A season-long team-building intervention: Examining the effect of team goal setting on cohesion

The purpose of the current study was to determine whether the implementation of a season-long team-building intervention program using team goal setting increased perceptions of cohesion. The participants were 86 female high school basketball players from 8 teams. The teams were randomly assigned to either an experimental team goal–setting or control condition. Each participant completed the Group Environment Questionnaire (GEQ; Carron, Brawley, & Widmeyer, 2002; Carron, Widmeyer, & Brawley, 1985), which assessed cohesion at both the beginning and end of the season. Overall, the results revealed a significant multivariate effect, Pillai’s trace F(12, 438) = 2.68, p = .002. Post hoc analyses showed that at the beginning of the season, athletes from both conditions did not differ in their perceptions of cohesion. However, at the end of the season, athletes in the team goal–setting condition held higher perceptions of cohesion than athletes in the control condition. Overall, the results indicated that team goal setting was an effective team-building tool for influencing cohesiveness in sport teams

Interlaced X-ray diffraction computed tomography

An X-ray diffraction computed tomography data-collection strategy that allows, post experiment, a choice between temporal and spatial resolution is reported. This strategy enables time-resolved studies on comparatively short timescales, or alternatively allows for improved spatial resolution if the system under study, or components within it, appear to be unchanging. The application of the method for studying an Mn–Na–W/SiO2 fixed-bed reactor in situ is demonstrated. Additionally, the opportunities to improve the data-collection strategy further, enabling post-collection tuning between statistical, temporal and spatial resolutions, are discussed. In principle, the interlaced scanning approach can also be applied to other pencil-beam tomographic techniques, like X-ray fluorescence computed tomography, X-ray absorption fine structure computed tomography, pair distribution function computed tomography and tomographic scanning transmission X-ray microscopy

Precision is in the Eye of the Beholder: Application of Eye Fixation-Related Potentials to Information Systems Research

This is the final version. Available from Association for Information Systems via the DOI in this recordThis paper introduces the eye-fixation related potential (EFRP) method to IS research. The EFRP method allows one to synchronize eye tracking with electroencephalographic (EEG) recording to precisely capture users’ neural activity at the exact time at which they start to cognitively process a stimulus (e.g., event on the screen). This complements and overcomes some of the shortcomings of the traditional event related potential (ERP) method, which can only stamp the time at which a stimulus is presented to a user. Thus, we propose a method conjecture of the superiority of EFRP over ERP for capturing the cognitive processing of a stimulus when such cognitive processing is not necessarily synchronized with the time at which the stimulus appears. We illustrate the EFRP method with an experiment in a natural IS use context in which we asked users to read an industry report while email pop-up notifications arrived on their screen. The results support our proposed hypotheses and show three distinct neural processes associated with 1) the attentional reaction to email pop-up notification, 2) the cognitive processing of the email pop-up notification, and 3) the motor planning activity involved in opening or not the email. Furthermore, further analyses of the data gathered in the experiment serve to validate our method conjecture about the superiority of the EFRP method over the ERP in natural IS use contexts. In addition to the experiment, our study discusses important IS research questions that could be pursued with the aid of EFRP, and describes a set of guidelines to help IS researchers use this method.Social Sciences and Humanities Research Council of Canada (SSHRC)Natural Sciences and Engineering Research Council of CanadaFonds Québécois pour la Recherche sur la Société et la Culture (FQRSC)Fonds de recherche Nature et Technologies (FQRNT

X-ray physico-chemical imaging during activation of cobalt-based Fischer-Tropsch synthesis catalysts

The imaging of catalysts and other functional materials under reaction conditions has advanced significantly in recent years. The combination of the computed tomography (CT) approach with methods such as X-ray diffraction (XRD), X-ray fluorescence (XRF) and X-ray absorption near-edge spectroscopy (XANES) now enables local chemical and physical state information to be extracted from within the interiors of intact materials which are, by accident or design, inhomogeneous. In this work, we follow the phase evolution during the initial reduction step(s) to form Co metal, for Co-containing particles employed as Fischer–Tropsch synthesis (FTS) catalysts; firstly, working at small length scales (approx. micrometre spatial resolution), a combination of sample size and density allows for transmission of comparatively low energy signals enabling the recording of ‘multimodal’ tomography, i.e. simultaneous XRF–CT, XANES–CT and XRD–CT. Subsequently, we show high-energy XRD–CT can be employed to reveal extent of reduction and uniformity of crystallite size on millimetre-sized TiO2 trilobes. In both studies, the CoO phase is seen to persist or else evolve under particular operating conditions and we speculate as to why this is observed

Modeling gaseous non-reactive flow in a lean direct injection gas turbine combustor through an advanced mesh control strategy

[EN] Fuel efficiency improvement and harmful emissions reduction are the main motivations for the development of gas turbine combustors. Numerical computational fluid dynamics (CFD) simulations of these devices are usually computationally expensive since they imply a multi-scale problem. In this work, gaseous non-reactive unsteady Reynolds-Averaged Navier-Stokes and large eddy simulations of a gaseous-fueled radial-swirled lean direct injection combustor have been carried out through CONVERGE (TM) CFD code by solving the complete inlet flow path through the swirl vanes and the combustor. The geometry considered is the gaseous configuration of the CORIA lean direct injection combustor, for which detailed measurements are available. The emphasis of the work is placed on the demonstration of the CONVERGE (TM) applicability to the multi-scale gas turbine engines field and the determination of an optimal mesh strategy through several grid control tools (i.e., local refinement, adaptive mesh refinement) allowing the exploitation of its automatic mesh generation against traditional fixed mesh approaches. For this purpose, the normalized mean square error has been adopted to quantify the accuracy of turbulent numerical statistics regarding the agreement with the experimental database. Furthermore, the focus of the work is to study the behavior when coupling several large eddy simulation sub-grid scale models (i.e., Smagorinsky, Dynamic Smagorinsky, and Dynamic Structure) with the adaptive mesh refinement algorithm through the evaluation of its specific performances and predictive capabilities in resolving the spatial-temporal scales and the intrinsically unsteady flow structures generated within the combustor. This investigation on the main non-reacting swirling flow characteristics inside the combustor provides a suitable background for further studies on combustion instability mechanisms.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partly sponsored by the program "Ayuda a Primeros Proyectos de Investigacion (PAID-06-18), Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia (UPV), Spain.'' The support given to Mr. Mario Belmar by Universitat Politecnica de Valencia through the "FPI-Subprograma 2'' grant within the "Programa de Apoyo para la Investigacion y Desarrollo (PAID-01-18)'' is gratefully acknowledged.Payri, R.; Novella Rosa, R.; Carreres, M.; Belmar-Gil, M. (2020). Modeling gaseous non-reactive flow in a lean direct injection gas turbine combustor through an advanced mesh control strategy. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering. 234(11):1788-1810. https://doi.org/10.1177/0954410020919619S1788181023411Patel, N., Kırtaş, M., Sankaran, V., & Menon, S. (2007). Simulation of spray combustion in a lean-direct injection combustor. Proceedings of the Combustion Institute, 31(2), 2327-2334. doi:10.1016/j.proci.2006.07.232Luo, K., Pitsch, H., Pai, M. G., & Desjardins, O. (2011). Direct numerical simulations and analysis of three-dimensional n-heptane spray flames in a model swirl combustor. Proceedings of the Combustion Institute, 33(2), 2143-2152. doi:10.1016/j.proci.2010.06.077Masri, A. R., Pope, S. B., & Dally, B. B. (2000). Probability density function computations of a strongly swirling nonpremixed flame stabilized on a new burner. Proceedings of the Combustion Institute, 28(1), 123-131. doi:10.1016/s0082-0784(00)80203-9Johnson, M. R., Littlejohn, D., Nazeer, W. A., Smith, K. O., & Cheng, R. K. (2005). A comparison of the flowfields and emissions of high-swirl injectors and low-swirl injectors for lean premixed gas turbines. Proceedings of the Combustion Institute, 30(2), 2867-2874. doi:10.1016/j.proci.2004.07.040Sankaran, V., & Menon †, S. (2002). LES of spray combustion in swirling flows. Journal of Turbulence, 3, N11. doi:10.1088/1468-5248/3/1/011Jones, W. P., Marquis, A. J., & Vogiatzaki, K. (2014). Large-eddy simulation of spray combustion in a gas turbine combustor. Combustion and Flame, 161(1), 222-239. doi:10.1016/j.combustflame.2013.07.016Ding, G., He, X., Xue, C., Zhao, Z., & Jin, Y. (2015). Preliminary design and experimental verification of a triple swirler combustor. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 229(12), 2258-2271. doi:10.1177/0954410015573555Menon, S., & Patel, N. (2006). Subgrid Modeling for Simulation of Spray Combustion in Large-Scale Combustors. AIAA Journal, 44(4), 709-723. doi:10.2514/1.14875Wang, P., Platova, N. A., Fröhlich, J., & Maas, U. (2014). Large Eddy Simulation of the PRECCINSTA burner. International Journal of Heat and Mass Transfer, 70, 486-495. doi:10.1016/j.ijheatmasstransfer.2013.11.025Cordier, M., Vandel, A., Cabot, G., Renou, B., & Boukhalfa, A. M. (2013). Laser-Induced Spark Ignition of Premixed Confined Swirled Flames. Combustion Science and Technology, 185(3), 379-407. doi:10.1080/00102202.2012.725791Patel, N., & Menon, S. (2008). Simulation of spray–turbulence–flame interactions in a lean direct injection combustor. Combustion and Flame, 153(1-2), 228-257. doi:10.1016/j.combustflame.2007.09.011Bang, B.-H., Kim, Y.-I., Jeong, S., Yoon, Y., Yarin, A. L., & Yoon, S. S. (2019). Theoretical model for swirling thin film flows inside nozzles with converging-diverging shapes. Applied Mathematical Modelling, 76, 607-616. doi:10.1016/j.apm.2019.06.025Linne, M., Paciaroni, M., Hall, T., & Parker, T. (2006). Ballistic imaging of the near field in a diesel spray. Experiments in Fluids, 40(6), 836-846. doi:10.1007/s00348-006-0122-0Desantes, J. M., Salvador, F. J., López, J. J., & De la Morena, J. (2010). Study of mass and momentum transfer in diesel sprays based on X-ray mass distribution measurements and on a theoretical derivation. Experiments in Fluids, 50(2), 233-246. doi:10.1007/s00348-010-0919-8Reddemann, M. A., Mathieu, F., & Kneer, R. (2013). Transmitted light microscopy for visualizing the turbulent primary breakup of a microscale liquid jet. Experiments in Fluids, 54(11). doi:10.1007/s00348-013-1607-2Chen, R.-H., & Driscoll, J. F. (1989). The role of the recirculation vortex in improving fuel-air mixing within swirling flames. Symposium (International) on Combustion, 22(1), 531-540. doi:10.1016/s0082-0784(89)80060-8Presser, C., Gupta, A. K., & Semerjian, H. G. (1993). Aerodynamic characteristics of swirling spray flames: Pressure-jet atomizer. Combustion and Flame, 92(1-2), 25-44. doi:10.1016/0010-2180(93)90196-aBulzan, D. L. (1995). Structure of a swirl-stabilized combusting spray. Journal of Propulsion and Power, 11(6), 1093-1102. doi:10.2514/3.23946Sommerfeld, M., & Qiu, H.-H. (1998). Experimental studies of spray evaporation in turbulent flow. International Journal of Heat and Fluid Flow, 19(1), 10-22. doi:10.1016/s0142-727x(97)10002-9Hadef, R., & Lenze, B. (2005). Measurements of droplets characteristics in a swirl-stabilized spray flame. Experimental Thermal and Fluid Science, 30(2), 117-130. doi:10.1016/j.expthermflusci.2005.05.002Soltani, M. R., Ghorbanian, K., Ashjaee, M., & Morad, M. R. (2005). Spray characteristics of a liquid–liquid coaxial swirl atomizer at different mass flow rates. Aerospace Science and Technology, 9(7), 592-604. doi:10.1016/j.ast.2005.04.004Tratnig, A., & Brenn, G. (2010). Drop size spectra in sprays from pressure-swirl atomizers. International Journal of Multiphase Flow, 36(5), 349-363. doi:10.1016/j.ijmultiphaseflow.2010.01.008Asgari, B., & Amani, E. (2017). A multi-objective CFD optimization of liquid fuel spray injection in dry-low-emission gas-turbine combustors. Applied Energy, 203, 696-710. doi:10.1016/j.apenergy.2017.06.080Moureau, V., Domingo, P., & Vervisch, L. (2011). From Large-Eddy Simulation to Direct Numerical Simulation of a lean premixed swirl flame: Filtered laminar flame-PDF modeling. Combustion and Flame, 158(7), 1340-1357. doi:10.1016/j.combustflame.2010.12.004Caraeni, D., Bergström, C., & Fuchs, L. (2000). Flow, Turbulence and Combustion, 65(2), 223-244. doi:10.1023/a:1011428926494Icardi, M., Gavi, E., Marchisio, D. L., Olsen, M. G., Fox, R. O., & Lakehal, D. (2011). Validation of LES predictions for turbulent flow in a Confined Impinging Jets Reactor. Applied Mathematical Modelling, 35(4), 1591-1602. doi:10.1016/j.apm.2010.09.035Sankaran, V., & Menon, S. (2002). Vorticity-scalar alignments and small-scale structures in swirling spray combustion. Proceedings of the Combustion Institute, 29(1), 577-584. doi:10.1016/s1540-7489(02)80074-8Lebas, R., Menard, T., Beau, P. A., Berlemont, A., & Demoulin, F. X. (2009). Numerical simulation of primary break-up and atomization: DNS and modelling study. International Journal of Multiphase Flow, 35(3), 247-260. doi:10.1016/j.ijmultiphaseflow.2008.11.005Zhou, Y., Huang, Y., & Mu, Z. (2017). Large eddy simulation of the influence of synthetic inlet turbulence on a practical aeroengine combustor with counter-rotating swirler. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233(3), 978-990. doi:10.1177/0954410017745900Torregrosa, A. J., Broatch, A., García-Tíscar, J., & Gomez-Soriano, J. (2018). Modal decomposition of the unsteady flow field in compression-ignited combustion chambers. Combustion and Flame, 188, 469-482. doi:10.1016/j.combustflame.2017.10.007Xu, L., Bai, X.-S., Jia, M., Qian, Y., Qiao, X., & Lu, X. (2018). Experimental and modeling study of liquid fuel injection and combustion in diesel engines with a common rail injection system. Applied Energy, 230, 287-304. doi:10.1016/j.apenergy.2018.08.104Broatch, A., Olmeda, P., Margot, X., & Gomez-Soriano, J. (2019). Numerical simulations for evaluating the impact of advanced insulation coatings on H2 additivated gasoline lean combustion in a turbocharged spark-ignited engine. Applied Thermal Engineering, 148, 674-683. doi:10.1016/j.applthermaleng.2018.11.106Esclapez, L., Riber, E., & Cuenot, B. (2015). Ignition probability of a partially premixed burner using LES. Proceedings of the Combustion Institute, 35(3), 3133-3141. doi:10.1016/j.proci.2014.07.040Rhie, C. M., & Chow, W. L. (1983). Numerical study of the turbulent flow past an airfoil with trailing edge separation. AIAA Journal, 21(11), 1525-1532. doi:10.2514/3.8284Gousseau, P., Blocken, B., & van Heijst, G. J. F. (2013). Quality assessment of Large-Eddy Simulation of wind flow around a high-rise building: Validation and solution verification. Computers & Fluids, 79, 120-133. doi:10.1016/j.compfluid.2013.03.006Hanna, S. ., Tehranian, S., Carissimo, B., Macdonald, R. ., & Lohner, R. (2002). Comparisons of model simulations with observations of mean flow and turbulence within simple obstacle arrays. Atmospheric Environment, 36(32), 5067-5079. doi:10.1016/s1352-2310(02)00566-6Hanna, S. R., Hansen, O. R., & Dharmavaram, S. (2004). FLACS CFD air quality model performance evaluation with Kit Fox, MUST, Prairie Grass, and EMU observations. Atmospheric Environment, 38(28), 4675-4687. doi:10.1016/j.atmosenv.2004.05.041Yakhot, V., Orszag, S. A., Thangam, S., Gatski, T. B., & Speziale, C. G. (1992). Development of turbulence models for shear flows by a double expansion technique. 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In Memoriam: Very Rev. Dr. Mateja Matejic

Special report dedicated to the memory of Professor Mateja Matejic (1924-2018) and his legacy of the Hilandar Research Library (HRL) and the Resource Center for Medieval Slavic Studies (RCMSS) at The Ohio State University.A four-page Special Report, inserted into Cyrillic Manuscript Heritage, v42 (October 2018), in memory of the Very Rev. Dr. Mateja Matejic. It includes an obituary written by Professor Predrag Matejic, his elder son, that was published in the local newspaper The Columbus Dispatch on July 30, 2018, (https://www.legacy.com/obituaries/dispatch/obituary.aspx?n=mateja-matejic&pid=189740357&fhid=8700) and on the website of the Rutherford Funeral Homes and Crematories, Columbus, Ohio (https://www.rutherfordfuneralhomes.com/obituaries/Mateja-Matejic/#!/Obituary), pp. I-II; Condolences - excerpts from comments regarding Father Matejic's legacy of the Hilandar Research Library and the Resource Center for Medieval Slavic Studies from researchers who have used the HRL/RCMSS resources, i.e., from Mirjana Živojinović, Adelina Angusheva, Svetlana Kujumdzhieva, and Enrique Santos Marinas, p. III; a list of donors who made contributions to the Hilandar Endowment Funds in memory of Father Matejic, p. IV. The Special Report is illustrated with photographs of Father Matejic by M.A. Johnson, Tatyana Nestorova-Matejic, Predrag Matejic, Walt Craig, Helene Senecal, and Pam McClung

Understanding the unsteady pressure field inside combustion chambers of compression-ignited engines using a computational fluid dynamics approach

[EN] In this article, a numerical methodology for assessing combustion noise in compression ignition engines is described with the specific purpose of analysing the unsteady pressure field inside the combustion chamber. The numerical results show consistent agreement with experimental measurements in both the time and frequency domains. Nonetheless, an exhaustive analysis of the calculation convergence is needed to guarantee an independent solution. These results contribute to the understanding of in-cylinder unsteady processes, especially of those related to combustion chamber resonances, and their effects on the radiated noise levels. The method was applied to different combustion system configurations by modifying the spray angle of the injector, evidencing that controlling the ignition location through this design parameter, it is possible to decrease the combustion noise by minimizing the resonance contribution. Important efficiency losses were, however, observed due to the injector/bowl matching worsening which compromises the performance and emissions levels.The authors want to express their gratitude to CONVERGENT SCIENCE Inc. and Convergent Science GmbH for their kind support for performing the CFD calculations using CONVERGE software.Torregrosa, AJ.; Broatch, A.; Margot, X.; Gómez-Soriano, J. (2018). Understanding the unsteady pressure field inside combustion chambers of compression-ignited engines using a computational fluid dynamics approach. International Journal of Engine Research. 1-13. https://doi.org/10.1177/1468087418803030S113Benajes, J., Novella, R., De Lima, D., & Tribotté, P. (2014). Analysis of combustion concepts in a newly designed two-stroke high-speed direct injection compression ignition engine. International Journal of Engine Research, 16(1), 52-67. doi:10.1177/1468087414562867Costa, M., Bianchi, G. M., Forte, C., & Cazzoli, G. (2014). A Numerical Methodology for the Multi-objective Optimization of the DI Diesel Engine Combustion. Energy Procedia, 45, 711-720. doi:10.1016/j.egypro.2014.01.076Navid, A., Khalilarya, S., & Taghavifar, H. (2016). Comparing multi-objective non-evolutionary NLPQL and evolutionary genetic algorithm optimization of a DI diesel engine: DoE estimation and creating surrogate model. Energy Conversion and Management, 126, 385-399. doi:10.1016/j.enconman.2016.08.014Benajes, J., García, A., Pastor, J. M., & Monsalve-Serrano, J. (2016). Effects of piston bowl geometry on Reactivity Controlled Compression Ignition heat transfer and combustion losses at different engine loads. Energy, 98, 64-77. doi:10.1016/j.energy.2016.01.014Masterton, B., Heffner, H., & Ravizza, R. (1969). The Evolution of Human Hearing. The Journal of the Acoustical Society of America, 45(4), 966-985. doi:10.1121/1.1911574Strahle, W. C. (1978). Combustion noise. Progress in Energy and Combustion Science, 4(3), 157-176. doi:10.1016/0360-1285(78)90002-3Flemming, F., Sadiki, A., & Janicka, J. (2007). Investigation of combustion noise using a LES/CAA hybrid approach. Proceedings of the Combustion Institute, 31(2), 3189-3196. doi:10.1016/j.proci.2006.07.060Klos, D., & Kokjohn, S. L. (2014). Investigation of the sources of combustion instability in low-temperature combustion engines using response surface models. International Journal of Engine Research, 16(3), 419-440. doi:10.1177/1468087414556135Cyclic dispersion in engine combustion—Introduction by the special issue editors. (2015). International Journal of Engine Research, 16(3), 255-259. doi:10.1177/1468087415572740Hickling, R., Feldmaier, D. A., & Sung, S. H. (1979). Knock‐induced cavity resonances in open chamber diesel engines. The Journal of the Acoustical Society of America, 65(6), 1474-1479. doi:10.1121/1.382910Torregrosa, A. J., Broatch, A., Margot, X., Marant, V., & Beauge, Y. (2004). Combustion chamber resonances in direct injection automotive diesel engines: A numerical approach. International Journal of Engine Research, 5(1), 83-91. doi:10.1243/146808704772914264Broatch, A., Margot, X., Gil, A., & Christian Donayre, (José). (2007). Computational study of the sensitivity to ignition characteristics of the resonance in DI diesel engine combustion chambers. Engineering Computations, 24(1), 77-96. doi:10.1108/02644400710718583Eriksson, L. J. (1980). Higher order mode effects in circular ducts and expansion chambers. The Journal of the Acoustical Society of America, 68(2), 545-550. doi:10.1121/1.384768Broatch, A., Margot, X., Novella, R., & Gomez-Soriano, J. (2017). Impact of the injector design on the combustion noise of gasoline partially premixed combustion in a 2-stroke engine. Applied Thermal Engineering, 119, 530-540. doi:10.1016/j.applthermaleng.2017.03.081Tutak, W., & Jamrozik, A. (2016). Validation and optimization of the thermal cycle for a diesel engine by computational fluid dynamics modeling. Applied Mathematical Modelling, 40(13-14), 6293-6309. doi:10.1016/j.apm.2016.02.021Payri, F., Benajes, J., Margot, X., & Gil, A. (2004). CFD modeling of the in-cylinder flow in direct-injection Diesel engines. Computers & Fluids, 33(8), 995-1021. doi:10.1016/j.compfluid.2003.09.003Benajes, J., Novella, R., De Lima, D., & Thein, K. (2017). Impact of injection settings operating with the gasoline Partially Premixed Combustion concept in a 2-stroke HSDI compression ignition engine. Applied Energy, 193, 515-530. doi:10.1016/j.apenergy.2017.02.044Lesieur, M., Métais, O., & Comte, P. (2005). Large-Eddy Simulations of Turbulence. doi:10.1017/cbo9780511755507Pope, S. B. (2004). Ten questions concerning the large-eddy simulation of turbulent flows. New Journal of Physics, 6, 35-35. doi:10.1088/1367-2630/6/1/035Silva, C. F., Leyko, M., Nicoud, F., & Moreau, S. (2013). Assessment of combustion noise in a premixed swirled combustor via Large-Eddy Simulation. Computers & Fluids, 78, 1-9. doi:10.1016/j.compfluid.2010.09.034Jamrozik, A., Tutak, W., Kociszewski, A., & Sosnowski, M. (2013). Numerical simulation of two-stage combustion in SI engine with prechamber. Applied Mathematical Modelling, 37(5), 2961-2982. doi:10.1016/j.apm.2012.07.040Qin, W., Xie, M., Jia, M., Wang, T., & Liu, D. (2014). Large eddy simulation of in-cylinder turbulent flows in a DISI gasoline engine. Applied Mathematical Modelling, 38(24), 5967-5985. doi:10.1016/j.apm.2014.05.004Broatch, A., Margot, X., Novella, R., & Gomez-Soriano, J. (2016). Combustion noise analysis of partially premixed combustion concept using gasoline fuel in a 2-stroke engine. Energy, 107, 612-624. doi:10.1016/j.energy.2016.04.045Torregrosa, A. J., Broatch, A., Martín, J., & Monelletta, L. (2007). Combustion noise level assessment in direct injection Diesel engines by means of in-cylinder pressure components. Measurement Science and Technology, 18(7), 2131-2142. doi:10.1088/0957-0233/18/7/045Payri, F., Broatch, A., Margot, X., & Monelletta, L. (2008). Sound quality assessment of Diesel combustion noise using in-cylinder pressure components. Measurement Science and Technology, 20(1), 015107. doi:10.1088/0957-0233/20/1/015107Ihlenburg, F. (2003). The Medium-Frequency Range in Computational Acoustics: Practical and Numerical Aspects. Journal of Computational Acoustics, 11(02), 175-193. doi:10.1142/s0218396x03001900Lapuerta, M., Armas, O., & Hernández, J. J. (1999). Diagnosis of DI Diesel combustion from in-cylinder pressure signal by estimation of mean thermodynamic properties of the gas. Applied Thermal Engineering, 19(5), 513-529. doi:10.1016/s1359-4311(98)00075-1Payri, F., Olmeda, P., Martín, J., & García, A. (2011). 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Quantifying Social Influence in an Online Cultural Market

We revisit experimental data from an online cultural market in which 14,000 users interact to download songs, and develop a simple model that can explain seemingly complex outcomes. Our results suggest that individual behavior is characterized by a two-step process–the decision to sample and the decision to download a song. Contrary to conventional wisdom, social influence is material to the first step only. The model also identifies the role of placement in mediating social signals, and suggests that in this market with anonymous feedback cues, social influence serves an informational rather than normative role