scholarly journals Prediction of scour at abutments using piecewise regression

Author(s):  
Amir Etemad-Shahidi ◽  
Mohammad Sadegh Rohani
Keyword(s):  
2021 ◽  
pp. 1-9
Author(s):  
Giulia Grande ◽  
Jing Wu ◽  
Petter L.S. Ljungman ◽  
Massimo Stafoggia ◽  
Tom Bellander ◽  
...  

Background: A growing but contrasting evidence relates air pollution to cognitive decline. The role of cerebrovascular diseases in amplifying this risk is unclear. Objectives: 1) Investigate the association between long-term exposure to air pollution and cognitive decline; 2) Test whether cerebrovascular diseases amplify this association. Methods: We examined 2,253 participants of the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). One major air pollutant (particulate matter ≤2.5μm, PM2.5) was assessed yearly from 1990, using dispersion models for outdoor levels at residential addresses. The speed of cognitive decline (Mini-Mental State Examination, MMSE) was estimated as the rate of MMSE decline (linear mixed models) and further dichotomized into the upper (25%fastest cognitive decline), versus the three lower quartiles. The cognitive scores were used to calculate the odds of fast cognitive decline per levels of PM2.5 using regression models and considering linear and restricted cubic splines of 10 years exposure before the baseline. The potential modifier effect of cerebrovascular diseases was tested by adding an interaction term in the model. Results: We observed an inverted U-shape relationship between PM2.5 and cognitive decline. The multi-adjusted piecewise regression model showed an increased OR of fast cognitive decline of 81%(95%CI = 1.2–3.2) per interquartile range difference up to mean PM2.5 level (8.6μg/m3) for individuals older than 80. Above such level we observed no further risk increase (OR = 0.89;95%CI = 0.74–1.06). The presence of cerebrovascular diseases further increased such risk by 6%. Conclusion: Low to mean PM2.5 levels were associated with higher risk of accelerated cognitive decline. Cerebrovascular diseases further amplified such risk.


2020 ◽  
Vol 134 (6) ◽  
pp. 2289-2296
Author(s):  
Gonzalo Saco-Ledo ◽  
Jordi Porta ◽  
Tesla A. Monson ◽  
Marianne F. Brasil ◽  
Derya Atamtürk ◽  
...  

2021 ◽  
Author(s):  
David W DeGroot ◽  
Catherine A Rappole ◽  
Paige McHenry ◽  
Robyn M Englert

ABSTRACT Introduction The incidence of and risk factors for exertional heat illness (EHI) and cold weather injury (CWI) in the U.S. Army have been well documented. The “heat season”, when the risk of EHI is highest and application of risk mitigation procedures is mandatory, has been arbitrarily defined as May 1 through September 30, while the “cold season” is understood to occur from October 1 to April 30 each year. The proportions of EHI and CWI that occur outside of the traditional heat and cold seasons are unknown. Additionally, it is unknown if either of the seasonal definitions are appropriate. The primary purpose of this study was to determine the proportion of EHI and of CWI that occur within the commonly accepted seasonal definitions. We also report the location-specific variability, seasonal definitions, and the demographic characteristics of the populations. Methods The U.S. Army installations with the highest frequency of EHI and of CWI from 2008 to 2013 were identified and used for analysis. In total there were 15 installations included in the study, with five installations used for analysis in both the EHI and CWI projects. In- and out-patient EHI and CWI data (ICD-9-CM codes 992.0 to 992.9 and ICD codes 991.0 to 991.9, respectively) were obtained from the Defense Medical Surveillance System. Installation-specific denominator data were obtained from the Defense Manpower Data Center, and incidence rates were calculated by week, for each installation. Segmental (piecewise) regression analysis was used to determine the start and end of the heat and cold seasons. Results Our analysis indicates that the heat season starts around April 22 and ends around September 9. The cold season starts on October 3 and ends on March 24. The majority (n = 6,445, 82.3%) of EHIs were diagnosed during the “heat season” of May 1 to September 30, while 10.3% occurred before the heat season started (January1 to April 30) and 7.3% occurred after the heat season ended (October 1 to December 31). Similar to EHI, 90.5% of all CWIs occurred within the traditionally defined cold season, while 5.7% occurred before and 3.8% occurred after the cold season. The locations with the greatest EHI frequency were Ft Bragg (n = 2,129), Ft Benning (n = 1,560), and Ft Jackson (n = 1,538). The bases with the largest proportion of CWI in this sample were Ft Bragg (17.8%), Ft Wainwright (17.2%), and Ft Jackson (12.7%). There were considerable inter-installation differences for the start and end dates of the respective seasons. Conclusions The present study indicates that the traditional heat season definition should be revised to begin  ∼3 weeks earlier than the current date of May 1; our data indicate that the current cold season definition is appropriate. Inter-installation variability in the start of the cold season was much larger than that for the heat season. Exertional heat illnesses are a year-round problem, with ∼17% of all cases occurring during non-summer months, when environmental heat strain and vigilance are lower. This suggests that EHI mitigation policies and procedures require greater year-round emphasis, particularly at certain locations.


2004 ◽  
Vol 1 (1) ◽  
pp. 131-142
Author(s):  
Ljupčo Todorovski ◽  
Sašo Džeroski ◽  
Peter Ljubič

Both equation discovery and regression methods aim at inducing models of numerical data. While the equation discovery methods are usually evaluated in terms of comprehensibility of the induced model, the emphasis of the regression methods evaluation is on their predictive accuracy. In this paper, we present Ciper, an efficient method for discovery of polynomial equations and empirically evaluate its predictive performance on standard regression tasks. The evaluation shows that polynomials compare favorably to linear and piecewise regression models, induced by the existing state-of-the-art regression methods, in terms of degree of fit and complexity.


2017 ◽  
Vol 16 (4) ◽  
pp. 279-282 ◽  
Author(s):  
Samuel Romano-Feinholz ◽  
Sergio Soriano-Solís ◽  
Julio César Zúñiga-Rivera ◽  
Carlos Francisco Gutiérrez-Partida ◽  
Manuel Rodríguez-García ◽  
...  

ABSTRACT Objective: To describe the learning curve that shows the progress of a single neurosurgeon when performing single-level MI-TLIF. Methods: We included 99 consecutive patients who underwent single-level MI-TLIF by the same neurosurgeon (JASS). Patient’s demographic characteristics were analyzed. In addition, surgical time, intraoperative blood loss and hospital stay were evaluated. The learning curves were calculated with a piecewise regression model. Results: The mean age was 54.6 years. The learning curves showed an inverse relationship between the surgical experience and the variable analyzed, reaching an inflection point for surgical time in case 43 and for blood loss in case 48. The mean surgical time was 203.3 minutes (interquartile range [IQR] 150-240 minutes), intraoperative bleeding was 97.4ml (IQR 40-100ml) and hospital stay of four days (IQR 3-5 days). Conclusions: MI-TLIF is a very frequent surgical procedure due to its effectiveness and safety, which has shown similar results to open procedure. According to this study, the required learning curve is slightly higher than for open procedures, and is reached after about 45 cases.


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