scholarly journals The influence of Morning Exercises Individual Differences on Mental Health Based on the Network Big Data

2017 ◽  
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Individual Differences ◽  
Network Big Data

Using Mobile Network Big Data for Informing Transportation and Urban Planning in Colombo

2014 ◽  
Author(s):  
Sriganesh Lokanathan ◽  
Gabriel Kreindler ◽  
Nisansa Dilushan de Silva ◽  
Yuhei Miyauchi ◽  
Dedunu Dhananjaya
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Mobile Network ◽  
Network Big Data

Toward Practical Anomaly Detection in Network Big Data

2017 ◽  
pp. 411-432
Author(s):  
Chengqiang Huang ◽  
Yulei Wu ◽  
Zuo Yuan ◽  
Geyong Min
Keyword(s):  
Big Data ◽  
Anomaly Detection ◽  
Network Big Data

scholarly journals Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students

2018 ◽  
Author(s):  
Jeremy F. Huckins ◽  
Alex W. daSilva ◽  
Rui Wang ◽  
Weichen Wang ◽  
Elin L. Hedlund ◽  
...  
Keyword(s):  
Mental Health ◽  
Individual Differences ◽  
Antidepressant Treatment ◽  
Brain Activity ◽  
Resting State Functional Connectivity ◽  
Neuroscience Research ◽  
Affective Processes ◽  
Usage Patterns ◽  
Mobile Phone Sensing ◽  
Functional Brain Activity

AbstractAs smartphone usage has become increasingly prevalent in our society, so have rates of depression, particularly among young adults. Individual differences in smartphone usage patterns have been shown to reflect individual differences in underlying affective processes such as depression (Wang et al., 2018). In the current study, we identified a positive relationship between smartphone screen time (e.g. phone unlock duration) and resting-state functional connectivity (RSFC) between the subgenual cingulate cortex (sgCC), a brain region implicated in depression and antidepressant treatment response, and regions of the ventromedial/orbitofrontal cortex, such that increased phone usage was related to stronger connectivity between these regions. We then used this cluster to constrain subsequent analyses looking at depressive symptoms in the same cohort and observed partial replication in a separate cohort. We believe the data and analyses presented here provide relatively simplistic initial analyses which replicate and provide a first step in combining functional brain activity and smartphone usage patterns to better understand issues related to mental health. Smartphones are a prevalent part of modern life and the usage of mobile sensing data from smartphones promises to be an important tool for mental health diagnostics and neuroscience research.

scholarly journals Optimization of Evacuation and Walking-Home Routes from Osaka City After a Nankai Megathrust Earthquake Using Road Network Big Data

2021 ◽  
pp. 369-389
Author(s):  
Atsushi Takizawa ◽  
Yutaka Kawagishi
Keyword(s):  
Big Data ◽  
Public Transportation ◽  
Urban Areas ◽  
Road Network ◽  
Large Earthquake ◽  
The Road ◽  
Return Home ◽  
Flooded Area ◽  
Network Big Data ◽  
Surrounding Areas

AbstractWhen a disaster such as a large earthquake occurs, the resulting breakdown in public transportation leaves urban areas with many people who are struggling to return home. With people from various surrounding areas gathered in the city, unusually heavy congestion may occur on the roads when the commuters start to return home all at once on foot. In this chapter, it is assumed that a large earthquake caused by the Nankai Trough occurs at 2 p.m. on a weekday in Osaka City, where there are many commuters. We then assume a scenario in which evacuation from a resulting tsunami is carried out in the flooded area and people return home on foot in the other areas. At this time, evacuation and returning-home routes with the shortest possible travel times are obtained by solving the evacuation planning problem. However, the road network big data for Osaka City make such optimization difficult. Therefore, we propose methods for simplifying the large network while keeping those properties necessary for solving the optimization problem and then recovering the network. The obtained routes are then verified by large-scale pedestrian simulation, and the effect of the optimization is verified.

scholarly journals Theories of Personality

2020 ◽  
Author(s):  
Cassandra M Brandes ◽  
Kathleen Wade Reardon ◽  
Jennifer L Tackett
Keyword(s):  
Mental Health ◽  
Academic Achievement ◽  
Individual Differences ◽  
Life Course ◽  
Personality Traits ◽  
Interpersonal Relationships ◽  
Personality Development ◽  
Adult Individual ◽  
Foundational Research ◽  
The Life Course

The study of personality development has seen significant advances in the last two decades. For many years, youth and adult individual differences were studied from separate theoretical standpoints. However, more recent research has indicated that teenagers display personality traits in many of the same ways as adults. These personality traits are moderately stable throughout the life course, but there are important developmental shifts in their expression, structure, and maturation, especially in adolescence. This has resulted in an effort to study youth personality “in its own right” (Tackett, Kushner, De Fruyt, & Mervielde, 2013). Early personality associations with important lifelong outcomes including academic achievement, mental health, and interpersonal relationships further underscore the importance of studying traits in youth. Here we discuss current consensus and controversy on adolescent personality and highlight foundational research on the topic.

Sign in / Sign up

Export Citation Format

Share Document