ON THE RELATIONSHIP BETWEEN FREEZING POINT LOWERING, Dgr, AND SPECIFIC ELECTRICAL CONDUCTIVITY, K, OF PLANT TISSUE FLUIDS

Science ◽  
1920 ◽  
Vol 52 (1351) ◽  
pp. 494-495 ◽  
Author(s):  
J. A. Harris ◽  
R. A. Gortner ◽  
J. V. Lawrence
Keyword(s):  
Electrical Conductivity ◽  
Plant Tissue ◽  
Freezing Point ◽  
Specific Electrical Conductivity ◽  
Tissue Fluids ◽  
The Relationship

Variation of Electrical Conductivity With Water Content in Agarose Gel

2002 ◽  
Author(s):  
Adriana L. Vega ◽  
Hai Yao ◽  
Marc-Antoine Justiz ◽  
Weiyong Gu
Keyword(s):  
Electrical Conductivity ◽  
Water Content ◽  
Normal Tissue ◽  
Tumor Tissue ◽  
Biological Tissues ◽  
Specific Electrical Conductivity ◽  
Agarose Gel ◽  
Similar Relationship ◽  
Ion Concentrations ◽  
The Relationship

Specific electrical conductivity, a material property of biological tissues, has been found to be greater in tumor tissue than in normal tissue on account of its higher water content [1]. Its value is related to water content, ion concentrations, and ion diffusivities within biological tissues [e.g., 1,2,3]. The variation in conductivity with water content is hypothesized to be related to the change in ion diffusivities [5,6]. The objective of this study is to investigate the relationship between conductivity and water content in hydrogels. The main goal is to develop a similar relationship for biological tissues and to understand deformation-dependent ion diffusivity in tissues under mechanical loading.

Relationship of the s1-elements halogenides melts specific electric conductivity with alkali metals specific electric conductivity

2019 ◽  
Vol 60 (12) ◽  
pp. 116-124
Author(s):  
Ivan K. Garkushin ◽  
◽  
Olga V. Lavrenteva ◽  
Yana A. Andreeva ◽  
◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Electric Conductivity ◽  
Alkali Metals ◽  
Analytical Description ◽  
Specific Electrical Conductivity ◽  
Molten Metals ◽  
Specific Electric Conductivity ◽  
Metal Melts ◽  
Relationship Of ◽  
The Relationship

The paper presents an analytical description of the relationship of the specific electrical conductivity æ of individual alkali metals haloganides melts (MHal) (M – Li, Na, K, Rb, Cs, Fr; Hal – F, Cl, Br, I) and the specific electrical conductivity æ(M) of alkali metal melts for temperatures (Тпл + n) (Tпл – melting temperature K; n = 5, 10, 50, 75, 100, 150, 200° higher melting temperatures of MHal and metals) and the specific electrical conductivity of alkali metals at standard temperature using M.Kh. Karapetyans comparative methods. The relationship of properties æ(MHal при Тпл+n) = f(æ(MHal при Тпл+5)), æ(FrHalТпл+n) = f(æ(FrHalТпл+5°)) is described in the "property-property" coordinates. A comparative analysis of the specific electrical conductivity values of francium haloganides melts obtained by the proposed methods was carried out. The possibility of calculating the electrical conductivity of molten salts from the electrical conductivity of molten metals is shown. It is shown that the equation æ(MHal)0.5 = a + bæ(M)1.5 can be used to calculate the specific electrical conductivity of francium haloganides melts. The calculation of the specific electrical conductivity using various equations shows the consistency of the numerical values obtained.

scholarly journals Prediction of Reproductive Success in Multiparous First Service Dairy Cows by Parameters from In-Line Sensors

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 334
Author(s):  
Ramūnas Antanaitis ◽  
Vida Juozaitienė ◽  
Dovilė Malašauskienė ◽  
Mindaugas Televičius ◽  
Mingaudas Urbutis ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Blood Parameters ◽  
Negative Energy ◽  
Hydroxybutyric Acid ◽  
Pregnancy Status ◽  
Group A ◽  
Biochemical Indices ◽  
Relationship Of ◽  
The Relationship

The aim of the current study was to evaluate the relationship of different parameters from an automatic milking system (AMS) with the pregnancy status of multiparous cows at first service and to assess the accuracy of such a follow-up with regard to blood parameters. Before the insemination of cows, blood samples for measuring biochemical indices were taken from the coccygeal vessels and the concentrations of blood serum albumin (ALB), cortisol, non-esterified fatty acids (NEFA) and the activities of aspartate aminotransferase (AST) and gamma glutamyltransferase (GGT) were determined. From oestrus day to seven days after oestrus, the following parameters were registered: milk yield (MY), electric milk conductivity, lactate dehydrogenase (LDH) and β-hydroxybutyric acid (BHB). The pregnancy status was evaluated using ultrasound “Easy scan” 30–35 days after insemination. Cows were grouped by reproductive status: PG− (non-pregnant; n = 48) and PG+ (pregnant; n = 44). The BHB level in PG− cows was 1.2 times higher (p < 0.005). The electrical conductivity of milk was statistically significantly higher in all quarters of PG− cows (1.07 times) than of PG+ cows (p < 0.05). The arithmetic mean of blood GGT was 1.61 times higher in PG− cows and the NEFA value 1.23 times higher (p < 0.05) compared with the PG+ group. The liver function was affected, the average ALB of PG− cows was 1.19 times lower (p < 0.05) and the AST activity was 1.16 times lower (p < 0.05) compared with PG+ cows. The non-pregnant group had a negative energy balance demonstrated by high in-line milk BHB and high blood NEFA concentrations. We found a greater number of cows with cortisol >0.0.75 mg/dL in the non-pregnant group. A higher milk electrical conductivity in the non-pregnant cows pointed towards a greater risk of mastitis while higher GGT activities together with lower albumin concentrations indicated that the cows were more affected by oxidative stress.

scholarly journals Investigation of the relationship between landform classes and electrical conductivity (EC) of water and soil using a fuzzy model in a GIS environment

Solid Earth ◽  
2016 ◽  
Vol 7 (3) ◽  
pp. 873-880
Author(s):  
Marzieh Mokarram ◽  
Dinesh Sathyamoorthy
Keyword(s):  
Electrical Conductivity ◽  
Fuzzy Model ◽  
Chemical Properties ◽  
Geographical Information ◽  
Fars Province ◽  
Fuzzy Method ◽  
The North ◽  
The Relationship ◽  
Very High ◽  
Soil And Water

Abstract. Soil genesis is highly dependent on landforms as they control the erosional processes and the soil physical and chemical properties. The relationship between landform classification and electrical conductivity (EC) of soil and water in the northern part of Meharloo watershed, Fars province, Iran, was investigated using a combination of a geographical information system (GIS) and a fuzzy model. The results of the fuzzy method for water EC showed 36.6 % of the land to be moderately land suitable for agriculture; high, 31.69 %; and very high, 31.65 %. In comparison, the results of the fuzzy method for soil EC showed 24.31 % of the land to be as not suitable for agriculture (low class); moderate, 11.78 %; high, 25.74 %; and very high, 38.16 %. In total, the land suitable for agriculture with low EC is located in the north and northeast of the study area. The relationship between landform and EC shows that EC of water is high for the valley classes, while the EC of soil is high in the upland drainage class. In addition, the lowest EC levels for soil and water are in the plains class.

Electrical Conductivity of Agricultural Drainage Water in Iowa

2017 ◽  
Vol 33 (3) ◽  
pp. 369-378 ◽  
Author(s):  
Brett A Zimmerman ◽  
Amy L Kaleita
Keyword(s):  
Electrical Conductivity ◽  
Environmental Conditions ◽  
Major Ions ◽  
Ionic Composition ◽  
Field Investigation ◽  
Drainage Water ◽  
Specific Electrical Conductivity ◽  
Agricultural Drainage ◽  
Bulk Electrical Conductivity ◽  
Drainage Waters

Abstract. Assessing the effectiveness of management strategies to reduce agricultural nutrient efflux is hampered by the lack of affordable, continuous monitoring systems. Generalized water quality monitoring is possible using electrical conductivity. However environmental conditions can influence the ionic ratios, resulting in misinterpretations of established electrical conductivity and ionic composition relationships. Here we characterize specific electrical conductivity (k25) of agricultural drainage waters to define these environmental conditions and dissolved constituents that contribute to k25. A field investigation revealed that the magnitude of measured k25 varied from 370 to 760 µS cm-1. Statistical analysis indicated that variability in k25 was not correlated with drainage water pH, temperature, nor flow rate. While k25 was not significantly different among drainage waters from growing and post-growing season, significant results were observed for different cropping systems. Soybean plots in rotation with corn had significantly lower conductivities than those of corn plots in rotation with soybeans, continuous corn plots, and prairie plots. In addition to evaluating k25 variability, regression analysis was used to estimate the concentration of major ions in solution from measured k25. Regression results indicated that HCO3-, Ca2+, NO3-, Mg2+, Cl-, Na2+, SO42- were the major drainage constituents contributing to the bulk electrical conductivity. Calculated ionic molal conductivities of these analytes suggests that HCO3-, Ca2+, NO3-, and Mg2+ account for approximately 97% of the bulk electrical conductivity. Keywords: Electrical conductivity, Salinity, Subsurface drainage, Total dissolved solids.

Laboratory test of snow wetness influence on electrical conductivity measured with ground penetrating radar

2009 ◽  
Vol 40 (1) ◽  
pp. 33-44 ◽  
Author(s):  
Nils Granlund ◽  
Angela Lundberg ◽  
James Feiccabrino ◽  
David Gustafsson
Keyword(s):  
Electrical Conductivity ◽  
Electrical Properties ◽  
Laboratory Test ◽  
Liquid Water ◽  
Ground Penetrating Radar ◽  
Wave Attenuation ◽  
Radar Measurements ◽  
Ground Penetrating ◽  
Radar Wave ◽  
The Relationship

Ground penetrating radar operated from helicopters or snowmobiles is used to determine snow water equivalent (SWE) for annual snowpacks from radar wave two-way travel time. However, presence of liquid water in a snowpack is known to decrease the radar wave velocity, which for a typical snowpack with 5% (by volume) liquid water can lead to an overestimation of SWE by about 20%. It would therefore be beneficial if radar measurements could also be used to determine snow wetness. Our approach is to use radar wave attenuation in the snowpack, which depends on electrical properties of snow (permittivity and conductivity) which in turn depend on snow wetness. The relationship between radar wave attenuation and these electrical properties can be derived theoretically, while the relationship between electrical permittivity and snow wetness follows a known empirical formula, which also includes snow density. Snow wetness can therefore be determined from radar wave attenuation if the relationship between electrical conductivity and snow wetness is also known. In a laboratory test, three sets of measurements were made on initially dry 1 m thick snowpacks. Snow wetness was controlled by stepwise addition of water between radar measurements, and a linear relationship between electrical conductivity and snow wetness was established.

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