A Comprehensive Decentralized Digital Identity System: Blockchain, Artificial Intelligence, Fuzzy Extractors, and NFTs for Secure Identity Management

Existing digital identification systems are often vulnerable to attacks as they are commonly based on authentication methods such as passwords, PIN codes, biometric data, etc., which can be easily forged or compromised. In this letter, we propose a digital identification system based on a unique set of user biometric data processed by Artificial Intelligence (AI) and fuzzy extractors to generate a cryptographically secure password linked to a unique Non-Fungible Token (NFT). Our system provides decentralized identification based on blockchain technology, which eliminates problems associated with centralized identification systems, such as cyber-attacks on central servers and data leaks. Our proposed system offers a higher level of user identification security by linking the user to their data through a unique NFT, generating a cryptographically secure password, and processing large volumes of biometric data using AI and fuzzy extractors. Our system provides a solution to many of these problems, making it important and relevant to many industries, including banking, medical, and financial sectors. The use of decentralized storage of information on the blockchain provides a high level of protection against hacking and reduces the likelihood of data breaches, making our system particularly relevant in the field of financial services and personal data protection

Near field of Hertzian dipole excited by impulse current

The angle dependence of near field radiated of Hertzian dipole excited by transient current is investigated. The more precise approximation in the solution for near field of the dipole is obtained in time domain by means of vector potential technique. As an example, the quickly decreasing excitation current case is considered

Gender-Age Distribution of Patients with COVID-19 at Different Stages of Epidemic in Moscow

The ongoing COVID-19 pandemic around the world and in Russia remains a major event of 2020. All over the world, research is being conducted to comprehensively study the patterns and manifestations of the epidemic  process. The main quantitative characteristics of SARS-CoV-2 transmission dynamics among the population, based on the data of official monitoring over the current situation, play an important role in the development of  the epidemiological surveillance system.The aim of this study is to explore the peculiarities of age-gender distribution of COVID-19 patients in Moscow.Material and methods. The data related to the epidemiological characteristics of age-gender structure of COVID-19 patients in Moscow between March 19, 2020 and April 15, 2020, at different stages of the  epidemic were retrospectively analyzed.Results and discussion. The mean age of COVID-19 patients in Moscow was 46,41±20,58 years. The gender ratio (male/female) among the patients was 52.7/47.3 %, wherein the indicators varied depending upon the  age. Male/female ratio in the age group “under 39” stood at 53.7/46.3 %, and “over 40 years of age” – at  39.3/60.7 %. The predominant age range among male cases was 19 to 39 years old – 35.4 %, while among female patients – 40–59 years (36.5 %). The age distribution of patients in Moscow is indicative of the fact that COVID-19 is a disease that primarily affects older age groups. The age structure of all COVID-19 cases during the observation period is characterized by predominance of adult patients over 19 years of age – 92,7 % (92,6–92,8 %), the share of patients aged 40–59 years is 35,7% (35,5–35,9 %). The differences in the age distribution in males and females are as follows: in the male cohort, the age groups 19–39 years old and 40–59 years old prevail – 35.4 % (35.1–35.7 %) and 34.9 % (34.6–35.2 %), respectively. The age group 40–59 years old – 36.5 % (36.3–36.8%) dominates in the female cohort

Identification of a ZEB2-MITF-ZEB1 transcriptional network that controls melanogenesis and melanoma progression

Deregulation of signaling pathways that control differentiation, expansion and migration of neural crest-derived melanoblasts during normal development contributes also to melanoma progression and metastasis. Although several epithelial-to-mesenchymal (EMT) transcription factors, such as zinc finger E-box binding protein 1 (ZEB1) and ZEB2, have been implicated in neural crest cell biology, little is known about their role in melanocyte homeostasis and melanoma. Here we show that mice lacking Zeb2 in the melanocyte lineage exhibit a melanoblast migration defect and, unexpectedly, a severe melanocyte differentiation defect. Loss of Zeb2 in the melanocyte lineage results in a downregulation of the Microphthalmia-associated transcription factor (Mitf) and melanocyte differentiation markers concomitant with an upregulation of Zeb1. We identify a transcriptional signaling network in which the EMT transcription factor ZEB2 regulates MITF levels to control melanocyte differentiation. Moreover, our data are also relevant for human melanomagenesis as loss of ZEB2 expression is associated with reduced patient survival