1,259 research outputs found

    Methanol dehydration over ZrO2 supported-activated carbons

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    Resumen comunicación congreso internacionalDME is playing an important role due to its potential use as an alternative fuel in diesel engines. The use of this fuel produces lower NOx emissions, and less engine noise compared to traditional diesel fuels. Moreover, this compound is used as building block for many value-added chemicals such as lower olefins. DME is usually produced via catalytic dehydration of methanol over a solid acid. The use of activated carbons in catalytic processes, acting directly as catalyst and as catalyst support, is focussing much attention. They can be obtained from different types of lignocellulosic waste, producing not only an environmental but an economical profit. In this sense, the preparation of activated carbons with phosphoric acid produces catalytic supports with certain surface acidity, which have shown high activity for alcohol dehydration. In this study, ZrO2 supported activated carbons were prepared from an industrial byproduct as lignin for the methanol dehydration to DME. The activated carbon was prepared by chemical activation with H3PO4, using Alcell® lignin as precursor. The impregnation ratio value (H3PO4/lignin) used was 3. The impregnated sample was activated under N2 flow at 500 ºC for 2h, washed and dried. The activated carbon was loaded with different amounts of ZrO(NO3)2, dried at 120ºC for 24h, and calcined in air at 250ºC for 2h, obtaining ZrO2 loadings of 5 and 10%, respectively. For the sake of comparison, pure ZrO2 was also used. Catalytic tests were performed at atmospheric pressure in a fixed bed reactor, at different space times and partial pressures. The activated carbon (ACP) prepared shows a well-developed porous structure, with an apparent surface area higher than 2000 m2/g, and a high contribution of mesoporosity. After metal loading, a maximum decrease of 20% in all structural parameters of the ACP was observed.The results show that ZrO2 loading produces an enhancing in the catalytic activity of the carbon materials compared to the parent activated carbon (0.1 g·s/μmol, PCH3OH= 0.02 atm in helium and 350 ºC). In this sense, a methanol conversion of 25% was observed with the addition of 10% w/w ZrO2 (ACP-10Zr), at steady state conditions (Figure 1). ACP shows negligible conversion, at the same conditions and for pure ZrO2 the methanol conversion was of 10%. Very high selectivity to DME (~100%) was found at temperatures lower than 350 ºC. The methanol conversion increases with temperature, reaching a value of 67% at 475ºC, but a slight decrease in DME selectivity is observed, resulting in a higher production of light hydrocarbons, mainly CH4. The results suggest that the addition of only a 10% of ZrO2 over an activated carbon prepared by chemical activation with H3PO4 enhances significantly the performance of the catalyst, compared to pure ZrO2.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Efecto del número de indicadores por factor sobre la identificación y estimación en modelos aditivos de análisis factorial confirmatorio

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    La matriz multirrasgo-multimétodo (MRMM) es un diseño de investigación de larga tradición en Psicología. Las técnicas de análisis de datos adecuadas para una correcta extracción de conclusiones han estado sujetas a controversia. Parece, no obstante, que diversos modelos de análisis factorial confirmatorio resultan muy adecuados. De entre los diversos modelos, dos de ellos han recibido gran atención, el modelo completo, que apareció primero en la literatura, y el de unicidades correlacionadas, que parece una alternativa razonable a los problemas que aparecen en el primero. Los resultados de ambos modelos en la literatura se refieren a situaciones con un solo indicador por combinación rasgo-método. La presente investigación simula datos de matrices MRMM para múltiples indicadores por combinación rasgo-método y somete a prueba la adecuación de las estimaciones de ambos modelos. Los resultados apuntan a un mejor comportamiento del modelo completo, si bien los sesgos, aunque triviales en cuantía, aumentan conforme aumenta la correlación entre los métodos

    Purification of Starch Granules from Arabidopsis Leaves and Determination of Granule-Bound Starch Synthase Activity

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    Starch constitutes the most important carbon reserve in plants and is composed of branched amylopectin and linear amylose. The latter is synthesized exclusively by the Granule-Bound Starch Synthase (GBSS, EC 2.4.1.21). Here we report a readily reproducible, specific and highly sensitive protocol, which includes the isolation of intact starch granules from Arabidopsis thaliana leaves and the subsequent determination of GBSS activity. We have applied this method to study GBSS activity in diurnal cycles in vegetative growth and during the photoperiodic transition to flowering in Arabidopsis (Tenorio et al., 2003; Ortiz-Marchena et al., 2014).España,MINECO CSD2007-00057, BIO2008-02292, and BIO2011-28847-C02-00España, Junta de Andalucía P06-CVI-01450 and P08-AGR-0358

    Three dimensional system of globally modified navier-Stokes equation with delay

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    We prove the existence and uniqueness of strong solutions of a three-dimensional system of glob-ally modified Navier–Stokes equations with delay in the locally Lipschitz case. The asymptoticbehavior of solutions, and the existence of pullback attractor are also analyzed

    Three-dimensional system of globally modified Navier-Stokes equations with delay

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    We prove the existence and uniqueness of strong solutions of a three-dimensional system of globally modified Navier–Stokes equations with delay in the locally Lipschitz case. The asymptotic behavior of solutions, and the existence of pullback attractor are also analyzed

    Identifying Opinion Leaders on Twitter during Sporting Events: Lessons from a Case Study

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    [EN] Social media platforms have had a significant impact on the public image of sports in recent years. Through the relational dynamics of the communication on these networks, many users have emerged whose opinions can exert a great deal of influence on public conversation online. This research aims to identify the influential Twitter users during the 2016 UCI Track Cycling World Championships using different variables which, in turn, represent different dimensions of influence (popularity, activity and authority). Mathematical variables of the social network analysis and variables provided by Twitter and Google are compared. First, we calculated the Spearman¿s rank correlation coefficient among all users (n = 20,175) in pairwise comparisons. Next, we performed a qualitative analysis of the top 25 influential users ranked by each variable. As a result, no single variable assessed is sufficient to identify the different kinds of influential Twitter users. The reason that some variables vary so greatly is that the components of influence are very different. Influence is a contextualised phenomenon. Having a certain type of account is not enough to make a user an influencer if they do not engage (actively or passively) in the conversation. Choosing the influencers will depend on the objectives pursued.Lamirán-Palomares, JM.; Baviera, T.; Baviera-Puig, A. (2019). Identifying Opinion Leaders on Twitter during Sporting Events: Lessons from a Case Study. Social Sciences. 8(5):1-18. https://doi.org/10.3390/socsci8050141S11885Abeza, G., Pegoraro, A., Naraine, M. L., Séguin, B., O’, N., & Reilly, N. A. (2014). Activating a global sport sponsorship with social media: an analysis of TOP sponsors, Twitter, and the 2014 Olympic Games. International Journal of Sport Management and Marketing, 15(3/4), 184. doi:10.1504/ijsmm.2014.072010Agre, P. E. (2002). Real-Time Politics: The Internet and the Political Process. The Information Society, 18(5), 311-331. doi:10.1080/01972240290075174Anagnostopoulos, C., Parganas, P., Chadwick, S., & Fenton, A. (2018). Branding in pictures: using Instagram as a brand management tool in professional team sport organisations. European Sport Management Quarterly, 18(4), 413-438. doi:10.1080/16184742.2017.1410202Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512. doi:10.1126/science.286.5439.509Barnes, J. A., & Harary, F. (1983). Graph theory in network analysis. Social Networks, 5(2), 235-244. doi:10.1016/0378-8733(83)90026-6Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. The Journal of Mathematical Sociology, 2(1), 113-120. doi:10.1080/0022250x.1972.9989806Borgatti, S. P., & Everett, M. G. (2006). A Graph-theoretic perspective on centrality. Social Networks, 28(4), 466-484. doi:10.1016/j.socnet.2005.11.005Bouguessa, M., & Romdhane, L. B. (2015). Identifying Authorities in Online Communities. ACM Transactions on Intelligent Systems and Technology, 6(3), 1-23. doi:10.1145/2700481BROSIUS, H.-B., & WEIMANN, G. (1996). Who Sets the Agenda. Communication Research, 23(5), 561-580. doi:10.1177/009365096023005002Carter, D. (2016). Hustle and Brand: The Sociotechnical Shaping of Influence. Social Media + Society, 2(3), 205630511666630. doi:10.1177/2056305116666305Chew, S., Metheney, E., & Teague, T. (2017). Modelling and Simulation of the Formation of Social Networks. Social Sciences, 6(3), 79. doi:10.3390/socsci6030079Clavio, G., Burch, L. M., & Frederick, E. L. (2012). Networked Fandom: Applying Systems Theory to Sport Twitter Analysis. International Journal of Sport Communication, 5(4), 522-538. doi:10.1123/ijsc.5.4.522Cleland, J. (2013). Racism, Football Fans, and Online Message Boards. Journal of Sport and Social Issues, 38(5), 415-431. doi:10.1177/0193723513499922Dahlgren, P. (2005). The Internet, Public Spheres, and Political Communication: Dispersion and Deliberation. Political Communication, 22(2), 147-162. doi:10.1080/10584600590933160Dart, J. (2012). New Media, Professional Sport and Political Economy. Journal of Sport and Social Issues, 38(6), 528-547. doi:10.1177/0193723512467356Campo-Ávila, J. del, Moreno-Vergara, N., & Trella-López, M. (2013). Bridging the Gap Between the Least and the Most Influential Twitter Users. Procedia Computer Science, 19, 437-444. doi:10.1016/j.procs.2013.06.059Delia, E. B., & Armstrong, C. G. (2015). #Sponsoring the #FrenchOpen: An Examination of Social Media Buzz and Sentiment. Journal of Sport Management, 29(2), 184-199. doi:10.1123/jsm.2013-0257Demir, R., & Söderman, S. (2015). Strategic sponsoring in professional sport: a review and conceptualization. European Sport Management Quarterly, 15(3), 271-300. doi:10.1080/16184742.2015.1042000Dubois, E., & Gaffney, D. (2014). The Multiple Facets of Influence. American Behavioral Scientist, 58(10), 1260-1277. doi:10.1177/0002764214527088Filo, K., Lock, D., & Karg, A. (2015). Sport and social media research: A review. Sport Management Review, 18(2), 166-181. doi:10.1016/j.smr.2014.11.001Freberg, K., Graham, K., McGaughey, K., & Freberg, L. A. (2011). Who are the social media influencers? A study of public perceptions of personality. Public Relations Review, 37(1), 90-92. doi:10.1016/j.pubrev.2010.11.001Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239. doi:10.1016/0378-8733(78)90021-7Freeman, L. C., Borgatti, S. P., & White, D. R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13(2), 141-154. doi:10.1016/0378-8733(91)90017-nGayo-Avello, D. (2013). Nepotistic relationships in Twitter and their impact on rank prestige algorithms. Information Processing & Management, 49(6), 1250-1280. doi:10.1016/j.ipm.2013.06.003Gibbs, C., O’Reilly, N., & Brunette, M. (2014). Professional Team Sport and Twitter: Gratifications Sought and Obtained by Followers. International Journal of Sport Communication, 7(2), 188-213. doi:10.1123/ijsc.2014-0005Hambrick, M. E. (2012). Six Degrees of Information: Using Social Network Analysis to Explore the Spread of Information Within Sport Social Networks. International Journal of Sport Communication, 5(1), 16-34. doi:10.1123/ijsc.5.1.16Hambrick, M. E., & Mahoney, T. Q. (2011). «It»s incredible trust me’: exploring the role of celebrity athletes as marketers in online social networks. International Journal of Sport Management and Marketing, 10(3/4), 161. doi:10.1504/ijsmm.2011.044794Hambrick, M. E., & Pegoraro, A. (2014). Social Sochi: using social network analysis to investigate electronic word-of-mouth transmitted through social media communities. International Journal of Sport Management and Marketing, 15(3/4), 120. doi:10.1504/ijsmm.2014.072005Hambrick, M. E., & Sanderson, J. (2013). Gaining Primacy in the Digital Network: Using Social Network Analysis to Examine Sports Journalists’ Coverage of the Penn State Football Scandal via Twitter. Journal of Sports Media, 8(1), 1-18. doi:10.1353/jsm.2013.0003Hambrick, M. E., Simmons, J. M., Greenhalgh, G. P., & Greenwell, T. C. (2010). Understanding Professional Athletes’ Use of Twitter: A Content Analysis of Athlete Tweets. International Journal of Sport Communication, 3(4), 454-471. doi:10.1123/ijsc.3.4.454Hofer, M., & Aubert, V. (2013). Perceived bridging and bonding social capital on Twitter: Differentiating between followers and followees. Computers in Human Behavior, 29(6), 2134-2142. doi:10.1016/j.chb.2013.04.038Hull, K., & Schmittel, A. (2014). A Fumbled Opportunity? A Case Study of Twitter’s Role in Concussion Awareness Opportunities During the Super Bowl. Journal of Sport and Social Issues, 39(1), 78-94. doi:10.1177/0193723514558928Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. doi:10.1016/j.bushor.2009.09.003Kassing, J. W., & Sanderson, J. (2010). Fan–Athlete Interaction and Twitter Tweeting Through the Giro: A Case Study. International Journal of Sport Communication, 3(1), 113-128. doi:10.1123/ijsc.3.1.113Katz, E. (1957). The Two-Step Flow of Communication: An Up-To-Date Report on an Hypothesis. Public Opinion Quarterly, 21(1, Anniversary Issue Devoted to Twenty Years of Public Opinion Research), 61. doi:10.1086/266687Khan, H. U., Daud, A., Ishfaq, U., Amjad, T., Aljohani, N., Abbasi, R. A., & Alowibdi, J. S. (2017). Modelling to identify influential bloggers in the blogosphere: A survey. Computers in Human Behavior, 68, 64-82. doi:10.1016/j.chb.2016.11.012Koenig-Lewis, N., Asaad, Y., & Palmer, A. (2017). Sports events and interaction among spectators: examining antecedents of spectators’ value creation. European Sport Management Quarterly, 18(2), 193-215. doi:10.1080/16184742.2017.1361459Kolyperas, D., Maglaras, G., & Sparks, L. (2018). Sport fans’ roles in value co-creation. European Sport Management Quarterly, 19(2), 201-220. doi:10.1080/16184742.2018.1505925Kunkel, T., Walker, M., & Hodge, C. M. (2018). The influence of advertising appeals on consumer perceptions of athlete endorser brand image. European Sport Management Quarterly, 19(3), 373-395. doi:10.1080/16184742.2018.1530688Lahuerta-Otero, E., & Cordero-Gutiérrez, R. (2016). Looking for the perfect tweet. The use of data mining techniques to find influencers on twitter. Computers in Human Behavior, 64, 575-583. doi:10.1016/j.chb.2016.07.035Lewin, K. (1939). Field Theory and Experiment in Social Psychology: Concepts and Methods. American Journal of Sociology, 44(6), 868-896. doi:10.1086/218177McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27(1), 415-444. doi:10.1146/annurev.soc.27.1.415Meenaghan, T., McLoughlin, D., & McCormack, A. (2013). New Challenges in Sponsorship Evaluation Actors, New Media, and the Context of Praxis. Psychology & Marketing, 30(5), 444-460. doi:10.1002/mar.20618Misener, L., & Mason, D. S. (2006). Creating community networks: Can sporting events offer meaningful sources of social capital? Managing Leisure, 11(1), 39-56. doi:10.1080/13606710500445676Morone, F., & Makse, H. A. (2015). Influence maximization in complex networks through optimal percolation. Nature, 524(7563), 65-68. doi:10.1038/nature14604Naraine, M. L., & Parent, M. M. (2016). Illuminating Centralized Users in the Social Media Ego Network of Two National Sport Organizations. Journal of Sport Management, 30(6), 689-701. doi:10.1123/jsm.2016-0067Naraine, M. L., Schenk, J., & Parent, M. M. (2016). Coordination in International and Domestic Sports Events: Examining Stakeholder Network Governance. Journal of Sport Management, 30(5), 521-537. doi:10.1123/jsm.2015-0273Norris, P., & Curtice, J. (2008). Getting the Message Out: A Two-Step Model of the Role of the Internet in Campaign Communication Flows During the 2005 British General Election. Journal of Information Technology & Politics, 4(4), 3-13. doi:10.1080/19331680801975359Pegoraro, A. (2010). Look Who’s Talking—Athletes on Twitter: A Case Study. International Journal of Sport Communication, 3(4), 501-514. doi:10.1123/ijsc.3.4.501Perić, M. (2018). Estimating the Perceived Socio-Economic Impacts of Hosting Large-Scale Sport Tourism Events. Social Sciences, 7(10), 176. doi:10.3390/socsci7100176Quatman, C., & Chelladurai, P. (2008). Social Network Theory and Analysis: A Complementary Lens for Inquiry. Journal of Sport Management, 22(3), 338-360. doi:10.1123/jsm.22.3.338Quatman, C., & Chelladurai, P. (2008). The Social Construction of Knowledge in the Field of Sport Management: A Social Network Perspective. Journal of Sport Management, 22(6), 651-676. doi:10.1123/jsm.22.6.651Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management, 52(5), 949-975. doi:10.1016/j.ipm.2016.04.003Santomier, J. (2008). New media, branding and global sports sponsorship. International Journal of Sports Marketing and Sponsorship, 10(1), 9-22. doi:10.1108/ijsms-10-01-2008-b005Small, T. A. (2011). WHAT THE HASHTAG? Information, Communication & Society, 14(6), 872-895. doi:10.1080/1369118x.2011.554572Towner, T., & Lego Munoz, C. (2016). Boomers versus Millennials: Online Media Influence on Media Performance and Candidate Evaluations. Social Sciences, 5(4), 56. doi:10.3390/socsci5040056Veglis, A., & Maniou, T. A. (2018). The Mediated Data Model of Communication Flow: Big Data and Data Journalism. KOME, 6(2), 32-43. doi:10.17646/kome.2018.23Wäsche, H. (2015). Interorganizational cooperation in sport tourism: A social network analysis. Sport Management Review, 18(4), 542-554. doi:10.1016/j.smr.2015.01.003Wäsche, H., Dickson, G., Woll, A., & Brandes, U. (2017). Social network analysis in sport research: an emerging paradigm. European Journal for Sport and Society, 14(2), 138-165. doi:10.1080/16138171.2017.1318198Yan, G., Pegoraro, A., & Watanabe, N. M. (2018). Student-Athletes’ Organization of Activism at the University of Missouri: Resource Mobilization on Twitter. Journal of Sport Management, 32(1), 24-37. doi:10.1123/jsm.2017-0031Yan, G., Watanabe, N. M., Shapiro, S. L., Naraine, M. L., & Hull, K. (2018). Unfolding the Twitter scene of the 2017 UEFA Champions League Final: social media networks and power dynamics. European Sport Management Quarterly, 19(4), 419-436. doi:10.1080/16184742.2018.1517272Yu, Y., & Wang, X. (2015). World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans’ tweets. Computers in Human Behavior, 48, 392-400. doi:10.1016/j.chb.2015.01.07

    ¿Apoyan los entrenadores la motivación de sus deportistas? Diferencias en la percepción del comportamiento

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    El objetivo de este trabajo era valorar la influencia de la percepción de apoyo a las necesidades psicológicas básicas (NPB) sobre la motivación intrínseca, disfrute, aburrimiento e intención de persistencia en el contexto deportivo de base, y como podía incidir la diferencia entre lo que los entrenadores creían proporcionar y lo que los deportistas percibían, en relación a dicho apoyo. Para ello, se llevaron a cabo 2 estudios diferentes. En el estudio 1 participaron 985 deportistas (M = 14,34; DT = 2,52) y se realizó un análisis de regresión, en el que se demostró la importancia de las percepciones de apoyo a las NPB sobre las variables analizadas. En función de estos resultados, se llevó a cabo el estudio 2, en el que participaron 91 entrenadores (M = 32,41; DT = 6,51) y 432 deportistas (M = 13,44; DT = 2,92), realizándose un análisis de diferencias entre grupos de entrenadores creados en función de la percepción de apoyo proporcionado y el apoyo percibido por los deportistas. Los resultados demostraron diferencias en la motivación intrínseca, aburrimiento e intención de persistencia entre los deportistas cuyos entrenadores creían proporcionar más apoyo a las NPB que el percibido por los atletas, respecto a aquellos cuyos entrenadores consideraban realizar un apoyo similar o inferior al percibido. Por tanto, los resultados hallados tienen implicaciones relevantes para explicar la adherencia deportiva en etapas de formación y en la consideración de los antecedentes motivacionales en el contexto deportivo.The aim of this study was focused on assessing the influence of perception of support for basic psychological needs (BPN) on variables such as intrinsic motivation, enjoyment, boredom, and intention to persist in the training sport context; and how this can affect the difference between what is provided and what coaches believe athletes perceive in relation to such support. To achieve this aim, two different studies were conducted. In the 1st study, 985 athletes participated (M = 14.34; SD = 2.52), and a regression analysis was performed, where the results showed the importance of perception of support for BPN in the variables analyzed. Regarding the outcomes found in the former section, the 2nd study was carried out, where 91 coaches (M = 32.41; SD = 6.51) and 432 athletes (M = 13.44; SD = 2.92) were involved, and an analysis of differences was conducted after the creation of groups of coaches formed with respect to the support given and the support perceived by athletes. The results show differences in intrinsic motivation, boredom and intention to persist between athletes whose coaches were believed to give more support for BPN than perceived by athletes, with respect to participants whose coaches were considered to give support similar to or lower than the perceived. Therefore, the outcomes found have relevant implications to explain sport adherence in training stages, as well as the consideration of motivational background in a sport context.O principal objectivo do presente estudo foi avaliar a influência da percepção de apoio às necessidades psicológicas básicas (NPB) em variáveis como a motivação intrínseca, divertimento, aborrecimento e intenção para persistir no treino desportivo, e como isso pode afectar as diferenças entre o que fornecem e o que pensam os treinadores que os atletas percepcionam no que concerne a esse apoio. Para alcançar este objectivo, foram desenvolvidos dois trabalhos. No primeiro estudo participaram 985 atletas (M = 14.34; DP = 2.52), e foi realizada uma análise de regressão sendo que os resultados demonstraram a importância da percepção de apoio às NPB nas variáveis analisadas. Tendo em conta os resultados obtidos no estudo anterior, o segundo estudo foi levado a cabo com a participação de 91 treinadores (M = 32.41; DP = 6.51) e 432 atletas (M = 13.44; DP = 2.92), e uma análise de diferenças efectuada após a criação de grupos de treinadores formados com base no critério de apoio prestado e o apoio percebido pelos atletas. Os resultados revelam diferenças na motivação intrínseca, aborrecimento e intenção de persistir entre atletas cujos treinadores acreditavam fornecer mais apoio pa as as NPB que o percepcionado pelos atletas, relativamente aos participantes cujos treinadores consideravam fornecer igual ou inferior apoio ao percepcionado. Contudo, os resultados obtidos apresentam implicações relevantes na explicação da adesão às etapas do treino desportivo, bem como relativamente à consideração de antecedentes motivacionais no contexto desportivo

    The Effect of the Launch of Bitcoin Futures on the Cryptocurrency Market: An Economic Efficiency Approach

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    We analyze the economic efficiency of the cryptocurrency market after the launch of Bitcoin futures by means of the Data Envelopment Analysis and Malmquist Indexes. Our results show that the introduction of Bitcoin futures did not affect the economic efficiency of the cryptocurrency market. However, we observe that Bitcoin obtained the highest risk-return trade-off due to its liquidity compared to the rest of cryptocurrencies. Therefore, our paper underlines the support of investors on Bitcoin to the detriment of the rest of cryptocurrencies

    Assessment of Fear of COVID-19 in Older Adults: Validation of the Fear of COVID-19 Scale

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    There is no information in Peru on the prevalence of mental health problems associated with COVID-19 in older adults. In this sense, the aim of the study was to gather evidence on the factor structure, criterion-related validity, and reliability of the Spanish version of the Fear of COVID-19 Scale (FCV-19S) in this population. The participants were 400 older adults (mean age = 68.04, SD = 6.41), who were administered the Fear of COVID-19 Scale, Revised Mental Health Inventory-5, Patient Health Questionnaire-2 items, and Generalized Anxiety Disorder Scale 2 items. Structural equation models were estimated, specifically confirmatory factor analysis (CFA), bifactor CFA, and structural models with latent variables (SEM). Internal consistency was estimated with composite reliability indexes (CRI) and omega coefficients. A bifactor model with both a general factor underlying all items plus a specific factor underlying items 1, 2, 4, and 5 representing the emotional response to COVID better represents the factor structure of the scale. This structure had adequate fit and good reliability, and additionally fear of COVID had a large effect on mental health. In general, women had more fear than men, having more information on COVID was associated to more fear, while having family or friends affected by COVID did not related to fear of the virus. The Spanish version of the Fear of COVID-19 Scale presents evidence of validity and reliability to assess fear of COVID-19 in the Peruvian older adult population
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