搜索结果：Biokhimiia (Moscow, Russia)

The Caucasus Mountain Observatory of the Sternberg Astronomical Institute of Moscow State University is the only one in Russia and one of the few in the world where is possible to obtain spectral data in the near-infrared (IR) range at $λ$=1-2.5 $μ$m. However, there is a problem of processing the spectra of extended objects, the angular dimensions of which exceed the length of the slit (4.5 arcmin). Obtaining additional spectra of the sky in the immediate vicinity of such objects does not solve the problem, since bright atmospheric hydroxyl lines at $λ$~2 $μ$m change their intensity significantly over a time shorter than the exposure time of a single frame. We have developed a technique that allows us to correctly account for and exclude the contribution of variable atmospheric lines in the spectra of extended objects. This technique has been successfully tested in spectroscopic studies of the star-forming region NGC 7538 (S158) in our Galaxy.

This paper examines how the Joint Institute for Nuclear Research (JINR), an international organization formally committed to peaceful science, is deeply embedded in an ecosystem of military-industrial enterprises in the city of Dubna in Russia, contributing to training specialists and developing technologies used in Russia's military operations, including attacks on civilian facilities in Ukraine. It also shows how JINR collaborates with scientific institutions on the Ukrainian territories occupied by Russia, legitimizing the occupation and exposing international partners to legal and ethical risks. Despite these ties, JINR maintains broad international collaborations, allowing its scientists and engineers to access advanced technologies and indirectly support Russia's military capabilities, highlighting the need for greater awareness in the global scientific community and coordinated sanctions enforcement.

This study examines how Russian far-right communities on Telegram shape perceptions of political figures through memes and visual narratives. Far from passive spectators, these actors co-produce propaganda, blending state-aligned messages with their own extremist framings. In Russia, such groups are central because they articulate the ideological foundations of the war against Ukraine and reflect the regime's gradual drift toward ultranationalist rhetoric. Drawing on a dataset of 200,000 images from expert-selected far-right Telegram channels, the study employs computer vision and unsupervised clustering to identify memes featuring Russian (Putin, Shoigu) and foreign politicians (Zelensky, Biden, Trump) and to reveal recurrent visual patterns in their representation. By leveraging the large-scale and temporal depth of this dataset, the analysis uncovers differential patterns of legitimation and delegitimation across actors and over time. These insights are not attainable in smaller-scale studies. Preliminary findings show that far-right memes function as instruments of propaganda co-production. These communities do not simply echo official messages but generate bottom-up narratives

This paper shares some experience in advanced mathematical education. We show how a high school student can be naturally and gradually introduced to basic steps of scientific research: developing intuition by finding and correcting mistakes through discussions and writing a paper, (transparent) anonymous peer review, recognition and award. We show that most of this can be done in research projects not aiming at scientific novelty. We share the experience (both principles and examples) of the Moscow Mathematical Conference of High School Students.

The present study aimed to improve upon the existing correlational literature on the parenthood penalty in Russia. An instrumental variables approach based on sibling sex composition and multiple births was employed alongside difference-in-differences designs to analyze rich census and longitudinal datasets. To the best of the authors' knowledge, this is the first study to provide causal estimates of the effect of fertility decisions on subsequent labor market outcomes for mothers and fathers in contemporary Russia. The study's primary finding is that, in contrast to the approximately 10 percent long-term motherhood penalty observed in developed countries, the causal impact of childbearing on women's employment in Russia is most significant in the first year after birth, reducing employment by around 15 percent. This penalty then rapidly declines to a modest 3 percent once children reach school age. The analysis indicates an absence of a systematic fatherhood penalty in terms of employment, although a modest increase in labor supply is observed.

The Russia-Ukraine conflict is a growing concern worldwide and poses serious threats to regional and global food security. Using monthly trade data for maize, rice, and wheat from 2016/1 to 2022/12, this paper constructs three international crop trade networks (iCTNs) and an aggregate international food trade network (iFTN). We aim to examine the structural changes following the occurrence of the Russia-Ukraine conflict. We find significant shifts in the number of edges, average degree, density, efficiency, and natural connectivity in the third quarter of 2022, particularly in the international wheat trade network. Additionally, we have shown that political reasons have caused more pronounced changes in the trade connections between the economies of the North Atlantic Treaty Organization and Russia than with Ukraine. This paper could provide insights into the negative impact of geopolitical conflicts on the global food system and encourage a series of effective strategies to mitigate the negative impact of the conflict on global food trade.

The main factors that influence the success of observations in the infrared range (central wavelengths of the photometric bands at 3.75 and 4.8~$μ$m) on the multipurpose optical telescope are considered. Estimates of the sky background brightness are obtained for the Caucasus Mountain Observatory (CMO) of Moscow State University: $1.3\cdot10^6$~photons/(s pixel) in the 3.75~$μ$m band and $3.4\cdot10^6$~photons/(s pixel) in the 4.8~$μ$m; and the instrumental background for the 2.5-m CMO telescope at $0^\circ$C: $3.2\cdot10^6$~photons/(s pixel) in the 3.75~$μ$m band and $4.3\cdot10^6$~photons/(s pixel) in the 4.8~$μ$m band. It is shown that at this background signal level with the currently available commercial cameras in the $3-5$~$μ$m spectral range, the telescope-camera coupling capabilities for observing faint objects will still be limited by the thermal background. For different observational conditions, estimates of the limiting magnitudes of objects available for observations in the 3.75 and 4.8~$μ$m ranges are obtained. For average observation conditions (instrument temperature of $0^\circ$C and stellar image size of 1 arcsec), the limit is $\sim10.6^m$ and $\sim8.4^m$, respe

This note highlights how Russia uses the international academic sphere-including scientometric databases, international publishers, and international organizations-as a propaganda tool to legitimize its appropriation of Ukrainian territories.

Inward Foreign Direct Investment (IFDI) into Europe and Asian developing countries like Bangladesh is experimentally examined in this study. IFDI in emerging markets has been boosted by global investment and inflow influenced by resource availability and public policy. The economic policy uncertainty on IFDI in 13 countries is explored at a time when the crisis between Russia and Ukraine war is having a global impact. Microeconomic factors affected Gross Domestic Product (GDP) growth, inflation, interest rates, and the currency rate fluctuated with IFDI, which mostly shocked during COVID-19 and the Russia-Ukraine war. With data from the World Bank and the United Nations Conference on Trade and Development (UNCTAD) database, we compile a panel dataset covering 2018-2022. The researchers used a mixture of panel and linear regression analysis using a random effect model. Our findings show that the impact of global rates hurts IFDI in 13 selected countries. There is a correlation between a country's ability to enforce contracts and the amount of Inward FDI it receives. Using the top 13 hosts of incoming FDI flows COVID-19 and Russia-Ukraine wartime series analysis gives valuable inform

This paper analyzes sustainable regional economic development and land use employing a case study of Russia. The economics of land management in Russia which is shaped by both historical legacies and contemporary policies represents an interesting conundrum. Following the dissolution of the Soviet Union, Russia embarked on a thorny and complex path towards the economic reforms and transformation characterized, among all, by the privatization and decentralization of land ownership. This transition was aimed at improving agricultural productivity and fostering sustainable regional economic development but also led to new challenges such as uneven distribution of land resources, unclear property rights, and underinvestment in rural infrastructure. However, managing all of that effectively poses significant challenges and opportunities. With the help of the comprehensive bibliographic network analysis, this study sheds some light on the current state of sustainable regional economic development and land use management in Russia. Its results and outcomes might be helpful for the researchers and stakeholders alike in devising effective strategies aimed at maximizing resources for sustain

In countries with a growing number of elderly and a shrinking workforce, one of which is Russia, it becomes impossible to maintain a solidary pension system and a need to switch to a more stable funded system appears. This paper analyzes various scenarios of Russia's transition to such a system. This is the first study on the Russian economy in which an Overlapping Generations Model is used to simulate the pension transition. It is demonstrated that in the long term, the transition to a funded system slightly reduces the welfare of pensioners, and during the transition, the situation of pensioners deteriorates strongly. However, it is also important to emphasize that the transition imposes a heavy burden on all generations living during the reform, they are forced to consume less and greatly change their savings, while also often starting to work more. Such conclusions are made concerning average population cohorts, and the results may not be the same for different groups of individuals within these cohorts. In different scenarios, the pension system transition can cause both economic growth and economic recession, as well as a corresponding increase or decrease in wages and consum

On the 21st of February 2022, Russia recognised the Donetsk People's Republic and the Luhansk People's Republic, three days before launching an invasion of Ukraine. Since then, an active debate has taken place on social media, mixing organic discussions with coordinated information campaigns. The scale of this discourse, alongside the role that information warfare has played in the invasion, make it vital to better understand this ecosystem. We therefore present a study of pro-Ukrainian vs. pro-Russian discourse through the lens of Twitter. We do so from two perspectives: (i) the content that is shared; and (ii) the users who participate in the sharing. We first explore the scale and nature of conversations, including analysis of hashtags, toxicity and media sharing. We then study the users who drive this, highlighting a significant presence of new users and bots.

Bronze cuboctahedral weights dated to the VIII-X centuries were found in northwest Russia near Ladoga, one of the most important trading centers in Eastern Europe in the VIII-X centuries. The history of the mathematical study of cuboctahedron and more generally of the entire family of Archimedean solids in the Middle East and Europe supports the archeological hypothesis about the origin of these artifacts and trading contacts between Europe and the Islamic Caliphate at that time when European mathematicians were not aware of such polyhedra, but Arab-Persian scientists and craftsmen were.

This paper presents an ongoing analyze of the Active Citizen e-voting system proposed by the Moscow city hall. This research points out that the main objective of the platform is not to enhance the democratic power of the Muscovites, but to strengthen the position of Moscow as a modern city at a world scale and the position of the city hall in the Russian political system.

A direct method for calculating default rates by industry and target corporate segments is not possible given the lack of statistical data. The proposed paper considers a model for filtering the dynamics of the probability of default of corporate companies and other borrowers based on indirect data on the dynamics of overdue debt supplied by the Bank of Russia. The model is based on the equation of the balance of total and overdue debts, the missing links of the corresponding time series are built using the Hodrick_Prescott filtering method. In retail lending segments (mortgage, consumer lending), default statistics are available and supplied by Credit Bureaus. The presented method is validated on this statistic. Over a historical limited period, validation has shown that the result is trustworthy. The resulting default probability series are exogenous variables for macro_economic modelling of sectoral credit risks.

Following the oil-price surge in the wake of Russia's invasion of Ukraine, many countries in the EU are cutting taxes on petrol and diesel. Using standard theory and empirical estimates, we assess how such tax cuts influence the oil income in Russia. We find that a tax cut of 20 euro cents per liter increase Russia's oil profits by around 11 million Euros per day in the short run and long run. This is equivalent to 4100 million Euros in a year, 0.3% of Russia's GDP or 7% of its military spending. We show that a cash transfer to EU citizens, with an equivalent fiscal burden as the tax cut, reduces these side effects to a fraction.

On February 24, 2022, Russia invaded Ukraine. In the days that followed, reports kept flooding in from layman to news anchors of a conflict quickly escalating into war. Russia faced immediate backlash and condemnation from the world at large. While the war continues to contribute to an ongoing humanitarian and refugee crisis in Ukraine, a second battlefield has emerged in the online space, both in the use of social media to garner support for both sides of the conflict and also in the context of information warfare. In this paper, we present a collection of over 63 million tweets, from February 22, 2022 through March 8, 2022 that we are publishing for the wider research community to use. This dataset can be found at https://github.com/echen102/ukraine-russia and will be maintained and regularly updated as the war continues to unfold. Our preliminary analysis already shows evidence of public engagement with Russian state sponsored media and other domains that are known to push unreliable information; the former saw a spike in activity on the day of the Russian invasion. Our hope is that this public dataset can help the research community to further understand the ever evolving role

I present the results of my observations (visual and photographic) of the Geminid meteor shower in 2016, 2018, 2019 and 2020. I observed meteors from different regions (Moscow and Primorsky Krai) of Russia, under different observation conditions: light pollution, Moon phases and weather. I used a DSLR camera with a lens to photograph meteor tracks. I compare the results of my visual observations in different years and determine the coordinates of the meteors from the photographs to graphically demonstrate the radiant.

Currently, the evolution of Covid-19 allows researchers to gather the datasets accumulated over 2 years and to use them in predictive analysis. In turn, this makes it possible to assess the efficiency potential of more complex predictive models, including neural networks with different forecast horizons. In this paper, we present the results of a consistent comparative study of different types of methods for predicting the dynamics of the spread of Covid-19 based on regional data for two countries: the United States and Russia. We used well-known statistical methods (e.g., Exponential Smoothing), a "tomorrow-as-today" approach, as well as a set of classic machine learning models trained on data from individual regions. Along with them, a neural network model based on Long short-term memory (LSTM) layers was considered, the training samples of which aggregate data from all regions of two countries: the United States and Russia. Efficiency evaluation was carried out using cross-validation according to the MAPE metric. It is shown that for complicated periods characterized by a large increase in the number of confirmed daily cases, the best results are shown by the LSTM model trained