The Relationship between high risk athletes risk perception of adventurous activities, their travel destinations and their vacation travel patterns

Adventure tourism is a growing market segment in the US; the decision to participate in adventurous activities and the selection of risky destinations while on vacation involves ones risk perception about the physical risks that can lead to fatal results from these activities. Existing literature suggest that risk perception is a determinate in choosing a leisure activity and destination while on vacation and that level of tolerance to uncertainty can determine travel behavior (Floyd & Gibson, 2004; Fuchs & Reichel, 2006; Hofstede, 2001; Iso- Ahola, 1980, 1982; Money & Crotts, 2003; Reisinger & Mavondo, 2005). People who have high tolerance to risk and uncertainty and are seeking high risk activities and destinations are defined in the literature as sensations seekers. Research shows that high risk athletes are sensation seekers that would take risks for the sake of novelty experience and that their risk perception of risky activities is different that of non-athletes (Straub, 1982; Woitas-Slubowska, 2006; Zuckerman, 2007). This research will look at high risk athletes risk perceptions of risky destinations and activities to determine the probability of them participating in adventure tourism

Occupational Risks of Health Professionals

Health service is an important work area which can lead to important risks related to occupational health and safety (OHS) of employees. This book chapter is prepared to evaluate the effects of occupational risks on health and decrease the exposure to occupational risks of health professionals by searching national and international literatüre. Thus, awareness can be raised to define occupational risks and help planning services for health professionals. American National Institute for Occupational Safety and Health (NIOSH) has reported 29 kinds of physical, 25 kinds of chemical, biological 24 varieties, 10 and six kinds of ergonomic and psycho‐social hazards and risks. According to ILO, it has been reported that there is 1.25 trillion dollars loss each year due to the OHS problems. In Turkey, the loss of only social security systems has been reported as approximately 4 million Turkish Liras per year. Health professionals have work stress, and they suffer from the inconvenient design and the hazards within the workplace. The health of the health professionals affects the health of the community. Thus, it is important to decrease the exposure to occupational risks of health professionals and diligently work on this issue

Multi-task Self-Supervised Learning for Human Activity Detection

Deep learning methods are successfully used in applications pertaining to ubiquitous computing, health, and well-being. Specifically, the area of human activity recognition (HAR) is primarily transformed by the convolutional and recurrent neural networks, thanks to their ability to learn semantic representations from raw input. However, to extract generalizable features, massive amounts of well-curated data are required, which is a notoriously challenging task; hindered by privacy issues, and annotation costs. Therefore, unsupervised representation learning is of prime importance to leverage the vast amount of unlabeled data produced by smart devices. In this work, we propose a novel self-supervised technique for feature learning from sensory data that does not require access to any form of semantic labels. We learn a multi-task temporal convolutional network to recognize transformations applied on an input signal. By exploiting these transformations, we demonstrate that simple auxiliary tasks of the binary classification result in a strong supervisory signal for extracting useful features for the downstream task. We extensively evaluate the proposed approach on several publicly available datasets for smartphone-based HAR in unsupervised, semi-supervised, and transfer learning settings. Our method achieves performance levels superior to or comparable with fully-supervised networks, and it performs significantly better than autoencoders. Notably, for the semi-supervised case, the self-supervised features substantially boost the detection rate by attaining a kappa score between 0.7-0.8 with only 10 labeled examples per class. We get similar impressive performance even if the features are transferred from a different data source. While this paper focuses on HAR as the application domain, the proposed technique is general and could be applied to a wide variety of problems in other areas

Distributed Fault Detection in Smart Spaces Based on Trust Management

AbstractApplication performance in a smart space is affected by faulty behaviours of nodes and communication networks. Detection of faults helps diagnosis of problems and maintenance can be done to restore performance, for example, by replacing or reconfiguring faulty parts. Fault detection methods in the literature are too complex for typical low-resource devices and they do not perform well in detecting intermittent faults. We propose a fully distributed fault detection method that relies on evaluating statements about trustworthiness of aggregated data from neighbors. Given one or more trust statements that describe a fault-free state, the trustor node determines for each observation coming from the trustee whether it is an outlier or not. Several fault types can be explored using different trust statements whose parameters are assessed differently. The trustor subsequently captures the observation history of the trustee node in only two evidence variables using evidence update rules that give more weight to recent observations. The proposed method detects not only permanent faults but also intermittent faults with high accuracy and low false alarm rate

The evaluation of physical activity and physical fitness levels of the adolescents staying in the orphanage

In this study, it was aimed to evaluate the physical activity and physical fitness levels of the adolescents who have stayed in the orphanage, to comment on the current situation of the playfields in which adolescents have participated the sport and recreational activities and to support the legislative regulations about the administration of the orphanages. The sample of the study was 83 adolescents (n=32 girls, n=51 boys and age=15.67±1.27) from Konya Orphanage. The physical activity levels of the subjects were evaluated by Physical Activity Questionnaire for Children (CFAO) adapted to Turkish from the Physical Activity Questionnaire for Older Children (PAQ-C) developed by Crocker et al (1997). Fitnessgram Test Battery was also used for the physical fitness levels of the subjects. According to gender, it was seen that there were some statistically significant differences in favor of boys in the anthropometric and physiological features (height, weight, BMI, VO2max). As it was understood that there were no significant differences in the push-up and sit-up test scores between the boys and girls, there were significant differences in the flexibility test scores in favor of girls. According to age and gender, 18,1% (n=15) of the subjects took part in the Needs Improvement-Health Risk (NI-HR), 19,3% (n=16) of them took part in the Needs Improvement (NI), 62,7% (n=52) of them took part in the Healthy Fitness Zone (HFZ) in aerobic capacity parameter (VO2max ml/kg/min); besides in Body Mass Index (BMI) parameter 13,3% (n=11) of the subjects took part in the Needs Improvement-Health Risk (NI-HR), 15,7% (n=13) of them took part in the Needs Improvement (NI), 71,1% (n=59) of them took part in the Healthy Fitness Zone (HFZ). It was also understood that 53% (n=44) of the subjects were inactive, 32,5% (n=27) of them were mid-active, 14,5% (n=12) of them were active. As a conclusion, it was seen that the adolescents who participated in the study were healthy according to the body composition, aerobic capacity and physical fitness standards determined by The Cooper Institute (2010). However, according to the classification which was applied in view of the reference values by Tanır (2013), it was concluded that most of the adolescents were inactive or mid-active in terms of physical activity. Even though the playfields in the orphanages were efficient, it has been thought that the legislative regulations for the reformation and diversification of the current playfields and organizing the recreational activities will not only support the maintenance and improvement of the current physical fitness levels of adolescents and also cause to enhancement in their physical activity levels

An abdominal tuberculosis case mimicking an abdominal mass

Abdominal tuberculosis is rare in childhood. It may be difficult to diagnose as it mimics various disorders. We present a 12-year-old child with an unusual clinical presentation who was diagnosed with abdominal tuberculosis only perioperatively

Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation

Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy. Despite its potential benefits, FL is hindered by excessive communication costs due to repeated server-client communication during training. To address this challenge, model compression techniques, such as sparsification and weight clustering are applied, which often require modifying the underlying model aggregation schemes or involve cumbersome hyperparameter tuning, with the latter not only adjusts the model's compression rate but also limits model's potential for continuous improvement over growing data. In this paper, we propose FedCompress, a novel approach that combines dynamic weight clustering and server-side knowledge distillation to reduce communication costs while learning highly generalizable models. Through a comprehensive evaluation on diverse public datasets, we demonstrate the efficacy of our approach compared to baselines in terms of communication costs and inference speed.Comment: 9 pages, 2 figures, Accepted on ICASSP 202