EFFECT OF WEARABLE FITNESS WATCH ON PHYSICAL ACTIVITY IN SCHOOL CHILDREN WITH OVERWEIGHT

Overweight and obesity in children and adolescents is a significant health problem, and lack of physical activity may be a factor related to obesity. In this study, we explored the wearable fitness watch to monitor the physical activity promoted situation of overweight and obese schoolchildren. Participants were overweight schoolchildren divided into two groups, the study group (n=41) and the control group (n=44). In the control group, the wearable fitness watch only displays the time. In the study group, each week, participants set up the goal, which increases the steps by 10% from baseline steps to reach 10,000 steps. The ANOVA is adopted to analyze the parameters such as the average daily number of steps, moderate to vigorous physical activity (MVPA). The results indicated the study group increase and improved daily walking steps and MVPA accumulation time in 8 weeks. The wearable fitness watch can help to promote the physical activity of overweight children

Adversarial Attack on Community Detection by Hiding Individuals

It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In this paper, we extend adversarial graphs to the problem of community detection which is much more difficult. We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models, which has many applications in real-world scenarios, for example, protecting personal privacy in social networks and understanding camouflage patterns in transaction networks. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model. We also find that the adversarial graphs generated by our method can be transferred to other learning based community detection models.Comment: In Proceedings of The Web Conference 2020, April 20-24, 2020, Taipei, Taiwan. 11 page

Optimization of Hydrolysis Conditions for the Production of Iron-Binding Peptides from Mackerel Processing Byproducts

Abstract: The aim of this study was focused on optimization of enzymatic hydrolysis conditions for the production of iron-binding peptides from marine mackerel processing byproducts. The marine mackerel processing byproducts protein were hydrolyzed using trypsin, Protamex, Flavourzyme, Alcalase and Neutrase. Alcalase and Protamex proteolytic hydrolysates exhibited the highest iron-binding capacity; however, Alcalase proteolytic hydrolysate had higher degree of hydrolysis than that of Protamex. A four-factor-three-level composition central design experiment in response surface methodology was used to optimize the enzymatic hydrolysis conditions of Alcalase. The optimal enzymatic hydrolysis conditions were temperature of 46.0°C, time of 2.01 h, pH 8.35 and enzyme to substrate 6460 U/mL. The quadratic model predicted well about the actual measured value. The average iron-binding capacity of three verification experiment was 6.62 mg-EDTA/g-protein, which was much closed to model predicted value of 6.69 mg-EDTA/g-protein

High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications