Attract the best: The attraction effect as an effective strategy to enhance healthy choices

Every day, people make many food decisions without thinking, repeatedly falling for the unhealthy option instead of the healthy option. While making these mindless decisions, people often rely on heuristics. In this paper, we demonstrate that these heuristics can be exploited to nudge consumers towards healthy alternatives. Specifically, we explore how the attraction effect (i.e., adding a decoy to a choice set) can nudge people to choose a healthy snack. The results of our choice experiment indicate that adding a decoy (i.e., a less attractive food alternative) to a self-control situation (i.e., choosing between a healthy and an unhealthy food alternative) can help people maintain self-control and choose the healthy option. This mixed choice set thus nudges people towards the healthy option. Moreover, our results show differential effects of the attraction effect depending on the (un)healthiness of the products in the choice set. Specifically, the attraction effect is prominent when the choice set consists of unhealthy products only (i.e., the unhealthy choice set), but not in the choice set that consists of only healthy products (i.e., healthy choice set). Importantly, our results indicate when the attraction effect can exploit consumers’ heuristics to help them make better, healthier food choices.

An Intelligent Mobile App to Detect Drowsy Driving with Artificial Intelligence

Drowsy driving is lethal- 793 died from accidents related to drowsy driving and 91000 accidents related to drowsy driving occurred [1]. However, drowsy driving and accidents related to drowsy driving are preventable. In this paper, we address the problem through an application that uses artificial intelligence to detect the eye openness of the user. The application can detect the eyes of the user via computer vision. Based on the user’s eye openness and frequencies, the sleepy driving condition can be inferred by this application. We applied our application to actual driving environments on the highway, both day and night, as well as within a normal control situation using a qualitative evaluation approach. The result shows that it is 88% effective during the day and 75% effective during nighttime. This result reveals effectiveness and accuracy of detection during daytime application under controlled testing, which is more flexible and efficient comparing to previous works. Effectiveness and accuracy for nighttime detection and detections with the presence of other distractions can be further improved.

The Impact of Chronic Stress and Eating Concern on Acylated Ghrelin Following Acute Psychological Stress in Healthy Men

Stress, mood, and eating behavior play an important role in appetite and weight regulation. In particular, ghrelin, as the only known orexigenic hormone, has been suggested to be an influential mediator in food intake responses to stress. The exact role of ghrelin in the hypothalamic–pituitary–adrenal axis is still unknown and further challenged by the psychological aspects of stress and eating behavior. This study aimed to assess the effect of chronic stress and subjective concern about eating on acute stress-induced changes in acylated ghrelin. In a 2-day study, sixteen healthy male participants were confronted with a stressful situation as well as a control situation. Additional measurements of heart rate, subjective hunger ratings, and subjective mood ratings were made to assess successful acute stress induction. The linear mixed model approach revealed a significant effect of acute stress on acylated ghrelin for a study-day*chronic-stress interaction (p < 0.001). Concern about eating did not affect acylated ghrelin levels after acute stress exposure. The significant interaction showed that lower chronic stress exposure was associated with a stronger acylated ghrelin response after acute stress exposure versus control condition. At the same time, participants with higher chronic stress exposure showed a blunted acylated ghrelin response after acute stress exposure compared to the control situation. Our findings indicate that chronic stress exposure can influence acylated ghrelin response after acute stress encounters, possibly affecting subsequent food intake and explaining the often diverse outcome in measurements of acute stress responses.

Monitoring multinomial processes based on a weighted chi-square control chart

Interpreting an out-of-control signal is a crucial step in monitoring categorical processes. For the Chi-Square Control Chart (CSCC), an out-of control situation does not specify if it was a process deterioration or a process improvement. For this reason, a weighted chi-square statistical control chart WSCC is proposed with different weighting categories in order to enable an accelerated disclosure of a control situation after a shift due to a deterioration of quality and on the other hand, decelerate an out of control situation after a shift due to a quality improvement. Furthermore, in comparison with Marcucci’s method, the new procedure provides an accurate and easier way to interpret several signals. In other words, the WSCC allows a faster detection of an out-of control situation in the case of a quality deterioration, however, an out-of control situation is not quickly detected in the case of a quality improvement. Indeed, comparative studies have been performed to find the best control chart for each combination. Concluding remarks with comments and recommendations are given based on Average Run Length (ARL) and standard deviation run length (SDRL).

Analysis of The Influence of Big Data on The Cost Control of Tianbao Green Food Company

In the era of big data, the cost control of enterprises is important, and the cost is still the main factor affecting the competitiveness of enterprises. Data information occurs in every part of an enterprise. If these data are collected reasonably and analyzed with professional software system, the effect of enterprise cost control could be quantified. This paper takes Tianbao green food company as an example, studies its cost control situation, summarizes the existing problems in cost control of the company, and analyzes how to use big data to control cost for purchasing, storage, producing and sales.

Effect of Dance on Postural Control in People with Parkinson’s Disease: A Meta-Analysis Review

This systematic review verified the effect of dance on postural control in people with Parkinson’s disease (PD) and investigated whether this practice can be as effective over a short period as when it is performed over a longer period in relation to the postural control of this population. The search was performed in April 2019 in nine databases. Only randomized/quasi-randomized controlled trials with participants with idiopathic PD were included. The meta-analysis of the 11 articles included, with 13 results, showed that the 211 participants with PD, who belonged to the group performing dance, had a standardized mean difference of postural control 0.82 [0.52, 1.12] greater than the 182 participants who were in a control situation. The statistically significant results of this meta-analysis indicate that dance can improve postural control in people with PD in a short period of time and therefore contribute to the prevention of falls.

Hadoop-Based Integrated Monitoring Platform for Risk Prediction Using Big Data

In this paper, we present an integrated monitoring platform framework to enable risk prediction using big data. By using social media and access records of their users, the proposed system determines uncertain risks they could create. By using the risk information that has been obtained from the platform, it can determine whether they should be granted the requested rights to access to restricted areas. And it can set some places that should be monitored as restricted areas. If the intrusion is detected in an area that has been set, then it promptly notifies the administrator with agile viewers showing him/her the intrusion for preparing the next step. So, it will provide a control situation for the administrator in real time and can detect the risky moments that may occur in advance.