing online application security by providing a methodical and scalable strategy for identifying SQL injection attacks.

Image Inpainting of Portraits Artwork Design and Implementation
Zhang H.
In modern society, the restoration of artwork has become increasingly important. Generative models can provide reference images for the damaged or blurred core areas of these artworks. This paper simulates artificial damage to classic portrait paintings in the Art Portraits dataset by adding center masks during data preprocessing and then implements the image inpainting task. During the training phase, the Denoising Diffusion Probabilistic Model (DDPM) is fine-tuned by progressively adding noise to the center-masked images in the noising stage, followed by denoising in the denoising stage to generate images. The generated images are compared with the original undamaged images through loss calculations to optimize the model. Additionally, a Generative Adversarial Network (GAN), which has shown promising results on other datasets, is used as a baseline for comparison. The damaged images are used as inputs, and the generated images are compared to the ground truth to evaluate the performance of both models. In the testing phase, two widely used metrics in image evaluation, Mean Squared Error (MSE) and Fréchet Inception Distance (FID), are introduced to assess the performance. The fine-tuned DDPM achieves an MSE of 0.2622 and an FID of 16.85, while the GAN scores 0.2835 and 22.78, respectively. Since lower values indicate higher fidelity in reproducing the original image, which is crucial for art restoration, the conclusion drawn from this paper is that the fine-tuned DDPM demonstrates higher accuracy and is more suitable for restoration projects related to Art Portraits.
Top-100
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Bentham Science Publishers Ltd.
7 citations, 0.01%
|
|
Begell House
7 citations, 0.01%
|
|
RTU MIREA
7 citations, 0.01%
|
|
Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
7 citations, 0.01%
|
|
National University of Science & Technology (MISiS)
7 citations, 0.01%
|
|
IGI Global
7 citations, 0.01%
|
|
7 citations, 0.01%
|
|
American Institute of Mathematical Sciences (AIMS)
6 citations, 0.01%
|
|
Korean Society of Industrial Engineering Chemistry
6 citations, 0.01%
|
|
Institute of Mathematical Problems of Biology of RAS (IMPB RAS)
6 citations, 0.01%
|
|
Electromagnetics Academy
6 citations, 0.01%
|
|
Chinese Ceramic Society
6 citations, 0.01%
|
|
Lviv Polytechnic National University
6 citations, 0.01%
|
|
Institute of Continuous Media Mechanics
6 citations, 0.01%
|
|
Moscow University Press
6 citations, 0.01%
|
|
IOS Press
5 citations, 0.01%
|
|
Infra-M Academic Publishing House
5 citations, 0.01%
|
|
South Ural State University
5 citations, 0.01%
|
|
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
5 citations, 0.01%
|
|
Southwest State University
5 citations, 0.01%
|
|
Scientific Publishers
4 citations, 0.01%
|
|
Institute of Computer Science
4 citations, 0.01%
|
|
Belarusian National Technical University
4 citations, 0.01%
|
|
NPG Publishing
4 citations, 0.01%
|
|
Social Science Electronic Publishing
4 citations, 0.01%
|
|
American Nuclear Society
4 citations, 0.01%
|
|
King Saud University
3 citations, 0.01%
|
|
Mary Ann Liebert
3 citations, 0.01%
|
|
3 citations, 0.01%
|
|
Public Library of Science (PLoS)
3 citations, 0.01%
|
|
Laser Institute of America
3 citations, 0.01%
|
|
Japan Institute of Metals
3 citations, 0.01%
|
|
Show all (70 more) | |
2000
4000
6000
8000
10000
12000
|
Publishing organizations
100
200
300
400
500
600
|
|
Lomonosov Moscow State University
566 publications, 8.78%
|
|
P.N. Lebedev Physical Institute of the Russian Academy of Sciences
372 publications, 5.77%
|
|
National Research Centre "Kurchatov Institute"
358 publications, 5.55%
|
|
Moscow Institute of Physics and Technology
272 publications, 4.22%
|
|
Landau Institute for Theoretical Physics of Russian Academy of Sciences
258 publications, 4%
|
|
Joint Institute for High Temperatures of the Russian Academy of Sciences
234 publications, 3.63%
|
|
Kirensky Institute of Physics of the Siberian Branch of the Russian Academy of Sciences
225 publications, 3.49%
|
|
M.N. Mikheev Institute of Metal Physics of the Ural Branch of the Russian Academy of Sciences
209 publications, 3.24%
|
|
Osipyan Institute of Solid State Physics of the Russian Academy of Sciences
201 publications, 3.12%
|
|
National Research Nuclear University MEPhI
198 publications, 3.07%
|
|
Ioffe Physical-Technical Institute of the Russian Academy of Sciences
184 publications, 2.85%
|
|
P.L. Kapitza Institute for Physical Problems of Russian Academy of Sciences
172 publications, 2.67%
|
|
Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences
147 publications, 2.28%
|
|
Prokhorov General Physics Institute of the Russian Academy of Sciences
145 publications, 2.25%
|
|
Ural Federal University
132 publications, 2.05%
|
|
Institute of Semiconductor Physics of the Siberian Branch of the Russian Academy of Sciences
119 publications, 1.85%
|
|
Institute of Spectroscopy of the Russian Academy of Sciences
115 publications, 1.78%
|
|
Lobachevsky State University of Nizhny Novgorod
111 publications, 1.72%
|
|
Saint Petersburg State University
111 publications, 1.72%
|
|
Novosibirsk State University
110 publications, 1.71%
|
|
Institute of Automation and Electrometry of the Siberian Branch of the Russian Academy of Sciences
102 publications, 1.58%
|
|
Joint Institute for Nuclear Research
100 publications, 1.55%
|
|
Shubnikov Institute of Crystallography
95 publications, 1.47%
|
|
Institute for High Pressure Physics of Russian Academy of Sciences
92 publications, 1.43%
|
|
A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences
91 publications, 1.41%
|
|
Petersburg Nuclear Physics Institute of NRC «Kurchatov Institute»
87 publications, 1.35%
|
|
Peter the Great St. Petersburg Polytechnic University
86 publications, 1.33%
|
|
Budker Institute of Nuclear Physics of the Siberian Branch of the Russian Academy of Sciences
83 publications, 1.29%
|
|
All-Russian Research Institute of Experimental Physics
81 publications, 1.26%
|
|
Federal Research Center of Problem of Chemical Physics and Medicinal Chemistry RAS
77 publications, 1.19%
|
|
Troitsk Institute for Innovation and Fusion Research
76 publications, 1.18%
|
|
N.N. Semenov Federal Research Center for Chemical Physics of the Russian Academy of Sciences
70 publications, 1.09%
|
|
Siberian Federal University
70 publications, 1.09%
|
|
Institute for Nuclear Research of the Russian Academy of Sciences
69 publications, 1.07%
|
|
![]() Alikhanov Institute for Theoretical and Experimental Physics of NRC «Kurchatov Institute»
57 publications, 0.88%
|
|
Ulyanovsk State University
57 publications, 0.88%
|
|
National University of Science & Technology (MISiS)
56 publications, 0.87%
|
|
Dagestan Scientific Center of the Russian Academy of Sciences
53 publications, 0.82%
|
|
Institute for Physics of Microstructures of the Russian Academy of Sciences
52 publications, 0.81%
|
|
Amirkhanov Institute of Physics DSC RAS
52 publications, 0.81%
|
|
Tomsk State University
51 publications, 0.79%
|
|
National Research University Higher School of Economics
49 publications, 0.76%
|
|
Institute of Electrophysics of the Ural Branch of the Russian Academy of Sciences
48 publications, 0.74%
|
|
Galkin Donetsk Institute for Physics and Engineering
47 publications, 0.73%
|
|
Scientific and Practical Center for Materials Science of the National Academy of Sciences of Belarus
44 publications, 0.68%
|
|
Space Research Institute of the Russian Academy of Sciences
42 publications, 0.65%
|
|
Federal Research Center "Krasnoyarsk Science Center" of the Siberian Branch of the Russian Academy of Sciences
42 publications, 0.65%
|
|
Institute of Laser Physics of the Siberian Branch of the Russian Academy of Sciences
40 publications, 0.62%
|
|
Kazan Federal University
40 publications, 0.62%
|
|
ITMO University
38 publications, 0.59%
|
|
Emanuel Institute of Biochemical Physics of the Russian Academy of Sciences
37 publications, 0.57%
|
|
Max Planck Institute for the Physics of Complex Systems
36 publications, 0.56%
|
|
A.M. Obukhov Institute of Atmospheric Physics of Russian Academy of Sciences
34 publications, 0.53%
|
|
Kazan E.K. Zavoisky Physical-Technical Institute of the Kazan Scientific Center of the Russian Academy of Sciences
33 publications, 0.51%
|
|
Reshetnev Siberian State University of Science and Technology
32 publications, 0.5%
|
|
Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences
31 publications, 0.48%
|
|
Nuclear Safety Institute of the Russian Academy of Sciences
30 publications, 0.47%
|
|
Nikolaev Institute of Inorganic Chemistry of the Siberian Branch of the Russian Academy of Sciences
29 publications, 0.45%
|
|
Institute of Solid State Chemistry of the Ural Branch of the Russian Academy of Sciences
28 publications, 0.43%
|
|
Perm State National Research University
28 publications, 0.43%
|
|
MIREA — Russian Technological University
28 publications, 0.43%
|
|
Saratov State University
28 publications, 0.43%
|
|
Voronezh State University
28 publications, 0.43%
|
|
Dostoevsky Omsk State University
28 publications, 0.43%
|
|
Institute of Theoretical and Applied Electrodynamics of Russian Academy of Sciences
27 publications, 0.42%
|
|
Vavilov State Optical Institute
27 publications, 0.42%
|
|
Institute of Microelectronics Technology and High Purity Materials of the Russian Academy of Sciences
26 publications, 0.4%
|
|
Dagestan State University
26 publications, 0.4%
|
|
Novosibirsk State Technical University
26 publications, 0.4%
|
|
Moscow Polytechnic University
26 publications, 0.4%
|
|
Southern Federal University
25 publications, 0.39%
|
|
B. I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus
25 publications, 0.39%
|
|
Kurchatov Complex of Crystallography and Photonics of NRC «Kurchatov Institute»
23 publications, 0.36%
|
|
Ogarev Mordovia State University
23 publications, 0.36%
|
|
Russian Quantum Center
21 publications, 0.33%
|
|
Institute of Strength Physics and Materials Science of the Siberian Branch of the Russian Academy of Sciences
20 publications, 0.31%
|
|
Voronezh State Technical University
20 publications, 0.31%
|
|
Northern (Arctic) Federal University
20 publications, 0.31%
|
|
Institute for Physical Research National Academy of Sciences of the Republic of Armenia
20 publications, 0.31%
|
|
Moscow Power Engineering Institute
19 publications, 0.29%
|
|
Chalmers University of Technology
19 publications, 0.29%
|
|
Chelyabinsk State University
18 publications, 0.28%
|
|
18 publications, 0.28%
|
|
National Institute for Nuclear Physics
18 publications, 0.28%
|
|
Institut Laue-Langevin
18 publications, 0.28%
|
|
Kazan Scientific Center of the Russian Academy of Sciences
17 publications, 0.26%
|
|
Yerevan State University
17 publications, 0.26%
|
|
Yu.G. Shafer Institute of Cosmophysical Research and Aeronomy of the Siberian Branch of the Russian Academy of Sciences
16 publications, 0.25%
|
|
Institute of Continuous Media Mechanics of the Ural Branch of the Russian Academy of Sciences
16 publications, 0.25%
|
|
Samara National Research University
16 publications, 0.25%
|
|
National Research Tomsk Polytechnic University
16 publications, 0.25%
|
|
Belgorod State University
16 publications, 0.25%
|
|
Aalto University
16 publications, 0.25%
|
|
Bauman Moscow State Technical University
15 publications, 0.23%
|
|
![]() Boreskov Institute of Catalysis of the Siberian Branch of the Russian Academy of Sciences
15 publications, 0.23%
|
|
Pushkov Institute of Terrestrial Magnetism, Ionosphere and Radiowave Propagation of Russian Academy of Sciences
15 publications, 0.23%
|
|
Physical-Technical Institute UFRC of the Ural Branch of the Russian Academy of Sciences
15 publications, 0.23%
|
|
Institute of Physics and Technology of NRC «Kurchatov Institute»
15 publications, 0.23%
|
|
Dukhov Research Institute of Automatics
15 publications, 0.23%
|
|
P.G. Demidov Yaroslavl State University
15 publications, 0.23%
|
|
Show all (70 more) | |
100
200
300
400
500
600
|
Publishing organizations in 5 years
10
20
30
40
50
60
70
80
90
100
|
|
Lomonosov Moscow State University
96 publications, 12.55%
|
|
Moscow Institute of Physics and Technology
62 publications, 8.1%
|
|
P.N. Lebedev Physical Institute of the Russian Academy of Sciences
50 publications, 6.54%
|
|
National Research Centre "Kurchatov Institute"
41 publications, 5.36%
|
|
Joint Institute for High Temperatures of the Russian Academy of Sciences
40 publications, 5.23%
|
|
Landau Institute for Theoretical Physics of Russian Academy of Sciences
38 publications, 4.97%
|
|
Kirensky Institute of Physics of the Siberian Branch of the Russian Academy of Sciences
32 publications, 4.18%
|
|
M.N. Mikheev Institute of Metal Physics of the Ural Branch of the Russian Academy of Sciences
32 publications, 4.18%
|
|
P.L. Kapitza Institute for Physical Problems of Russian Academy of Sciences
30 publications, 3.92%
|
|
Ioffe Physical-Technical Institute of the Russian Academy of Sciences
30 publications, 3.92%
|
|
Saint Petersburg State University
28 publications, 3.66%
|
|
National Research University Higher School of Economics
27 publications, 3.53%
|
|
Osipyan Institute of Solid State Physics of the Russian Academy of Sciences
27 publications, 3.53%
|
|
Ural Federal University
25 publications, 3.27%
|
|
Novosibirsk State University
24 publications, 3.14%
|
|
Lobachevsky State University of Nizhny Novgorod
23 publications, 3.01%
|
|
Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences
19 publications, 2.48%
|
|
Institute for Nuclear Research of the Russian Academy of Sciences
19 publications, 2.48%
|
|
National Research Nuclear University MEPhI
18 publications, 2.35%
|
|
All-Russian Research Institute of Experimental Physics
18 publications, 2.35%
|
|
Federal Research Center "Krasnoyarsk Science Center" of the Siberian Branch of the Russian Academy of Sciences
18 publications, 2.35%
|
|
Institute of Semiconductor Physics of the Siberian Branch of the Russian Academy of Sciences
17 publications, 2.22%
|
|
Peter the Great St. Petersburg Polytechnic University
17 publications, 2.22%
|
|
National University of Science & Technology (MISiS)
15 publications, 1.96%
|
|
Federal Research Center of Problem of Chemical Physics and Medicinal Chemistry RAS
15 publications, 1.96%
|
|
Institute of Spectroscopy of the Russian Academy of Sciences
13 publications, 1.7%
|
|
Siberian Federal University
13 publications, 1.7%
|
|
Joint Institute for Nuclear Research
13 publications, 1.7%
|
|
Petersburg Nuclear Physics Institute of NRC «Kurchatov Institute»
12 publications, 1.57%
|
|
Russian Quantum Center
12 publications, 1.57%
|
|
Dagestan Scientific Center of the Russian Academy of Sciences
12 publications, 1.57%
|
|
N.N. Semenov Federal Research Center for Chemical Physics of the Russian Academy of Sciences
11 publications, 1.44%
|
|
Kurchatov Complex of Crystallography and Photonics of NRC «Kurchatov Institute»
11 publications, 1.44%
|
|
Institute for High Pressure Physics of Russian Academy of Sciences
11 publications, 1.44%
|
|
Institute of Laser Physics of the Siberian Branch of the Russian Academy of Sciences
11 publications, 1.44%
|
|
Institute for Physics of Microstructures of the Russian Academy of Sciences
11 publications, 1.44%
|
|
Amirkhanov Institute of Physics DSC RAS
11 publications, 1.44%
|
|
Space Research Institute of the Russian Academy of Sciences
10 publications, 1.31%
|
|
Tomsk State University
10 publications, 1.31%
|
|
Troitsk Institute for Innovation and Fusion Research
9 publications, 1.18%
|
|
Prokhorov General Physics Institute of the Russian Academy of Sciences
8 publications, 1.05%
|
|
Shubnikov Institute of Crystallography
8 publications, 1.05%
|
|
Novosibirsk State Technical University
8 publications, 1.05%
|
|
Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences
8 publications, 1.05%
|
|
Steklov Mathematical Institute of Russian Academy of Sciences
8 publications, 1.05%
|
|
Skolkovo Institute of Science and Technology
7 publications, 0.92%
|
|
A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences
7 publications, 0.92%
|
|
Nuclear Safety Institute of the Russian Academy of Sciences
7 publications, 0.92%
|
|
Saratov State University
7 publications, 0.92%
|
|
Nizhny Novgorod State Technical University n.a. R.E. Alekseev
7 publications, 0.92%
|
|
Institute of Solid State Chemistry of the Ural Branch of the Russian Academy of Sciences
6 publications, 0.78%
|
|
Institute of Automation and Electrometry of the Siberian Branch of the Russian Academy of Sciences
6 publications, 0.78%
|
|
Voronezh State Technical University
6 publications, 0.78%
|
|
Dukhov Research Institute of Automatics
6 publications, 0.78%
|
|
Emanuel Institute of Biochemical Physics of the Russian Academy of Sciences
5 publications, 0.65%
|
|
Kazan Scientific Center of the Russian Academy of Sciences
5 publications, 0.65%
|
|
Institute of Strength Physics and Materials Science of the Siberian Branch of the Russian Academy of Sciences
5 publications, 0.65%
|
|
ITMO University
5 publications, 0.65%
|
|
St Petersburg National Research Academic University of the Russian Academy of Sciences
5 publications, 0.65%
|
|
Kazan State Power Engineering University
5 publications, 0.65%
|
|
Kazan E.K. Zavoisky Physical-Technical Institute of the Kazan Scientific Center of the Russian Academy of Sciences
5 publications, 0.65%
|
|
A.M. Obukhov Institute of Atmospheric Physics of Russian Academy of Sciences
5 publications, 0.65%
|
|
Yerevan State University
5 publications, 0.65%
|
|
Institute for Physical Research National Academy of Sciences of the Republic of Armenia
5 publications, 0.65%
|
|
Institute of Theoretical and Applied Electrodynamics of Russian Academy of Sciences
4 publications, 0.52%
|
|
Kazan Federal University
4 publications, 0.52%
|
|
Saint Petersburg Electrotechnical University "LETI"
4 publications, 0.52%
|
|
Polzunov Altai State Technical University
4 publications, 0.52%
|
|
Ton Duc Thang University
4 publications, 0.52%
|
|
University of Lorraine
4 publications, 0.52%
|
|
École Polytechnique Fédérale de Lausanne
4 publications, 0.52%
|
|
Grenoble Alpes University
4 publications, 0.52%
|
|
Aalto University
4 publications, 0.52%
|
|
National Institute for Materials Science
4 publications, 0.52%
|
|
Yu.G. Shafer Institute of Cosmophysical Research and Aeronomy of the Siberian Branch of the Russian Academy of Sciences
3 publications, 0.39%
|
|
Institute of Electrophysics of the Ural Branch of the Russian Academy of Sciences
3 publications, 0.39%
|
|
Institute of Applied Mechanics of the Russian Academy of Sciences
3 publications, 0.39%
|
|
Institute for Information Transmission Problems of the Russian Academy of Sciences
3 publications, 0.39%
|
|
Perm State National Research University
3 publications, 0.39%
|
|
National Research Tomsk Polytechnic University
3 publications, 0.39%
|
|
Sechenov First Moscow State Medical University
3 publications, 0.39%
|
|
Dubna State University
3 publications, 0.39%
|
|
Alikhanov Institute for Theoretical and Experimental Physics of NRC «Kurchatov Institute»
3 publications, 0.39%
|
|
Vladimir State University
3 publications, 0.39%
|
|
Herzen State Pedagogical University of Russia
3 publications, 0.39%
|
|
Irkutsk State University
3 publications, 0.39%
|
|
V.I. Vernadsky Crimean Federal University
3 publications, 0.39%
|
|
V.S. Sobolev Institute of Geology and Mineralogy of the Siberian Branch of the Russian Academy of Sciences
3 publications, 0.39%
|
|
Saint Petersburg State Marine Technical University
3 publications, 0.39%
|
|
Donetsk State University
3 publications, 0.39%
|
|
Rostov State Transport University
3 publications, 0.39%
|
|
Central Scientific Research Institute of Machine Building Technology
3 publications, 0.39%
|
|
Voronezh State Pedagogical University
3 publications, 0.39%
|
|
Ufa University of Science and Technology
3 publications, 0.39%
|
|
University of the Punjab
3 publications, 0.39%
|
|
Radboud University Nijmegen
3 publications, 0.39%
|
|
ETH Zurich
3 publications, 0.39%
|
|
Max Planck Institute for the Physics of Complex Systems
3 publications, 0.39%
|
|
University of Minnesota
3 publications, 0.39%
|
|
A.N. Frumkin Institute of Physical Chemistry and Electrochemistry of the Russian Academy of Sciences
2 publications, 0.26%
|
|
Show all (70 more) | |
10
20
30
40
50
60
70
80
90
100
|
Publishing countries
1000
2000
3000
4000
5000
6000
|
|
Russia
|
Russia, 5410, 83.9%
Russia
5410 publications, 83.9%
|
Ukraine
|
Ukraine, 377, 5.85%
Ukraine
377 publications, 5.85%
|
Germany
|
Germany, 291, 4.51%
Germany
291 publications, 4.51%
|
USA
|
USA, 190, 2.95%
USA
190 publications, 2.95%
|
France
|
France, 186, 2.88%
France
186 publications, 2.88%
|
Belarus
|
Belarus, 96, 1.49%
Belarus
96 publications, 1.49%
|
Poland
|
Poland, 85, 1.32%
Poland
85 publications, 1.32%
|
Japan
|
Japan, 73, 1.13%
Japan
73 publications, 1.13%
|
United Kingdom
|
United Kingdom, 61, 0.95%
United Kingdom
61 publications, 0.95%
|
China
|
China, 55, 0.85%
China
55 publications, 0.85%
|
Italy
|
Italy, 52, 0.81%
Italy
52 publications, 0.81%
|
Sweden
|
Sweden, 51, 0.79%
Sweden
51 publications, 0.79%
|
Armenia
|
Armenia, 45, 0.7%
Armenia
45 publications, 0.7%
|
India
|
India, 38, 0.59%
India
38 publications, 0.59%
|
Netherlands
|
Netherlands, 37, 0.57%
Netherlands
37 publications, 0.57%
|
Switzerland
|
Switzerland, 37, 0.57%
Switzerland
37 publications, 0.57%
|
Moldova
|
Moldova, 34, 0.53%
Moldova
34 publications, 0.53%
|
Israel
|
Israel, 29, 0.45%
Israel
29 publications, 0.45%
|
Finland
|
Finland, 29, 0.45%
Finland
29 publications, 0.45%
|
Pakistan
|
Pakistan, 26, 0.4%
Pakistan
26 publications, 0.4%
|
Iran
|
Iran, 24, 0.37%
Iran
24 publications, 0.37%
|
Spain
|
Spain, 23, 0.36%
Spain
23 publications, 0.36%
|
Republic of Korea
|
Republic of Korea, 23, 0.36%
Republic of Korea
23 publications, 0.36%
|
Belgium
|
Belgium, 21, 0.33%
Belgium
21 publications, 0.33%
|
Vietnam
|
Vietnam, 18, 0.28%
Vietnam
18 publications, 0.28%
|
Georgia
|
Georgia, 17, 0.26%
Georgia
17 publications, 0.26%
|
Czech Republic
|
Czech Republic, 17, 0.26%
Czech Republic
17 publications, 0.26%
|
Canada
|
Canada, 16, 0.25%
Canada
16 publications, 0.25%
|
Slovakia
|
Slovakia, 15, 0.23%
Slovakia
15 publications, 0.23%
|
Greece
|
Greece, 14, 0.22%
Greece
14 publications, 0.22%
|
Denmark
|
Denmark, 14, 0.22%
Denmark
14 publications, 0.22%
|
Egypt
|
Egypt, 13, 0.2%
Egypt
13 publications, 0.2%
|
Mexico
|
Mexico, 13, 0.2%
Mexico
13 publications, 0.2%
|
Uzbekistan
|
Uzbekistan, 13, 0.2%
Uzbekistan
13 publications, 0.2%
|
Kazakhstan
|
Kazakhstan, 12, 0.19%
Kazakhstan
12 publications, 0.19%
|
Brazil
|
Brazil, 12, 0.19%
Brazil
12 publications, 0.19%
|
Turkey
|
Turkey, 12, 0.19%
Turkey
12 publications, 0.19%
|
Austria
|
Austria, 10, 0.16%
Austria
10 publications, 0.16%
|
Norway
|
Norway, 10, 0.16%
Norway
10 publications, 0.16%
|
Portugal
|
Portugal, 9, 0.14%
Portugal
9 publications, 0.14%
|
Australia
|
Australia, 8, 0.12%
Australia
8 publications, 0.12%
|
Morocco
|
Morocco, 7, 0.11%
Morocco
7 publications, 0.11%
|
Serbia
|
Serbia, 7, 0.11%
Serbia
7 publications, 0.11%
|
Bulgaria
|
Bulgaria, 6, 0.09%
Bulgaria
6 publications, 0.09%
|
Saudi Arabia
|
Saudi Arabia, 6, 0.09%
Saudi Arabia
6 publications, 0.09%
|
Slovenia
|
Slovenia, 6, 0.09%
Slovenia
6 publications, 0.09%
|
South Africa
|
South Africa, 5, 0.08%
South Africa
5 publications, 0.08%
|
Azerbaijan
|
Azerbaijan, 4, 0.06%
Azerbaijan
4 publications, 0.06%
|
Hungary
|
Hungary, 4, 0.06%
Hungary
4 publications, 0.06%
|
Singapore
|
Singapore, 4, 0.06%
Singapore
4 publications, 0.06%
|
Croatia
|
Croatia, 4, 0.06%
Croatia
4 publications, 0.06%
|
Algeria
|
Algeria, 3, 0.05%
Algeria
3 publications, 0.05%
|
Ireland
|
Ireland, 3, 0.05%
Ireland
3 publications, 0.05%
|
North Macedonia
|
North Macedonia, 3, 0.05%
North Macedonia
3 publications, 0.05%
|
Tajikistan
|
Tajikistan, 3, 0.05%
Tajikistan
3 publications, 0.05%
|
Tunisia
|
Tunisia, 3, 0.05%
Tunisia
3 publications, 0.05%
|
Estonia
|
Estonia, 2, 0.03%
Estonia
2 publications, 0.03%
|
Cyprus
|
Cyprus, 2, 0.03%
Cyprus
2 publications, 0.03%
|
Latvia
|
Latvia, 2, 0.03%
Latvia
2 publications, 0.03%
|
Palestine
|
Palestine, 2, 0.03%
Palestine
2 publications, 0.03%
|
Philippines
|
Philippines, 2, 0.03%
Philippines
2 publications, 0.03%
|
Chile
|
Chile, 2, 0.03%
Chile
2 publications, 0.03%
|
Argentina
|
Argentina, 1, 0.02%
Argentina
1 publication, 0.02%
|
Ghana
|
Ghana, 1, 0.02%
Ghana
1 publication, 0.02%
|
Iraq
|
Iraq, 1, 0.02%
Iraq
1 publication, 0.02%
|
Iceland
|
Iceland, 1, 0.02%
Iceland
1 publication, 0.02%
|
Cameroon
|
Cameroon, 1, 0.02%
Cameroon
1 publication, 0.02%
|
New Zealand
|
New Zealand, 1, 0.02%
New Zealand
1 publication, 0.02%
|
UAE
|
UAE, 1, 0.02%
UAE
1 publication, 0.02%
|
Romania
|
Romania, 1, 0.02%
Romania
1 publication, 0.02%
|
Thailand
|
Thailand, 1, 0.02%
Thailand
1 publication, 0.02%
|
Turkmenistan
|
Turkmenistan, 1, 0.02%
Turkmenistan
1 publication, 0.02%
|
Show all (42 more) | |
1000
2000
3000
4000
5000
6000
|
Publishing countries in 5 years
100
200
300
400
500
600
700
|
|
Russia
|
Russia, 643, 84.05%
Russia
643 publications, 84.05%
|
France
|
France, 27, 3.53%
France
27 publications, 3.53%
|
Ukraine
|
Ukraine, 17, 2.22%
Ukraine
17 publications, 2.22%
|
Germany
|
Germany, 16, 2.09%
Germany
16 publications, 2.09%
|
USA
|
USA, 16, 2.09%
USA
16 publications, 2.09%
|
Armenia
|
Armenia, 10, 1.31%
Armenia
10 publications, 1.31%
|
Switzerland
|
Switzerland, 9, 1.18%
Switzerland
9 publications, 1.18%
|
China
|
China, 8, 1.05%
China
8 publications, 1.05%
|
Vietnam
|
Vietnam, 7, 0.92%
Vietnam
7 publications, 0.92%
|
India
|
India, 7, 0.92%
India
7 publications, 0.92%
|
Finland
|
Finland, 7, 0.92%
Finland
7 publications, 0.92%
|
Japan
|
Japan, 7, 0.92%
Japan
7 publications, 0.92%
|
Brazil
|
Brazil, 6, 0.78%
Brazil
6 publications, 0.78%
|
Italy
|
Italy, 6, 0.78%
Italy
6 publications, 0.78%
|
Netherlands
|
Netherlands, 5, 0.65%
Netherlands
5 publications, 0.65%
|
Poland
|
Poland, 5, 0.65%
Poland
5 publications, 0.65%
|
Kazakhstan
|
Kazakhstan, 4, 0.52%
Kazakhstan
4 publications, 0.52%
|
Belarus
|
Belarus, 4, 0.52%
Belarus
4 publications, 0.52%
|
United Kingdom
|
United Kingdom, 4, 0.52%
United Kingdom
4 publications, 0.52%
|
Pakistan
|
Pakistan, 4, 0.52%
Pakistan
4 publications, 0.52%
|
Serbia
|
Serbia, 4, 0.52%
Serbia
4 publications, 0.52%
|
Czech Republic
|
Czech Republic, 4, 0.52%
Czech Republic
4 publications, 0.52%
|
Egypt
|
Egypt, 3, 0.39%
Egypt
3 publications, 0.39%
|
Israel
|
Israel, 3, 0.39%
Israel
3 publications, 0.39%
|
Iran
|
Iran, 3, 0.39%
Iran
3 publications, 0.39%
|
Spain
|
Spain, 3, 0.39%
Spain
3 publications, 0.39%
|
Moldova
|
Moldova, 3, 0.39%
Moldova
3 publications, 0.39%
|
Republic of Korea
|
Republic of Korea, 3, 0.39%
Republic of Korea
3 publications, 0.39%
|
Belgium
|
Belgium, 2, 0.26%
Belgium
2 publications, 0.26%
|
Canada
|
Canada, 2, 0.26%
Canada
2 publications, 0.26%
|
Morocco
|
Morocco, 2, 0.26%
Morocco
2 publications, 0.26%
|
Norway
|
Norway, 2, 0.26%
Norway
2 publications, 0.26%
|
Slovakia
|
Slovakia, 2, 0.26%
Slovakia
2 publications, 0.26%
|
Philippines
|
Philippines, 2, 0.26%
Philippines
2 publications, 0.26%
|
Estonia
|
Estonia, 1, 0.13%
Estonia
1 publication, 0.13%
|
Azerbaijan
|
Azerbaijan, 1, 0.13%
Azerbaijan
1 publication, 0.13%
|
Algeria
|
Algeria, 1, 0.13%
Algeria
1 publication, 0.13%
|
Ghana
|
Ghana, 1, 0.13%
Ghana
1 publication, 0.13%
|
Cameroon
|
Cameroon, 1, 0.13%
Cameroon
1 publication, 0.13%
|
Mexico
|
Mexico, 1, 0.13%
Mexico
1 publication, 0.13%
|
UAE
|
UAE, 1, 0.13%
UAE
1 publication, 0.13%
|
Thailand
|
Thailand, 1, 0.13%
Thailand
1 publication, 0.13%
|
Turkmenistan
|
Turkmenistan, 1, 0.13%
Turkmenistan
1 publication, 0.13%
|
Turkey
|
Turkey, 1, 0.13%
Turkey
1 publication, 0.13%
|
Uzbekistan
|
Uzbekistan, 1, 0.13%
Uzbekistan
1 publication, 0.13%
|
Sweden
|
Sweden, 1, 0.13%
Sweden
1 publication, 0.13%
|
Show all (16 more) | |
100
200
300
400
500
600
700
|
35 profile journal articles
Filippov Anatoly
🥼 🤝
DSc in Physics and Mathematics
103 publications,
773 citations
h-index: 16
Research interests
Dust plasma
Gas discharge
Imperfect plasma
Plasma Physics
14 profile journal articles
Sobolev Alexey
PhD in Chemistry, Associate Professor

Lomonosov Moscow State University
149 publications,
1 483 citations
h-index: 19
Research interests
Data analysis
Inorganic Chemistry
Magnetism
Mossbauer spectroscopy
Spin waves
Synchrotron radiation
13 profile journal articles
Alshits Vladimir
DSc in Physics and Mathematics, Professor

Shubnikov Institute of Crystallography

Kurchatov Complex of Crystallography and Photonics of NRC «Kurchatov Institute»
135 publications,
1 631 citations
h-index: 20
13 profile journal articles
Prudnikov P
DSc in Physics and Mathematics, Professor

Dostoevsky Omsk State University
121 publications,
704 citations
h-index: 15
11 profile journal articles
Chulkov Evgeny
DSc

Tomsk State University

Saint Petersburg State University
642 publications,
23 078 citations
h-index: 75
11 profile journal articles
Lundin Andrey

N.N. Semenov Federal Research Center for Chemical Physics of the Russian Academy of Sciences
34 publications,
178 citations
h-index: 8
10 profile journal articles
Magomedov Magomed
46 publications,
290 citations
h-index: 10
8 profile journal articles
Stegailov Vladimir
🥼
DSc in Physics and Mathematics

Moscow Institute of Physics and Technology

Joint Institute for High Temperatures of the Russian Academy of Sciences
158 publications,
2 439 citations
h-index: 27
8 profile journal articles
Shikin A
DSc

Saint Petersburg State University
181 publications,
5 383 citations
h-index: 34
8 profile journal articles
Ogloblichev Vasiliy
PhD in Physics and Mathematics

M.N. Mikheev Institute of Metal Physics of the Ural Branch of the Russian Academy of Sciences
55 publications,
410 citations
h-index: 11
8 profile journal articles
Zelener Boris
🤝
DSc in Physics and Mathematics

National Research Nuclear University MEPhI

Joint Institute for High Temperatures of the Russian Academy of Sciences
96 publications,
412 citations
h-index: 11
7 profile journal articles
Obraztsov Alexander

Lomonosov Moscow State University
216 publications,
3 477 citations
h-index: 31