scholarly journals Temporal-Spatial Analysis of Traffic Congestion Based on Modified CTM

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Chenglong Chu ◽  
Na Xie ◽  
Xiqun Chen ◽  
Yuxin Wu ◽  
Xiaoxiao Sun
Keyword(s):  
Traffic Congestion ◽  
Regression Models ◽  
Piecewise Linear ◽  
Transmission Model ◽  
Spatial Evolution ◽  
Cell Transmission Model ◽  
Linear Regression Models ◽  
Piecewise Linear Regression ◽  
Relationship Of ◽  
The Relationship

A modified cell transmission model (CTM) is proposed to depict the temporal-spatial evolution of traffic congestion on urban freeways. Specifically, drivers’ adaptive behaviors and the corresponding influence on traffic flows are emphasized. Two piecewise linear regression models are proposed to describe the relationship of flow and density (occupancy). Several types of cellular connections are designed to depict urban rapid roads with on/off-ramps and junctions. Based on the data collected on freeway of Queen Elizabeth, Ontario, Canada, we show that the new model provides a relatively higher accuracy of temporal-spatial evolution of traffic congestions.

Relationship of body mass index and other anthropometric measures and prostate specific antigen, NHANES 2001–2002

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 4649-4649
Author(s):  
M. Saraiya ◽  
D. Werny ◽  
T. Thompson
Keyword(s):  
Prostate Cancer ◽  
Regression Models ◽  
Specific Antigen ◽  
Linear Regression Models ◽  
Anthropometric Measures ◽  
Severely Obese ◽  
Relationship Of ◽  
The Relationship ◽  
Mexican Men ◽  
Racial Ethnic

4649 Background: Obesity has been suggested to be a risk factor for prostate cancer. However, there has been limited information examining the relationship between body mass index (BMI) and other anthropometric measures such as triceps thickness (TT) and waist circumference (WC) and prostate specific antigen (PSA), a marker used in screening for prostate cancer. Methods: We analyzed data from the 2001–2002 National Health and Nutrition Examination Survey (NHANES), a cross-sectional survey of a nationally representative sample of the non-institutionalized civilian US population. Participants in this study were limited to men aged 40 years and older without previously diagnosed prostate cancer (n = 1320). Weighted analyses were done overall and within three racial/ethnic groups [non-Hispanic whites (white), non-Hispanic blacks (black), or Mexican Americans (Mexican)]. BMI, TT, and WC were examined as both as a continuous and as a categorical variable. Three linear regression models were fit to determine the individual effect of BMI, TT, and WC on PSA within each racial/ethnic group after adjusting for age. Results: Among white men, the median PSA was 1.09 ng/mL [95% CI (0.96, 1.30)] for men with a normal BMI, 0.80 [95% CI (0.70, 0.94)] for overweight men, 0.83 [95% CI (0.74,0.95)] for obese men, and 0.74 [95% CI (0.56,1.22)] for severely obese men. Among black men, the median PSA for men was 0.83 [95% CI (0.70, 1.94)] and remained constant across BMI levels. Among Mexican men, the median PSA was 0.92 [95% CI (0.82, 1.23)] for men with a normal BMI, 0.91 [95% CI (0.81, 1.07) ] for overweight, 0.73 [95% CI (0.54,1.14)] for obese, and 0.59 [95% CI (0.39,1.06)] for severely obese men. In linear regression models controlling for age, a significant non-linear relationship was seen with BMI and PSA for both white (p = 0.010) and Mexican men (p < 0.001) but not for black men (p = 0.167). Increased TT was associated with decreasing PSA for black and Mexican men. Increased WC was associated with decreasing PSA for white and Mexican men. Conclusion: These data do suggest racial/ethnic differences of PSA across BMI categories as well as other anthropometric measures. This finding may help inform practitioners about the relationship of anthropometric measures on PSA levels. No significant financial relationships to disclose.

CSF Aβ 42 levels correlate with amyloid-neuropathology in a population-based autopsy study

Neurology ◽  
2003 ◽  
Vol 60 (4) ◽  
pp. 652-656 ◽  
Author(s):  
D. Strozyk ◽  
K. Blennow ◽  
L. R. White ◽  
L. J. Launer
Keyword(s):  
Regression Models ◽  
Population Based ◽  
Japanese American ◽  
Amyloid Angiopathy ◽  
Linear Regression Models ◽  
Autopsy Study ◽  
Aging Study ◽  
Relationship Of ◽  
The Relationship ◽  
Diffuse Plaques

Objective: To investigate the relationship of amyloid neuropathology to postmortem CSF Aβ 42 levels in an autopsy sample of Japanese American men from the population-based Honolulu–Asia Aging Study.Methods: In 1991, participants were assessed and diagnosed with dementia (including subtype) based on published criteria. At death CSF was obtained from the ventricles. Neuritic plaques (NP) and diffuse plaques in areas of the neocortex and hippocampus were examined using Bielschowsky silver stains. Cerebral amyloid angiopathy (CAA) was measured by immunostaining for β4 amyloid in cerebral vessels in the neocortex. Neuropathologically confirmed AD was diagnosed using Consortium to Establish a Registry for Alzheimer’s Disease criteria. In 155 autopsy samples, log transformed linear regression models were used to examine the association of NP and CAA to Aβ 42 levels, controlling for clinical dementia severity, time between diagnosis and death, age at death, brain weight, hours between death and collection of CSF, education, and APOE genotype.Results: Higher numbers of NP in the neocortex (p trend = 0.001) and in the hippocampus (p trend = 0.03) were strongly associated with lower levels of Aβ 42. Individuals with CAA had lower Aβ 42 levels (β coefficient = −0.48; 95% CI −0.9, −0.1). Compared to participants with a diagnosis of clinical dementia, those with pathologically confirmed AD had lower Aβ 42 levels (β coefficient = −0.74; 95% CI −1.4, −0.1).Conclusion: The current study suggests that lower Aβ 42 levels reflect neuropathologic processes implicated in amyloid-related pathologies, such as NP and CAA.

Modeling Traffic Flows on Urban Arterials Considering the Downstream Influence

2020 ◽  
Vol 2674 (5) ◽  
pp. 475-485
Author(s):  
Hong Zhu ◽  
Hideki Nakamura ◽  
Wael Alhajyaseen ◽  
Miho Iryo-Asano
Keyword(s):  
Traffic Congestion ◽  
Regression Models ◽  
Heavy Traffic ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Piecewise Regression ◽  
Start Up ◽  
Signalized Arterials ◽  
Saturation Flow ◽  
Capacity Drop

Arterials are important transportation facilities, undertaking the two functions of mobility and accessibility. In the urban area, signalized intersections along arterials are usually closely spaced and bear heavy traffic pressure. Capacities of intersections can be reduced by downstream intersections even without having spillback. This effect will be accumulated and amplified back along the traffic direction and may lead to severe congestion in the upstream intersection which can be frequently observed, especially during peak hours. However, existing traffic simulators cannot capture this phenomenon accurately because they ignore the capacity drop before spillback happens. In this study, downstream influence is quantified by a virtual optimal speed ( vop). vop is the speed by which the upstream platoon reaches the endpoint of the queue ahead when the last vehicle in the downstream queue just starts. Based on that, two piecewise regression models, for start-up lost time and saturation flow rate, are formulated to estimate the capacity reduction. These regression models are further introduced to improve the modified cell transmission model (CTM). The result of the simulation experiment shows that the proposed CTM model has better performance in simulating traffic flow on signalized arterials than the existing CTM, especially in reproducing the traffic congestion in upstream. The analysis emphasized the importance of considering the downstream influence when simulating the traffic on signalized arterials. Finally, a sensitivity analysis is designed to further reveal the causes of upstream congestion.

Relationship Between Mental Health, Resilience, and Physical Activity in Older Adults: A 2-Year Longitudinal Study

2021 ◽  
pp. 1-9
Author(s):  
Maria Priscila Wermelinger Ávila ◽  
Jimilly Caputo Corrêa ◽  
Alessandra Lamas Granero Lucchetti ◽  
Giancarlo Lucchetti
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Older Adults ◽  
Regression Models ◽  
Linear Regression Models ◽  
Physically Active ◽  
Effective Interventions ◽  
Self Sufficiency ◽  
The Relationship

The aim of this study was to longitudinally investigate the association between resilience and mental health in older adults and to determine the influence of physical activity on this relationship. A total of 291 older adults were included in a 2-year follow-up study. Adjusted linear regression models evaluated the association between resilience at baseline and mental health after 2 years in sufficiently and insufficiently physically active older adults. A negative correlation was found between resilience at baseline and depression, anxiety, and stress after 2 years for the overall sample. This association changed after stratifying the group. Sufficiently physically active individuals made greater use of the resilience components “Self-Sufficiency” and “Perseverance,” whereas insufficiently physically active individuals made greater use of “Meaning of Life” and “Existential Singularity.” Physical activity can influence the relationship between resilience and mental health. These results can help guide the devising of more effective interventions for this age group.

scholarly journals The effects of ambient temperature and heatwaves on dailyCampylobactercases in Adelaide, Australia, 1990–2012

2017 ◽  
Vol 145 (12) ◽  
pp. 2603-2610 ◽  
Author(s):  
A. MILAZZO ◽  
L. C. GILES ◽  
Y. ZHANG ◽  
A. P. KOEHLER ◽  
J. E. HILLER ◽  
...  
Keyword(s):  
Regression Models ◽  
South Australia ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Infectious Gastroenteritis ◽  
Food Borne ◽  
Relationship Of ◽  
The Relationship

SUMMARYCampylobacterspp. is a commonly reported food-borne disease with major consequences for morbidity. In conjunction with predicted increases in temperature, proliferation in the survival of microorganisms in hotter environments is expected. This is likely to lead, in turn, to an increase in contamination of food and water and a rise in numbers of cases of infectious gastroenteritis. This study assessed the relationship ofCampylobacterspp. with temperature and heatwaves, in Adelaide, South Australia.We estimated the effect of (i) maximum temperature and (ii) heatwaves on dailyCampylobactercases during the warm seasons (1 October to 31 March) from 1990 to 2012 using Poisson regression models.There was no evidence of a substantive effect of maximum temperature per 1 °C rise (incidence rate ratio (IRR) 0·995, 95% confidence interval (95% CI) 0·993–0·997) nor heatwaves (IRR 0·906, 95% CI 0·800–1·026) onCampylobactercases. In relation to heatwave intensity, which is the daily maximum temperature during a heatwave, notifications decreased by 19% within a temperature range of 39–40·9 °C (IRR 0·811, 95% CI 0·692–0·952). We found little evidence of an increase in risk and lack of association betweenCampylobactercases and temperature or heatwaves in the warm seasons. Heatwave intensity may play a role in that notifications decreased with higher temperatures. Further examination of the role of behavioural and environmental factors in an effort to reduce the risk of increasedCampylobactercases is warranted.

scholarly journals Relation between swine weight and morphometric measurements

2020 ◽  
Vol 9 (9) ◽  
pp. e891998013
Author(s):  
Mônica Calixto Ribeiro de Holanda ◽  
Marco Aurélio Carneiro de Holanda ◽  
Leandro Ricardo Rodrigues de Lucena
Keyword(s):  
Mathematical Model ◽  
Body Weight ◽  
Regression Models ◽  
Hip Circumference ◽  
Morphometric Measurements ◽  
Large White ◽  
Male And Female ◽  
Lactation Phase ◽  
Relationship Of ◽  
The Relationship

Objective was to define a mathematical model that better explain the relationship of the animals weight depending not only on the animals age but also on the animals morphometric measurements. 40 piglets, half Duroc-Large White blood, were used, 20 males and 20 females, from 3 to 35 days of age (lactation phase) initially weighing 1.518 ± 0.121 kg and from 36 to 66 days of age (calving phase) with a body weight of 7.010 ± 0.704 kg. The animals were weighed weekly on a digital balance. The relationship of animal weight, age and morphometric measurements of male and female piglets were performed using regression models: existing, linear and power. The models were evaluated according to nine criterialinear model was the most adequate to explain the weight of male pigs, while for female pigs was the power. The age of the pig, the shank and palette length, as well as the circumference of the shank jointly explain the weight of the male piglets. The weight of females is explained jointly by age, body length, thorax and hip circumference.

scholarly journals Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

2020 ◽  
Vol 12 (17) ◽  
pp. 2716
Author(s):  
Shuang Liang ◽  
Xiaofeng Li ◽  
Xingming Zheng ◽  
Tao Jiang ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Linear Regression ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regression Models ◽  
Linear Regression Models ◽  
Winter Snow ◽  
The Relationship

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

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