Activity Pattern Discovery from Network Captures

2016 ◽  
Author(s):  
Alan C. Lin ◽  
Gilbert L. Peterson
Keyword(s):  
Activity Pattern ◽  
Pattern Discovery

scholarly journals Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery

2016 ◽  
Vol 10 (7) ◽  
pp. 1238-1253 ◽  
Author(s):  
Gal Mishne ◽  
Ronen Talmon ◽  
Ron Meir ◽  
Jackie Schiller ◽  
Maria Lavzin ◽  
...  
Keyword(s):  
Activity Pattern ◽  
Pattern Discovery ◽  
Neuronal Structure ◽  
Geometry Analysis ◽  
Structure And Activity

Intensity of Human Predation on Rocky Shores at Las Cruces in Central Chile

1987 ◽  
Vol 14 (2) ◽  
pp. 143-149 ◽  
Author(s):  
L. René Durán ◽  
Juan Carlos Castilla ◽  
Doris Oliva
Keyword(s):  
Activity Pattern ◽  
Rocky Shore ◽  
Rocky Shores ◽  
Central Chile ◽  
Temporal Grouping ◽  
Human Predation

The rocky shore of central Chile is heavily harvested by mariscadores de orilla and skin-divers, but their catches are not considered in the fishery statistics. The aim of the present paper is to estimate the intensity of human predation and annual catch of each of the species taken at Las Cruces, Central Chile. The activity pattern of both categories of collectors demonstrate a temporal grouping. The observation of mariscadores de orilla and skin-divers in 3 sectors of fringe totalling 1,500 m of rocky shore during 12 months allowed us to estimate the annual catch per species caught (kg per year).

scholarly journals Image-based activity pattern segmentation using longitudinal data of the German Mobility Panel

2020 ◽  
Vol 8 ◽  
pp. 100264
Author(s):  
Sascha von Behren ◽  
Tim Hilgert ◽  
Sophia Kirchner ◽  
Bastian Chlond ◽  
Peter Vortisch
Keyword(s):  
Longitudinal Data ◽  
Activity Pattern ◽  
Pattern Segmentation

scholarly journals Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing

2021 ◽  
Vol 18 (11) ◽  
pp. 5884
Author(s):  
Junli Liu ◽  
Panli Cai ◽  
Jin Dong ◽  
Junshun Wang ◽  
Runkui Li ◽  
...  
Keyword(s):  
Land Use ◽  
Exposure Assessment ◽  
Activity Pattern ◽  
Cell Phone ◽  
Total Population ◽  
Cell Phones ◽  
Land Use Regression ◽  
Pm2.5 Exposure ◽  
Dynamic Exposure ◽  
Pm2.5 Concentration

The spatiotemporal locations of large populations are difficult to clearly characterize using traditional exposure assessment, mainly due to their complicated daily intraurban activities. This study aimed to extract hourly locations for the total population of Beijing based on cell phone data and assess their dynamic exposure to ambient PM2.5. The locations of residents were located by the cellular base stations that were keeping in contact with their cell phones. The diurnal activity pattern of the total population was investigated through the dynamic spatial distribution of all of the cell phones. The outdoor PM2.5 concentration was predicted in detail using a land use regression (LUR) model. The hourly PM2.5 map was overlapped with the hourly distribution of people for dynamic PM2.5 exposure estimation. For the mobile-derived total population, the mean level of PM2.5 exposure was 89.5 μg/m3 during the period from 2013 to 2015, which was higher than that reported for the census population (87.9 μg/m3). The hourly activity pattern showed that more than 10% of the total population commuted into the center of Beijing (e.g., the 5th ring road) during the daytime. On average, the PM2.5 concentration at workplaces was generally higher than in residential areas. The dynamic PM2.5 exposure pattern also varied with seasons. This study exhibited the strengths of mobile location in deriving the daily spatiotemporal activity patterns of the population in a megacity. This technology would refine future exposure assessment, including either small group cohort studies or city-level large population assessments.

scholarly journals A Pilot Study of 24-h Motor Activity Patterns in Multiple Sclerosis: Pre-Planned Follow-Up at 2 Years

2021 ◽  
Vol 3 (3) ◽  
pp. 366-376
Author(s):  
Lorenzo Tonetti ◽  
Federico Camilli ◽  
Sara Giovagnoli ◽  
Vincenzo Natale ◽  
Alessandra Lugaresi
Keyword(s):  
Multiple Sclerosis ◽  
Motor Activity ◽  
Activity Pattern ◽  
Activity Patterns ◽  
Expanded Disability Status Scale ◽  
Disability Status ◽  
Relapsing Remitting ◽  
Edss Score ◽  
Activity Prognosis

Early multiple sclerosis (MS) predictive markers of disease activity/prognosis have been proposed but are not universally accepted. Aim of this pilot prospective study is to verify whether a peculiar hyperactivity, observed at baseline (T0) in early relapsing-remitting (RR) MS patients, could represent a further prognostic marker. Here we report results collected at T0 and at a 24-month follow-up (T1). Eighteen RRMS patients (11 females, median Expanded Disability Status Scale-EDSS score 1.25, range EDSS score 0–2) were monitored at T0 (mean age 32.33 ± 7.51) and T1 (median EDSS score 1.5, range EDSS score 0–2.5). Patients were grouped into two groups: responders (R, 14 patients) and non-responders (NR, 4 patients) to treatment at T1. Each patient wore an actigraph for one week to record the 24-h motor activity pattern. At T0, NR presented significantly lower motor activity than R between around 9:00 and 13:00. At T1, NR were characterized by significantly lower motor activity than R between around 12:00 and 17:00. Overall, these data suggest that through the 24-h motor activity pattern, we can fairly segregate at T0 patients who will show a therapeutic failure, possibly related to a more active disease, at T1. These patients are characterized by a reduced morning level of motor activation. Further studies on larger populations are needed to confirm these preliminary findings.

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