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Lithium Abundances in Asymptotic Giant Branch Stars Department of Physics & Astronomy, Michigan State University, East Lansing, MI 48824-1116 When stars undergo helium shell burning, they are subject to many different mixing processes which contribute to unusual elemental abundances found in these stars. 7Lithium burns atrelatively low temperatures; however, it is found in these asymptotic giant branch (AGB) stars.
This should not be possible, except through the production of lithium via hot bottom burning andthe Cameron-Fowler mechanism. In this study, 122 AGB candidates were analyzed for possiblelithium production. Lithium abundances (or upper limits) were determined for these stars usingMOOG, as well as [Fe/H] and rotational velocity estimates.
Subject headings: stars: Abundances –stars: Asymptotic Giant Branch Stars dran 2000, Luck & Lambert 1982, Lebre et al.
2006); however, the majority of the stars in these The understanding of the abundance of lithium studies have been on the red giant branch, as op- in stars is an important key to understanding the posed to the AGB. One of the intentions of this chemical nature of the universe. Lithium is one study is to populate a previously under-sampled of the three elements that were produced in the Big Bang; lithium is also an integral part of the The AGB is a fairly inaccessible region of the proton-proton chain. Lithium is continually de- H-R diagram because it’s difficult to ascertain that stroyed throughout the lifetime of a star. Even stars are indeed on the AGB. Luminosities must before a star enters the main sequence, convection be known, which is not an easy task due to the un- dilutes the primal lithium from the surface of the certainty that arises in distance calculations. De- star where it is eventually burned deeper inside spite the difficulties, it is important to understand the star through the proton-proton (PP) chain.
these stars, as they provide observable clues to the Lithium is also destroyed in PPII as the final stage inside of their cores given their highly convective of helium production. As a star ascends the RGB, envelope. This convection allows for material in what little lithium is left at the surface is con- the core of the star to be brought to the surface vected away from the surface, making these stars and often causes hot bottom burning where the highly lithium deficient. By the time a star enters bottom of the convective envelope of the star be- the AGB, there should essentially be zero lithium; gins to undergo hydrogen to helium fusion. It also however, this is not necessarily the case.
works to enable the Cameron-Fowler mechanism There have been many studies about the abun- which is responsible for the production of lithium dance of lithium in stars because its primary fea- ture is at an accessible area of the spectrum and In the PPII and PPIII, 7Be is one in a step of can be easily identified. The studies usually in- many to form alpha particles. 7Be can gain a pro- volve dwarf stars or stars on the red giant branch; ton to become 8B and complete PPIII, or it can extensive surveys of the lithium abundance in gain an electron to form 7Li and complete PPII.
AGB stars have not really been conducted to In order for 7Be to gain a proton, the temperature date. Studies of the lithium content of giant stars must be sufficiently high. In the Cameron-Fowler have been conducted (Charbonnel & Balachan- transport mechanism 7Be is taken from a hot re- gion in the star to a cooler region where it’s only Atmospheric models were created using Kurucz able to capture protons. This facilitates the pro- duction of 7Li and serves as an explanation of the tive temperature, surface gravity, and microtur- bulence. They also required the overall metallic- In this analysis, 122 AGB stars are studied ity of the star in the form of [Fe/H]. Kurucz mod- to attain metallicities, v sin i, and the lithium els were used for stars without significant TiO. In abundance. Thirty-seven positive detections were general, the coolest stars with Kurucz models were around 3950 K. They were also made specifically for each star. MARCS models were much moreappropriate for cooler stars, as they accounted for line blanketing caused by the TiO, whereas the 122 stars were observed between three observa- Kurucz models did not. MARCS models were not tories. 92 stars were observed at McDonald Obser- made to be star specific; rather a matrix of models vatory on the 2.1m telescope using the Sandiford Cassegrain Echelle Spectrometer. 16 stars were observed at the European Southern Observatoryusing the Fiber-fed Extended Range Optical Spec- trograph (FEROS) Instrument on the 1.52m tele-scope. 16 stars were also observed at the Haute- The lithium line is notoriously temperature sen- provedce Observatory using the 1.52m telescope sitive. It is crucial to attain a correct value for the effective temperature of the star. First guess ef-fective temperatures were based on the Ramirez & Melendez (2005) paper for a temperature scalefor FGK stars. Temperatures were determined by the V-I, V-J, and V-K colors, with the median be- IRAF using the echelle, rv, and onedspec pack- ing taken as the value. V and I magnitudes were ages. Aurelie data were reduced using MIDAS.
known for these stars and the J and K colors were Instrument independent data are necessary to taken from the 2MASS catalog. An extensive liter- calculate abundances, as well as to enable the ob- ature search was also conducted to find other effec- server to compare data taken with different in- tive temperature estimates for the program stars.
struments. In echelle spectroscopy, instrument in- When available, the literature temperatures were dependence is maintained by removing the Blaze used if the calculated temperatures were clearly function of the echelle, which differs from instru- ment to instrument; this process is known as nor- Reddening is essential to photometric deter- malization. Normalization of the spectra was car- mination of effective temperatures. It provided ried out by the splot routine for all the McDonald a problem with many of the stars. The Schel- and FEROS spectra. Aurelie data were reduced gal (1998) dust maps were used as an indicator using MIDAS. A fit of a hot star was made for of reddening; however, given its notable problem each instrument for each night. Cool stars with with the overestimation of the reddening of disk strong TiO bandheads were often fit using the the stars, Neckel (1980) or Savage (1985) reddening hot star fits because the continuum was engulfed.
The final determination of effective temper- ature was conducted through iteration using MOOG. For hotter stars, the exact temperature to perform abundance calculations of lithium.
calculated (or quoted from the literature) was used MOOG requires a parameter file which includes an as a first guess for the effective temperature; for atmospheric model, a line list, and the normalized cooler stars, a matrix of models was used, with the temperature being forced to the closest avail- able in the matrix. Five iron lines in the lithium region were used as an indicator of the tempera- Microturbulence is a way to account for the tur- ture. They all had differing oscillator strengths, bulent stellar atmosphere. Line broadening that which corresponded differently to incorrect tem- cannot be attributed to other factors (e.g. rota- tional velocity) is attributed to the microturbu- The uncertainties associated with effective tem- lence of the star. Microturbulence was derived perature were assigned to be 100K given that the from the relation of microturbulence and surface reddening was not always correct. This temper- ature effect corresponded to the largest source oferror determined in the lithium abundance.
The imprecise nature of the determination of The surface gravity, log g, was derived from first the microturbulence reflects the relative impor- tance of the parameter. Surface gravity was moreimportant than microturbulence; however, tem- perature was much more important than surface gravity and was proved to be so in error analysis.
Errors were assumed to be 0.5 km/s.
Metallicities were taken from a literature search. When no metallicity was available, [Fe/H] was initially assumed to be solar. Metallicity was adjusted through MOOG, using the New Abun-dances option. For changes in [Fe/H] greater than This equation (2) can then be substituted in the or equal to 0.1 dex, another model was used with the corresponding change in metallicity. Lithium is a metallicity sensitive line; there is also a strong iron feature near the lithium line with which it is often blended. It is therefore essential to correctly Surface gravity, much like [Fe/H], is really com- puted in comparison to the solar value, so con- matching several nearby Fe I lines in the spec- stants G, π, σ, and 4 drop out of the equation Given the parameters required to run MOOG, synthetic spectra were created. This was the vehi- cle through which the lithium abundance was de- termined. The fit of the lithium line determined In the final step of the derivation, surface grav- the abundance. However, the stellar parameters ity is actually taken as a logarithm in the atmo- must first be correct and the abundance of C, Fe, spheric models given its large values. The final and Ti are crucial to correctly fitting the region.
The shift in velocity must be accounted for as well.
This is trivial in MOOG because there is an op- tion that allows the user to shift the normalized spectrum to match the rest wavelengths of the fea- tures of the synthetic spectrum. However, calcu- The uncertainties associated with the surface grav- lating the rotational and radial velocities of the ity relied most heavily upon the uncertainties in star, which necessitate the shift, is a nontrivial the luminosity. This produced an error of 0.4.
process that has been performed on only a subset tion and computation of these stars is to be com- Iron is important to correctly synthesize be- cause it accounts for the overall metallicity of the star as well as shaping the lithium region. TiO Of the 122 stars, 37 positive lithium detections molecules swamp the area in cool stars, beginning were found. Log values ranged between 1.22 and at about 3800 K, and essentially destroy the con- -1.10 dex. As effective temperature goes up, the tinuum. Adjusting the Ti is important so that ability to detect lithium and establish upper limits the features are well matched. Carbon also plays goes up. This is due to the temperature sensitivity an important role. In hot stars, the C2 feature of the lithium line; as the temperature increases, can dominate the lithium region. In cooler stars, the line weakens. High S/N with high resolution the carbon must be adjusted to ensure proper TiO is required to determine abundances in hot stars abundances. High carbon abundances in cool stars with weak lithium, but this is extremely difficult creates CO, which depletes the available oxygen to to do, given that high resolution reduces the S/N.
The largest source of error was due to effec- tive temperature. Errors in effective temperaturecaused the abundances to vary by +0.17 and -0.2 Rotational velocities were computed for all of in the population of stars hotter than 3900. For the stars, whilst radial velocities were computed the cooler stars, errors of +0.25 and -0.3 were more for the McDonald stars. The rv package in IRAF appropriate because the temperatures were even was utilized. The fxcor routine was used to as- more uncertain given the difficulty fitting the TiO certain full width half maximums (FWHM), helio- continuum. Changes in surface gravity and mirco- centric velocities, and error estimates on the helio- turbulence did not change the value of the lithium centric velocities. The rotational velocities, which line. This should be expected because changes in correspond to the rotation of the star itself, were surface gravity really only effect the comparison calculated using the smallest FWHM per each in- of ionized species of elements. The lithium here was 7Li. The resolution of the data was not highenough to really distinguish between 6Li and 7Li.
Microturbulence was also not expected to play a huge role in error analysis because of its relatively small contribution to the synthetic spectrum.
In recent papers (Lebre et al. 2006) it has been is the FWHM of the star. This method does not suggested that rotational velocity and the lithium account well for rotational velocities below about abundance are linked. However, in this data set, 5 km/s. The vrot is obviously wrong for the small- there are no correlations to be found in rotational est FWHM of the set and is better understood as an upper limit for all vrot less than 5 km/s.
abundances having high and low rotation. The Radial velocities were computed by using the upper limits were similarly scattered. Though this rvcorrect routine. It was only performed on the does not support the studies linking rotation and McDonald data because those were the only stars lithium abundances, this also does not negate the with complete header files which included right as- work. Errors are sufficiently high enough on the cension, declination, and the UT at which the ex- rotational velocities to question the validity of the posure was taken. Without this information it is numbers. A link between metallicity and lithium not possible to complete the calculation. The val- abundance has also been suggested. However, this ues computed by rvcorrect must be added to the heliocentric velocity to account for the earth’s mo-tion around the sun. This is the actual radial ve- locity computed for the star. Aurelie and FEROSdata did not include the aforementioned informa- The objective of this project was to analyze 122 AGB stars for lithium abundance; this has been accomplished with 37 stars yielding positively forthe detection of lithium. An increase in the popu-lation of a previously undersampled region of theH-R diagram has been achieved. Future work onthis project could include calculations of the car-bon isoptic ratio of these AGB stars, as well asthe completion of the radial velocity calculationfor the Aurelie and FEROS data.
Fig. 1.— H-R Diagram of the program stars. Open stars are abundances and the other symbol indicates anupper limit.
Fig. 2.— Plot of the lithium abundances and upper limits versus the effective temperature. Abundancesappear as open stars.
Fe value. There is no obvious correlation between the two parameters. Abundances appear as open stars.
Fig. 4.— Plot of the log Li value versus the rotational velocity value. Again, there is no obvious correlationbetween the two parameters. Abundances appear as open stars.
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Charbonnel, C., Balachandran, S. C., 2000, AandA 359, 563C.
do Nascimento, J. D., Jr., et al., 2000, AandA, 357, 931D.
Karakas, A., Ph.D. Thesis 2003.
Lebre, A., et al. 2006, AandA, 450, 1173L.
Luck, R. E., and Luck, D.L. 1982, Ap.J., 256, 189.


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Technical Report TR-01-001, Equidyne Systems, Inc. Retention of structural/potency characteristics of Lantus Insulin, AKA Authored by: Vision Biotechnology Consulting 315 S. Coast Hwy. 101, Suite U, PMB144 Encinitas, CA 92024 Phone/FAX: 760-634-2999 Technical Report TR-01-001, Equidyne Systems, Inc. This document contains information that is privileged or confidential

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