A Dataset on Population Activity Patterns in Typical Regions of North China

This data article describes the "Typical Regional Activity Patterns" (TRAP) dataset, which is based on the Tackling Key Problems in Air Pollution Control Program. In order to explore the interaction between air pollution and physical activity, we collected activity patterns of 9,221 residents with different occupations and lifestyles for three consecutive days in typical regions (Jinan and Baoding) where air pollutant concentrations were higher than those in neighboring areas. The TRAP dataset consists of two aspects of information: demographic indicators (personal information, occupation, personal habits, and living situation) and physical activity pattern data (activity location and intensity); additionally, the exposure measures of physical activity patterns are included, which data users can match to various endpoints for their specific purpose. This dataset provides evidence for exploring the attributes of activity patterns of residents in northern China and for interdisciplinary researchers to develop strategies and measures for health education and health promotion.

Research on the Recognition and Tracking of Group-Housed Pigs' Posture Based on Edge Computing

The existing algorithms for identifying and tracking pigs in barns generally have a large number of parameters, relatively complex networks and a high demand for computational resources, which are not suitable for deployment in embedded-edge nodes on farms. A lightweight multi-objective identification and tracking algorithm based on improved YOLOv5s and DeepSort was developed for group-housed pigs in this study. The identification algorithm was optimized by: (i) using a dilated convolution in the YOLOv5s backbone network to reduce the number of model parameters and computational power requirements; (ii) adding a coordinate attention mechanism to improve the model precision; and (iii) pruning the BN layers to reduce the computational requirements. The optimized identification model was combined with DeepSort to form the final Tracking by Detecting algorithm and ported to a Jetson AGX Xavier edge computing node. The algorithm reduced the model size by 65.3% compared to the original YOLOv5s. The algorithm achieved a recognition precision of 96.6%; a tracking time of 46 ms; and a tracking frame rate of 21.7 FPS, and the precision of the tracking statistics was greater than 90%. The model size and performance met the requirements for stable real-time operation in embedded-edge computing nodes for monitoring group-housed pigs.

A Mousepad Triboelectric-Piezoelectric Hybrid Nanogenerator (TPHNG) for Self-Powered Computer User Behavior Monitoring Sensors and Biomechanical Energy Harvesting

Hybrid nanogenerators based on the principle of surface charging of functional films are significant in self-powering sensing and energy conversion devices due to their multiple functions and high conversion efficiency, although applications remain limited due to a lack of suitable materials and structures. Here, we investigate a triboelectric-piezoelectric hybrid nanogenerator (TPHNG) in the form of a mousepad for computer user behavior monitoring and energy harvesting. Triboelectric and piezoelectric nanogenerators with different functional films and structures work independently to detect sliding and pressing movements, and the profitable coupling between the two nanogenerators leads to enhanced device outputs/sensitivity. Different mouse operations such as clicking, scrolling, taking-up/putting-down, sliding, moving rate, and pathing can be detected by the device via distinguishable patterns of voltage ranging from 0.6 to 36 V. Based on operation recognition, human behavior monitoring is realized, with monitoring of tasks such as browsing a document and playing a computer game being successfully demonstrated. Energy harvesting from mouse sliding, patting, and bending of the device is realized with output voltages up to 37 V and power up to 48 μW while exhibiting good durability up to 20,000 cycles. This work presents a TPHNG utilizing surface charging for self-powered human behavior sensing and biomechanical energy harvesting.

Power Up: Combining Behavior Monitoring Software with Business Intelligence Tools to Enhance Proactive Animal Welfare Reporting

Simple Summary

Monitoring animal behavior over time is important for zoos and aquariums seeking to continually evaluate animal welfare. Although new digital tools are making behavior monitoring more accessible, analyzing behavior data in a timely manner to draw meaningful insights can be challenging. Business intelligence software has the potential to help address these challenges. Business intelligence software is a class of tools that combines the ability to integrate multiple data streams with advanced analytics and robust data visualizations. Here, I highlight features of the Microsoft Power BI platform as an example. Power BI is a leading option in business intelligence software and is freely available. To demonstrate the potential of business intelligence tools for behavior monitoring, I provide two example data dashboards of data recorded using the ZooMonitor behavior recording software. The first dashboard illustrates a simple quarterly behavior summary to track behavior changes in an ongoing manner. The second dashboard visualizes data relating to enrichment evaluation. I hope this introduction to business intelligence software and the Microsoft Power BI platform can provide researchers and managers in zoos and aquariums with new tools to support their evidence-based decision-making processes.

Abstract

Animal welfare is a dynamic process, and its evaluation must be similarly dynamic. The development of ongoing behavior monitoring programs in zoos and aquariums is a valuable tool for identifying meaningful changes in behavior and allows proactive animal management. However, analyzing observational behavior data in an ongoing manner introduces unique challenges compared with traditional hypothesis-driven studies of behavior over fixed time periods. Here, I introduce business intelligence software as a potential solution. Business intelligence software combines the ability to integrate multiple data streams with advanced analytics and robust data visualizations. As an example, I provide an overview of the Microsoft Power BI platform, a leading option in business intelligence software that is freely available. With Power BI, users can apply data cleaning and shaping in a stepwise fashion, then build dashboards using a library of visualizations through a drag-and-drop interface. I share two examples of data dashboards built with Power BI using data from the ZooMonitor behavior recording app: a quarterly behavior summary and an enrichment evaluation summary. I hope this introduction to business intelligence software and Microsoft Power BI empowers researchers and managers working in zoos and aquariums with new tools to enhance their evidence-based decision-making processes.

Three-Dimensional Tracking of Multiple Small Insects by a Single Camera

Many systems to monitor insect behavior have been developed recently. Yet most of these can only detect two-dimensional behavior for convenient analysis and exclude other activities, such as jumping or flying. Therefore, the development of a three-dimensional (3D) monitoring system is necessary to investigate the 3D behavior of insects. In such a system, multiple-camera setups are often used to accomplish this purpose. Here, a system with a single camera for tracking small insects in a 3D space is proposed, eliminating the synchronization problems that typically occur when multiple cameras are instead used. With this setup, two other images are obtained via mirrors fixed at other viewing angles. Using the proposed algorithms, the tracking accuracy of five individual drain flies, Clogmia albipunctata (Williston) (Diptera: Psychodidae), flitting about in a spherical arena (78 mm in diameter) is as high as 98.7%, whereas the accuracy of 10 individuals is 96.3%. With this proposed method, the 3D trajectory monitoring experiments of insects can be performed more efficiently.

Youth and Provider Perspectives on Behavior-Tracking Mobile Apps: Qualitative Analysis

BACKGROUND

Mobile health apps stand as one possible means of improving evidence-based mental health interventions for youth. However, a better understanding of youth and provider perspectives is necessary to support widespread implementation.

OBJECTIVE

The objective of this research was to explore both youth and provider perspectives on using mobile apps to enhance evidence-based clinical care, with an emphasis on gathering perspectives on behavior-tracking apps.

METHODS

Inductive qualitative analysis was conducted on data obtained from semistructured interviews held with 10 youths who received psychotherapy and 12 mental health care providers who conducted therapy with youths aged 13-26 years. Interviews were independently coded by multiple coders and consensus meetings were held to establish reliability.

RESULTS

During the interviews, the youths and providers broadly agreed on the benefits of behavior tracking and believed that tracking via app could be more enjoyable and accessible. Providers and youths also shared similar concerns that negative emotions and user burden could limit app usage. Participants also suggested potential app features that, if implemented, would help meet the clinical needs of providers and support long-term use among youth. Such features included having a pleasant user interface, reminders for clients, and graphical output of data to clients and providers.

CONCLUSIONS

Youths and providers explained that the integration of mobile health into psychotherapy has the potential to make treatment, particularly behavior tracking, easy and more accessible. However, both groups had concerns about the increased burden that could be placed on the clients and providers.

The Relationship Between Childhood Stress and Distinct Stages of Dynamic Behavior Monitoring in Adults: Neural and Behavioral Correlates

Childhood adversity is a major risk factor for emotional and cognitive disorders later in adulthood. Behavior monitoring, one of the most important components of cognitive control, plays a crucial role in flexible interaction with the environment. Here, we test a novel conceptual model discriminating between two distinct dimensions of childhood adversity (i.e., deprivation and threat) and examine their relations to dynamic stages of behavior monitoring. Sixty young healthy adults participated in this study using event-related potentials (ERPs) and the dynamic stages of behavior monitoring including response inhibition, error detection, and post-error adjustments were investigated in a classical Go/NoGo task. Multiple regression analyses revealed that participants with higher severity of childhood adversity recruited more controlled attention, as indicated by larger (more negative) conflict detection-related NoGo-N2 amplitudes and larger (more negative) error detection-related ERN amplitudes. Higher severity of childhood abuse (an indicator of threat) was related to smaller (less positive) error appraisal-related Pe amplitudes on the neural level and subsequently lower post-error accuracy on the behavioral level. These results suggested that prefrontal-supported controlled attention is influenced by universal adversity in childhood while the error-related behavioral adjustment is mainly affected by childhood abuse, indicating the dimensions of deprivation and threat are at least partially distinct.

Ensuring Elderly Well-Being During COVID-19 by Using the Internet of Things

Coronavirus disease (COVID-19) cases continue to surge, and the world must learn from this disaster. Most of the world economies are shattered due to this pandemic. The development of infrastructure to counter such deadly viral attacks in the future is the wisest investment that can be made. The elderly population is the most vulnerable age group affected by the pandemic, and the threat to their lives becomes manifold if they are living alone. Thus, a well-formed elderly support framework is required to safeguard this vulnerable population from COVID-like disasters in the future. We report here on the research findings we conducted by laying out a mitigation system for elderly well-being during disastrous times. The proposed system demands a sound collaboration between software, hardware devices, the state, and social agencies.

Mapping Shade Availability and Use in Zoo Environments: A Tool for Evaluating Thermal Comfort

For many species in zoos, particularly megafauna vulnerable to heat stress, shade is a key environmental resource. However, shade availability has received comparatively less attention than other aspects of the zoo environment. In this study, we share a simple low-cost approach that we applied to document shade availability across 33 zoo enclosures. We then combined these assessments with behavioral observations of enclosure use and shade-seeking behavior during summer months in a case study focused on Sichuan takin (Budorcas taxicolor tibetana) (n = 3), a large cold-adapted bovid. Behavioral observations were conducted before and after installation of a shade sail for the takin. Results indicated that shade availability varied widely across zoo enclosures, with the percent of shaded space ranging from 85 % to 22 % across enclosures during summer months. Shade was a dynamic resource and increased throughout the year and fluctuated across the day, with the least shade available midday. Takin showed general preferences for shaded areas near the walls of their enclosure but were observed using newly available shade from the shade sail after its installation. These accessible methods can be easily applied to assess shade within existing enclosures, evaluate enclosure modifications, and provide guidance for the design of new enclosures.