Observations and modeling of the surface seiches of Lake Tahoe, USA

A rich array of spatially complex surface seiche modes exists in lakes. While the amplitude of these oscillations is often small, knowledge of their spatio-temporal characteristics is valuable for understanding when they might be of localized hydrodynamic importance. The expression and impact of these basin-scale barotropic oscillations in Lake Tahoe are evaluated using a finite-element numerical model and a distributed network of ten high-frequency nearshore monitoring stations. Model-predicted nodal distributions and periodicities are confirmed using the presence/absence of spectral power in measured pressure signals, and using coherence/phasing analysis of pressure signals from stations on common and opposing antinodes. Surface seiches in Lake Tahoe have complex nodal distributions despite the relative simplicity of the basin morphometry. Seiche amplitudes are magnified on shallow shelves, where they occasionally exceed 5 cm; elsewhere, amplitudes rarely exceed 1 cm. There is generally little coherence between surface seiching and littoral water quality. However, pressure–temperature coherence at shelf sites suggests potential seiche-driven pumping. Main-basin seiche signals are present in attached marinas, wetlands, and bays, implying reversing flows between the lake and these water bodies. On the shallow sill connecting Emerald Bay to Lake Tahoe, the fundamental main-basin seiche combines with a zeroth-mode harbor seiche to dominate the cross-sill flow signal, and to drive associated temperature fluctuations. Results highlight the importance of a thorough descriptive understanding of the resonant barotropic oscillations in any lake basin in a variety of research and management contexts, even when the magnitude of these oscillations tends to be small

Inkjet printed multimetal microelectrodes on PDMS for functionalized microfluidic systems

A novel direct method of metal microelectrode patterning on polydimethylsiloxane (PDMS) using inkjet printed gold and silver nanoparticles to form electrochemical sensors is presented. Inkjet printing is an additive microfabrication technique enabling microelectrode patterning directly over large areas at low-temperatures. (3-mercaptopropyl) trimethoxysilane (MPTMS) to promote PDMS surface wettability and improve metal adhesion and a pixel-printing subsampling method to overcome surface tension driven ink-droplet coalescence, are then employed to form a robust fabrication process. The resulting printed gold and silver microelectrodes exhibit good compactness, continuity and conductivity, and are used to manufacture functionalized microfluidic systems with in-situ three-electrode electrochemical sensors.published_or_final_versio

The effect of inhibition of leukotriene synthesis on the activity of interleukin-8 and granulocyte-macrophage colony-stimulating factor

The cytokines interleukin-8 (IL-8) and granulocyte-macrophage colony-stimulating factor (GM-CSF) enhanced the extracellular release of arachidonate metabolites from ionophore-stimulated neutrophils by 145 +/- 10% (mean +/- S.E.M., n = 13) and 182 +/- 11% (n = 16), respectively. To determine whether enhanced leukotriene production mediates the effects of these cytokines on neutrophil activity, two different specific arachidonate 5-lipoxygenase (5-LO) inhibitors, piriprost and MK-886, were used to inhibit leukotriene synthesis. Neither inhibitor affected the upregulation of CD11b beta2-integrin expression or priming of superoxide generation stimulated by IL-8 and GM-CSF. It is concluded that leukotrienes do not mediate either the direct or priming effects of these cytokines and that these classes of anti-inflammatory drugs are therefore unlikely to inhibit the effects of IL-8 and GM-CSF on neutrophil activation

Reductions in global biodiversity loss predicted from conservation spending

Halting global biodiversity loss is central to both the Convention on Biological Diversity (CBD) and United Nations Sustainable Development Goals (SDGs)1,2, but success to date has been very limited3–5. A critical determinant of overall strategic success (or failure) is the financing committed to biodiversity6–9; however, financing decisions are still hindered by considerable uncertainty over what any investment is likely to achieve6–9.. For greater effectiveness, we need an evidence-based model (EBM)10–12 showing how conservation spending quantitatively reduces the rate of loss. Here, we empirically quantify how i$14.4 billion of conservation investment reduced biodiversity loss across 109 signatory countries between 1996 and 2008, by an average 29% per country. We also show that biodiversity change in signatory countries can be predicted with high accuracy, using a dual model that combines the positive impact of conservation investment with the negative impact of economic, agricultural and population growth (i.e. human development pressures)13–18. Decision-makers can use this dual model to forecast the improvement that any proposed biodiversity budget would achieve under various scenarios of human development pressure, comparing those forecasts to any chosen policy target (including the CBD and SDGs). Importantly, we further find that spending impacts shrink as human development pressures grow, implying that funding may need to increase over time. The model therefore offers a flexible tool for balancing the SDGs of human development and biodiversity, by predicting the dynamic changes needed in conservation finance as human development proceeds

A Universal Model of Global Civil Unrest

Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of universal patterns of behavior. In contrast, the possibility that civil unrest activities, across countries and over long time periods, are governed by universal mechanisms has not been explored. Here, we analyze records of civil unrest of 170 countries during the period 1919-2008. We demonstrate that the distributions of the number of unrest events per year are robustly reproduced by a nonlinear, spatially extended dynamical model, which reflects the spread of civil disorder between geographic regions connected through social and communication networks. The results also expose the similarity between global social instability and the dynamics of natural hazards and epidemics.Comment: 8 pages, 3 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. 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Application of a central design composed of surface of response for the determination of the flatness in the steel sheets of a Colombian steel

In this paper the results of research are shown in which the response surface was applied with central composite design for estimating the values of flatness as shown and accepted by the quality department and production company of the Caribbean region, taking into account factors such as voltage control in the winding sheet and the coefficient of friction. Initially, a first-order model was proposed to estimate the variance of the error and consider a quantitative model that takes into account the effects of combination. The method of steepest descent is used to sequentially move the descent path, i.e., in the direction of the minimum response decrement, until a new interval near optimal experimentation. Subsequently, a model of higher order was developed using a central composite design with center points and stars, taking into account control other variables additional response flatness, such as the roughness and hardness of the sheets in a range of flexible and acceptable tolerances for the company through a canonical model, and that will be the focus of future research