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Journal articles on the topic 'Sample rate'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Van Den Enden, Adrianus Wilhelmus Maria. "Sample rate converter." Journal of the Acoustical Society of America 113, no. 2 (2003): 694. http://dx.doi.org/10.1121/1.1560289.

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Choi, Jong-Won, TaekJoon Yi, Joo-Hyeok Kw, Jooeon Ahn, and Bokki Kim. "Arbitrary Sample Rate Conversion Architecture for High Output Sample Rate." Journal of Korean Institute of Communications and Information Sciences 44, no. 7 (July 31, 2019): 1248–56. http://dx.doi.org/10.7840/kics.2019.44.7.1248.

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3

Rittinghaus, Tim, and Birgit Glasmacher. "Is freezer cooling rate equal to sample cooling rate?" Cryobiology 85 (December 2018): 135. http://dx.doi.org/10.1016/j.cryobiol.2018.10.070.

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4

Hentschel, T., and G. Fettweis. "Sample rate conversion for software radio." IEEE Communications Magazine 38, no. 8 (2000): 142–50. http://dx.doi.org/10.1109/35.860866.

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5

Yao, Lin Xin, and Gan Yao. "High sample rate Doppler ultrasound system." Journal of the Acoustical Society of America 113, no. 1 (2003): 18. http://dx.doi.org/10.1121/1.1554183.

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6

Putzeys, Bruno J. G. "Sample rate converter using polynomial interpolation." Journal of the Acoustical Society of America 113, no. 5 (2003): 2394. http://dx.doi.org/10.1121/1.1584178.

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7

Kim, Jae-Hun, and Young-Sik Kim. "25MHz Sample Rate 8bit Thermometer DAC Design." Journal of the Institute of Electronics and Information Engineers 55, no. 3 (March 31, 2018): 29–33. http://dx.doi.org/10.5573/ieie.2018.55.3.29.

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8

Birru, D. "Optimized reduced sample rate sigma-delta modulation." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 44, no. 11 (1997): 896–906. http://dx.doi.org/10.1109/82.644043.

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9

Logsdon, S. D. "WITHIN SAMPLE VARIATION OF OXYGEN DIFFUSION RATE." Soil Science 168, no. 8 (August 2003): 531–39. http://dx.doi.org/10.1097/01.ss.0000085052.25696.c0.

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10

McFee, Brian. "resampy: efficient sample rate conversion in Python." Journal of Open Source Software 1, no. 8 (December 5, 2016): 125. http://dx.doi.org/10.21105/joss.00125.

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11

Obrecht, Natalie A., and Dana L. Chesney. "Sample representativeness affects whether judgments are influenced by base rate or sample size." Acta Psychologica 142, no. 3 (March 2013): 370–82. http://dx.doi.org/10.1016/j.actpsy.2013.01.012.

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12

Whikehart, Bill. "Sample-Rate Conversion for Non-Clocked Audio Sources." SAE International Journal of Passenger Cars - Electronic and Electrical Systems 7, no. 2 (April 1, 2014): 418–21. http://dx.doi.org/10.4271/2014-01-0261.

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13

ANTONESEI, G., C. TURCU, and A. GRAUR. "Conceptual Implementation of Sample Rate Convertors for DACs." Advances in Electrical and Computer Engineering 10, no. 2 (2010): 53–60. http://dx.doi.org/10.4316/aece.2010.02009.

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14

MacCarley, C. A., and D. G. Meyer. "Sample Rate Selection for Discrete Time Switching Controls." IFAC Proceedings Volumes 23, no. 8 (August 1990): 305–9. http://dx.doi.org/10.1016/s1474-6670(17)51933-3.

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15

Al-Abdulmunem, Mosa’ad, and Stella T. Briggs. "Spontaneous blink rate of a normal population sample." International Contact Lens Clinic 26, no. 2 (March 1999): 29–32. http://dx.doi.org/10.1016/s0892-8967(99)00016-4.

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16

Abu-Al-Saud, W. A., and G. L. Stuber. "Efficient sample rate conversion for software radio systems." IEEE Transactions on Signal Processing 54, no. 3 (March 2006): 932–39. http://dx.doi.org/10.1109/tsp.2005.861737.

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17

Lake, Douglas E., Joshua S. Richman, M. Pamela Griffin, and J. Randall Moorman. "Sample entropy analysis of neonatal heart rate variability." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 283, no. 3 (September 1, 2002): R789—R797. http://dx.doi.org/10.1152/ajpregu.00069.2002.

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Abnormal heart rate characteristics of reduced variability and transient decelerations are present early in the course of neonatal sepsis. To investigate the dynamics, we calculated sample entropy, a similar but less biased measure than the popular approximate entropy. Both calculate the probability that epochs of window length m that are similar within a tolerance r remain similar at the next point. We studied 89 consecutive admissions to a tertiary care neonatal intensive care unit, among whom there were 21 episodes of sepsis, and we performed numerical simulations. We addressed the fundamental issues of optimal selection of m and r and the impact of missing data. The major findings are that entropy falls before clinical signs of neonatal sepsis and that missing points are well tolerated. The major mechanism, surprisingly, is unrelated to the regularity of the data: entropy estimates inevitably fall in any record with spikes. We propose more informed selection of parameters and reexamination of studies where approximate entropy was interpreted solely as a regularity measure.
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18

Tassart, Stéphan. "Time-Invariant Context for Sample Rate Conversion Systems." IEEE Transactions on Signal Processing 60, no. 3 (March 2012): 1098–107. http://dx.doi.org/10.1109/tsp.2011.2176336.

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19

Chakrabarti, C. "High sample rate array architectures for median filters." IEEE Transactions on Signal Processing 42, no. 3 (March 1994): 707–12. http://dx.doi.org/10.1109/78.277872.

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20

Tewksbury, S., M. Hatamian, P. Franzon, L. Hornak, C. Siller, and V. Lawrence. "FIR digital filters for high sample rate applications." IEEE Communications Magazine 25, no. 7 (July 1987): 62–72. http://dx.doi.org/10.1109/mcom.1987.1093656.

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21

Pounds, S., and C. Cheng. "Sample size determination for the false discovery rate." Bioinformatics 21, no. 23 (October 4, 2005): 4263–71. http://dx.doi.org/10.1093/bioinformatics/bti699.

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22

Howes, Stephen, and Jean Olson Lanjouw. "DOES SAMPLE DESIGN MATTER FOR POVERTY RATE COMPARISONS?" Review of Income and Wealth 44, no. 1 (March 1998): 99–109. http://dx.doi.org/10.1111/j.1475-4991.1998.tb00254.x.

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23

Stutzman, Warren L., and P. Will Remaklus. "The effect of sample rate on propagation measurements." Microwave and Optical Technology Letters 7, no. 9 (June 20, 1994): 404–6. http://dx.doi.org/10.1002/mop.4650070907.

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24

Fahey, S. O'F, and A. L. Wicks. "DIGITAL DATA: SAMPLE RATE, ALIASING AND SIGNAL ATTENUATION." Experimental Techniques 23, no. 5 (September 1999): 38–40. http://dx.doi.org/10.1111/j.1747-1567.1999.tb01304.x.

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25

Keefer, Lyndon M. "Method for sample rate conversion of digital data." Journal of the Acoustical Society of America 111, no. 6 (2002): 2537. http://dx.doi.org/10.1121/1.1492951.

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26

Zeineddine, Ali, Amor Nafkha, Stéphane Paquelet, Christophe Moy, and Pierre Yves Jezequel. "Comprehensive Survey of FIR-Based Sample Rate Conversion." Journal of Signal Processing Systems 93, no. 1 (July 27, 2020): 113–25. http://dx.doi.org/10.1007/s11265-020-01575-6.

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27

Jiao, L. C., Jianrui Chen, Jianshe Wu, Xiaodong Wang, and Shuang Zhang. "Efficient Collaborative Spectrum Sensing with Low Sample Rate." Wireless Personal Communications 67, no. 4 (November 23, 2011): 923–36. http://dx.doi.org/10.1007/s11277-011-0419-z.

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28

Sudarma, Nyoman, and I. Made Oka Adi Parwata. "Determination Ethanol In Arak With Gas Chromatography." Bali Medika Jurnal 4, no. 2 (December 30, 2017): 126–35. http://dx.doi.org/10.36376/bmj.v4i2.10.

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Ethanol is a type of alcohol that can be consumed. One of drinks containing ethanol is Arak. Due to ethanol drink containing in excessive levels, has many causes and event such as accidents caused by drunken brawl. The purpose of this research was to determine the levels of ethanol in Arak is sold in the Merita village of Karangasem regency. Determination levels of ethanol done by gas chromatography. Results of the determination of levels ethanol of 5 samples taken at random to meet the standard required by Menkes RI number 86/Menkes/Per/IV/77, alcohol group B at the rate of 5-20% is sampel ‘a’ that is at the rate of 18.47%, sample ‘c’ with the rate of 16.11% and sampel ‘d’ with the rate 11.68%. Alcohol group C with 20-55% rate that is in the sample ‘b’ with a sample rate of 25.28% and sampel ‘e’ with the rate 25.94%.
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29

Göckler, Heinz G., Gennaro Evangelista, and Alexandra Groth. "Minimal block processing approach to fractional sample rate conversion." Signal Processing 81, no. 4 (April 2001): 673–91. http://dx.doi.org/10.1016/s0165-1684(00)00221-8.

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30

Carlson, Robert H. "‘Blood Culture Club’ Reduces Rate of Blood Sample Contamination." Oncology Times 28, no. 14 (July 2006): 36. http://dx.doi.org/10.1097/01.cot.0000303165.11327.76.

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31

Wei Qin, Bo Hu, and Xieting Ling. "Sigma-delta ADC with reduced sample rate multibit quantizer." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 46, no. 6 (June 1999): 824–28. http://dx.doi.org/10.1109/82.769792.

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32

Bridgelall, Raj. "Inertial Sensor Sample Rate Selection for Ride Quality Measures." Journal of Infrastructure Systems 21, no. 2 (June 2015): 04014039. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000225.

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33

Pounds, Stan, and Cheng Cheng. "Erratum: sample size determination for the false discovery rate." Bioinformatics 25, no. 5 (January 6, 2009): 698–99. http://dx.doi.org/10.1093/bioinformatics/btn661.

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34

Weiss, S. M. "Small sample error rate estimation for k-NN classifiers." IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 3 (March 1991): 285–89. http://dx.doi.org/10.1109/34.75516.

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35

Birru, Dagnachew. "Reduced-sample-rate sigma–delta modulation using recursive deconvolution." International Journal of Circuit Theory and Applications 25, no. 5 (September 1997): 419–37. http://dx.doi.org/10.1002/(sici)1097-007x(199709/10)25:5<419::aid-cta982>3.0.co;2-1.

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36

Jain, Vivek, and Navneet Agrawal. "Implement Multichannel Fractional Sample Rate Convertor using Genetic Algorithm." International Journal of Multimedia Data Engineering and Management 8, no. 2 (April 2017): 10–21. http://dx.doi.org/10.4018/ijmdem.2017040102.

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In this paper reduce power of multichannel fractional sample rate convertor by minimized hamming distance between consecutive coefficients of filter using Genetic algorithm. The main component of multichannel fractional sample rate convertor is Cascaded multiple architecture finite impulse response filter (CMFIR filter). CMFIR is implemented by cascading of cascaded integrator-comb (CIC) & multiply accumulate architecture (MAC) FIR filter. Genetic algorithm minimizes the hamming distance between consecutive coefficients of CMFIR filter. By Minimizing the hamming distance of consecutive filter coefficient reduces the transaction from 0 to 1 or 1 to 0. These techniques reduce the switching activity of CMOS transistor which is directly reduces Dynamic power consumption by multichannel sample rate convertor, it also minimizes the total power consumption of multichannel fractional sample rate convertor. later than use genetic algorithm on 1 to 128 channel Down sample rate convertor total power reduced by 3.44% to 61.56%, dynamic power reduced by 9.09% to 56.25% .1 to 128 channel Up sample rate convertor total power reduced by 2.81% to 45.42%, dynamic power reduced by 4.76% to 56%, 1 to 128 channel fractional sample rate convertor total power reduced by 1.44% to 17.17%, dynamic power reduced by 6.25% to 19.92%.
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37

Walker, Linda J., Phillip O. Moreno, and Håkon Hope. "Cryocrystallography: effect of cooling medium on sample cooling rate." Journal of Applied Crystallography 31, no. 6 (December 1, 1998): 954–56. http://dx.doi.org/10.1107/s0021889898005299.

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The rates of cooling of small samples with cold N2gas (100 K), liquid N2(77 K) and liquid propane (100 K) have been measured. The samples were one bare Cu-constantan thermocouple and one coated with a 0.25 mm layer of silicone rubber cement. Gas cooling yielded the lowest rate, liquid N2the highest. With the gas, cooling of the centers of the samples from 295 to 140 K took 0.8 and 2 s for the bare and coated samples, respectively; with liquid N2the times were 0.15 and 0.6 s, and with liquid propane they were 0.15–0.18 and 1.2 s, respectively (time reproducibility is within ±10%).
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38

Sabraj, Manish. "Performance Analysis of Upsample Filter for Sample Rate Converter." Signal & Image Processing : An International Journal 3, no. 1 (February 29, 2012): 59–65. http://dx.doi.org/10.5121/sipij.2012.3105.

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39

Bagheri, Fathollah, and Orhan Kara. "Rate of Return on Education and Sample Selection Bias." Atlantic Economic Journal 33, no. 3 (August 12, 2005): 359–60. http://dx.doi.org/10.1007/s11293-005-8190-8.

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40

Maier, Thomas D., and Tai-Huang Huang. "A microprocessor-based magic-angle-sample-spinning rate controller." Journal of Magnetic Resonance (1969) 91, no. 1 (January 1991): 165–69. http://dx.doi.org/10.1016/0022-2364(91)90420-x.

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41

Armand, J. Y., R. Gonnet, R. Jones, J. Bouzon, M. Touchard, and J. M. Vergnaud. "Effect of heating rate and sample size on heat transfer through the sample in DSC." Thermochimica Acta 103, no. 2 (July 1986): 341–51. http://dx.doi.org/10.1016/0040-6031(86)85171-1.

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42

Deng, Xin-Fa. "Dependence of the clustering properties of galaxies on star formation rate and specific star formation rate." Canadian Journal of Physics 91, no. 1 (January 2013): 12–18. http://dx.doi.org/10.1139/cjp-2012-0310.

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The primary goal of this study is to explore the dependence of the clustering properties of galaxies on star formation rate (SFR) and specific star formation rate (SSFR). From the main galaxy sample of the Sloan Digital Sky Survey Data Release 7, we construct two volume-limited samples with absolute magnitudes above and below [Formula: see text], and then divide each volume-limited main galaxy sample into two subsamples with low SFRs and high SFRs or low SSFRs and high SSFRs. A strong dependence of the clustering properties on SFR and SSFR is found: high SFR and SSFR galaxies are preferentially isolated or found in close pairs and small groups, whereas low SFR and SSFR galaxies preferentially inhabit dense groups and clusters.
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43

Jacka, T. H. "Investigations of discrepancies between laboratory studies of the flow of ice: density, sample shape and size, and grain-size." Annals of Glaciology 19 (1994): 146–54. http://dx.doi.org/10.3189/1994aog19-1-146-154.

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Laboratory results are presented concerning ice creep at minimum creep rate (at ~1% strain) for fine-grained, initially isotropic, polycrystalline samples. The effect on the creep rate of ice density, sample shape (aspect ratio) and size, grain-size and ratio of grain-size to sample size is examined. Provided sample density is above ~0.83 Mg m−3 (i.e. the close-off density), there is no effect of density on ice-creep rate. Results provide no evidence of a creep rate dependence on test sample length for cylindrical samples. Sample diameter, however, does affect creep rate. Over the range of sample diameters studied (16.2 to 90 mm) creep rate decreases monotonically by a factor of ~4. This effect is independent of sample aspect ratio. Experiments examining size effects in simple shear indicate no dependence of minimum flow rate on shape or size in this stress configuration. Two grain-sizes were represented within the samples tested for the effect of sample size. As expected from earlier work, no grain-size effect on minimum creep rate is evident. In addition, there was no evidence of an effect on creep rate of the ratio of grain-size to sample size.
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44

Jacka, T. H. "Investigations of discrepancies between laboratory studies of the flow of ice: density, sample shape and size, and grain-size." Annals of Glaciology 19 (1994): 146–54. http://dx.doi.org/10.1017/s0260305500011137.

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Laboratory results are presented concerning ice creep at minimum creep rate (at ~1% strain) for fine-grained, initially isotropic, polycrystalline samples. The effect on the creep rate of ice density, sample shape (aspect ratio) and size, grain-size and ratio of grain-size to sample size is examined. Provided sample density is above ~0.83 Mg m−3 (i.e. the close-off density), there is no effect of density on ice-creep rate. Results provide no evidence of a creep rate dependence on test sample length for cylindrical samples. Sample diameter, however, does affect creep rate. Over the range of sample diameters studied (16.2 to 90 mm) creep rate decreases monotonically by a factor of ~4. This effect is independent of sample aspect ratio. Experiments examining size effects in simple shear indicate no dependence of minimum flow rate on shape or size in this stress configuration. Two grain-sizes were represented within the samples tested for the effect of sample size. As expected from earlier work, no grain-size effect on minimum creep rate is evident. In addition, there was no evidence of an effect on creep rate of the ratio of grain-size to sample size.
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45

Deng, Xin-Fa. "A comparison of the star formation rate and the specific star formation rate distributions between paired galaxies and isolated ones." Canadian Journal of Physics 91, no. 4 (April 2013): 337–42. http://dx.doi.org/10.1139/cjp-2012-0487.

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From each of two volume-limited main galaxy samples of the Sloan Digital Sky Survey data release 7, a paired galaxy sample and an isolated galaxy sample are constructed and comparative studies between the star formation of galaxies in pairs and isolated are performed to explore influences of galaxy interactions on star formation. It is found that isolated galaxies have an enhancement of the star formation rate and the specific star formation rate, which suggests that interactions between galaxies are not the trigger of enhanced star formation.
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46

García Trillos, Nicolás, and Dejan Slepčev. "On the Rate of Convergence of Empirical Measures in ∞-transportation Distance." Canadian Journal of Mathematics 67, no. 6 (December 1, 2015): 1358–83. http://dx.doi.org/10.4153/cjm-2014-044-6.

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AbstractWe consider random i.i.d. samples of absolutely continuous measures on bounded connected domains. We prove an upper bound on the ∞-transportation distance between the measure and the empirical measure of the sample. The bound is optimal in terms of scaling with the number of sample points.
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47

Ju, Hai Lang, Xiao Bai Chen, Ci Zhao, Xing Lai Che, Xiong Li, Jun Gao, Liang Ting Cai, and Yan An Hu. "The Study on Magnetron Sputtering Rate of Metal Targets." Advanced Materials Research 926-930 (May 2014): 371–74. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.371.

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The sputtering rate of Pt, Au, Fe and Co targets was researched and they were 0.059 nm/s, 0.076 nm/s, 0.050 nm/s, and 0.030nm/s respectively. A series of magnetic multilayers were prepared using the calibrated rate and the anomalous hall effect of the samples were tested, and Hall curve was in line with expectations. The sputtering rate of Pt, Au, Fe and Co targets was appropriate for sample preparation. Suitable sputtering rate play a decisive role in sample preparation.
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48

Tracy, Russell P., Anne B. Newman, Jeff D. Williamson, Tamara B. Harris, and Steve R. Cummings. "Interleukin-6 and heart rate in a population-based sample:." Circulation 103, suppl_1 (March 2001): 1348. http://dx.doi.org/10.1161/circ.103.suppl_1.9999-22.

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0022 Inflammatory cytokines enhance the spontaneous beating rate of cardiac myocytes. We hypothesize that higher levels of interleukin-6 (IL-6) may be associated with a higher resting heart rate in a population-based sample. IL-6 (mean±SEM 2.39±0.5 ng/ml, range 0.21-15.96 ng/ml, n=2824) was measured in Health ABC, a cohort study of 3075 well functioning older adults living in Memphis, TN, and Pittsburgh, PA (age 73.6±0.3 years, 51.5% women, 41.7% African American). Heart rate was calculated from electrocardiogram strips recorded at the baseline clinic visit after 15 min resting in supine position. Participants with arrhythmias or conduction anomalies were excluded. After adjustment for demographics, body-mass index, smoking, history of cardiovascular disease, and use of digoxin, beta-blockers, calcium antagonists, anti-inflammatory drugs and antiarrhythmic drugs, higher log (IL-6) was significantly correlated with a higher heart rate (β=.17, p<0.001, n=2377). Such an association was significant in all race and gender strata (white men β=0.17, p<0.001; white women β=0.13, p=0.001; black men β=0.18, p<0.001; black women β=0.18, p<0.001). The overall il-6/heart rate association was even more evident when the analyses were restricted to the participants who had no history of cardiovascular disease and were not using any these cardiovascular drugs (β=0.21, p<0.001, n=1196). The table shows heart rate according to IL-6 quintiles. Circulating IL-6 was strongly and independently correlated with resting heart rate. Circulating IL-6 is a possible biological mediator that may contribute to explain the increased mortality associated with high heart rate. Table 1.
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49

Porter, Charlotte A., Kevin M. Bradley, and Daniel R. McGowan. "A comparison of four-sample slope–intercept and single-sample 51Cr-EDTA glomerular filtration rate measurements." Nuclear Medicine Communications 39, no. 5 (May 2018): 465–68. http://dx.doi.org/10.1097/mnm.0000000000000815.

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50

Drummond, Gordon B., Darius Fischer, and D. K. Arvind. "Current clinical methods of measurement of respiratory rate give imprecise values." ERJ Open Research 6, no. 3 (July 2020): 00023–2020. http://dx.doi.org/10.1183/23120541.00023-2020.

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BackgroundRespiratory rate is a basic clinical measurement used for illness assessment. Errors in measuring respiratory rate are attributed to observer and equipment problems. Previous studies commonly report rate differences ranging from 2 to 6 breaths·min−1 between observers.MethodsTo study why repeated observations should vary so much, we conducted a virtual experiment, using continuous recordings of breathing from acutely ill patients. These records allowed each breathing cycle to be precisely timed. We made repeated random measures of respiratory rate using different sample durations of 30, 60 and 120 s. We express the variation in these repeated rate measurements for the different sample durations as the interquartile range of the values obtained for each subject. We predicted what values would be found if a single measure, taken from any patient, were repeated and inspected boundary values of 12, 20 or 25 breaths·min−1, used by the UK National Early Warning Score, for possible mis-scoring.ResultsWhen the sample duration was nominally 30 s, the mean interquartile range of repeated estimates was 3.4 breaths·min−1. For the 60 s samples, the mean interquartile range was 3 breaths·min−1, and for the 120 s samples it was 2.5 breaths·min−1. Thus, repeat clinical counts of respiratory rate often differ by >3 breaths·min−1. For 30 s samples, up to 40% of National Early Warning Scores could be misclassified.ConclusionsEarly warning scores will be unreliable when short sample durations are used to measure respiratory rate. Precision improves with longer sample duration, but this may be impractical unless better measurement methods are used.
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