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\"Author of the phenomenal national best seller, Born to Run, Christopher McDougall now travels to the Mediterranean where he discovers that the secrets of ancient Greek heroes are still alive and well on the island of Crete and in the muscles and minds of fitness enthusiasts everywhere. While researching Born to Run, Chris McDougall encountered the story of Pheidippides, the legendary ancient Greek 'all-day runner.' Later, when McDougall met a dedicated amateur historian, he saw a connection to one of the most fascinating mysteries of World War II: How did a small band of Resistance fighters surrounded by German troops kidnap a top German general? What he discovered is that ancestral techniques for extraordinary endurance, natural movement, and nutrition allowed ancient Greek soldiers and Cretan shepherds to race across mountains on all-night missions. Inspired by their heroic acts, McDougall sets off to discover the lost art of the hero, both throughout history and across the world. Just as Born to Run inspired casual runners to get off the treadmill, out of their shoes, and into nature, Natural-Born Heroes will inspire casual athletes to leave the gym and take their fitness to nature doing cross-training, mud runs, parkour and free-running to bound--and climb, swim, skip, wade, and jump--their way to heroic feats\"-- Provided by publisher.

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.

Prior research has found that people perceive positive objects and locations as physically closer than negative ones. Yet, other work has found the opposite to be true for perceptions of temporal distance, where negative future events feel closer than positive ones. Motivated by this seeming discrepancy, we propose that (a) feelings of control can differentially influence how far away valenced (i.e., positive or negative) targets feel in space and time and that (b) the difference in perceived control over space versus time can account for these opposite findings. First, across four studies, we show that high (vs. low) control makes positive targets feel closer and negative targets feel more distant in both physical space (Studies 1 and 1a) and time (Studies 2 and 2a). Then, in Studies 3 and 4, we simultaneously examine perceptions of spatial and temporal distance and show that baseline differences in perceived control between these domains can explain the prior discrepant findings. Finally, a within-paper meta-analysis offers further support to these findings.

On January 31, 2020, the World Health Organization (WHO) declared the outbreak of a novel coronavirus responsible for an infection termed COVID-19 as a global public health emergency. To slow the spread of the coronavirus, countries around the world have been implementing various measures, including school and institutional closures, lockdown and targeted quarantine for suspected infected individuals. More than a third of the world's population have been home confined less than 4 months after the start of the outbreak. The present article aims to advise healthy individuals and athletes who are in lockdown regarding their lifestyle in order to keep healthy, safe and fit. The advice contained in the present article could apply to anyone aiming at remaining in good physical and mental health while forced to undergo lockdown, quarantine, or limited movement (movement control order). Boosting the immune system is crucial during such periods for confined people and especially for confined athletes. Specific recommendations must be followed concerning boosting the immune system through physiological and psychological management. This article analyses the available scientific evidence in order to recommend a practical approach, focusing on nutrition, intermittent fasting or caloric restriction, vitamin D insufficiency, sleep pattern, exercise, and psychodynamic aspects as factors impacting the immune system and human health in general.

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.

PurposeArtificial intelligence (AI) has a large number of applications at the industry and user levels. However, AI's uniqueness neglect is becoming an obstacle in the further application of AI. Based on the theory of innovation resistance, this paper aims to explore the effect of AI's uniqueness neglect on consumer resistance to AI.Design/methodology/approachThe authors tested four hypothesis across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI's uniqueness neglect leads to consumer resistance to AI; Studies 2 focused on the role of human–AI interaction trust as an underlying driver of resistance to medical AI. Study 3–4 provided process evidence by way of a measured moderator, testing whether participants with a greater sense of non-verbal human–AI communication are more reluctant to have consumer resistance to AI.FindingsThe authors found that AI's uniqueness neglect increased users' resistance to AI. This occurs because the uniqueness neglect of AI hinders the formation of interaction trust between users and AI. The study also found that increasing the gaze behavior of AI and increasing the physical distance in the interaction can alleviate the effect of AI's uniqueness neglect on consumer resistance to AI.Originality/valueThis paper explored the effect of AI's uniqueness neglect on consumer resistance to AI and uncovered human–AI interaction trust as a mediator for this effect and gaze behavior and physical distance as moderators for this effect.

As part of the global response to COVID-19, categorized by the WHO as a pandemic, social distancing has emerged as a key concept in the management of the bioemergency. This article discusses social distancing from a sociological perspective, drawing on Simmel, Cantó-Milà and Sabido Ramos’ notions of proximity and distance. We intend to illustrate the implicit and explicit meanings and dimensions given to the concepts of social distance and distancing during the initial months of the pandemic, along with their impact on relationships, interactions, and ties. We reflect on the function of framing all social relations in terms of distance and proximity, analytically differentiating between the concepts of relationship, interaction, and ties.

Under the framework of payment for transboundary watershed ecosystem services (ESs), identifying midstream and downstream residents’ preference and willingness to pay (WTP) for ESs provided by the upstream is crucial for sustainable watershed management. The residents’ preferences and WTP are not evenly distributed within the watershed. This study uses a choice experiment assessing the spatial impact of physical distance (including residents’ watershed location and distance to waterbody) and psychological distance on residents’ preference and WTP for the Wei River Basin ESs. The results showed that midstream and downstream residents’ preference and WTP for the ecological attributes have a significant distance-decay effect, either the physical distance to the upstream exit or physical and psychological distance to the waterbody. However, compared with the residents in the midstream, the residents downstream have a stronger preference and WTP for upstream ecological governance. Besides, the distance-decay effect differs between urban and rural residents. There is a psychological distance-decay effect in rural residents’ preference for water quality and a physical distance-decay effect in their preference for water quantity, entertainment area, and cost; there is a physical distance decay effect in urban residents’ preference for entertainment areas. The above differences lead to heterogeneity in WTP and total economic value (TEV) for ESs. When calculating the TEV of the transboundary watershed ES and charging the public, policymakers should consider the location of the residents, the physical and psychological distance to the water body, and the difference between urban and rural areas.

The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.

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.