Effects of ICTs in mega events on national image formation: the case of PyeongChang Winter Olympic Games in South Korea

Purpose Among a wide array of information and communication technologies (ICTs) used to directly or indirectly support the mega events are reality boosting technologies and smart tourism technologies. Building upon the halo effect, this study aims to explain the connection between satisfaction with ICTs used in mega event, national image and event participants’ future behavior. Design/methodology/approach The analyzed data included survey responses from 246 foreigners who visited PyeongChang as a visitor for the 2018 Winter Olympics. Findings The results showed that both reality boosting technologies and smart tourism technologies directly or indirectly influence overall experience satisfaction by way of transaction satisfaction. Furthermore, the two types of satisfaction were found to positively influence the national image of the host country, which consequently has a positive effect on visitors’ future behavior. Originality/value This study aimed to explore two different roles of ICTs in mega events by focusing more on the visitors who came to PyeongChang for the Olympics. The originality of this study lies in its attempt to examine the mechanisms in which visitors’ satisfaction from ICT-based experiences in mega events contributes to forming a positive image toward the host country.

Vehicle Interaction Behavior Prediction with Self-Attention

The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.

History of one’s own performance modulates evaluative processing of another’s action outcomes, but not vice versa

In action monitoring, i.e., evaluating an outcome of our behavior, a reward prediction error signal is calculated as the difference between actual and predicted outcomes and is used to adjust future behavior. Previous studies demonstrate that this signal, which is reflected by an event-related brain potential called feedback-related negativity (FRN), occurs in response to not only one's own outcomes, but also those of others. However, it is still unknown if predictions of different actors' performance interact with each other. Thus, we investigated how predictions from one’s own and another’s performance history affect each other by manipulating the task difficulty for participants themselves and their partners independently. Pairs of participants performed a time estimation task, randomly switching the roles of actor and observer from trial to trial. Results show that the history of the other’s performance did not modulate the amplitude of the FRN for the evaluation of one’s own outcomes. In contrast, the amplitude of the observer FRN for the other’s outcomes differed according to the frequency of one’s own action outcomes. In conclusion, the monitoring system tracks the histories of one’s own and observed outcomes separately and considers information related to one’s own action outcomes to be more important.

Pandemic-related decline in injuries related to women wearing high-heeled shoes: Analysis of U.S. data for 2016-2020

Background Wearing high-heeled shoes is associated with injury risk. During the COVID-19 pandemic, changes in work and social behavior may have reduced women's use of such footwear. Methods This study assessed the trend in high-heel related injuries among U.S. women, using 2016-2020 data from the U.S. Consumer Product Safety Commission's National Electronic Injury Surveillance System (NEISS). Results In 2020 there were an estimated 6,290 high-heel related emergency department visits involving women ages 15-69, down from 16,000 per year in 2016-2019. The 2020 decline began after the start of the COVID-19 shutdowns on March 15. There was no significant change in the percentage of fractures or hospital admissions. Conclusions The COVID-19 pandemic was associated with a decline in reported injuries related to high-heeled shoes among US women. If this resulted from fewer women wearing such shoes, and such habits influence future behavior, the result may be fewer injuries in the future.

Relationships Between University Students’ Gender and Physical Activity (PA) and Their Exercise Self-Schemas, Processing of PA-Related Information, and PA Decision-Making

In the current study, we investigated the effects of gender and regular physical activity (PA) on PA decision-making and speed of information processing. We enrolled 110 university students ( Mage = 20.91, SD =2.28 years) in an experiment involving two tasks and a questionnaire. One of the two tasks assessed how much participants agreed with presented PA words and phrases and the other task predicted behavior and responses to future situations. We collected and measured the participants’ choices and the time they took to make them. The questionnaire, the International Physical Activity Questionnaire (IPAQ), consisted of exercise self-schema and PA questions. We conducted a 2 (gender: male or female) ×2 (regular PA or not) multivariate analysis of variance (MANOVA) and found statistically significant differences between variables as a function of participants’ gender (λ = .66, p < .001) and regular PA engagement (λ = .51, p < .001). In a regression analysis, we also found gender differences [males showed relationships between agreement with PA information and information processing speed for decisions on future behavior ( R 2 = .31, F = 12.50); females showed relationships between their exercise self-schema ( R 2 = .26, F = 18.18) and regular PA such that, in the non-regular PA group, exercise self-schema was related to reaction time in making decisions on future behavior ( R 2 = .29, F = 11.23), and in the regular PA group, agreement with PA information was related to reaction time for PA-related words, and agreement with non-PA information ( R 2 = .29, F = 8.91)]. These results highlight the need to consider participant characteristics when designing exercise interventions, and we present supplementary data regarding exercise self-schemas, decision-making, and the speed of processing PA information.

Tourism Market Segmentation Applied to Coastal and Marine Destinations: A Study from Acapulco, Mexico

Coastal and marine destinations offer alternate options for the sun and the beach, options that are related to nature and culture. This empirical study aims to segment the demand of domestic tourism in coastal and marine destinations and its relationship with satisfaction and loyalty. A factorial analysis and an analysis of K-means clusters were used to reduce and group data. Six motivational dimensions are evident heritage and nature, learning, and sun and beach; and physical, authentic coastal experience, novelty, and social interaction. Two segments were found: the “multiple coastal motives,” which returned a high motivation among the motivational variables proposed and are related to all the factors found, and the “beach lovers”, with high motivation in the aspects of sun and beach, resting, and wanting to see things they do not usually see. These two segments are related to the dimensions of sun and beach and novelty. The multiple coastal motives rendered higher levels of satisfaction and in some variables of future behavior, which shows the relationship of the motivation with the visit. The findings are used to develop marketing plans appropriate to the characteristics of the demand found in each group.

The effect of COVID-19 on the Egyptian exchange using principal component analysis

PurposeSince the beginning of 2020, economies faced many changes as a result of coronavirus disease 2019 (COVID-19) pandemic. The effect of COVID-19 on the Egyptian Exchange (EGX) is investigated in this research.Design/methodology/approachTo explore the impact of COVID-19, three periods were considered: (1) 17 months before the spread of COVID-19 and the start of the lockdown, (2) 17 months after the spread of COVID-19 and the during the lockdown and (3) 34 months comprehending the whole period (before and during COVID-19). Due to the large number of variables that could be considered, dimensionality reduction method, such as the principal component analysis (PCA) is followed. This method helps in determining the most individual stocks contributing to the main EGX index (EGX 30). The PCA, also, addresses the multicollinearity between the variables under investigation. Additionally, a principal component regression (PCR) model is developed to predict the future behavior of the EGX 30.FindingsThe results demonstrate that the first three principal components (PCs) could be considered to explain 89%, 85%, and 88% of data variability at (1) before COVID-19, (2) during COVID-19 and (3) the whole period, respectively. Furthermore, sectors of food and beverage, basic resources and real estate have not been affected by the COVID-19. The resulted Principal Component Regression (PCR) model performs very well. This could be concluded by comparing the observed values of EGX 30 with the predicted ones (R-squared estimated as 0.99).Originality/valueTo the best of our knowledge, no research has been conducted to investigate the effect of the COVID-19 on the EGX following an unsupervised machine learning method.

Intelligent Collision Avoidance Method for Motion Obstacles of Unmanned Vehicles

At present, the active technology of automobiles is becoming more and more mature and the emergence of driverless vehicles makes it a hotspot in the field of road safety. A new intelligent collision avoidance method for unmanned vehicle motion obstacles is proposed. The kinematics model of unmanned vehicles is established and linearized to obtain the kinematics linear tracking error model of unmanned vehicles and predict the future behavior of unmanned vehicles. The intelligent collision avoidance can be achieved by improving the artificial potential field model of the unmanned vehicle after understanding the future behavior and obstacle information of the unmanned vehicle. The experimental results show that the method has a high detection rate and success rate of obstacle avoidance and low total time-consuming in the process of behavior selection and path planning. It can quickly make collision avoidance responses and reduce the possibility of collision.

Localizing Epileptic Foci Using Simultaneous EEG-fMRI Recording: Template Component Cross-Correlation

Conventional EEG-fMRI methods have been proven to be of limited use in the sense that they cannot reveal the information existing in between the spikes. To resolve this issue, the current study obtains the epileptic components time series detected on EEG and uses them to fit the Generalized Linear Model (GLM), as a substitution for classical regressors. This approach allows for a more precise localization, and equally importantly, the prediction of the future behavior of the epileptic generators. The proposed method approaches the localization process in the component domain, rather than the electrode domain (EEG), and localizes the generators through investigating the spatial correlation between the candidate components and the spike template, as well as the medical records of the patient. To evaluate the contribution of EEG-fMRI and concordance between fMRI and EEG, this method was applied on the data of 30 patients with refractory epilepsy. The results demonstrated the significant numbers of 29 and 24 for concordance and contribution, respectively, which mark improvement as compared to the existing literature. This study also shows that while conventional methods often fail to properly localize the epileptogenic zones in deep brain structures, the proposed method can be of particular use. For further evaluation, the concordance level between IED-related BOLD clusters and Seizure Onset Zone (SOZ) has been quantitatively investigated by measuring the distance between IED/SOZ locations and the BOLD clusters in all patients. The results showed the superiority of the proposed method in delineating the spike-generating network compared to conventional EEG-fMRI approaches. In all, the proposed method goes beyond the conventional methods by breaking the dependency on spikes and using the outside-the-scanner spike templates and the selected components, achieving an accuracy of 97%. Doing so, this method contributes to improving the yield of EEG-fMRI and creates a more realistic perception of the neural behavior of epileptic generators which is almost without precedent in the literature.

Violence in Mexico: the politicides in Puebla from 2010-2020

This paper argues that the decision making of the political class is one of the main causes of violence and poverty, by presenting statistics that show the evolution of violence in the state of Puebla, Mexico, evidencing manifestations of violence: kidnappings, extortions and murders in the political class. The method used in the process is of a mixed type, used to collect and present statistical data and, through inferential analysis, project their future behavior, thus seeking a change in their behavior, which gives it a normative character. Among the main results, there is evidence of a rise in politicides in Puebla, normalizing violence within the population, this leads to identify a dystopia, following Lorenzo Meyer "the negative aspects of the exercise of power dominate to an extreme degree "(Meyer, 2017, p. 13). There remains for discussion the need to reinterpret the scope of violence, which, although it may reflect a sense of defense, the evidence shows the opposite. In this way, a reflection is drawn on the risks of such violence and the possibilities of turning the state apparatus into a failed one.