In the framework of the joint project of LIT and LRB JINR, aimed to the creation of an information system for the tasks of radiation biology, a module is being developed to study the behavioral patterns of small laboratory animals exposed to radiation. The module for behavioral analysis automates the analysis of video data obtained by testing of the laboratory animals in the different test...
Hyperspectral images are a unique source for obtaining many kinds of information about the Earth's surface. Modern platforms support users to perform complex analyses with a collection of images without the use of any specialized software. Google Earth Engine (GEE) is a planetary-scale platform for Earth science data & analysis. Atmospheric, radiometric, and geometric corrections have been...
Cloud cover is the main physical factor limiting the downward shortwave (SW) solar radiation flux. In modern models of climate and weather forecasts, physical models describing radiative transfer through clouds may be used. However, this is a computationally expensive option. Instead, one may use parameterizations which are simplified schemes for approximating environmental variables. The...
Cloudiness plays an important role in the hydrological cycle of the atmosphere. Cloud types and other cloud spatial and temporal characteristics privide the ability to make short-term in situ weather forecasts. With the help of clouds, one may also track the content of various impurities in the air. Most importantly, clouds are the major obstacle on the pathway of incoming solar radiation,...
Surface wind is one of the most important fields in climate change research. Accurate prediction of high-resolution surface wind has a wide variety of applications, such as renewable energy and extreme weather forecasts. Downscaling is a methodology for high-resolution approximation of physical variables from low-resolution modeling outputs. Statistical downscaling methods allow to avoid...
The task of analysing the inhabitants of the underwater world is applicable to a wide range of applied problems: construction, fishing, and mining. Currently, this task is applied on an industrial scale by a rigorous review done by human experts in the field of underwater life. In this work, we present a tool that we have created that allows us to significantly reduce the time spent by a...
Recently, the haze removal methods have taken increasing attention of researchers. An objective comparison of haze removal methods struggles because of the lack of real data. Capturing pairs of images of the same scene with presence/absence of haze in real environment is a very complicated task. Therefore, the most of modern haze datasets contain artificial images, generated by some model of...
This study is devoted to the inverse problems of exploration geophysics, which consist in reconstructing the spatial distribution of the properties of the medium in the Earth's thickness from the geophysical fields measured on its surface. We consider the methods of gravimetry, magnetometry, and magnetotelluric sounding, as well as their integration, i.e. simultaneous use of data from several...
В данной работе предлагается рассмотреть метод предсказания матрицы контактов для пептидов. В данной статье были выбраны пептиды с длинной до 45 аминокислотных остатков для упрощения расчётов. Для предсказания использовались свёрточные нейронные сети (CNN) из-за схожести пространства признаков белков и изображений, к котором обычно успешно применяются свёрточные нейронные сети. Признаки были...
Quantitative, granulometric and classification-based distribution of oceanic sediment grains are important indicators in paleo-reconstruction of the characteristics of marine waters. Currently, the classification of grains is performed visually by an expert on a limited subset of a sediment sample using a binocular microscope. It is a highly time-consuming process in which geological expertise...
Currently, there are more than two years of statistics accumulated on COVID-19 for a large number of regions, which allows the use of algorithms that require large training sets, such as neural networks, to predict the dynamics of the disease.
The article provides a comparative analysis of various COVID-19 models based on forecasting for the period from 07/20/2020 to 05/05/2022 using...
In neural network solutions to many physical problems, there is a need to reduce the dimension of the input data in order to achieve a more accurate and stable solution while reducing computational complexity.
When solving an inverse problem in spectroscopy, multicollinearity is often observed between the input features, making it necessary to use a selection method that takes into account...
The weather forecast has a significant impact on a variety of human industries. In particular, knowledge of the short-term wind speed conditions is essential for fishery, energy management, surfing and others. One of the most effective neural network models for time series forecasting is LSTM (Long short-term memory), however, the accuracy of its forecast decreases significantly with...
