Developing of Distributed Virtual Laboratories for Smart Sensor System Design Based on Multi-Dimensional Access Method

In the article it is considered preconditions and main principles of creation of virtual laboratories for computer-aided design, as tools for interdisciplinary researches. Virtual laboratory, what are offered, is worth to be used on the stage of the requirements specification or EFT-stage, because it gives the possibility of fast estimating of the project realization, certain characteristics and, as a result, expected benefit of its applications. Using of these technologies already increase automation level of design stages of new devices for different purposes. Proposed computer technology gives possibility to specialists from such scientific fields, as chemistry, biology, biochemistry, physics etc, to check possibility of device creating on the basis of developed sensors. It lets to reduce terms and costs of designing of computer devices and systems on the early stages of designing, for example on the stage of requirements specification or EFT-stage. An important feature of this project is using the advanced multi-dimensional access method for organizing the information base of the Virtual laboratory

Students' mental health during the pandemic:results of the observational cross-sectional COVID-19 MEntal health inTernational for university Students (COMET-S) study

Introduction: The aim of the study was to search rates of depression and mental health in university students, during the COVID-19 pandemic. Materials and methods: This is an observational cross-sectional study. A protocol gathering sociodemographic variables as well as depression, anxiety and suicidality and conspiracism was assembled, and data were collected anonymously and online from April 2020 through March 2021. The sample included 12,488 subjects from 11 countries, of whom 9,026 were females (72.2%; aged 21.11 ± 2.53), 3,329 males (26.65%; aged 21.61 ± 2.81) and 133 “non-binary gender” (1.06%; aged 21.02 ± 2.98). The analysis included chi-square tests, correlation analysis, ANCOVA, multiple forward stepwise linear regression analysis and Relative Risk ratios. Results: Dysphoria was present in 15.66% and probable depression in 25.81% of the total study sample. More than half reported increase in anxiety and depression and 6.34% in suicidality, while lifestyle changes were significant. The model developed explained 18.4% of the development of depression. Believing in conspiracy theories manifested a complex effect. Close to 25% was believing that the vaccines include a chip and almost 40% suggested that facemask wearing could be a method of socio-political control. Conspiracism was related to current depression but not to history of mental disorders. Discussion: The current study reports that students are at high risk for depression during the COVID-19 pandemic and identified specific risk factors. It also suggested a role of believing in conspiracy theories. Further research is important, as it is targeted intervention in students' groups that are vulnerable both concerning mental health and conspiracism

A Randomized Study of Lenvatinib 18 mg vs 24 mg in Patients With Radioiodine-Refractory Differentiated Thyroid Cancer

Background: Lenvatinib is a multikinase inhibitor approved to treat radioiodine-refractory differentiated thyroid cancer (RR-DTC) at a starting dose of 24 mg/day. This study explored, in a double-blinded fashion, whether a starting dose of 18 mg/day would provide comparable efficacy with reduced toxicity. Methods: Patients with RR-DTC were randomized to lenvatinib 24 mg/day or 18 mg/day. The primary efficacy endpoint was objective response rate as of week 24 (ORRwk24); the odds ratio noninferiority margin was 0.4. The primary safety endpoint was frequency of grade ≥3 treatment-emergent adverse events (TEAEs) as of week 24. Tumors were assessed using RECIST v1.1. TEAEs were monitored and recorded. Results: The ORRwk24 was 57.3% (95% CI 46.1, 68.5) in the lenvatinib 24-mg arm and 40.3% (95% CI 29.3, 51.2) in the lenvatinib 18-mg arm, with an odds ratio (18/24 mg) of 0.50 (95% CI 0.26, 0.96). As of week 24, the rates of TEAEs grade ≥3 were 61.3% in the lenvatinib 24-mg arm and 57.1% in the lenvatinib 18-mg arm, a difference of -4.2% (95% CI -19.8, 11.4). Conclusion: A starting dose of lenvatinib 18 mg/day did not demonstrate noninferiority compared to a starting dose of 24 mg/day as assessed by ORRwk24 in patients with RR-DTC. The results represent a clinically meaningful difference in ORRwk24. The safety profile was comparable, with no clinically relevant difference between arms. These results support the continued use of the approved starting dose of lenvatinib 24 mg/day in patients with RR-DTC and adjusting the dose as necessary. Trial registration: ClinicalTrials.gov NCT02702388

Cryptology transmitted message protection: from deterministic chaos up to optical vortices

This book presents methods to improve information security for protected communication. It combines and applies interdisciplinary scientific engineering concepts, including cryptography, chaos theory, nonlinear and singular optics, radio-electronics and self-changing artificial systems. It also introduces additional ways to improve information security using optical vortices as information carriers and self-controlled nonlinearity, with nonlinearity playing a key "evolving" role. The proposed solutions allow the universal phenomenon of deterministic chaos to be discussed in the context of information security problems on the basis of examples of both electronic and optical systems. Further, the book presents the vortex detector and communication systems and describes mathematical models of the chaos oscillator as a coder in the synchronous chaotic communication and appropriate decoders, demonstrating their efficiency both analytically and experimentally. Lastly it discusses the cryptologic features of analyzed systems and suggests a series of new structures for confident communication

Graz Lagrangian Model (GRAL) for Pollutants Tracking and Estimating Sources Partial Contributions to Atmospheric Pollution in Highly Urbanized Areas

Computational modeling allows studying the air quality problems in depth and provides the best solution reducing the population risks. This research demonstrates the Graz Lagrangian model effectiveness for assessing emission sources contributions to the air pollution: particles tracking and accumulation estimate. The article describes model setting up parameters and datasets preparation for the analysis. The experiment simulated the dispersion from the main groups of emission sources for real weather conditions during 96 h of December 2018, when significant excess of NO2, CO, SO2, PM10, and benzo(a)pyrene concentrations were observed in the Krasnoyarsk surface atmospheric layer. The computational domain was a parallelepiped of 40 × 30 × 2.5 km, which was located deep inside the Eurasian continent on a heterogeneous landscape exaggerated by high-rise buildings, with various pollutions sources and the ice-free Yenisei River. The results demonstrated an excellent applicability of the Lagrange model for hourly tracking of particle trajectories, taking into account the urban landscape. For values <1 MPC (maximum permissible concentration) of peak pollutants concentrations, the coincidences were 93 cases, and for values < 0.1 shares of MPC, there were 36 cases out of the total number of 97. The same was found for the average daily concentration for values <1 MPC—31, and for values <0.1 MPC—5 matches out of 44. Wind speeds COR—65.3%, wind directions COR—68.6%. The Graz Lagrangian model showed the ability to simulate air quality problems in the Krasnoyarsk greater area conditions

The Ti wire functionalized with inherent TiO2 nanotubes by anodization as one-electrode gas sensor

We propose a Ti wire functionalized with inherent anatase TiO2 nanotubes by an anodization process to serve as a one-electrode gas sensor. The design is similar to other known one-electrode gas sensors when the wire is employed both as a heater and measuring resistive element. We discuss the low-cost fabrication protocol, the structure of the sensor characterized by Raman spectroscopy and electron microscopy, and show the proof-of-concept sensor responses to a few organic vapors, acetone and alcohols, in mixture with air. We have found the response-to-concentration curves to follow a linear fit with a detection limit below 1000 ppm. These findings enlarge the range of possible gas sensor architectures based on nanostructured material for research and practical applications.Peer reviewe

Analysis of Posit and Bfloat Arithmetic of Real Numbers for Machine Learning

Modern computational tasks are often required to not only guarantee predefined accuracy, but get the result fast. Optimizing calculations using floating point numbers, as opposed to integers, is a non-trivial task. For this reason, there is a need to explore new ways to improve such operations. This paper presents analysis and comparison of various floating point formats – float, posit and bfloat. One of the promising areas in which the problem of using such values can be considered to be the most acute is neural networks. That is why we pay special attention to algorithms of linear algebra and artificial intelligence to assess efficiency of new data types in this area. The research results showed that software implementations of posit16 and posit32 have high accuracy, but they are not particularly fast; on the other hand, bfloat16 is not much different from float32 in accuracy, but significantly surpasses it in performance for large amounts of data and complex machine learning algorithms. Thus, posit16 can be used in systems with less stringent performance requirements, as well as in conditions of limited computer memory; and also in cases when bfloat16 cannot provide required accuracy. As for bfloat16, it can speed up systems based on the IEEE 754 standard, but it cannot solve all the problems of conventional floating point arithmetic. Thus, although posits and bfloats are not a full fledged replacement for float, they provide (under certain conditions) advantages that can be useful for implementation of machine learning algorithms