ANALYSIS OF EXISTING SOLUTIONS FOR IMPROVING THE MEASURING AND COMPUTING COMPLEXES OF HEAT SUPPLY NETWORKS

In the presented article, the team of authors considers the existing methods and the main modern technical solutions that are currently implemented in different countries in the diagnosis of heat supply networks. There is a selection of the main directions in the development and design of heat supply networks, which have already been implemented or supported by scientific teams from different countries. Various methods and technical features of diagnostics are reviewed, strengths and weaknesses of the presented solutions are highlighted. The reviewed works were subjected to detailed analysis, which revealed the presence of a high interest of the scientific and industrial community in the integration and improvement of existing digital technologies in the development of heat supply systems, which would be closely related to forecasting and modeling processes in this industry. The team of authors highlights the main vectors for the development of this sector, citing an example of a significant increase in the degree of digitalization of final products, which makes it possible to use data analytics to obtain effective technical solutions regarding heat supply networks. Separately, the positive experience of different countries in this industry is noted when using neural networks not only in the design of heat supply networks, but also as a target industry as a whole. Assumptions are put forward about the need for a detailed analysis of the existing foreign and domestic experience, as well as scientific developments in this area, in order to determine the most suitable technical solutions on the territory of the Russian Federation, which will take into account the climatic characteristics of the country and be based on methods of large data analysis, computer vision and simulation. modeling.

Analysis of COVID-19 Test Positivity Rate in West Sumatra, Indonesia: A Cross-sectional Study of One-year Observation

Background: The test positivity rate (TPR) of COVID-19 is an epidemiological indicator used to estimate SARS-CoV-2 transmission in a population at a certain time. However, large data analysis on the TPR in Indonesia is still limited. In this study, we determined COVID-19 TPR dynamics of Indonesian West Sumatra Province in the first year of cases were recorded.Method: We conducted an observational study with a cross-sectional approach from one-year secondary data of COVID-19 test using qualitative reverse transcription polymerase chain reaction (q-RT-PCR) in West Sumatra collected from April 2020 until March 2021. The TPR(s) in the province, its regions (cities/ regencies), and districts were determined annually, quarterly, and monthly to analyze their trends.Results: From a total of 410,424 individuals taking COVID-19 q-RT-PCR examination during one-year observation, the provincial TPR was 8.11%. The highest TPR quarterly and monthly was detected in the third quarter (October 2020 – December 2020, 12.18%) and October 2020 (15.62%) respectively. The TPR of cities was likely two times higher than regencies. There were significant differences in annual TPR between regions, districts, and any period of times detected in this study.Conclusion: We have shown the COVID-19 q-RT-PCR TPR dynamics to describe SARS-CoV-2 transmission control among different areas in West Sumatra. This study should be beneficial to ensure an effective COVID-19 preventive strategy in the future.

Digital Transformation: From Data Analytics to Customer Solutions. A Framework of Types, Techniques and Tools

It has become clear by now that the digital transformation has an obvious, lasting impact as much on the economic systems and commercial players as on the lives of individuals and on society at large. The decisions we make, our actions, even our existence in the digital world result in the production of massive amounts of data. These data can be integrated into large data analysis ecosystems and contribute positively to the revision of current business models and practices. Machine learning algorithms combined with the suitable tools, such as Python, turn raw data into useful information and lead to critical and correct decisions. The aim of this paper is to present a review of current popular and useful data analytics techniques and tools that lead to custom solutions for both customer and business. The most famous techniques based on Machine learning and visualization tools are represented here.