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In connection with the COVID-19 pandemic, university education has undergone dramatic changes, resulting in a justified increase in the need for the development of distant learning. Today, information technology is an invaluable part of the learning process around the world. The purpose of this article is to analyze positive and negative aspects of applying information and communication technologies to the process of teaching foreign languages to students of mining specialties. The ways of solving the problems, identified in the discipline "Foreign language" in the course of the practical implementation of distance learning, are outlined. The possibilities of using such information tools as video lectures, video conferences, electronic coursebooks, computer testing are considered. Particular attention is devoted to the Moodle course management system as the most effective technology contributing to the formation of the language skills of future mining engineers. The authors come to the conclusion that each of the considered innovative educational technologies has a number of undeniable advantages, thus having a positive impact on both teachers and students. The recommendations developed by the authors provide a means for improving pedagogical design at the level of both individual teachers and universities.

The Shor are a small indigenous people of Western Siberia. In total, there are 13 thousand representatives of this nation in the world. The ethnos was formed in the 6-9 centuries. Kuzbass – the largest coal cluster in Russia – is a home for a significant part of the Shor population, being the indigenous minority of the region. The development of mining operations (mainly coal and iron ore mining) negatively affects the territory of the Shor people habitation. Therefore, the preservation of their cultural traditions, including the original language, is an important part of the sustainable development of the mining region Kuzbass. The suggested study is based on the Shor language material, which belongs to the Turkic branch of the Altai language family. The purpose of the study is to describe the synthetic type of complex sentences in the Shor language, which is designed to help preserve their culture in the system of sustainable development of Kuzbass.

A unified approach has been developed to assess the integral alongshore sediment transport for the conditions of heterogeneous pebble and sandy beaches. The energy formulas for determining the sediment discharge have been modified for the conditions for calculating the integral alongshore sediment transport on heterogeneous pebble and sandy beaches. For this, the coefficient included in the energy formulas is determined using the Ackers-White method.

A mathematical model for beach reshaping in the vicinity of transverse hydraulic structures has been developed on the basis of the developed unified approach for determining the alongshore transport of heterogeneous sediments as applied to the conditions of pebble and sandy beaches.

Modern society needs to establish a balance between social and economic development and environment conservation. This target can be met through the integrated use of legislative, economic and administrative measures developed and implemented by various international, national and regional organizations. Environmental management can achieve the maximum efficiency only through the joint use of three environmental policy instruments: direct regulating, market-based instruments, and voluntary programs. Direct regulating is inherently directive in terms of establishing emission standards / discharge limits, industry product and process specifications, and transparency reporting requirements. The implementation of market-based instruments depends on the day-to-day use of private sector financial calculations. Economic methods include price-based instruments and property-based instruments. Voluntary programs, actively encouraged by governments, offer intangible rewards such as public recognition. The vast legislative framework and activities of governmental and public organizations encourage mining and processing enterprises to strictly conform to environmental rules and regulations. While multiple market-based instruments of environmental management and voluntary programs provides businesses with greater autonomy in choosing ways, means and terms of restructuring their environmental policies to meet pressing environmental challenges.

The coastal marine transport is one of the important components defining development of all Black Sea coast of Russia. Questions of need of restoration of coastal passenger traffic within the coast of Greater Sochi in connection with intensive development of tourism in Krasnodar Region of Russia and the questions of the organization of the transport communication between a coastal towns and settlements are considered. Need of further implementation of the program of reconstruction and construction of port points (moorings) and objects of coastal transport infrastructure in the Sochi region and in all Black Sea coast of Russia is shown. The analysis of a problem of environmental protection, which can arise at implementation of the project of reconstruction and development of coastal sea transport infrastructure in the Sochi region, is made. Possible ways of their solution for reduction of impacts on the environment are proposed. Introduction of the program of development of objects of coastal transport in the region, taking into account environment will contribute to sustainable development of the region.

Nowadays foreign language competence is one of the main professional skills of mining engineers. Modern competitive conditions require the ability for meeting production challenges in a foreign language from specialists and managers of mining enterprises. This is the reason of high demand on foreign language training/retraining courses. Language training of adult learners fundamentally differs from children and adolescent education. The article describes the features of andragogical learning model. The authors conclude that distance learning is the most productive education form having a number of obvious advantages over traditional (in-class) one. Interactive learning method that involves active engagement of adult trainees appears to be of the greatest interest due to introduction of modern information and communication technologies for distance learning.

Globalization of mining education imposes new requirements for mining engineer competence. Nowadays linguistic competence is one of the most demanded. It guarantees technical university graduates the possibility of global employment, on the one hand, and the chance of getting cutting edge education in leading training centers of the world, on the other hand. Distance education is actively developing all over the world and is widely used in technical colleges and universities, as well. Interactive method that involves active engagement of students appears to be of the greatest interest due to introduction of modern information and communication technologies for distance learning. The paper presents step-by-step implementation of several interactive technologies (jigsaw, case study, brainstorming, and role-play) that can be used in distance education in the process of teaching subjects in foreign languages with the help of information and communication technologies. In response to the changes in the conditions of educational process, the implementation of the methods has been transformed to a combination of traditional (in-class) and distance (online) learning.

Background: To date, performance comparisons between men and machines have been carried out in many health domains. Yet machine learning (ML) models and human performance comparisons in audio-based respiratory diagnosis remain largely unexplored., Objective: The primary objective of this study was to compare human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings., Methods: In this study, we compared human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. Prediction performance on 24 audio samples (12 tested positive) made by 36 clinicians with experience in treating COVID-19 or other respiratory illnesses was compared with predictions made by an ML model trained on 1162 samples. Each sample consisted of voice, cough, and breathing sound recordings from 1 subject, and the length of each sample was around 20 seconds. We also investigated whether combining the predictions of the model and human experts could further enhance the performance in terms of both accuracy and confidence., Results: The ML model outperformed the clinicians, yielding a sensitivity of 0.75 and a specificity of 0.83, whereas the best performance achieved by the clinicians was 0.67 in terms of sensitivity and 0.75 in terms of specificity. Integrating the clinicians' and the model's predictions, however, could enhance performance further, achieving a sensitivity of 0.83 and a specificity of 0.92., Conclusions: Our findings suggest that the clinicians and the ML model could make better clinical decisions via a cooperative approach and achieve higher confidence in audio-based respiratory diagnosis., (©Jing Han, Marco Montagna, Andreas Grammenos, Tong Xia, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Ting Dang, Dimitris Spathis, R Andres Floto, Pietro Cicuta, Cecilia Mascolo. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.05.2023.)

Stress is often considered the 21 st century's epidemic, affecting more than a third of the globe's population. Long-term exposure to stress has significant side effects on physical and mental health. In this work we propose a methodology for detecting stress using abdominal sounds. For this study, eight participants were either exposed to a stressful (Stroop test) or a relaxing (guided meditation) stimulus for ten days. In total, we collected 104 hours of abdominal sounds using a custom wearable device in a belt form-factor. We explored the effect of various features on the binary stress classification accuracy using traditional machine learning methods. Namely, we observed the impact of using acoustic features on their own, as well as in combination with features representing current mood state, and hand-crafted domain-specific features. After feature extraction and reduction, by utilising a multilayer perceptron classifier model we achieved 77% accuracy in detecting abdominal sounds under stress exposure. Clinical relevance- This feasibility study confirms the link between the gastrointestinal system and stress and uncovers a novel approach for stress inference via abdominal sounds using machine learning.

Background: Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems., Objective: The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques., Methods: Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning-enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95% CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels., Results: We first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1%) of 21 COVID-19-positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery., Conclusions: An audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general., (©Ting Dang, Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, R Andres Floto, Pietro Cicuta, Cecilia Mascolo. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.06.2022.)

To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent work has shown how audio (including cough, breathing and voice) based approaches can be used for testing. However, there is a lack of exploration of how biases and methodological decisions impact these tools' performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside symptoms and COVID-19 test results. Within the collected dataset, we selected 5240 samples from 2478 English-speaking participants and split them into participant-independent sets for model development and validation. In addition to controlling the language, we also balanced demographics for model training to avoid potential acoustic bias. We used these audio samples to construct an audio-based COVID-19 prediction model. The unbiased model took features extracted from breathing, coughs and voice signals as predictors and yielded an AUC-ROC of 0.71 (95% CI: 0.65-0.77). We further explored several scenarios with different types of unbalanced data distributions to demonstrate how biases and participant splits affect the performance. With these different, but less appropriate, evaluation strategies, the performance could be overestimated, reaching an AUC up to 0.90 (95% CI: 0.85-0.95) in some circumstances. We found that an unrealistic experimental setting can result in misleading, sometimes over-optimistic, performance. Instead, we reported complete and reliable results on crowd-sourced data, which would allow medical professionals and policy makers to accurately assess the value of this technology and facilitate its deployment., (© 2022. Crown.)

The ability of cells to adhere to substrates is an important factor for the effectiveness of biotechnologies and bioimplants. This research demonstrates that the statistical distribution of the sizes of the cells ( Saccharomyces cerevisiae ) attached to the substrate surface correlates with the statistical distribution of electrical potential on the substrate's surface. Hypothetically, this behavior should be taken into consideration during the processing of surfaces when cell adhesion based on cell size is required.

Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination. The ability to diagnose a patient based on their heart sounds is a rather difficult skill to master. Thus, many approaches for automated heart auscultation have been explored. However, most of the previously proposed methods involve a segmentation step, the performance of which drops significantly for high pulse rates or noisy signals. In this work, we propose a novel segmentation-free heart sound classification method. Specifically, we apply discrete wavelet transform to denoise the signal, followed by feature extraction and feature reduction. Then, Support Vector Machines and Deep Neural Networks are utilised for classification. On the PASCAL heart sound dataset our approach showed superior performance compared to others, achieving 81% and 96% precision on normal and murmur classes, respectively. In addition, for the first time, the data were further explored under a user-independent setting, where the proposed method achieved 92% and 86% precision on normal and murmur, demonstrating the potential of enabling automatic murmur detection for practical use.

Background: Normal weight obesity in children has been associated with excessive body fat, lower bone density and decreased total lean mass. However, no studies have been done into whether normal weight obese children differ in skeletal robustness or lean mass development on the extremities from normal weight non-obese, overweight, and obese peers although these are important indicators of healthy development of children. Methods: Body height, body weight, BMI, four skinfolds, and two limb circumferences were assessed. We calculated total body fat using Slaughter's equations, the Frame index for skeletal robustness and muscle area for the upper arm and calf using Rolland-Cachera equations. Using national references of BMI and measured skinfolds, three subgroups of participants (9-12 years) consisting of 210 middle-school-aged children (M-age = 11.01 ± 1.05)-110 girls and 100 boys-were selected: (A) overweight obese (OWOB) ( n = 72); (B) normal weight obese (NWO) ( n = 69); and, (C) normal weight non-obese (NWNO) ( n = 69). All values, were converted to Z-scores to take account of participant's sex and age. Results: NWO children had significantly poorer skeletal robustness on lower extremities and poorer muscle area on the upper arm and calf compared to NWNO counterparts with significantly higher evidence in boys-skeletal robustness NWO boys: Z-score = -0.85; NWO girls: Z-score = -0.43; lean mass on the calf: NWO boys Z-score = -1.34; NWO girls: Z-score = -0.85. The highest skeletal robustness-but not muscle area on the calf-was detected in OWOB children. Conclusions: Further research should focus on whether this poor skeletal and lean mass development: (1) is a consequence of insufficient physical activity regimes; (2) affects physical fitness of NWO children and could contribute to a higher prevalence of health problems in them. We have highlighted the importance of the development of a simple identification of NWO children to be used by pediatricians.

We studied the interrelations between G/A-polymorphism of EPAS 1 gene(rs1867785) and macimal oxygen consumption (VO2max) in Russian male athletes. There were two groups in the study: athletes of different sport specialization (n = 241) and nonathletic males (n = 92). It was found that the frequency of AA and AG genotype of EPAS1 gene in the group of athletes was higher (χ2 = 14.16, p = 0.03). In the subgroups with average (EPAS1*A 38.1% and EPAS1*G 61.9%) and high ((EPAS1*A 41.8% and EPAS1*G 58.2%) VO2max values, the frequency of these alleles was significantly different from those in the control group (χ2 = 7.53, p = 0.006 and χ2 = 6.58, p = 0.01, respectively). This suggests that higher aerobic capacities are caused by the presence of at least one minor A-Allele of the EPAS1 gene in the genome of an athlete.