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For millions of people, the COVID-19 pandemic situation and its accompanying restrictions have been a source of threat and confrontation with negative emotions. The pandemic's universal and long-term character, as well as the ensuing drastic limitation of control over one's life, have made it necessary to work out adaptive strategies that would reduce negative experiences and eventually lead to the restoration of well-being. The aim of this research was to identify strategies that people use in response to a long-term threat that restore affective balance and a subjective sense of security. We registered selected manifestations of affective reactions to the pandemic situation. The researchers focused on the dynamics of changes in the areas of (1) experienced negative emotions (asked in an indirect way) and (2) a subjective feeling of threat regarding the pandemic (in three different contexts: Poland, Europe, and worldwide) during the first phase of the pandemic in Poland. It was expected that both the negative emotions and the sense of threat would decrease with time. In addition, it was anticipated that the physical distance would modify the assessment of the situation as threatening depending on the geographical proximity: in Poland, Europe, and worldwide. We used the mixed quasi-experimental design in the series of four studies conducted by Internet in March, May, June, and July 2020. The intensity of negative emotions and the sense of threat caused by the pandemic situation in Poland, Europe, and worldwide were measured. Despite the objective number of confirmed COVID-19 cases during each of the stages of the study, both the intensity of emotions attributed by participants as well as the feeling of threat were found to have decreased. In addition, surprisingly, a reversed effect of the distance was revealed: namely, a sense of threat experienced towards distant locations (Europe and the world) was found to be more acute when compared with the threat experienced in Poland. The obtained results are interpreted as a manifestation of adaptive perception of the threat that lies beyond one's control, which takes the form of unconscious, biased distortions: unrealistic optimism. The decrease in the intensity of negative emotions explains unrealistic absolute optimism, while the perception of the situation in Poland as less threatening than in Europe and around the world is predicted by unrealistic comparative optimism.

Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.

Simple Summary: The usefulness of genomic prediction (GP) has been widely proofed by breeding analysis in livestock, plants and aquatic populations. It is well known that 'marker density' is a critical factor that affects the accuracy of GP, however, how to properly measure 'marker density' in GP is yet to be determined. With population-level whole-genome sequence data or high-density single nucleotide polymorphism (SNP) data available, this question seems to be answered more convincingly. In this study, we investigated and discussed the impact of four 'marker density' measures that reflect genetic or physical distances between SNPs on the accuracy of GP in a Germany Holstein dairy cattle population. Our results showed that the degree of variation of physical distance between adjacent SNPs had significant effects on the accuracy of GP, while the genetic distance between SNPs had no relationship with the accuracy of GP. Therefore, for studies based on high-density SNP data, the default strategy of pruning SNPs based on genetic distance is detrimental to heritability estimation and genomic prediction. The results extended the communities knowledge of 'marker density' and provided useful suggestions for the application and research on genome prediction. With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of 'marker density' seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of 'marker density' based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean ( d ¯ ) and variance ( σ d 2 ) of the physical distance between SNPs and the mean ( r 2 ¯ ) and variance ( σ r 2 2 ) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the d ¯ of all panels is basically the same, but the σ d 2 , r 2 ¯ and σ r 2 2 are different. Therefore, we only investigated the effects of σ d 2 , r 2 ¯ and σ r 2 2 on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with σ d 2 , but not with r 2 ¯ and σ r 2 2 . Compared with GenD and RanD, the σ d 2 of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large σ d 2 , which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20–30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is posing a major health crisis. Spanish legislation establishes the mandatory use of masks and the implementation of hygienic measures such as hand washing and physical distancing. The aim of this study is to describe access to information and the level of community knowledge/adoption about the preventive measures proposed by the Spanish health authorities in response to the COVID-19 pandemic and to analyze the influence of socio-demographic factors in compliance among people over 18 years of age resident in Spain. An observational, descriptive and transversal study was conducted. Data was collected on sociodemographic variables, access to information and the degree of knowledge/adoption about the preventive measures: use of masks, hand hygiene and physical distancing. A total of 1811 people participated. The average age was 45.1 ± 15.1 years, predominantly female (69.3%), from an urban geographical area (74%), with a higher education level of 53.2%. Most of the respondents (57.5%) are or live with people at risk. The main access to information on preventive measures was from secondary sources (49.2%), with television being the main medium; 72.3% think that there are some difficulties in accessing information, while 8.7% of the participants do not consider the use of masks to be useful. As regards the choice of type of mask, the majority of people (44.8%) opt for the surgical variety; 88.5% of respondents believe that the physical distancing established is at least 1.5 m. This study confirmed that socio-demographic factors influence compliance with or the degree of knowledge/adoption of the preventive measures proposed to combat the COVID-19 pandemic and has made it possible to ascertain the sectors of the population with the greatest deficiencies in this respect. It shows the importance of implementing health information and education systems in the community, and it is advisable to promote specific programs aimed at men, people living in rural areas and people with a low level of education. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the temporary closure of physical activity and sports facilities, and the generalized cancellation or postponement of sports events have a massive impact on social and economic development. In this study, we explored the feasibility of using tracking data from a football match to assess interpersonal contact between individuals by calculating two measures of respiratory exposure. The dynamic tracking positioning of all players and referees during one international football match was analyzed. For each individual, two measures of respiratory exposure were calculated, based on the 2 m interpersonal distance recommendations for contact tracing for COVID-19 control. Overall, individuals spent a median of 0:12 mm:ss (IQR = 0:45 mm:ss) exposed to interpersonal contact of fewer than 2 m from others. The highest value of exposure was observed between two players of opposing teams (6:35 mm:ss). The results suggest that tracking data can be used to assess respiratory exposure to interpersonal contact in team sports, such as football. The measures of exposure calculated can be used to the prompt identification of high-risk contacts of COVID-19 cases during a match or a training session, but also the risk stratification of different sports and physical activities. [ABSTRACT FROM AUTHOR]

Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone. [ABSTRACT FROM AUTHOR]

WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-based WKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy. [ABSTRACT FROM AUTHOR]