Validation and adjustment of the mathematical prediction model for human sweat rate responses to outdoor environmental conditions

Abstract

Based on indoor laboratory studies, a mathematical model to predict sweat loss response was suggested as follows:

Under outdoor conditions this model was over estimating sweat loss response in shaded (low solar radiation) environments, and underestimating the response when solar radiation was high (open field areas). The present study was conducted in order to adjust the model to be applicable under outdoor environmental conditions. Four groups of fit acclimated subjects participated in the study. They were exposed to three climatic conditions (30°, 65% rh; 31°C, 40% rh; and 40°C, 20% rh) and three levels of metabolic rate (100, 300 and 450W) in shaded and sunny areas while wearing shorts, cotton fatigues (BDUs) or protective garments. The original predictive equation for sweat loss was adjusted for the outdoor conditions by evaluating separately the radiative heat exchange, short-wave absorption in the body and long-wave emission from the body to the atmosphere and integrating them in the required evaporation component (E_req) of the model, as follows:

where SL is solar radiation (W m ^-2), M_ethis the Stephan Boltzman constant, and /T is the effective clothing insulation coefficient. This adjustment revealed a high correlation between the measured and expected values of sweat loss { r= 099, p< 0-0001).     

Comparison of different methods to extract the required coefficient of friction for level walking

The required coefficient of friction (RCOF) is an important predictor for slip incidents. Despite the wide use of the RCOF there is no standardised method for identifying the RCOF from ground reaction forces. This article presents a comparison of the outcomes from seven different methods, derived from those reported in the literature, for identifying the RCOF from the same data. While commonly used methods are based on a normal force threshold, percentage of stance phase or time from heel contact, a newly introduced hybrid method is based on a combination of normal force, time and direction of increase in coefficient of friction. Although no major differences were found with these methods in more than half the strikes, significant differences were found in a significant portion of strikes. Potential problems with some of these methods were identified and discussed and they appear to be overcome by the hybrid method. PRACTITIONER SUMMARY: No standard method exists for determining the required coefficient of friction (RCOF), an important predictor for slipping. In this study, RCOF values from a single data set, using various methods from the literature, differed considerably for a significant portion of strikes. A hybrid method may yield improved results.     

Comparison of land surface temperature (LST) modeled with a spatially-distributed solar radiation model (SRAD) and remote sensing data

The solar radiation model SRAD was applied to a Mediterranean mountainous environment in southern California for estimating land surface temperature (LST). The simulated SRAD LST results were compared with high- and meso-resolution satellite-based LST data at daily, monthly and annual temporal scales to identify potential ways of improving the LST accuracy in either the SRAD or satellite-based approaches. It was found that mean monthly and annual LST from SRAD closely matched the MODIS LST observations (but for the fact they were from 0.7 to 1.5 °C lower) while the daily LST from SRAD agreed less well with ASTER observations and were 2.1 °C and 4.8 °C higher for 01/23/05 and 07/21/06, respectively. High mountainous, steep and south/north facing slopes resulted in large discrepancies in LST estimates and the winter LST estimates are more sensitive to terrain factors and their associated land use/cover characteristics than the summer estimates. It is therefore suggested that vertically variable elevation lapse rates, spatially distributed surface albedo and leaf area index for the time-specific simulations that represent the heterogeneity of land surface characteristics be used in SRAD inputs. The results also show how instantaneous model outputs rather than lumped average daily outputs offer better comparisons with satellite based data as well.     

Simulation of mortality dynamics for populations of mammals (mice) exposed to radiation

Mathematical models are developed which describe the dynamics of radiation-induced mortality of homogeneous and nonhomogeneous (in radiosensitivity) populations of mammals (mice). They relate statistical biometric functions with statistical characteristics and dynamics of a critical body system of individuals composing populations. The model of mortality of a nonhomogeneous population involves two types of distributions, normal and log-normal, of individuals with respect to the radiosensitivity of cells of the critical system. These models outline new pathways in developing the methods of radiation risk assessment.     

The signal-to-noise ratio (SNR) required for the estimation of foliar biochemical concentrations

Abstract This paper compares estimates of the signal-to-noise ratio(SNR) required by imaging spectrometers for the estimation of foliar biochemical concentrations and the SNR currently achieved by the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS). The work was comprised of three sections. Section 1: the SNR required by imaging spectrometers was estimated by modelling three data sets, each of which more closely approximated the data recorded by the AVIRIS. The remaining stages were concerned with estimating the SNR currently achieved by the AVIRIS. Section 2: SNR estimates made as part of instrument calibration were scaled to those that would be expected when viewing vegetation, and section 3: SNR was estimated directly from AVIRIS imagery. The results of these three sections were then compared to assess the SNR performance of the AVIRIS and its utility for the estimation of foliar biochemical concentrations. The SNR of the AVIRIS is planned to double between 1994-5 and while this sensor was barely adequate for the estimation of foliar biochemical concentrations in 1992-3 it should be more than adequate from 1995 onwards.     

Modeling the interaction of infrared radiation (750 to 2500 nm) with bifacial and unifacial plant leaves

Plants are arguably among the most investigated remote sensing targets. Due to their economical and environmental importance, several models to simulate radiation transport and absorption by foliar tissues have been proposed in remote sensing and related fields. The main goal of this research is to present alternative modeling strategies for the investigation of these phenomena. These solutions consist in algorithmic models specifically designed to simulate the interaction of radiation with bifacial and unifacial plant leaves. Their flexible formulations based on standard Monte Carlo techniques make their implementation straightforward and allow their use in investigations involving different regions of the electromagnetic spectrum of radiation. In this paper, they are examined in the context of infrared applications. This choice is motivated by the simulation challenges posed by the processes that relate biophysical characteristics to optical properties of plant leaves in this domain. The accuracy and predictability of the proposed models have been evaluated through comparisons between modeled results and measured data. The results of these evaluations illustrate the applicability of the proposed models to investigations involving the predictive simulation of foliar spectral signatures.     

Vibrational investigation of the spinning bi-dimensional functionally graded (2-FGM) micro plate subjected to thermal load in thermal environment

Microsystem Technologies - This article presented a numerical method for discovering the free vibration of a spinning bi-dimensional functionally graded materials (FGM) micro circular plate exposed...     

On properties of the second order generalized autoregressive GAR(2) model with index

In this paper we consider a new class of time series models generated by a second order autoregressive type operator with an index. Autocorrelation and spectral properties are discussed and some explicit results are derived for a restricted class in the family. The parameter estimation is discussed using the Whittle procedure. Some numerical results are presented to support the theoretical results.     

Detecting coal fires with a neural network to reduce the effect of solar radiation on Landsat Thematic Mapper thermal infrared images

Coal fires in the north of China have already resulted in serious problems, including huge losses in coal resources, air pollution and so on. Thermal infrared images by Landsat Thematic Mapper (TM) can be used to detect some thermal anomalies. However, an initial necessity is to reduce the effect of solar radiation on TM thermal infrared images. In this paper, a neural network is used to set up a mathematical model of ground temperature for the first time. After the neural network completes training, we can use it to calculate the ground temperature caused by solar radiation. Thus, the result can be used to reduce the effect of solar radiation on TM thermal infrared images, and extract the thermal anomalies caused by coal fires.     

Developing the vegetation drought response index for South Korea (VegDRI-SKorea) to assess the vegetation condition during drought events

South Korea has experienced severe droughts and water scarcity problems that have influenced agriculture, food prices, and crop production in recent years. Traditionally, climate-based drought indices using point-based meteorological observations have been used to help quantify drought impacts on the vegetation in South Korea. However, these approaches have a limited spatial precision when mapping detailed vegetation stress caused by drought. For these reasons, the development of a drought index that provides detailed spatial-resolution information on drought-affected vegetation conditions is essential to improve the country’s drought monitoring capabilities, which are needed to help develop more effective adaptation and mitigation strategies. The objective of this study was to develop a satellite-based hybrid drought index called the vegetation drought response index for South Korea (VegDRI-SKorea) that could improve the spatial resolution of agricultural drought monitoring on a national scale. The VegDRI-SKorea was developed for South Korea, modifying the original VegDRI methodology (developed for the USA) by tailoring it to the available local data resources. The VegDRI-SKorea utilizes a classification and regression tree (CART) modelling approach that collectively analyses remote-sensing data (e.g. normalized difference vegetation index (NDVI)), climate-based drought indices (e.g. self-calibrated Palmer drought severity index (PDSI) and standardized precipitation index (SPI)), and biophysical variables (e.g. elevation and land cover) that influence the drought-related vegetation stress. This study evaluates the performance of the recently developed VegDRI-SKorea for severe and extreme drought events that occurred in South Korea in 2001, 2008, and 2012. The results demonstrated that the hybrid drought index improved the more spatially detailed drought patterns compared to the station-based drought indices and resulted in a better understanding of drought impacts on the vegetation conditions. The VegDRI-SKorea model is expected to contribute to the monitoring of drought conditions nationally. In addition, it will provide the necessary information on the spatial variations of those conditions to evaluate local and regional drought risk assessment across South Korea and assist local decision-makers in drought risk management.     

Assessment of the 1-fluid method for DNS of particulate flows: Sedimentation of a single sphere at moderate to high Reynolds numbers

This paper introduces an original 1-fluid method for direct simulation of the motion of rigid particles in fluids. The model is based on the implicit treatment of a single fictitious fluid over a fixed grid, and uses an augmented Lagrangian optimization algorithm for the velocity-pressure coupling. The paper focuses on the case of a rigid sphere settling in a viscous medium. For validation purposes, simulations of the transient motion of a sedimenting sphere at Reynolds numbers ranging from 1.5 to 31.9 are compared to the PIV data published by Ten Cate et al. [Ten Cate A, Nieuwstad CH, Derksen JJ, Van den Akker HEA. Particle imaging velocimetry experiments and lattice-Boltzmann simulations on a single sphere settling under gravity. Phys Fluids 2002;14(11):4012-25]. Accurate reproduction of the experimental data is obtained. Further simulations are intended to investigate higher Reynolds numbers. Predictions of transient particle sedimentation at Reynolds number 280 are performed and compared with experimental data of the sedimentation trajectory, as well as with simulation results based on the lattice-Boltzmann method.     

Prediction of the thermal imaging minimum resolvable (circle) temperature difference with neural network application

Thermal imaging is an important technology in both national defense and the private sector. An advantage of thermal imaging is its ability to be deployed while fully engaged in duties, not limited by weather or the brightness of indoor or outdoor conditions. However, in an outdoor environment, many factors, including atmospheric decay, target shape, great distance, fog, temperature out of range and diffraction limits can lead to bad image formation, which directly affects the accuracy of object recognition. The visual characteristics of the human eye mean that it has a much better capacity for picture recognition under normal conditions than artificial intelligence does. However, conditions of interference significantly reduce this capacity for picture recognition for instance, fatigue impairs human eyesight. Hence, psychological and physiological factors can affect the result when the human eye is adopted to measure MRTD (minimum resolvable temperature difference) and MRCTD (minimum resolvable circle temperature difference). This study explores thermal imaging recognition, and presents a method for effectively choosing the characteristic values and processing the images fully. Neural network technology is successfully applied to recognize thermal imaging and predict MRTD and MRCTD (Appendix A), exceeding thermal imaging recognition under fatigue and the limits of the human eye.     

Comparison of four different mobile devices for measuring heart rate and ECG with respect to aspects of usability and acceptance by older people

In the area of product design and usability, most products are developed for the mass-market by technically oriented designers and developers for use by persons who themselves are also technically adept by today's standards. The demands of older people are commonly not given sufficient consideration within the early developmental process. In the present study, the usability and acceptability of four different devices meant to be worn for the measurement of heart rate or ECG were analyzed on the basis of qualitative subjective user ratings and structured interviews of twelve older participants. The data suggest that there was a relatively high acceptance concerning these belts by older adults but none of the four harnesses was completely usable. Especially problematic to the point of limiting satisfaction among older subjects were problems encountered while adjusting the length of the belt and/or closing the locking mechanism. The two devices intended for dedicated heart rate recording yielded the highest user ratings for design, and were clearly preferred for extended wearing time. Yet for all the devices participants identified several important deficiencies in their design, as well as suggestions for improvement. We conclude that the creation of an acceptable monitoring device for older persons requires designers and developers to consider the special demands and abilities of the target group.     

A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil

Engineering with Computers - Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has...     

On the use of laboratory ocean circulation models to simulate mesoscale (10–100 km) spreading

The development of hydrodynamic numerical models for environmental studies depends on good benchmarks to calibrate and validate the physics and numerical codes. Laboratory models of non-linear and coupled physics in topography for which no analytical solutions are available can provide such valuable benchmarks. Although field data are necessary for a final validation, they are often of less value for developing numerical models, since a truly synoptic coverage of a scenario is seldom found, knowledge of the forcing conditions is imperfect and average conditions of a non-linear system are seldom obtained by applying average boundary conditions.The role of laboratory models and experiments for providing information on turbulence in complicated topography is indisputable. The high topographical resolution of these models reveals how narrow and filamentous many of the flow features can be, as often seen in satellite images. Such filaments enhance diffusion through a process known as shear dispersion. The filaments are also of concern for the interpretation of sparse field measurements and for computing the mesoscale (10–100 km) spreading characteristics. Time histories of dye clouds and clusters of particles in laboratory simulations of ocean currents, without wind, show much larger spreading than particle spreading due to strong winds. The results demonstrate that numerical models need high resolution and/or good parametrization of the spreading characteristics, which vary both in space and time, to achieve their goals. It is proposed that the differences between numerical and laboratory simulations of dispersion, with identical forcing, be parametrized as a size-dependent, or time-dependent random walk diffusion in the numerical code.The laboratory results amply show that spreading is greatly enhanced by shear dispersion, and that assessments of the consequences of accidental oil spills or releases of radionuclides, for example, must take this into account. Island communities in tidally active regions are particularly prone to the consequences of a rapid dispersion of contaminants.     

Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days

A simple scheme is proposed to estimate instantaneous net radiation over large heterogeneous areas for clear sky days using only remote sensing observations. Our method attempts to develop an algorithm which primarily uses remote sensing information and eliminates the need for ground information as model input, by using various land and atmospheric data products available from Terra-MODIS. It explicitly recognizes the need for spatially varied parameters and provides a distributed net radiation map over large heterogeneous domain with fine spatial resolution. Since instantaneous net radiation estimates have limited scope compared to daily average values or diurnal cycle, a sinusoidal model is proposed to estimate diurnal cycle of net radiation. The sinusoidal model is capable of retrieving the diurnal variations of net radiation with a single instantaneous net radiation estimate from the satellite. Preliminary results, using data over Southern Great Plains, show good agreement with ground-based observations. It appears that the methodology presented here can estimate instantaneous and daily net radiation with comparable accuracy to those of current methods that use ground-based observations and mainly provide point estimates.     

A comparison of thermal infrared emissivity spectra measured in situ,in the laboratory,and derived from thermal infrared multispectral scanner (TIMS) data in Cuprite,Nevada, U.S.A.

In order to obtain ground truth data for multispectral thermal infrared sensors such as TIMS and ASTER, in situ spectral emissivity measurements were made during field surveys. These spectral emissivity measurements and laboratory spectral reflectance measurements of field samples were compared to emissivity spectra extracted from TIMS data at the surveyed points. The results indicate that emissivity spectra derived from the TIMS data agree well in shape with the spectra measured in situ or in the laboratory.