2,931 research outputs found
Impact of gaps in the asteroseismic characterization of pulsating stars. I. On the efficiency of pre-whitening
It is known that the observed distribution of frequencies in CoRoT and Kepler
{\delta} Scuti stars has no parallelism with any theoretical model.
Pre-whitening is a widespread technique in the analysis of time series with
gaps from pulsating stars located in the classical instability strip such as
{\delta} Scuti stars. However, some studies have pointed out that this
technique might introduce biases in the results of the frequency analysis. This
work aims at studying the biases that can result from pre-whitening in
asteroseismology. The results will depend on the intrinsic range and
distribution of frequencies of the stars. The periodic nature of the gaps in
CoRoT observations, just in the range of the pulsational frequency content of
the {\delta} Scuti stars, is shown to be crucial to determine their oscillation
frequencies, the first step to perform asteroseismolgy of these objects. Hence,
here we focus on the impact of pre-whitening on the asteroseismic
characterization of {\delta} Scuti stars. We select a sample of 15 {\delta}
Scuti stars observed by the CoRoT satellite, for which ultra-high quality
photometric data have been obtained by its seismic channel. In order to study
the impact on the asteroseismic characterization of {\delta} Scuti stars we
perform the pre-whitening procedure on three datasets: gapped data, linearly
interpolated data, and ARMA interpolated data. The different results obtained
show that at least in some cases pre-whitening is not an efficient procedure
for the deconvolution of the spectral window. therefore, in order to reduce the
effect of the spectral window to the minimum it is necessary to interpolate
with an algorithm that is aimed to preserve the original frequency content, and
not only to perform a pre-whitening of the data.Comment: 27 pages, 47 figures Tables and typos fixe
An Augmented Reality App to Learn to Interpret the Nutritional Information on Labels of Real Packaged Foods
[EN] Healthy eating habits involve controlling your diet. It is important to know how to
interpret the nutritional information of the packaged foods that you consume. These
packaged foods are usually processed and contain carbohydrates and fats. Monitoring
carbohydrates intake is particularly important for weight-loss diets and for some
pathologies such as diabetes. In this paper, we present an augmented reality app for
helping interpret the nutritional information about carbohydrates in real packaged foods
with the shape of boxes or cans. The app tracks the full object and guides the user in
finding the surface or area of the real package where the information about carbohydrates
is located using augmented reality and helps the user to interpret this information. The
portions of carbohydrates (also called carb choices or carb servings) that correspond
to the visualized food are shown. We carried out a study to check the effectiveness of
our app regarding learning outcomes, usability, and perceived satisfaction. A total of 40
people participated in the study (20 men and 20 women). The participants were between
14 and 55 years old. The results reported that their initial knowledge about carb choices
was very low. This indicates that education about nutritional information in packaged
foods is needed. An analysis of the pre-knowledge and post-knowledge questionnaires
showed that the users had a statistically significant increase in knowledge about carb
choices using our app. Gender and age did not influence the knowledge acquired. The
participants were highly satisfied with our app. In conclusion, our app and similar apps
could be used to effectively learn how to interpret the nutritional information on the labels
of real packaged foods and thus help users acquire healthy life habits.Juan, M.; Charco, JL.; García García, I.; Mollá Vayá, RP. (2019). An Augmented Reality App to Learn to Interpret the Nutritional Information on Labels of Real Packaged Foods. Frontiers in Computer Science. 1(1):1-16. https://doi.org/10.3389/fcomp.2019.00001S11611Akçayır, M., & Akçayır, G. (2017). Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educational Research Review, 20, 1-11. doi:10.1016/j.edurev.2016.11.002Azuma, R. T. (1997). A Survey of Augmented Reality. Presence: Teleoperators and Virtual Environments, 6(4), 355-385. doi:10.1162/pres.1997.6.4.355Barsom, E. Z., Graafland, M., & Schijven, M. P. (2016). Systematic review on the effectiveness of augmented reality applications in medical training. Surgical Endoscopy, 30(10), 4174-4183. doi:10.1007/s00464-016-4800-6Billinghurst, M., & Kato, H. (2002). Collaborative augmented reality. Communications of the ACM, 45(7), 64-70. doi:10.1145/514236.514265Bowman, D. A., & McMahan, R. P. (2007). Virtual Reality: How Much Immersion Is Enough? Computer, 40(7), 36-43. doi:10.1109/mc.2007.257Calle-Bustos, A.-M., Juan, M.-C., García-García, I., & Abad, F. (2017). An augmented reality game to support therapeutic education for children with diabetes. PLOS ONE, 12(9), e0184645. doi:10.1371/journal.pone.0184645Chatzopoulos, D., Bermejo, C., Huang, Z., & Hui, P. (2017). Mobile Augmented Reality Survey: From Where We Are to Where We Go. IEEE Access, 5, 6917-6950. doi:10.1109/access.2017.2698164Chen, P., Liu, X., Cheng, W., & Huang, R. (2016). A review of using Augmented Reality in Education from 2011 to 2016. Lecture Notes in Educational Technology, 13-18. doi:10.1007/978-981-10-2419-1_2Domhardt, M., Tiefengrabner, M., Dinic, R., Fötschl, U., Oostingh, G. J., Stütz, T., … Ginzinger, S. W. (2015). Training of Carbohydrate Estimation for People with Diabetes Using Mobile Augmented Reality. Journal of Diabetes Science and Technology, 9(3), 516-524. doi:10.1177/1932296815578880Furió, D., González-Gancedo, S., Juan, M.-C., Seguí, I., & Costa, M. (2013). The effects of the size and weight of a mobile device on an educational game. Computers & Education, 64, 24-41. doi:10.1016/j.compedu.2012.12.015Furió, D., González-Gancedo, S., Juan, M.-C., Seguí, I., & Rando, N. (2013). Evaluation of learning outcomes using an educational iPhone game vs. traditional game. Computers & Education, 64, 1-23. doi:10.1016/j.compedu.2012.12.001Harris, J. L., Bargh, J. A., & Brownell, K. D. (2009). Priming effects of television food advertising on eating behavior. Health Psychology, 28(4), 404-413. doi:10.1037/a0014399Ibáñez, M.-B., & Delgado-Kloos, C. (2018). Augmented reality for STEM learning: A systematic review. Computers & Education, 123, 109-123. doi:10.1016/j.compedu.2018.05.002Juan, M. C., Alcaniz, M., Monserrat, C., Botella, C., Banos, R. M., & Guerrero, B. (2005). Using Augmented Reality to Treat Phobias. IEEE Computer Graphics and Applications, 25(6), 31-37. doi:10.1109/mcg.2005.143Juan, M.-C., García-García, I., Mollá, R., & López, R. (2018). Users’ Perceptions Using Low-End and High-End Mobile-Rendered HMDs: A Comparative Study. Computers, 7(1), 15. doi:10.3390/computers7010015Juan, M.-C., Mendez-Lopez, M., Perez-Hernandez, E., & Albiol-Perez, S. (2014). Augmented Reality for the Assessment of Children’s Spatial Memory in Real Settings. PLoS ONE, 9(12), e113751. doi:10.1371/journal.pone.0113751Kerawalla, L., Luckin, R., Seljeflot, S., & Woolard, A. (2006). «Making it real»: exploring the potential of augmented reality for teaching primary school science. Virtual Reality, 10(3-4), 163-174. doi:10.1007/s10055-006-0036-4Kesim, M., & Ozarslan, Y. (2012). Augmented Reality in Education: Current Technologies and the Potential for Education. Procedia - Social and Behavioral Sciences, 47, 297-302. doi:10.1016/j.sbspro.2012.06.654Li, W., Nee, A., & Ong, S. (2017). A State-of-the-Art Review of Augmented Reality in Engineering Analysis and Simulation. Multimodal Technologies and Interaction, 1(3), 17. doi:10.3390/mti1030017Macias M, A. I., Gordillo S, L. G., & Camacho R, E. J. (2012). Hábitos alimentarios de niños en edad escolar y el papel de la educación para la salud. Revista chilena de nutrición, 39(3), 40-43. doi:10.4067/s0717-75182012000300006Augmented Reality Market by Offering (Hardware (Sensor, Displays and Projectors, Cameras), and Software), Device Type (Head-Mounted, Head-Up, Handheld), Application (Enterprise, Consumer, Commercial, Automotive) and Geography - Global forecast to 2023Augmented and Virtual Reality in Healthcare Market by Offering (Hardware and Software), Device Type, End User, Application (Patient Care Management, Medical Training and Education, Pharmacy Management, Surgery), and Geography - Global Forecast to 2023Meola, A., Cutolo, F., Carbone, M., Cagnazzo, F., Ferrari, M., & Ferrari, V. (2016). Augmented reality in neurosurgery: a systematic review. Neurosurgical Review, 40(4), 537-548. doi:10.1007/s10143-016-0732-9Nincarean, D., Alia, M. B., Halim, N. D. A., & Rahman, M. H. A. (2013). Mobile Augmented Reality: The Potential for Education. Procedia - Social and Behavioral Sciences, 103, 657-664. doi:10.1016/j.sbspro.2013.10.385Nishida, C., & Martinez Nocito, F. (2007). FAO/WHO Scientific Update on carbohydrates in human nutrition: introduction. European Journal of Clinical Nutrition, 61(S1), S1-S4. doi:10.1038/sj.ejcn.1602935Palmarini, R., Erkoyuncu, J. A., Roy, R., & Torabmostaedi, H. (2018). A systematic review of augmented reality applications in maintenance. Robotics and Computer-Integrated Manufacturing, 49, 215-228. doi:10.1016/j.rcim.2017.06.002Patrick, H., & Nicklas, T. A. (2005). A Review of Family and Social Determinants of Children’s Eating Patterns and Diet Quality. Journal of the American College of Nutrition, 24(2), 83-92. doi:10.1080/07315724.2005.10719448R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing2018Radu, I. (2014). Augmented reality in education: a meta-review and cross-media analysis. Personal and Ubiquitous Computing, 18(6), 1533-1543. doi:10.1007/s00779-013-0747-yRollo, M. E., Bucher, T., Smith, S., & Collins, C. E. (2017). The effect of an augmented reality aid on error associated with serving food. Journal of Nutrition & Intermediary Metabolism, 8, 90. doi:10.1016/j.jnim.2017.04.111RStudio: Integrated Development Environment for R (Version 1.1.463). Boston, MA2018Schmalsteig, D., & Hollerer, T. (2016). Augmented reality. ACM SIGGRAPH 2016 Courses. doi:10.1145/2897826.2927365Shelton, B. E., & Hedley, N. R. (s. f.). Using augmented reality for teaching Earth-Sun relationships to undergraduate geography students. The First IEEE International Workshop Agumented Reality Toolkit,. doi:10.1109/art.2002.1106948Sielhorst, T., Feuerstein, M., & Navab, N. (2008). Advanced Medical Displays: A Literature Review of Augmented Reality. Journal of Display Technology, 4(4), 451-467. doi:10.1109/jdt.2008.2001575SIRAKAYA, M., & ALSANCAK SIRAKAYA, D. (2018). Trends in Educational Augmented Reality Studies: A Systematic Review. Malaysian Online Journal of Educational Technology, 6(2), 60-74. doi:10.17220/mojet.2018.02.005Number of Mobile Phone Users Worldwide From 2015 to 2020 (in billions)2016STORY, M., NANNEY, M. S., & SCHWARTZ, M. B. (2009). Schools and Obesity Prevention: Creating School Environments and Policies to Promote Healthy Eating and Physical Activity. Milbank Quarterly, 87(1), 71-100. doi:10.1111/j.1468-0009.2009.00548.xVávra, P., Roman, J., Zonča, P., Ihnát, P., Němec, M., Kumar, J., … El-Gendi, A. (2017). Recent Development of Augmented Reality in Surgery: A Review. Journal of Healthcare Engineering, 2017, 1-9. doi:10.1155/2017/4574172Witmer, B. G., & Singer, M. J. (1998). Measuring Presence in Virtual Environments: A Presence Questionnaire. Presence: Teleoperators and Virtual Environments, 7(3), 225-240. doi:10.1162/105474698565686Healthy Diet2015Zhu, E., Hadadgar, A., Masiello, I., & Zary, N. (2014). Augmented reality in healthcare education: an integrative review. PeerJ, 2, e469. doi:10.7717/peerj.46
Visualization of multifractal superconductivity in a two-dimensional transition metal dichalcogenide in the weak-disorder regime
Eigenstate multifractality is a distinctive feature of non-interacting
disordered metals close to a metal-insulator transition, whose properties are
expected to extend to superconductivity. While multifractality in three
dimensions (3D) only develops near the critical point for specific
strong-disorder strengths, multifractality in 2D systems is expected to be
observable even for weak disorder. Here we provide evidence for multifractal
features in the superconducting state of an intrinsic weakly disordered
single-layer NbSe by means of low-temperature scanning tunneling
microscopy/spectroscopy. The superconducting gap, characterized by its width,
depth and coherence peaks' amplitude, shows a characteristic spatial modulation
coincident with the periodicity of the quasiparticle interference pattern.
Spatial inhomogeneity of the superconducting gap width, proportional to the
local order parameter in the weak-disorder regime, follows a log-normal
statistical distribution as well as a power-law decay of the two-point
correlation function, in agreement with our theoretical model. Furthermore, the
experimental singularity spectrum f() shows anomalous scaling behavior
typical from 2D weakly disordered systems
Montera: A Framework for Efficient Execution of Monte Carlo Codes on Grid Infrastructures
he objective of this work is to improve the performance of Monte Carlo codes on Grid production infrastructures. To do so, the codes and the grid sites are characterized with simple parameters to model their behaviors. Then, a new performance model for grid infrastructures is proposed, and an algorithm that employs this information is described. This algorithm dynamically calculates the number and size of tasks to execute on each site to maximize the performance and reduce makespan. Finally, a newly developed framework called Montera is presented. Montera deals with the execution of Monte Carlo codes in an unattended way, isolating the complexity of the problem from the final user. By employing two fusion Monte Carlo codes as example cases, along with the described characterizations and scheduling algorithm, a performance improvement up to 650 % over current best results is obtained on a real production infrastructure, together with enhanced stability and robustness
Diagnóstico diferencial de las enfermedades prostáticas
Este trabajo incluye una revisión de los métodos de diagnóstico de las enfermedades prostáticas; además describe cómo podemos distinguir entre cada una de ellas, dependiendo del método de diagnóstico utilizado.This paper reviews the prostatic pathologies diagnosis methods; it also describes the way we can distinguish between each one depending on the one we use
Genomic Evolution of Two Acinetobacter baumannii Clinical Strains from ST-2 Clones Isolated in 2000 and 2010 (ST-2_clon_2000 and ST-2_clon_2010)
Acinetobacter baumannii is a successful nosocomial pathogen due to its ability to persist in hospital environments by acquiring
mobile elements such as transposons, plasmids, and phages. In this study, we compared two genomes of A. baumannii clinical
strains isolated in 2000 (ST-2_clon_2000) and 2010 (ST-2_clon_2010) from GenBank project PRJNA308422
- …