Seasonal variations in wool growth and heat tolerance of sheep. II. Heat tolerance

1960 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
M Wodzicka
Keyword(s):  
Water Vapour ◽  
Air Temperature ◽  
Heat Tolerance ◽  
Seasonal Variations ◽  
Vapour Pressure ◽  
Evaporative Cooling ◽  
Water Vapour Pressure ◽  
Heat Acclimatization ◽  
Wool Growth ◽  
Heat Tolerant

The heat tolerance of rams at an air temperature of 105°F (40.6°C) and 34 mm Hg water vapour pressure was studied over a period of 1 year at Beltsville, Maryland. The rams were more heat tolerant in summer than in winter. This difference was significant (P < 0.001). Tolerance was less in July than in June and August. The reasons for this are discussed. Shearing increased the heat tolerance of rams (P < 0.001), presumably by enhancing the efficiency of evaporative cooling from the skin. There is evidence that shearing reduced heat acclimatization, especially during the cooler months.

scholarly journals Decomposition and modelling of the annual cycle of meteorological variables in the Ukrainian Carpathians

2018 ◽  
Keyword(s):  
Wind Speed ◽  
Water Vapour ◽  
Air Temperature ◽  
Annual Cycle ◽  
Vapour Pressure ◽  
Seasonal Cycle ◽  
Meteorological Variables ◽  
Water Vapour Pressure ◽  
Seasonal Fluctuations ◽  
Saturation Deficit

Formulation of the problem. In this paper we consider some structural peculiarities of the seasonal cycle of a number of meteorological variables (air temperature, sum of precipitations, saturation deficit, relative humidity, water vapour pressure, station level and sea level pressure, wind speed) in the Ukrainian Carpathians, with the annual cycle being interpreted as a superposition of six harmonics with the period ranging from 2 months to 1 year. Data and methods. This research is based on the average monthly values of seven meteorological variables in the Ukrainian Carpathians within a standard climatological period of 1961-1990. Implicit frequencies in seasonal fluctuations were revealed and evaluated with the help of harmonic analysis method. Presentation of the main research material. A particular emphasis has been laid on the parameters of the first (annual) and second (semiannual) harmonics. It was found out that, on average, the annual harmonic explains some 87% of the total variance of the variables, while the semiannual harmonic accounts for more than 7%. It is shown that when considering seasonal fluctuations of air temperature, saturation deficit and water vapour pressure annual harmonic will suffice. Analyzing fluctuations of other meteorological variables requires a semiannual harmonic to be taken into account. Higher order harmonics (from third to fifth) are to be taken into consideration when analyzing relative humidity, sum of precipitations, station level pressure, and wind speed. The last harmonic (with a period of 2 months) does not play any significant role at all. It was found out that the seasonal cycle structure of these meteorological variables at mountain weather stations and at foothill ones differ noticeably. For some meteorological variables, namely saturation deficit, air temperature, water vapour pressure and atmospheric pressure, orographic effects that manifest in either delayed or early phase of the annual cycle with relation to altitude, have proved to be statistically significant. Phases of this harmonic are typical of meteorological variables that are directly interdependent, while variables that are inversely interdependent normally fluctuate in antiphase.

scholarly journals Deep learning and regression modelling of cloudless downward longwave radiation

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Nsikan I. Obot ◽  
Ibifubara Humphrey ◽  
Michael A. C. Chendo ◽  
Sunday O. Udo
Keyword(s):  
Neural Networks ◽  
Regression Model ◽  
Water Vapour ◽  
Air Temperature ◽  
Vapour Pressure ◽  
Regression Models ◽  
Longwave Radiation ◽  
Water Vapour Pressure ◽  
Analytical Relationship ◽  
Downward Longwave Radiation

Abstract Background Though downward longwave radiation (DLR) models curb the paucity of data, they are mostly location dependent. Therefore, there is a need to evaluate their relevance given the increasing use of machine learning techniques. In this study, cloudless DLR estimates from regression models and soft computing models of neural networks (NN), support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS) were compared. Clear days from September 1992 to August 1994 and July 1995 to March 1998 in Ilorin (8.50 °N, 4.55 °E), Nigeria were considered, while the predictors for the models were water vapour pressure, e and air temperature, T. Results A new regression model in relation to the Boltzmann constant, σ: $$ \left(1.014\left(\frac{1.0\times {10}^{30}\times e}{T^{13}}\right)+0.699\right)\sigma {T}^4 $$1.0141.0×1030×eT13+0.699σT4, was better than other regression models and applicable at another location. Between 1 and 8, the sixth degree was the best polynomial kernel function in SVR models’ estimations of cloudless DLR. Though the new regression model was comparable to expert systems, ANFIS was still the best model due to its consistent high correlations and lowest estimation errors. Conclusions Experience-based computational procedures that combine enough logics with neural networks respond effectively to other data. Furthermore, the analytical relationship between water vapour pressure and air temperature in DLR’s mechanism should be redefined accordingly, while the sixth polynomial should be used as the default setting in SVR systems.

Water vapour pressure above saturated salt solutions at low temperatures

1999 ◽  
Vol 155 (2) ◽  
pp. 297-309 ◽  
Author(s):  
V Morillon ◽  
F Debeaufort ◽  
J Jose ◽  
J.F Tharrault ◽  
M Capelle ◽  
...  
Keyword(s):  
Water Vapour ◽  
Vapour Pressure ◽  
Low Temperatures ◽  
Water Vapour Pressure ◽  
Salt Solutions

The Study of Evaporation from Small Surfaces by the Direct Measurement of Water Vapour Pressure Gradients

1970 ◽  
Vol 53 (3) ◽  
pp. 753-762
Author(s):  
JOHN MACHIN
Keyword(s):  
Water Vapour ◽  
Direct Measurement ◽  
Vapour Pressure ◽  
Point Method ◽  
Dew Point ◽  
Water Vapour Pressure ◽  
Pressure Gradients ◽  
Pressure Measurements ◽  
Wind Speeds ◽  
Experimental Values

1. The construction, maintenance and calibration of a sensitive instrument capable of making numerous vapour-pressure measurements within humidity gradients by the dew-point method is described. 2. Coefficients of diffusion of water vapour in air, calculated from observed vapour-pressure gradients and measured rates of evaporation agree with theoretical and other experimental values in still air. 3. Apparent coefficients in wind speeds between 10 and 100 cm/s were significantly lower than those in still air. 4. This finding, together with the performance of the dew-point probe, is discussed in relation to its possible use in the study of evaporation from animals and plants.

scholarly journals Properties of hydrated TiO2 and SiO2 nanoclusters: dependence on size, temperature and water vapour pressure

Nanoscale ◽  
2018 ◽  
Vol 10 (45) ◽  
pp. 21518-21532 ◽  
Author(s):  
Andi Cuko ◽  
Antoni Macià Escatllar ◽  
Monica Calatayud ◽  
Stefan T. Bromley
Keyword(s):  
Water Vapour ◽  
Systematic Study ◽  
Vapour Pressure ◽  
Water Vapour Pressure ◽  
Nano Silica

The stabilities and properties of globally optimised (TiO2)M(H2O)N and (SiO2)M(H2O)N clusters with M = 4–16 and a range of N/M ratios are studied with respect temperature and water vapour pressure. Our systematic study provides a comparative reference for understanding hydration of nano-silica and nano-titania.

Study of the reaction CaSO4.½H2O (β-hemihydrate) = CaSO4 (β-soluble anhydrite)+½H2O in the temperature range 20–100° C

1965 ◽  
Vol 34 (268) ◽  
pp. 327-345 ◽  
Author(s):  
J. D. C. McConnell
Keyword(s):  
Water Vapour ◽  
Vapour Pressure ◽  
Physical Adsorption ◽  
Linear Increase ◽  
Water Vapour Pressure ◽  
Hydration Reaction ◽  
Temperature Range ◽  
Dehydration Reactions ◽  
Adsorption Of Water ◽  
Hydration And Dehydration

SummaryA thermogravimetric vacuum microbalance has been used to study the reaction between β-soluble anhydrite and water vapour in the temperature range 20–100° C. Equilibrium water-vapour pressures for the hydration reaction in this temperature range were determined directly and have been compared with available data obtained by Kelly, Southard, and Anderson (1941) in the temperature range 80–120° C. The kinetics of the hydration and dehydration reactions have also been studied in a series of isothermal experiments with varying water-vapour pressure. These experiments indicate that in a vapour-pressure range close to the equilibrium value very low rates for both hydration and dehydration are observed. Outside this range of vapour pressures both hydration and dehydration rates increase suddenly and show an approximately linear increase with imposed water-vapour pressure.At low temperatures (25° C) the dehydration reaction has an associated activation energy of approximately 10 kcal mole−1. In the same temperature range additional, physical adsorption of water vapour by the specimen was noted.

Techniques and Applications for Imaging Biological Samples in a Low Vacuum Environmental Scanning Electron Microscope

2001 ◽  
Vol 7 (S2) ◽  
pp. 782-783
Author(s):  
C. J. Gilpin.
Keyword(s):  
Water Vapour ◽  
Biological Samples ◽  
Vapour Pressure ◽  
Water Vapour Pressure ◽  
Environmental Scanning ◽  
Sample Collection ◽  
Electron Microscopes ◽  
A Site ◽  
Scanning Electron Microscopes ◽  
Scanning Electron

Of all the commercially available scanning electron microscopes which operate at “low vacuum” the ESEM is the most suitable for examining biological samples. in order to maintain samples with liquid water present the specimen chamber must be capable of operating at a pressure of at least 4.6 Torr (611 pascals) of water vapour pressure (the vapour pressure of water at 0°C). Use of lower pressures or a chamber gas other than water vapour will result in evaporation of water from the sample at a rate dependant on the partial pressure difference between the sample and its surrounding environment. Tables of relative humidity as a function of water vapour pressure and temperature are readily available to calculate desired settings for the microscope.One of the difficulties associated with examining fresh biological material is the need to have the microscope and sample available in the same location at the same time.If sample collection occurs at a site remote from the microscope inevitable necrotic changes will occur before examination can be carried out.

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