Event analysis based on multiple video sensors for cooperative environment perception

Author(s):  
Tian Wang ◽  
Jie Chen ◽  
Aichun Zhu ◽  
Hichem Snoussi
Impact ◽  
2020 ◽  
Vol 2020 (3) ◽  
pp. 26-28
Author(s):  
Tsukasa Ohba

Volcanology is an extremely important scientific discipline. Shedding light on how and why volcanoes erupt, how eruptions can be predicted and their impact on humans and the environment is crucial to public safety, economies and businesses. Understanding volcanoes means eruptions can be anticipated and at-risk communities can be forewarned, enabling them to implement mitigation measures. Professor Tsukasa Ohba is a scientist based at the Graduate School of International Resource Studies, Akita University, Japan, and specialises in volcanology and petrology. Ohba and his team are focusing on volcanic phenomena including: phreatic eruptions (a steam-driven eruption driven by the heat from magma interacting with water); lahar (volcanic mudflow); and monogenetic basalt eruptions (which consist of a group of small monogenetic volcanoes, each of which erupts only once). The researchers are working to understand the mechanisms of these phenomena using Petrology. Petrology is one of the traditional methods in volcanology but has not been applied to disastrous eruptions before. The teams research will contribute to volcanic hazard mitigation.


Author(s):  
Hojune E. Chung ◽  
Jessica Chen ◽  
Dhairyasheel Ghosalkar ◽  
Jared L. Christensen ◽  
Alice J. Chu ◽  
...  

Background: While an association between atherosclerosis and dementia has been identified, few studies have assessed the longitudinal relationship between aortic valve calcification (AVC) and cognitive impairment (CI). Objective: We sought to determine whether AVC derived from lung cancer screening CT (LCSCT) was associated with CI in a moderate-to-high atherosclerotic risk cohort. Methods: This was a single site, retrospective analysis of 1401 U.S. veterans (65 years [IQI: 61, 68] years; 97%male) who underwent quantification of AVC from LCSCT indicated for smoking history. The primary outcome was new diagnosis of CI identified by objective testing (Mini-Mental Status Exam or Montreal Cognitive Assessment) or by ICD coding. Time-to-event analysis was carried out using AVC as a continuous variable. Results: Over 5 years, 110 patients (8%) were diagnosed with CI. AVC was associated with new diagnosis of CI using 3 Models for adjustment: 1) age (HR: 1.104; CI: 1.023–1.191; p = 0.011); 2) Model 1 plus hypertension, hyperlipidemia, diabetes, CKD stage 3 or higher (glomerular filtration rate <  60 mL/min) and CAD (HR: 1.097; CI: 1.014–1.186; p = 0.020); and 3) Model 2 plus CVA (HR: 1.094; CI: 1.011–1.182; p = 0.024). Sensitivity analysis demonstrated that the association between AVC and new diagnosis of CI remained significant upon exclusion of severe AVC (HR: 1.100 [1.013–1.194]; p = 0.023). Subgroup analysis demonstrated that this association remained significant when including education in the multivariate analysis (HR: 1.127 [1.030–1.233]; p = 0.009). Conclusion: This is the first study demonstrating that among mostly male individuals who underwent LCSCT, quantified aortic valve calcification is associated with new diagnosis of CI.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bethany E. Higgins ◽  
Giovanni Montesano ◽  
Alison M. Binns ◽  
David P. Crabb

AbstractIn age-related macular degeneration (AMD) research, dark adaptation has been found to be a promising functional measurement. In more severe cases of AMD, dark adaptation cannot always be recorded within a maximum allowed time for the test (~ 20–30 min). These data are recorded either as censored data-points (data capped at the maximum test time) or as an estimated recovery time based on the trend observed from the data recorded within the maximum recording time. Therefore, dark adaptation data can have unusual attributes that may not be handled by standard statistical techniques. Here we show time-to-event analysis is a more powerful method for analysis of rod-intercept time data in measuring dark adaptation. For example, at 80% power (at α = 0.05) sample sizes were estimated to be 20 and 61 with uncapped (uncensored) and capped (censored) data using a standard t-test; these values improved to 12 and 38 when using the proposed time-to-event analysis. Our method can accommodate both skewed data and censored data points and offers the advantage of significantly reducing sample sizes when planning studies where this functional test is an outcome measure. The latter is important because designing trials and studies more efficiently equates to newer treatments likely being examined more efficiently.


Sign in / Sign up

Export Citation Format

Share Document