Time-dependent trapping of pollinators driven by the alignment of floral phenology with insect circadian rhythms

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Evaluating rapid evolutionary radiation in Goniothalamus (Annonaceae)

Oral Presentation - Session 1: Taxonoly & Biosystematic-1Main Theme: Contributions of Flora Malesiana to the Welfare of People in AsiaBoth intrinsic and environmental factors may result in changes in diversification rate in a lineage. Significant shifts in evolutionary tempo, including rapid evolutionary radiation, are of particular interest as they are key to understanding how factors such as the timing of diversifications, species attributes, environmental conditions and the size and complexity of geographical regions have shaped current patterns of biodiversity. The Annonaceae is a species-rich fami...postprin

Fractional quantum Hall effect in the absence of Landau levels

It has been well-known that topological phenomena with fractional excitations, i.e., the fractional quantum Hall effect (FQHE) \cite{Tsui1982} will emerge when electrons move in Landau levels. In this letter, we report the discovery of the FQHE in the absence of Landau levels in an interacting fermion model. The non-interacting part of our Hamiltonian is the recently proposed topologically nontrivial flat band model on the checkerboard lattice \cite{sun}. In the presence of nearest-neighboring repulsion ($U$), we find that at 1/3 filling, the Fermi-liquid state is unstable towards FQHE. At 1/5 filling, however, a next-nearest-neighboring repulsion is needed for the occurrence of the 1/5 FQHE when $U$ is not too strong. We demonstrate the characteristic features of these novel states and determine the phase diagram correspondingly.Comment: 6 pages and 4 figure

An EWMA control chart for the multivariate coefficient of variation

This is the peer reviewed version of the following article: Giner-Bosch, V, Tran, KP, Castagliola, P, Khoo, MBC. An EWMA control chart for the multivariate coefficient of variation. Qual Reliab Engng Int. 2019; 35: 1515-1541, which has been published in final form at https://doi.org/10.1002/qre.2459. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Monitoring the multivariate coefficient of variation over time is a natural choice when the focus is on stabilising the relative variability of a multivariate process, as is the case in a significant number of real situations in engineering, health sciences, and finance, to name but a few areas. However, not many tools are available to practitioners with this aim. This paper introduces a new control chart to monitor the multivariate coefficient of variation through an exponentially weighted moving average (EWMA) scheme. Concrete methodologies to calculate the limits and evaluate the performance of the chart proposed and determine the optimal values of the chart's parameters are derived based on a theoretical study of the statistic being monitored. Computational experiments reveal that our proposal clearly outperforms existing alternatives, in terms of the average run length to detect an out-of-control state. A numerical example is included to show the efficiency of our chart when operating in practice.Generalitat Valenciana, Grant/Award Number: BEST/2017/033 and GV/2016/004; Ministerio de Economia y Competitividad, Grant/Award Number: MTM2013-45381-PGiner-Bosch, V.; Tran, KP.; Castagliola, P.; Khoo, MBC. (2019). An EWMA control chart for the multivariate coefficient of variation. Quality and Reliability Engineering International. 35(6):1515-1541. https://doi.org/10.1002/qre.2459S15151541356Kang, C. W., Lee, M. S., Seong, Y. J., & Hawkins, D. M. (2007). A Control Chart for the Coefficient of Variation. Journal of Quality Technology, 39(2), 151-158. doi:10.1080/00224065.2007.11917682Amdouni, A., Castagliola, P., Taleb, H., & Celano, G. (2015). Monitoring the coefficient of variation using a variable sample size control chart in short production runs. The International Journal of Advanced Manufacturing Technology, 81(1-4), 1-14. doi:10.1007/s00170-015-7084-4Amdouni, A., Castagliola, P., Taleb, H., & Celano, G. (2017). A variable sampling interval Shewhart control chart for monitoring the coefficient of variation in short production runs. International Journal of Production Research, 55(19), 5521-5536. doi:10.1080/00207543.2017.1285076Yeong, W. C., Khoo, M. B. C., Tham, L. K., Teoh, W. L., & Rahim, M. A. (2017). Monitoring the Coefficient of Variation Using a Variable Sampling Interval EWMA Chart. Journal of Quality Technology, 49(4), 380-401. doi:10.1080/00224065.2017.11918004Teoh, W. L., Khoo, M. B. C., Castagliola, P., Yeong, W. C., & Teh, S. Y. (2017). Run-sum control charts for monitoring the coefficient of variation. European Journal of Operational Research, 257(1), 144-158. doi:10.1016/j.ejor.2016.08.067Sharpe, W. F. (1994). The Sharpe Ratio. The Journal of Portfolio Management, 21(1), 49-58. doi:10.3905/jpm.1994.409501Van Valen, L. (1974). Multivariate structural statistics in natural history. Journal of Theoretical Biology, 45(1), 235-247. doi:10.1016/0022-5193(74)90053-8Albert, A., & Zhang, L. (2010). A novel definition of the multivariate coefficient of variation. Biometrical Journal, 52(5), 667-675. doi:10.1002/bimj.201000030Aerts, S., Haesbroeck, G., & Ruwet, C. (2015). Multivariate coefficients of variation: Comparison and influence functions. Journal of Multivariate Analysis, 142, 183-198. doi:10.1016/j.jmva.2015.08.006Bennett, B. M. (1977). On multivariate coefficients of variation. Statistische Hefte, 18(2), 123-128. doi:10.1007/bf02932744Underhill, L. G. (1990). The coefficient of variation biplot. Journal of Classification, 7(2), 241-256. doi:10.1007/bf01908718Boik, R. J., & Shirvani, A. (2009). Principal components on coefficient of variation matrices. Statistical Methodology, 6(1), 21-46. doi:10.1016/j.stamet.2008.02.006MacGregor, J. F., & Kourti, T. (1995). Statistical process control of multivariate processes. Control Engineering Practice, 3(3), 403-414. doi:10.1016/0967-0661(95)00014-lBersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview. Quality and Reliability Engineering International, 23(5), 517-543. doi:10.1002/qre.829Yeong, W. C., Khoo, M. B. C., Teoh, W. L., & Castagliola, P. (2015). A Control Chart for the Multivariate Coefficient of Variation. Quality and Reliability Engineering International, 32(3), 1213-1225. doi:10.1002/qre.1828Lim, A. J. X., Khoo, M. B. C., Teoh, W. L., & Haq, A. (2017). Run sum chart for monitoring multivariate coefficient of variation. Computers & Industrial Engineering, 109, 84-95. doi:10.1016/j.cie.2017.04.023Roberts, S. W. (1966). A Comparison of Some Control Chart Procedures. Technometrics, 8(3), 411-430. doi:10.1080/00401706.1966.10490374Roberts, S. W. (1959). Control Chart Tests Based on Geometric Moving Averages. Technometrics, 1(3), 239-250. doi:10.1080/00401706.1959.10489860Lucas, J. M., & Saccucci, M. S. (1990). Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements. Technometrics, 32(1), 1-12. doi:10.1080/00401706.1990.10484583Wijsman, R. A. (1957). Random Orthogonal Transformations and their use in Some Classical Distribution Problems in Multivariate Analysis. The Annals of Mathematical Statistics, 28(2), 415-423. doi:10.1214/aoms/1177706969The general sampling distribution of the multiple correlation coefficient. (1928). Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 121(788), 654-673. doi:10.1098/rspa.1928.0224Paolella, M. S. (2007). Intermediate Probability. doi:10.1002/9780470035061WalckC.Handbook on statistical distributions for experimentalists. Tech. Rep. SUFPFY/96‐01 Stockholm   Particle Physics Group Fysikum University of Stockholm;2007. http://inspirehep.net/record/1389910BROOK, D., & EVANS, D. A. (1972). An approach to the probability distribution of cusum run length. Biometrika, 59(3), 539-549. doi:10.1093/biomet/59.3.539Castagliola, P., Celano, G., & Psarakis, S. (2011). Monitoring the Coefficient of Variation Using EWMA Charts. Journal of Quality Technology, 43(3), 249-265. doi:10.1080/00224065.2011.11917861Vining, G. (2009). Technical Advice: Phase I and Phase II Control Charts. Quality Engineering, 21(4), 478-479. doi:10.1080/08982110903185736Scilab Enterprises: Scilab: Free and open source software for numerical computation Version 6.0.0.http://www.scilab.org;2017.Nelder, J. A., & Mead, R. (1965). A Simplex Method for Function Minimization. The Computer Journal, 7(4), 308-313. doi:10.1093/comjnl/7.4.308PAGE, E. S. (1954). CONTINUOUS INSPECTION SCHEMES. Biometrika, 41(1-2), 100-115. doi:10.1093/biomet/41.1-2.100Über die hypergeometrische Reihe . (1836). Journal für die reine und angewandte Mathematik (Crelles Journal), 1836(15), 39-83. doi:10.1515/crll.1836.15.3

Lithium distribution across the membrane of motoneurons in the isolated frog spinal cord

Lithium sensitive microelectrodes were used to investigate the transmembrane distribution of lithium ions (Li+) in motoneurons of the isolated frog spinal cord. After addition of 5 mmol·l–1 LiCl to the bathing solution the extracellular diffusion of Li+ was measured. At a depth of 500 m, about 60 min elapsed before the extracellular Li+ concentration approached that of the bathing solution. Intracellular measurements revealed that Li+ started to enter the cells soon after reaching the motoneuron pool and after up to 120 min superfusion, an intra — to extracellular concentration ratio of about 0.7 was obtained. The resting membrane potential and height of antidromically evoked action potentials were not altered by 5 mmol·l–1 Li+

3D Visualisation of Additive Occlusion and Tunable Full-Spectrum Fluorescence in Calcite

From biomineralization to synthesis, organic additives provide an effective means of controlling crystallisation processes. There is growing evidence that these additives are often occluded within the crystal lattice, where this promises an elegant means of creating nanocomposites and tuning physical properties. Here, we use the incorporation of sulfonated fluorescent dyes to gain new understanding of additive occlusion in calcite (CaCO3), and to link morphological changes to occlusion mechanisms. We demonstrate that these additives are incorporated within specific zones, as defined by the growth conditions, and show how occlusion can govern changes in crystal shape. Fluorescence spectroscopy and lifetime imaging microscopy also show that the dyes experience unique local environments within different zones. Our strategy was then extended to simultaneously incorporate mixtures of dyes, whose fluorescence cascade creates calcite nanoparticles that fluoresce white. This offers a simple strategy for generating biocompatible and stable fluorescent nanoparticles whose output can be tuned as required