Evidence for phase formation in potassium intercalated 1,2;8,9-dibenzopentacene

We have prepared potassium intercalated 1,2;8,9-dibenzopentacene films under vacuum conditions. The evolution of the electronic excitation spectra upon potassium addition as measured using electron energy-loss spectroscopy clearly indicate the formation of particular doped phases with compositions K$_x$dibenzopentacene ($x$ = 1,2,3). Moreover, the stability of these phases as a function of temperature has been explored. Finally, the electronic excitation spectra also give insight into the electronic ground state of the potassium doped 1,2;8,9-dibenzopentacene films.Comment: 6 pages, 5 figures. arXiv admin note: text overlap with arXiv:1201.200

Ionic liquid-derived carbon-supported metal electrocatalysts as anodes in direct borohydride-peroxide fuel cells

Three different carbon-supported metal (gold, platinum, nickel) nanoparticle (M/c-IL) electrocatalysts are prepared by template-free carbonization of the corresponding ionic liquids, namely [Hmim][AuCl4 ], [Hmim]2 [PtCl4 ], and [C16mim]2 [NiCl4 ], as confirmed by X-ray diffraction analysis, scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy and Raman spectroscopy. The electrochemical investigation of borohydride oxidation reaction (BOR) at the three electrocatalysts by cyclic voltammetry reveals different behavior for each material. BOR is found to be a first-order reaction at the three electrocatalysts, with an apparent activation energy of 10.6 and 13.8 kJ mol−1 for Pt/c-IL and Au/c-IL electrocatalysts, respectively. A number of exchanged electrons of 5.0, 2.4, and 2.0 is obtained for BOR at Pt/c-IL, Au/c-IL, and Ni/c-IL electrodes, respectively. Direct borohydride-peroxide fuel cell (DBPFC) tests done at temperatures in the 25–65◦C range show ca. four times higher power density when using a Pt/c-IL anode than with an Au/c-IL anode. Peak power densities of 40.6 and 120.5 mW cm−2 are achieved at 25 and 65◦C, respectively, for DBPFC with a Pt/c-IL anode electrocatalyst. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)

Basic Module Theory over Non-Commutative Rings with Computational Aspects of Operator Algebras

The present text surveys some relevant situations and results where basic Module Theory interacts with computational aspects of operator algebras. We tried to keep a balance between constructive and algebraic aspects.Comment: To appear in the Proceedings of the AADIOS 2012 conference, to be published in Lecture Notes in Computer Scienc

Genome-wide association meta-analysis of fish and EPA+DHA consumption in 17 US and European cohorts

Background: Regular fish and omega-3 consumption may have several health benefits and are recommended by major dietary guidelines. Yet, their intakes remain remarkably variable both within and across populations, which could partly owe to genetic influences. Objective: To identify common genetic variants that influence fish and dietary eicosapentaenoic acid plus docosahexaenoic acid (EPA+DHA) consumption. Design: We conducted genome-wide association (GWA) meta-analysis of fish (n = 86, 467) and EPA +DHA (n = 62, 265) consumption in 17 cohorts of European descent from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Nutrition Working Group. Results from cohort-specific GWA analyses (additive model) for fish and EPA+DHA consumption were adjusted for age, sex, energy intake, and population stratification, and meta-analyzed separately using fixed-effect meta-analysis with inverse variance weights (METAL software). Additionally, heritability was estimated in 2 cohorts. Results: Heritability estimates for fish and EPA+DHA consumption ranged from 0.13

Sloan Digital Sky Survey IV: mapping the Milky Way, nearby galaxies, and the distant universe

We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median ). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July

Mortality forecasting in Colombia from abridged life tables by sex

[EN] BACKGROUND: An adequate forecasting model of mortality that allows an analysis of different population changes is a topic of interest for countries in demographic transition. Phenomena such as the reduction of mortality, ageing, and the increase in life expectancy are extremely useful in the planning of public policies that seek to promote the economic and social development of countries. To our knowledge, this paper is one of the first to evaluate the performance of mortality forecasting models applied to abridged life tables. OBJECTIVE: Select a mortality model that best describes and forecasts the characteristics of mortality in Colombia when only abridged life tables are available. DATA AND METHOD: We used Colombian abridged life tables for the period 1973-2005 with data from the Latin American Human Mortality Database. Different mortality models to deal with modeling and forecasting probability of death are presented in this study. For the comparison of mortality models, two criteria were analyzed: graphical residuals analysis and the hold-out method to evaluate the predictive performance of the models, applying different goodness of fit measures. RESULTS: Only three models did not have convergence problems: Lee-Carter (LC), Lee-Carter with two terms (LC2), and Age-Period-Cohort (APC) models. All models fit better for women, the improvement of LC2 on LC is mostly for central ages for men, and the APC model's fit is worse than the other two. The analysis of the standardized deviance residuals allows us to deduce that the models that reasonably fit the Colombian mortality data are LC and LC2. The major residuals correspond to children's ages and later ages for both sexes. CONCLUSION: The LC and LC2 models present better goodness of fit, identifying the principal characteristics of mortality for Colombia.Mortality forecasting from abridged life tables by sex has clear added value for studying differences between developing countries and convergence/divergence of demographic changes.Support for the research presented in this paper was provided by a grant from the Ministerio de Economía y Competitividad of Spain, project no. MTM2013-45381-P.Diaz-Rojo, G.; Debón Aucejo, AM.; Giner-Bosch, V. (2018). Mortality forecasting in Colombia from abridged life tables by sex. Genus. Journal of Population Sciences (Online). 74(15):1-23. https://doi.org/10.1186/s41118-018-0038-6S1237415Aburto, J.M., & García-Guerrero, V.M. (2015). El modelo aditivo doble multiplicativo. Una aplicacion a la mortalidad mexicaná. Papeles de Población, 21(84), 9–44.Acosta, K., & Romero, J. (2014). Cambios recientes en las principales causas de mortalidad en Colombia. Technical report: Banco de la Banco de la República- Serie Documentos de Trabajo Sobre Economía Regional.Aguilar, E. (2013). Estimación y proyección de la mortalidad para Costa Rica con la aplicación del método Lee-Carter con dos variantes. Población y, Salud en Mesoamérica, 11(1), 3–24.Andreozzi, L (2012). Estimación y pronósticos de la mortalidad de Argentina utilizando el modelo de Lee-Carter. Revista de la Sociedad Argentina de Estadística, 10(1), 21–43.Andreozzi, L, & Blaconá, MT (2011). Estimación y pronóstico de las tasas de mortalidad y la esperanza de vida en la República Argentina. In Proceedings of Anales de las Decimosextas Jornadas Investigaciones en la Facultad de Ciencias Económicas y Estadística. Universidad Nacional de Rosario, Argentina.Andres, V, Millossovich, P, Vladimir, K (2018). StMoMo: Stochastic Mortality Modeling in R. Journal of Statistical Software, 84(3), 1–38.Belliard, M., & Williams, I. (2013). Proyección estocástica de la mortalidad. Una aplicación de Lee-Carter en la Argentina. Revista Latinoamericana de Población, 7(13), 129–148.Bertranou, E. (2008). Tendencias demográficas y protección social en América Latina y el Caribe: CEPAL. http://repositorio.cepal.org/handle/11362/7224 . Accessed 8 Aug 2017.Blaconá, M.T, & Andreozzi, L. (2014). Análisis de la mortalidad por edad y sexo mediante modelos para datos funcionales. Estadística, 66(186–187), 65–89.Blum, A., Kalai, A., Langford, J. (1999). Beating the hold-out: bounds for k-fold and progressive cross-validation. In Proceedings of the twelfth annual Conference on Computational Learning Theory. ACM, (pp. 203–208).Booth, H., Maindonald, J., Smith, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325–336.Booth, H., & Tickle, L. (2008). Mortality modelling and forecasting: a review of methods. Annals of Actuarial Science, 3(1–2), 3–43.Butt, Z., Haberman, S., Shang, H.L. (2014). ilc: Lee-Carter mortality models using iterative fitting algorithms. http://cran.r-project.org/package=ilc .Cairns, A.J., Blake, D., Dowd, K. (2006). A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. Journal of Risk and Insurance, 73(4), 687–718.Cairns, A.J., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Khalaf-Allah, M. (2011). Mortality density forecasts: an analysis of six stochastic mortality models. Insurance: Mathematics and Economics, 48(3), 355–367.Cairns, A.J., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A., Balevich, I. (2009). A quantitative comparison of stochastic mortality models using data from England and Wales and the United States. North American Actuarial Journal, 13(1), 1–35.Canudas-Romo, V. (2008). The modal age at death and the shifting mortality hypothesis. Demographic Research, 19(30), 1179–1204.Carfora, M.F., Cutillo, L., Orlando, A. (2017). A quantitative comparison of stochastic mortality models on Italian population data. Computational Statistics & Data Analysis, 112, 198–214.Currie, I.D., Durban, M., Eilers, P.H.C. (2006). Generalized linear array models with applications to multidimensional smoothing. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(2), 259–280.Debón, A., Martínez-Ruiz, F., Montes, F. (2012). Temporal evolution of mortality indicators: application to Spanish data. North American Actuarial Journal, 16(3), 364–377.Debón, A., Montes, F., Puig, F. (2008). Modelling and forecasting mortality in Spain. European Journal of Operation Research, 189(3), 624–637.Debón, A., Martínez-Ruiz, F., Montes, F. (2010). A geostatistical approach for dynamic life tables: the effect of mortality on remaining lifetime and annuities. Insurance: Mathematics and Economics, 47(3), 327–336.Díaz, G., & Debón, A. (2016). Tendencias y comportamiento de la mortalidad en Colombia entre 1973 y 2005. Estadística Española, 58(191), 277–300.García-Guerrero, V.M., & Mellado, M.O. (2012). Proyección estocástica de la mortalidad mexicana por medio del método de Lee-Carter. Estudios Demográficos y Urbanos, 27(2), 409–448.Garfield, R., & Llanten, C. (2004). The public health context of violence in Colombia. Revista Panamericana de Salud Pública, 16(4), 266–271.Haberman, S. (2011). A comparative study of parametric mortality projection models. Insurance: Mathematics and Economics, 48(1), 35–55.Holford, T.R. (2006). Approaches to fitting age-period-cohort models with unequal intervals. Statistics in Medicine, 25(6), 977–993.Hunt, A., & Villegas, A.M. (2015). Robustness and convergence in the Lee-Carter model with cohort effects. Insurance: Mathematics and Economics, 64, 186–202.Hyndman, R.J. (2016). Forecast: forecasting functions for time series and linear models. R package version 7.3. https://CRAN.R-project.org/package=forecast .Hyndman, R.J, Booth, H., Tickle, L., Maindonald, J. (2014). Demography: forecasting mortality, fertility, migration and population data. R package version 1.18. https://CRAN.R-project.org/package=demography .Hyndman, R.J., & Ullah, M.S (2007). Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics & Data Analysis, 51(10), 4942–4956.Kennes, T. (2017). The convergence and robustness of cohort extensions of mortality models. MaRBLe, 1, 36–53.Lee, R., & Carter, L. (1992). Modelling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659–671.Lee, R., & Rofman, R. (1994). Modelación y proyección de la mortalidad en Chile. Notas de Poblacion, 6(59), 183–213.Lee, W. (1997). Characterizing exposure-disease association in human populations using the Lorenz curve and Gini index. Statistics in Medicine, 16(7), 729–739.Levitt, S., & Rubio, M. (2000). Understanding crime in Colombia and what can be done about it. Technical Report 20, FEDESARROLLO.Llorca, J., Prieto, M.D., Alvarez, C.F., Delgado-Rodriguez, M. (1998). Age differential mortality in Spain, 1900–1991. Journal of Epidemiology & Community Health, 52, 259–261.Llorca, J, Prieto, M.D, Delgado-Rodriguez, M (2000). Medición de las desigualdades en la edad de muerte: cálculo del índice de Gini a partir de las tablas de mortalidad. Revista Española de Salud Pública, 74(1), 5–12.Lora, E. (2008). Técnicas de medición económica. Metodología y aplicaciones en Colombia, Ed. Siglo Veintiuno XXI y Fedesarrollo, fourth edition. Bogotá D.C.: Alfaomega Colombiana S.A.Ochoa, C.A. (2015). El modelo Lee-Carter para estimar y pronosticar mortalidad: una aplicación para Colombia. Master’s thesis: Universidad Nacional de Colombia-Sede Medellín.O’hare, C., & Li, Y. (2017). Modelling mortality: are we heading in the right direction?Applied Economics, 49(2), 170–187.Ornelas, A. (2015). La mortalidad y la longevidad en la cuantificación del riesgo actuarial para la población de México. PhD thesis: Universitat de Barcelona.Remund, A., Camarda, C.G., Riffe, T., et al. (2017). A cause-of-death decomposition of the young adult mortality hump. Technical report. Rostock, Germany: Max Planck Institute for Demographic Research.Renshaw, A.E., & Haberman, S. (2003). Lee-Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33(2), 255–272.Renshaw, A.E., & Haberman, S. (2006). A cohort-based extensionto the Lee-Carter model for mortality reduction factors. Insurance: Mathematic and Economics, 38(3), 556–570.Reyes, A.R. (2010). Una aproximación al costo fiscal en pensiones como consecuencia del envejecimiento de la población en Colombia y el efecto de la sobre-mortalidad masculina. Master’s thesis: Universidad Nacional de Colombia.Richards, S. (2008). Detecting year-of-birth mortality patterns with limited data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(1), 279–298.Rodríguez, J. (2007). Desigualdades socioeconómicas entre departamentos y su asociación con indicadores de mortalidad en Colombia en 2000. Rev Panam Salud Publica, 21(2/3), 111–124.Shkolnikov, V.M., Andreev, E.M., Begun, A. (2003). Gini coefficient as a life table function: computation from discrete data, decomposition of differences and empirical examples. Demographic Research, 8, 305–358.Singh, A., Shukla, A., Ram, F., Kumar, K. (2017). Trends in inequality in length of life in India: a decomposition analysis by age and causes of death. Genus, 73(1), 5.Tabeau, E. (2001). A review of demographic forecasting models for mortality. In: Tabeau, E., van den Berg Jeths A., Heathcote C. (Eds.) In Forecasting Mortality in Developed Countries. European Studies of Population, vol 9. Springer, Dordrecht, (pp. 1–32).Turner, H., & Firth, D. (2015). gnm: Generalized nonlinear models in R. R package version 1.0-8. https://CRAN.R-project.org/package=gnm .Urdinola, B.P., & Queiroz, B.L. (2017). Latin American Human Mortality Database. http://www.lamortalidad.org . Accessed 21 Nov 2015

Search for Gravitational Waves Associated with Gamma-Ray Bursts Detected by Fermi and Swift during the LIGO-Virgo Run O3b

We search for gravitational-wave signals associated with gamma-ray bursts (GRBs) detected by the Fermi and Swift satellites during the second half of the third observing run of Advanced LIGO and Advanced Virgo (2019 November 1 15:00 UTC-2020 March 27 17:00 UTC). We conduct two independent searches: A generic gravitational-wave transients search to analyze 86 GRBs and an analysis to target binary mergers with at least one neutron star as short GRB progenitors for 17 events. We find no significant evidence for gravitational-wave signals associated with any of these GRBs. A weighted binomial test of the combined results finds no evidence for subthreshold gravitational-wave signals associated with this GRB ensemble either. We use several source types and signal morphologies during the searches, resulting in lower bounds on the estimated distance to each GRB. Finally, we constrain the population of low-luminosity short GRBs using results from the first to the third observing runs of Advanced LIGO and Advanced Virgo. The resulting population is in accordance with the local binary neutron star merger rate. © 2022. The Author(s). Published by the American Astronomical Society