Top PDF Comparison of UTCI to selected thermal indices

Comparison of UTCI to selected thermal indices

Comparison of UTCI to selected thermal indices

Perceived temperature Fanger’s ( 1970 ) PMV-equation, including Gagge’s improve- ment of the description of latent heat fluxes (Gagge et al. 1986 ), is the basis for the operational thermal assessment procedure entitled the Klima-Michel-model (Jendritzky 1990 ; Jendritzky et al. 1979 ). This is used by the German Meteorological Service (Deutsche Wetterdienst, DWD). The output parameter is perceived temperature (PT; °C) (VDI 2008 ; Staiger et al. 2011 ), because an equivalent temperature makes the evaluation of thermal perception to the public more comprehensible than PMV. PT is defined as the equivalent temperature of an isothermal reference environ- ment with a wind reduced to light air and a relative humidity of 50%, where the same perception of warm or cold assessed by PMV would occur as under the actual environment. The reference person has a metabolic rate of 135 (W m −2 ) and the clothing insulation can be varied between 0.5 clo (warm / summer) and 1.75 clo (cold / winter) to achieve as much thermal comfort as possible. Hence, PT is related to outdoor conditions. Due to the PMV base, its assessment scale (Table 5 ) is comfort based. The implemented enthalpy correction (Gagge et al. 1986 ) increases the humidity sensitivity of PT under warm conditions markedly compared to Fanger’s original PMV.
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The Universal Thermal Climate Index UTCI in operational use

The Universal Thermal Climate Index UTCI in operational use

This paper presents the application of the operational procedure, which is accessible both as software source code and executable program at the project’s website (www.utci.org), on studying the sensitivity of UTCI to wind, humidity and radiation under heat and cold stress conditions compared with responses and predictions of some ergonomics standards concerned with the thermal environment. Concerning the reactions to humidity and radiation in warm environments, the Wet Bulb Globe Temperature (WBGT, ISO 7243, 1989) and the Predicted Heat Strain (PHS, ISO 7933, 2004) were used for comparison, respectively, and the response of UTCI to wind in the cold was compared to the Wind-Chill Temperature (ISO 11079, 2007; Osczevski & Bluestein, 2005).
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The Universal Thermal Climate Index UTCI Compared to Ergonomics Standards for Assessing the Thermal Environment

The Universal Thermal Climate Index UTCI Compared to Ergonomics Standards for Assessing the Thermal Environment

experiments where three acclimated male participants each walked for 3 h with 4 km/h on a treadmill at the level wearing clothing insulation of Icl=0.7 clo, with radiant temperature equal to air temperature and air velocity va = 0.3 m/s. For each participant there were 12 to 15 exposures with different air temperatures and humidities. The averaged rectal temperatures (Tre) and sweat rates during the third hour of exposure were used to calculate a best-fit area according to a heuristic approach. By this the intra-individual variation is averaged as to facilitate a comparison with the predictions for the corresponding climatic conditions of the PHS and of the UTCI-Fiala model in psychrometric diagrams, for details cf. 20, 21) . Predictions of Tre and sweat rates by PHS and by the UTCI-Fiala model were calculated with clothing insulation, activity level, air velocity and radiation corresponding to the experimental
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The Universal Thermal Climate Index UTCI compared to ergonomics standards for assessing the thermal environment

The Universal Thermal Climate Index UTCI compared to ergonomics standards for assessing the thermal environment

experiments where three acclimated male participants each walked for 3 h with 4 km/h on a treadmill at the level wearing clothing insulation of Icl=0.7 clo, with radiant temperature equal to air temperature and air velocity va = 0.3 m/s. For each participant there were 12 to 15 exposures with different air temperatures and humidities. The averaged rectal temperatures (Tre) and sweat rates during the third hour of exposure were used to calculate a best-fit area according to a heuristic approach. By this the intra-individual variation is averaged as to facilitate a comparison with the predictions for the corresponding climatic conditions of the PHS and of the UTCI-Fiala model in psychrometric diagrams, for details cf. 20, 21) . Predictions of Tre and sweat rates by PHS and by the UTCI-Fiala model were calculated with clothing insulation, activity level, air velocity and radiation corresponding to the experimental
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Validation of the Fiala multi-node thermophysiological model for UTCI application

Validation of the Fiala multi-node thermophysiological model for UTCI application

The essential requirement that COST Action 730 demanded of the model to be used for the UTCI was the capability to accurate simulate human thermophysiological responses across a very wide range of thermal environmental conditions. As the index was intended for the assessment of outdoor conditions, the applicable range of environmental conditions should correspond to the selection of all habitable climates and their seasonal changes. The physiological model should also be able to cope with transient conditions such as continuous variability of outdoor conditions. Therefore, the aim of this study was to evaluate available thermophysio- logical models by direct comparison and plausibility analysis. In the second stage, the selected model was validated for a range of relevant (steady-state and transient) environmental conditions using a number of human datasets collected from the literature and from laboratories participating in this project.
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An introduction to the Universal Thermal Climate Index (UTCI)

An introduction to the Universal Thermal Climate Index (UTCI)

The applicability of the UTCI was compared with selected biometeorological indices (e.g. Effec- tive Temperature – TE, Wet-Bulb Globe Tempera- ture – WBGT, Heat Stress Index – HSI, Wind Chill Temperature – WCT, Perceived Temperature – PT, Physiological Equivalent Temperature – PET, Stand- ard Effective Temperature – SET*, Physiological Subjective Temperature – PST, and Physiological Strain – PhS). The main finding from the compari- sons made for various sets of data (global, region- al, local, daily and momentary data) was that the UTCI offers a better portrayal of biothermal conditions for human beings than do other indices (Błażejczyk et al. 2012).
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Am empirical comparison of the performance of classical power indices

Am empirical comparison of the performance of classical power indices

The model underlying the measurement of voting power is a game played by n players who co- operate by forming coalitions 3 by simply casting their votes; that is, they vote for or against a motion in a hypothetical meeting. Coalitions may be winning or losing according to the rules of decision making in the meeting and each member’s power is then measured by his or her ability to influence the outcome by changing a coalition from losing to winning by voting with it rather than against it, an effect referred to henceforth as a swing. A power index is calculated for each player by considering each possible coalition of which he or she is not a member and evaluating the number of swings. The indices are not described in full detail here but in a later section after the discussion of methodology and the description of data because the form of the data available affects the precise nature of the games studied and their computational details. Before that I consider some theoretical aspects of the indices' comparative properties which have been discussed in the literature.
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Assessment of the Relationship between Stock Indices and Selected Macroeconomic Indicators

Assessment of the Relationship between Stock Indices and Selected Macroeconomic Indicators

Významnou součástí finančního systému tržních ekonomik je kapitálový trh, na kterém jsou obchodovány dlouhodobé finanční instrumenty. Nejrozšířenějším a nejvýznamnějším instrument[r]

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A Comparison of Hotel Indices with Hotel Properties and Portfolios

A Comparison of Hotel Indices with Hotel Properties and Portfolios

T he development of hotel real estate indices raises the possibility that investors could benchmark hotel price appreciation using hotel indices. However, individual hotel property transactions are poorly tracked by the aggregate hotel index that is available. Moreover, for individual assets, the level of tracking error is larger for hotels than non-hotel properties. Forming portfolios reduces tracking error quite dramatically, but the level of tracking error for hotel portfolios still appears to be higher than for other commercial properties. These results demonstrate that indexing individual properties is difficult, since any individual hotel investment is unlikely to perform like an aggregate index. However, it may be reasonable to compare a portfolio aggregate hotel indices. In that instance, this would require a fairly large portfolio of hotels where the properties are of approximately the same value.
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The correlation between pancreatic dysfunction markers and selected indices of periodontitis

The correlation between pancreatic dysfunction markers and selected indices of periodontitis

The control group consisted of 50 patients of the Gas- troenterology Ward, Department of Internal Medicine and Endocrinology Clinic. The age range was 29–69 years (median: 45.6 years). The probands selected to this group evinced no clinically relevant conditions regarding the digestive system organs. The inclusion to the group was made based on computed tomography and/or abdominal ultrasonography in order to exclude signs of pancreatic conditions including enlarged head of pancreas, and a di- lated pancreatic duct and common bile duct. 11 Moreover, to

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DETERMINATION OF FIRE PERFOMANCE INDICES OF SELECTED BUILDING MATERIALS IN NIGERIA

DETERMINATION OF FIRE PERFOMANCE INDICES OF SELECTED BUILDING MATERIALS IN NIGERIA

times interval increased. Hardwood started producing smoke before the expatriation of the 20 minutes. This finding was in agreement with Simms and Law (2005) and Li and Drysdale (1992) that wooden materials exhibited physical changes (char and smoke) during burning. The value of the index of the individual material was equal to their indices and it was occurred at 0.5 to 3 mins respectively (Table 3).

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Comparison of Performance of Selected DBMS

Comparison of Performance of Selected DBMS

1 V tomto návrhu budou veškeré tabulky typu halda a na některé z těchto tabulek bude vytvořen index typu B-Strom.. Index bude vytvářen s ohle- dem na co nejvyšší efektivitu dotazování.[r]

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Comparison of log transformed and scaled cone indices

Comparison of log transformed and scaled cone indices

It is difficult to quantify the soil strength build-up or the differences between treatments because cone indices fluctuate throughout the growing season, changing with water content or [r]

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Comparison of Sticky Cotton Indices and Sugar Composition

Comparison of Sticky Cotton Indices and Sugar Composition

Sticky cotton lint contaminates equipment in gins and textile mills, and requires costly stop- pages for cleaning and repair. The primary source of stickiness is sugars from insect honeydew. This manuscript assesses relationships among mea- surements of sugar composition and two methods used to measure stickiness. The hypothesis was that the measures of stickiness and sugar com- position will be highly correlated. Variables were compared to look for patterns that could be used to improve our understanding and management of stickiness. Experimental trials were conducted in seven fields over 3 years. A total of 724 samples of cotton lint were tested for stickiness with the Lintronics Fiber Contamination Tester (LFCT) and the High Speed Stickiness Detector (H2SD). Sugar composition testing was also performed on a subset of 325 samples. Coefficients of varia- tion, correlations, and factor analyses were used to identify relationships among the variables reported from each source of test data. The stron- gest relationships were between large spots on the H2SD and intermediate sized spots on the LFCT (R = 0.46). The LFCT demonstrated greater precision than the H2SD, based on coefficients of variation. Sugar composition was dominated by those found in plants (72%) with 6% from melezitose and 22% from trehalulose. The H2SD was more closely associated with aphid-derived sugars, and the LFCT was more closely associ- ated with whitefly-derived sugars. Relationships between sugar content and stickiness indices indicate complex biochemical interactions that
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Critical thermal maxima in knockdown selected Drosophila: are
thermal endpoints correlated?

Critical thermal maxima in knockdown selected Drosophila: are thermal endpoints correlated?

have resulted in photo- and geotactic behaviors (discussed above) genetically correlated to metabolic performance. Selection on phototaxis and geotaxis in Drosophila results in correlated changes in multiple traits, including eye size, testis color, wing venation and mating behavior (Del Solar, 1966; Dobzhansky and Spassky, 1969). Toma et al. compared gene expression of 250 genes in populations of D. melanogaster selected for negative or positive geotaxis and found that ~5% of the genes were differentially expressed (Toma et al., 2002). Further testing strongly implicated three candidate genes, whose mechanistic involvement in geotaxis is unclear. We speculate that selection on photo- or geotaxis (via selection on low knockdown) may lead to genetic changes influencing the thermal sensitivity of metabolism. An informative study would
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Monitoring land-use change using selected indices

Monitoring land-use change using selected indices

The land divisions were based on geographical dividing lines (to a stream, road, forest, or village) or the cadastral municipality boundary. We selected a contiguous area of land showing the characteristics of arable land division (Ilešič 1950; Foški 2017). For Zatolmin and Predoslje, all of the agricultural land is considered within a cadastral municipality. For Kleče and Podgora, the agricultural land between Podgora and Kleče that is still divided into strips is considered; furthermore, nine contact enclosures within the cadastral municipality of Pernice are considered. In Vinjole we address the geographically coherent areas of a hamlet based on geographical divisions (i.e., stream, ridge, forest, or road). The sites were named after the closest settlement or hamlet. The analysis of land use in 2015 was carried out using data from the Land Use data base (Evidenca dejanske rabe … 2015). Using graphic cross-sections, the Land Use data base data were trimmed to the area of the sites. All patches smaller than 20 m², which are a consequence of the graph- ic cross-section of the Land Use data base with the area of a site and which are at the edges of an area, were aggregated with contiguous land use.
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Thermal Analysis. Application Handbook. Thermal Analysis of Polymers Selected Applications

Thermal Analysis. Application Handbook. Thermal Analysis of Polymers Selected Applications

In a typical measurement, an oscillating force is applied to the sample at different frequencies. The elastic modulus is mea- sured as the shear storage modulus, G', and loss modulus, G". This data is used to calculate tan delta, the loss factor, or the damping coefficient, G"/G'. DMA is much more sensitive than other methods. For example, it can measure glass tran- sitions of filled materials or thin layers on substrate material, that is, transitions which are difficult to detect by DSC. Figure 16 displays the DMA measurement curve of a shock-cooled PET sample 5 mm in diameter and 0.49 mm thick in the shear mode at 1 Hz in the range –150 °C to +270 °C. The heating rate was 2 K/min. The DMA curve also shows other effects such as  relaxation (local movement of polymer groups) or recrystallization in addition to the effects detected by TMA or TGA/DSC such as the glass transition, crystallization and melting.  relaxation is weak and can only be measured by DMA. Other thermal analysis techniques such as DSC or TGA cannot detect this transition.
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A new era for sustainable development : a comparison for sustainability indices

A new era for sustainable development : a comparison for sustainability indices

In DJSI, total return index series are calculated for the indices as well as the price return series. Ordinary cash dividends are applied on the ex-date in calculating the total return series. “Special dividends” are those dividends that are outside of the normal payment pattern established historically by the issuing corporation. These may be described by the corporation as “special,” “extra,” “year-end,” or “return of capital.” Whether a dividend is funded from operating earnings or from other sources of cash does not affect the determination of whether it is ordinary or special. “Special dividends” are treated as corporate actions with offsetting price and divisor adjustments; the total return index series reflect both ordinary and special dividends (S&P, 2015).
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FORECASTING INDEX MOVEMENTS: A STUDY WITH REFERENCE TO SELECTED NSE SECTORAL INDICES.

FORECASTING INDEX MOVEMENTS: A STUDY WITH REFERENCE TO SELECTED NSE SECTORAL INDICES.

1.4856 from the starting period of time. After that, the R/S plot close to Log E R/S pot from 600 th day at the Point 21 th i.e., (1.638) to 25 th Point 750 day i.e., 1.5416. From the overall analysis of the above chart, the series of log R/S and log E(R/S) were identical with the random walk. This clearly shows the fact that there was absence of long memory in the case of S&P NSE Auto, NSE FMCG, NSE IT and NSE Pharama during the study period from 1 st April 2005 to 31 st March 2017. Hence the null hypothesis H04,namely, “There is no long range dependence in the returns of Selected Sample Listed in NSE”, isaccepted.
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ANALYSIS OF VOLATILITY DYNAMICS IN SELECTED SECTORAL INDICES OF NATIONAL STOCK EXCHANGE

ANALYSIS OF VOLATILITY DYNAMICS IN SELECTED SECTORAL INDICES OF NATIONAL STOCK EXCHANGE

We have used AR (1) for Auto and realty and for remaining ARMA (1, 1) model to model the mean equations and used their residuals and squared residuals to check the serial correlation in them and then ARCH effect is also checked at lag 1 and 2 to find out the return series suitability to model the volatility through GARCH models. We can see in Table II the residuals diagnostics of ARMA model. Ljung box Q-statistics is used to find the serial correlation in residuals and squared residuals series. In residual series and squared residual series Q statistics have been checked up to 36 lags. But here in table 2 up to lag 5 this statistics have been shown. We can see that in all sectoral indices along with Nifty residual series Q statistics is statistically insignificant because P value is more than 0.05 except realty at lag 3 after that it is also more than 0.05. Thus all P values are showing more than 0.05 values indicating Null hypothesis of No serial correlation is accepted in residual series. While if we see the squared residual series Q- statistics then we can found that all p values are less than 0.05 except IT sector. It means null hypothesis of no autocorrelation get rejected here and there is serial correlation in squared residual series. Further we have checked for ARCH effect of heteroskedasticity up to lag 2. At lag 2 all p values are less than 0.05 in nifty and all 5 sectors except IT so there is ARCH effect in Nifty and all sectors except IT sector. So we can find that our volatility model pre conditions like- No Auto correlation in residual series, Serial correlation in Squared residual series or ARCH effect in residual series have been met in Nifty and all sectors except IT. Now we can fit GARCH family models to model the volatility in Nifty and 4 sectors. IT sector can’t be used for volatility models because there is no ARCH effect found.
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