Dynamic sound attenuation at hypersonic frequencies in silica glass

In order to clarify the origin of the dominant processes responsible for the acoustic attenuation of phonons, which is a much debatted topic, we present Bril louin scattering experiments in various silica glasses of different OH impurities content. A large temperature range, from 5 to 1500 K is investigated, up to the glass transition temperature. Comparison of the hypersonic wave attenuation in various samples allows to identify two different processes. The first one induce s a low temperature peak related to relaxational processes; it is strongly sensitive to the extrinsic defects. The second, dominant in the hig h temperature range, is weakly dependent on the impurities and can be ascribed to anharmonic interactions

In situ measurements of density fluctuations and compressibility in silica glass as a function of temperature and thermal history

In this paper, small-angle X-ray scattering measurements are used to determine the different compressibility contributions, as well as the isothermal compressibility, in thermal equilibrium in silica glasses having different thermal histories. Using two different methods of analysis, in the supercooled liquid and in the glassy state, we obtain respectively the temperature and fictive temperature dependences of the isotheraml compressibility. The values obtained in the glass and supercooled liquid states are very close to each other. They agree with previous determinations of the literature. The compressibility in the glass state slightly decreases with increasing fictive temperature. The relaxational part of the compressibility is also calculated and compared to previous determinations. We discussed the small differences between the different determinations

Water Dynamics at Protein Interfaces: Ultrafast Optical Kerr Effect Study

The behavior of water molecules surrounding a protein can have an important bearing on its structure and function. Consequently, a great deal of attention has been focused on changes in the relaxation dynamics of water when it is located at the protein surface. Here we use the ultrafast optical Kerr effect to study the H-bond structure and dynamics of aqueous solutions of proteins. Measurements are made for three proteins as a function of concentration. We find that the water dynamics in the first solvation layer of the proteins are slowed by up to a factor of 8 in comparison to those in bulk water. The most marked slowdown was observed for the most hydrophilic protein studied, bovine serum albumin, whereas the most hydrophobic protein, trypsin, had a slightly smaller effect. The terahertz Raman spectra of these protein solutions resemble those of pure water up to 5 wt % of protein, above which a new feature appears at 80 cm–1, which is assigned to a bending of the protein amide chain

RAYLEIGH SCATTERING: COLLISIONAL MOTIONS IN LIQUIDS∗LIQUIDS^{*}

$^{*}$This research was supported by the Atomic Energy Commission. $^{\dagger}$Present address: Naval Research Laboratory, Washington, D.C.""Author Institution: Department of Physics, The Catholic University of AmericaMeasurements of light scattered in the Rayleigh wing were made over the range from 5 to $500 cm^{-1}$ in $CCI_{4}$, $C_{6}H_{12}$, $C_{5}H_{12}$, $CH_{3}OH$, $C_{2}H_{5}OH$, $H_{2}O$, $NH_{3}$ and $CHCl_{3}$. These data when compared with earlier data on Ar, Xe, and SnBr, indicate that in all of these liquids there is present the essentially exponential frequency dependence typical of collision induced effects. A calculation of the spectrum for large frequency shifts based on a binary interaction picture employing a Lennard-Jones potential and a short range electronic overlap distortion model agrees well with the experimental results in liquid argon. Further, assuming that molecular frame distortion is proportional to the interaction force, a similar calculation yields excellent agreement for the molecular systems. It is concluded that isolated binary interactions are mainly responsible for the spectral density in the wings of the Rayleigh Spectrum

FUME projection data

# FUME data Population data and migration flows from FUME projections. ## Introduction International projection model with dimensions Age, Sex, Education and Country of Birth. Projected from 2015 to 2050, four different scenarios; Benchmark, Short War, Scenario B and Scenario C. Additional scenario with no migration also included. Benchmark scenario: Identical to SSP2 from Koch & Leimbach (2022), including COVID shock but not Ukraine war. Short-war scenario: Same as benchmark scenario but using the IMF estimate (International Monetary Fund, 2022) until 2027, then linear transition over 5 years back to SSP2 growth rates. Scenario B - Recovery in Europe, stagnation in developing countries: Same as short-war scenario, but instead of all countries transitioning to SSP2, European countries transition towards the SSP in which they have the highest growth rates; while developing countries (including emerging economies) transition towards the SSP in which they have the lowest growth rates. These might be different SSPs for different countries. All other countries (e.g. USA, Australia etc.) transition towards SSP2. Scenario C - Rise of the East: Same as Scenario B, but opposite: European countries transition towards the SSP in which they have the lowest growth rates; while developing countries (including emerging economies) transition towards the SSP in which they have the highest growth rates. All other countries (E.g. USA, Australia etc.) transition towards SSP2. ## Variables period: Start year of projection step dest: Country of residency / Migration destination country CoB: Country of Birth area: ISO3 numeric country code of destination age: Age, five year groups, 0 - 100+ edu: Education, 6 levels, (e1 = No Education, e2 = Some Primary, e3 = Primary, e4 = Lower Secondary, e5 = Upper Secondary, e6 = Post Secondary) sex: Sex, two categories pop: Population ### Migration rate data specific variable names POB: Place Of Birth (Country of Birth) Orig: Country of origin Dest: Country of destination flow: Migration rate Skill: Skill categories, (Low (Secondary and Less) and High (Post secondary+)) age: Age groups (1 (0-24), 2 (25-64), 3 (65+)) flowM: Male specific migration rate flowF: Female specific migration rate ## Countries Countries currently included in the model are in total 171 (given in ISO3 country codes): ``` "AFG" "AUT" "BEL" "BGR" "CYP" "CZE" "DEU" "DNK" "ESP" "EST" "FIN" "FRA" "GBR" "GRC" "HRV" "HUN" "IRL" "ITA" "LTU" "LUX" "LVA" "MLT" "NLD" "POL" "PRT" "ROU" "SVK" "SVN" "SWE" "AGO" "ALB" "ARE" "ARG" "ARM" "AUS" "AZE" "BDI" "BEN" "BFA" "BGD" "BHR" "BHS" "BIH" "BLR" "BLZ" "BOL" "BRA" "BTN" "BWA" "CAF" "CAN" "CHE" "CHL" "CHN" "CIV" "CMR" "COD" "COG" "COL" "COM" "CPV" "CRI" "CUB" "DOM" "DZA" "ECU" "EGY" "ETH" "FJI" "GAB" "GEO" "GHA" "GIN" "GMB" "GNB" "GNQ" "GTM" "GUY" "HKG" "HND" "HTI" "IDN" "IND" "IRN" "IRQ" "ISL" "ISR" "JAM" "JOR" "JPN" "KAZ" "KEN" "KGZ" "KHM" "KOR" "KWT" "LAO" "LBN" "LBR" "LCA" "LKA" "LSO" "MAC" "MAR" "MDA" "MDG" "MDV" "MEX" "MKD" "MLI" "MMR" "MNE" "MNG" "MOZ" "MUS" "MWI" "MYS" "NAM" "NER" "NGA" "NIC" "NOR" "NPL" "NZL" "OMN" "PAK" "PAN" "PER" "PHL" "PRI" "PRY" "PSE" "QAT" "RUS" "RWA" "SAU" "SDN" "SEN" "SGP" "SLB" "SLE" "SLV" "SOM" "SRB" "STP" "SUR" "SWZ" "SYR" "TCD" "TGO" "THA" "TJK" "TKM" "TLS" "TTO" "TUN" "TUR" "TZA" "UGA" "UKR" "URY" "USA" "VCT" "VEN" "VNM" "VUT" "WSM" "YEM" "ZAF" "ZMB" "ZWE" ``