A simple and valid method to determine thermoregulatory sweating threshold and sensitivity

2009 ◽  
Vol 107 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Samuel N. Cheuvront ◽  
Shawn E. Bearden ◽  
Robert W. Kenefick ◽  
Brett R. Ely ◽  
David W. DeGroot ◽  
...  
Keyword(s):  
Temperature Sensor ◽  
Sweat Rate ◽  
Dew Point ◽  
Threshold Temperature ◽  
Data Sets ◽  
Empirical Methods ◽  
Dew Point Temperature ◽  
The Relationship ◽  
Sweating Threshold

Sweating threshold temperature and sweating sensitivity responses are measured to evaluate thermoregulatory control. However, analytic approaches vary, and no standardized methodology has been validated. This study validated a simple and standardized method, segmented linear regression (SReg), for determination of sweating threshold temperature and sensitivity. Archived data were extracted for analysis from studies in which local arm sweat rate (ṁsw; ventilated dew-point temperature sensor) and esophageal temperature (Tes) were measured under a variety of conditions. The relationship ṁsw/Tes from 16 experiments was analyzed by seven experienced raters (Rater), using a variety of empirical methods, and compared against SReg for the determination of sweating threshold temperature and sweating sensitivity values. Individual interrater differences ( n = 324 comparisons) and differences between Rater and SReg ( n = 110 comparisons) were evaluated within the context of biologically important limits of magnitude (LOM) via a modified Bland-Altman approach. The average Rater and SReg outputs for threshold temperature and sensitivity were compared ( n = 16) using inferential statistics. Rater employed a very diverse set of criteria to determine the sweating threshold temperature and sweating sensitivity for the 16 data sets, but interrater differences were within the LOM for 95% (threshold) and 73% (sensitivity) of observations, respectively. Differences between mean Rater and SReg were within the LOM 90% (threshold) and 83% (sensitivity) of the time, respectively. Rater and SReg were not different by conventional t-test ( P > 0.05). SReg provides a simple, valid, and standardized way to determine sweating threshold temperature and sweating sensitivity values for thermoregulatory studies.

scholarly journals Sensitivity Analysis of Extreme Daily Rainfall Depth in Summer Season on Surface Air Temperature and Dew-Point Temperature

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 771 ◽  
Author(s):  
Inkyeong Sim ◽  
Okjeong Lee ◽  
Sangdan Kim
Keyword(s):  
Climate Models ◽  
Surface Air Temperature ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Summer Season ◽  
Dew Point ◽  
Dew Point Temperature ◽  
Rainfall Depth ◽  
Future Data ◽  
The Relationship

Looking at future data obtained from global climate models, it is expected that future extreme rainfall will increase in many parts of the world. The Clausius-Clapeyron equation provides a physical basis for understanding the sensitivity of rainfall in response to warming, but the relationship between rainfall and temperature is still uncertain. The purpose of this study is to analyze the sensitivity of extreme daily rainfall depth during the summer season (June–September) to climate change in Korea. The relationship between the observed extreme daily rainfall depth and the surface air temperature (SAT) and dew-point temperature (DPT), which were observed in the 60 sites of the Korea Meteorological Administration, were analyzed. The same analysis was also performed using future data provided in various climate models. In addition, the future trends of extreme rainfall, SAT, and DPT were analyzed using future data obtained from climate models, and the effects of increasing SAT and DPT on future extreme rainfall changes were investigated. Finally, it has been confirmed that using changes in SAT and DPT to look at changes in future extreme rainfall can give more consistent future projection results than using future rainfall data directly.

Evaluation of the relationship between daily extreme precipitation and temperature over Eastern China

2020 ◽  
Author(s):  
Rui Wang
Keyword(s):  
Surface Temperature ◽  
Extreme Precipitation ◽  
Eastern China ◽  
Precipitable Water ◽  
Precipitation Intensity ◽  
Dew Point ◽  
Near Surface ◽  
Dew Point Temperature ◽  
The Relationship ◽  
Precipitation And Temperature

<p>    In this work, the relationship between daily extreme precipitation and temperature is investigated by using rain gauge precipitation data and corresponding the Integrated Global Radiosonde Archive over eastern China during 1998-2012. Eventually, 14 stations are selected to explore the relationship in eastern China (MEC) and southeastern China (SEC). The result shows that daily extreme precipitation intensity increases approximately 7% when near surface temperature increases 1 °C in MEC and SEC, which generally follows Clausius–Clapeyron (CC) rate (CC rate describes the increasing rate of water vapor with temperature). Moreover, the regression slopes for the logarithmic daily extreme precipitation intensity and near surface temperature range from 3% °C<sup>-1</sup> to 9% °C<sup>-1</sup> at the selected stations in MEC and SEC. However, extreme precipitation intensity decreases with near surface temperature when the temperature is higher than 25 °C. That is, the increase of extreme precipitation with near surface temperature performances single peak structure in MEC and SEC. The variation of extreme precipitation and near surface dew point temperature shows the similar pattern in MEC and SEC (The transition dew point temperature is also about 25 °C). Therefore, <strong>it could be deduced that extreme precipitation intensity does not always increase with climate warming in MEC and SEC.</strong> In addition, precipitable water, which corresponds to extreme precipitation event, increases with near surface temperature at CC rate. <strong>It is found that the increase rate of precipitable water with temperature is closer to CC rate than that of extreme precipitation.</strong></p>

Determination of Optimal Clusters Using a Genetic Algorithm

2008 ◽  
pp. 98-117 ◽  
Author(s):  
Tushar ◽  
Tushar ◽  
Shibendu Shekhar Roy ◽  
Dilip Kumar Pratihar
Keyword(s):  
Genetic Algorithm ◽  
Threshold Value ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Fcm Algorithm ◽  
Data Points ◽  
The Relationship

Clustering is a potential tool of data mining. A clustering method analyzes the pattern of a data set and groups the data into several clusters based on the similarity among themselves. Clusters may be either crisp or fuzzy in nature. The present chapter deals with clustering of some data sets using Fuzzy C-Means (FCM) algorithm and Entropy-based Fuzzy Clustering (EFC) algorithm. In FCM algorithm, the nature and quality of clusters depend on the pre-defined number of clusters, level of cluster fuzziness and a threshold value utilized for obtaining the number of outliers (if any). On the other hand, the quality of clusters obtained by the EFC algorithm is dependent on a constant used to establish the relationship between the distance and similarity of two data points, a threshold value of similarity and another threshold value used for determining the number of outliers. The clusters should ideally be distinct and at the same time compact in nature. Moreover, the number of outliers should be as minimum as possible. Thus, the above problem may be posed as an optimization problem, which will be solved using a Genetic Algorithm (GA). The best set of multi-dimensional clusters will be mapped into 2-D for visualization using a Self-Organizing Map (SOM).

Design of Demisting System in Vehicle Dome Based on Fuzzy Control Algorithm

2014 ◽  
Vol 635-637 ◽  
pp. 1438-1442
Author(s):  
Peng Han ◽  
Xu Ying Wang
Keyword(s):  
Fuzzy Control ◽  
Temperature Sensor ◽  
Control Algorithm ◽  
Humidity Sensor ◽  
Dew Point ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Fuzzy Control Algorithm

This paper describes one demisting system in vehicle camera. On the basis of fuzzy control algorithm, temperature sensor and humidity sensor were used to sample the temperature and humidity inside the dome's cover, single-chip microcomputer was used to get the dew point temperature and window glass's actual temperature through calculation and adjust the heating power consumption automatically. With these processes, the target of intelligent demisting system finally was achieved. This design is practical in vehicle domes.

Study on Evaluation Index of Cold Insulation Performance

2012 ◽  
Vol 516-517 ◽  
pp. 1201-1204
Author(s):  
Hai Qian Zhao ◽  
Zhong Hua Wang ◽  
Xiao Yan Liu ◽  
Zhi Guo Wang
Keyword(s):  
Surface Temperature ◽  
Outer Surface ◽  
Environmental Parameters ◽  
Dew Point ◽  
Evaluation Index ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Two Temperatures ◽  
The Relationship ◽  
Insulation Performance

The outer surface temperature of cold insulation structure must be higher than air dew point temperature is stipulated in national standard.But the outer surface temperature of cold insulation structure and air dew point temperature normally wave in a certain range with the change of environmental parameters. In Practical application, it is difficult to determine the relationship between these two temperatures. Functional relationship between the outer temperature, air dew point temperature and environmental temperature or humidity is fitted.The influence of the air temperature and humidity is analyzed. Some suggestions about design and evaluation index of cold insulation are offered based on this research.

scholarly journals Tropospheric profiles of wet refractivity and humidity from the combination of remote sensing data sets and measurements on the ground

2013 ◽  
Vol 6 (11) ◽  
pp. 3083-3098 ◽  
Author(s):  
F. Hurter ◽  
O. Maier
Keyword(s):  
Relative Humidity ◽  
Lower Troposphere ◽  
Dew Point ◽  
Data Sets ◽  
Temperature Profiles ◽  
Least Squares Collocation ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Reconstruction Quality ◽  
Humidity Profiles

Abstract. We reconstruct atmospheric wet refractivity profiles for the western part of Switzerland with a least-squares collocation approach from data sets of (a) zenith path delays that are a byproduct of the GPS (global positioning system) processing, (b) ground meteorological measurements, (c) wet refractivity profiles from radio occultations whose tangent points lie within the study area, and (d) radiosonde measurements. Wet refractivity is a parameter partly describing the propagation of electromagnetic waves and depends on the atmospheric parameters temperature and water vapour pressure. In addition, we have measurements of a lower V-band microwave radiometer at Payerne. It delivers temperature profiles at high temporal resolution, especially in the range from ground to 3000 m a.g.l., though vertical information content decreases with height. The temperature profiles together with the collocated wet refractivity profiles provide near-continuous dew point temperature or relative humidity profiles at Payerne for the study period from 2009 to 2011. In the validation of the humidity profiles, we adopt a two-step procedure. We first investigate the reconstruction quality of the wet refractivity profiles at the location of Payerne by comparing them to wet refractivity profiles computed from radiosonde profiles available for that location. We also assess the individual contributions of the data sets to the reconstruction quality and demonstrate a clear benefit from the data combination. Secondly, the accuracy of the conversion from wet refractivity to dew point temperature and relative humidity profiles with the radiometer temperature profiles is examined, comparing them also to radiosonde profiles. For the least-squares collocation solution combining GPS and ground meteorological measurements, we achieve the following error figures with respect to the radiosonde reference: maximum median offset of relative refractivity error is −16% and quartiles are 5% to 40% for the lower troposphere. We further added 189 radio occultations that met our requirements. They mostly improved the accuracy in the upper troposphere. Maximum median offsets have decreased from 120% relative error to 44% at 8 km height. Dew point temperature profiles after the conversion with radiometer temperatures compare to radiosonde profiles as to: absolute dew point temperature errors in the lower troposphere have a maximum median offset of −2 K and maximum quartiles of 4.5 K. For relative humidity, we get a maximum mean offset of 7.3%, with standard deviations of 12–20%. The methodology presented allows us to reconstruct humidity profiles at any location where temperature profiles, but no atmospheric humidity measurements other than from GPS are available. Additional data sets of wet refractivity are shown to be easily integrated into the framework and strongly aid the reconstruction. Since the used data sets are all operational and available in near-realtime, we envisage the methodology of this paper to be a tool for nowcasting of clouds and rain and to understand processes in the boundary layer and at its top.

Maturity and Molt Status of Crustacea: Determining Classification without Prior Criteria

1994 ◽  
Vol 51 (2) ◽  
pp. 408-416 ◽  
Author(s):  
William G. Warren
Keyword(s):  
Iterative Procedure ◽  
A Priori ◽  
Carapace Width ◽  
Snow Crab ◽  
Data Sets ◽  
Chionoecetes Opilio ◽  
Relationship Of ◽  
The Relationship

Existing methodology for estimation is reviewed for the situation where, a priori, the existence of two or more groups can be postulated but, in contrast with discriminant analysis, there is no sample in which the correct categories are known. Such mixture models are applied to two data sets related to the maturity and molt status of snow crab, Chionoecetes opilio, namely (1) the classification of crab as morphometrically mature or immature on the basis of the chela height – carapace width relationship and (2) the determination of the number of molts of crab, during a known period at liberty, from the relationship of size at release and size at recapture of tagged animals, in the latter example, a theoretical constraint is imposed that links the relationships at the different stages. The solution is obtained by "nesting" an iterative procedure within an EM algorithm. The method permits hypotheses concerning the number of groups to be tested, including the hypothesis that the data come from a single homogenous group, and each individual is assigned a probability of belonging to a group.

scholarly journals Relationship between weather parameters and risk of exertional heat injuries during military training

2021 ◽  
Author(s):  
BH Poon ◽  
AW Gorny ◽  
KY Zheng ◽  
WK Cheong
Keyword(s):  
Risk Mitigation ◽  
Meteorological Data ◽  
Military Training ◽  
Armed Forces ◽  
Dew Point ◽  
Heat Injury ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Weather Parameters ◽  
The Relationship

Introduction: The Singapore Armed Forces (SAF) collaborated with the Meteorological Service Singapore (MSS) to study the relationship between weather parameters and the incidents of exertional heat injury (EHI) to mitigate the risk of EHI in a practical manner. Methods: Data from the SAF’s heat injury registry and MSS’ meteorological data from 2012 to 2018 were used to establish a consolidated dataset of EHI incidents and same-day weather parameters rank-ordered in deciles. Poisson regression modelling was used to determine the incidence rate ratios (IRRs) of the EHI, referencing the first decile of weather parameters. Two frames of analysis were performed - the first described the relationship between the weather parameters and the adjusted IRR for the same day (D), and the second described the relationship between the weather parameters and the adjusted IRR on the following day (D+1). Results: For wet-bulb temperature, the IRR on D+1 approximated unity for the first nine deciles but rose to 3.09 at the tenth decile. For dew-point temperature, the IRR on D+1 approximated unity for the first nine deciles but rose to 3.48 at the tenth decile. By designating a single dew-point temperature cut-off at  25.1°C (transition between the ninth and tenth decile), the adjusted IRR on D +1 was 2.26 on days with dew-point temperature  25.1°C,. Conclusion: Integrating the data from the SAF and MSS demonstrated that a dew-point temperature ≥ 25.1°C on D correlates statistically with the risk of EHI on D +1and could be used to supplement the risk mitigation system.

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