The integral indexes are used to measure trends and monitor progress in transportation complex development. The selection of the indicators, included in indexes, is related to the data availability (depends on existence of a specific data sources). The aim of this paper is to provide a development methodology of Integral Index of Traffic Planning (Integral TP Index), which is based on the primary data on vehicle speeds, traffic volumes, number of accidents, etc., for Moscow and allows for basic assessment of transport situation in Russian’s capital. The proposed methodology is a combination of economic and urban approaches to analyze the key indicators of transportation planning efficiency in the metropolis. Four groups of indicators are considered: traffic management efficiency, traffic management quality, transit efficiency and road safety. The integral index considers traffic volumes for various roads and their contributions to the overall transportation system. Division of streets by type (highways, rings, center) makes it possible to take into account specifications the radial-ring streets structure of Moscow. The constructed index is applied to the analysis of Moscow transportation statistics in 2012–2017 provided by the Moscow Traffic Management Center, Yandex and TomTom.

(1) Background: As global urbanization accelerates, effective mobility in metropolitan areas becomes crucial. City transportation systems, often congested, have diverse transit modes and numerous access points. Our study focuses on the transportation accessibility of the various districts within Moscow, a city with a population of over 12 million and covering approximately 900 square kilometers. (2) Methods: The city was divided into 2 km-by-2 km squares, and we used both personal and public transportation data. This allowed us to analyze spatiotemporal mobility patterns, calculating travel times and distances between these defined centroids. Our assessment not only considered transportation to key hubs, such as major train stations, airports, and the city center, but also weighed the integral interconnectedness of individual districts. Various time frames, including morning and evening peak hours and quieter weekend periods, were used. (3) Results: The study pinpointed the most and least convenient districts for various transit options across the city. Our findings underscore the intricacies of daily commuting patterns in Moscow, highlighting bottlenecks and areas for potential infrastructure enhancement. (4) Conclusions: Using Moscow’s case, we demonstrated the methodology to better understand and improve strategic urban planning and intelligent mobility solutions, aiming to bolster transportation accessibility.

This paper investigates the utilization of news in predicting market regimes. The findings illustrate that employing an ensemble of multiple FinBERT models can outperform straightforward time-series prediction by 73% in accuracy and 110% in F1 score. The NLP models demonstrate strong performance across two different market-regime scenarios and show the ability to detect market shifts.

Since the beginning of the COVID-19 pandemic almost two years ago, there have been more than 700,000 scientific papers published on the subject. An individual researcher cannot possibly get acquainted with such a huge text corpus and, therefore, some help from artificial intelligence (AI) is highly needed. We propose the AI-based tool to help researchers navigate the medical papers collections in a meaningful way and extract some knowledge from scientific COVID-19 papers. The main idea of our approach is to get as much semi-structured information from text corpus as possible, using named entity recognition (NER) with a model called PubMedBERT and Text Analytics for Health service, then store the data into NoSQL database for further fast processing and insights generation. Additionally, the contexts in which the entities were used (neutral or negative) are determined. Application of NLP and text-based emotion detection (TBED) methods to COVID-19 text corpus allows us to gain insights on important issues of diagnosis and treatment (such as changes in medical treatment over time, joint treatment strategies using several medications, and the connection between signs and symptoms of coronavirus, etc.).

Real-time estimation of the parameters characterising infectious disease transmission is important for optimization quarantine interventions during outbreaks. One of the most significant parameters is the effective reproduction number - number of secondary cases produced by a single infection. The current study presents an approach for estimating the effective reproduction number and its application to COVID-19 outbreak. The method is based on fitting SIR epidemic model to observation data in a sliding time window and allows to show real-time dynamics of reproduction number at any phase of epidemic for countries globally. Online data on COVID-19 daily cases of infections, recoveries, deaths are used.Finally, time-dependent reproduction number is explored in connection with dynamics of peoples mobility. The method allows to assess the disease transmission potential and understand the effect of interventions on epidemics spread. It also can be easily adapted to future outbreaks of different pathogens. The tool is available online as Python code from the Github repository.

An extremely interesting problem in aero-hydrodynamics is the sound radiation of a single vortical structure. Currently, this type of problem is mainly considered for an incompressible medium. In this paper a method was developed to take into account the viscosity and thermal conductivity of gas. The acoustic radiation frequency of a cylindrical vortex on a flat wall in viscous heat-conducting gas (air) has been investigated. The problem is solved on the basis of the Navier–Stokes equations using the small initial vorticity approach. The power expansion of unknown functions in a series with a small parameter (vorticity) is used. It is shown that there are high-frequency oscillations modulated by a low-frequency signal. The value of the high frequency remains constant for a long period of time. Thus the high frequency can be considered a natural frequency of the vortex radiation. The value of the natural frequency depends only on the initial radius of the cylindrical vortex, and does not depend on the intensity of the initial vorticity. As expected from physical considerations, the natural frequency decreases exponentially as the initial radius of the cylinder increases. Furthermore, the natural frequency differs from that of the oscillations inside the initial cylinder and in the outer domain. The results of the paper may be of interest for aeroacoustics and tornado modeling.