1-D Eulerian Vlasov-Maxwell simulations are presented which show kinetic enhancement of stimulated Raman backscatter (SRBS) due to electron trapping in regimes of heavy linear Landau damping. The conventional Raman Langmuir wave is transformed into a set of beam acoustic modes [L. Yin et al., Phys. Rev. E 73, 025401 (2006)]. For the first time, a low phase velocity electron acoustic wave (EAW) is seen developing from the self-consistent Raman physics. Backscatter of the pump laser off the EAW fluctuations is reported and referred to as electron acoustic Thomson scatter. This light is similar in wavelength to, although much lower in amplitude than, the reflected light between the pump and SRBS wavelengths observed in single hot spot experiments, and previously interpreted as stimulated electron acoustic scatter [D. S. Montgomery et al., Phys. Rev. Lett. 87, 155001 (2001)]. The EAW observed in our simulations is strongest well below the phase-matched frequency for electron acoustic scatter, and therefore the EAW is not produced by it. The beating of different beam acoustic modes is proposed as the EAW excitation mechanism, and is called beam acoustic decay. Supporting evidence for this process, including bispectral analysis, is presented. The linear electrostatic modes, found by projecting the numerical distribution function onto a GaussHermite basis, include beam acoustic modes (some of which are unstable even without parametric coupling to light waves) and a strongly-damped EAW similar to the observed one. This linear EAW results from non-Maxwellian features in the electron distribution, rather than nonlinearity due to electron trapping.
With digitisation and the rise of e-learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper-based course materials, registration, admissions and support services to the mobile, always-on and always accessible data has driven demand for information and generated new forms of data observable through consumption behaviours. These changes have led to a plethora of data sets that store learning content and track user behaviours. Most recently, new data analytics approaches are creating new ways of understanding trends and behaviours in students that can be used to improve learning design, strengthen student retention, provide early warning signals concerning individual students and help to personalise the learner's experience. This paper proposes a foundational learning analytics model (LAM) for higher education that focuses on the dynamic interaction of stakeholders with their data supported by visual analytics, such as self-organising maps, to generate conversations, shared inquiry and solution-seeking. The model can be applied for other educational institutions interested in using learning analytics processes to support personalised learning and support services. Further work is testing its efficacy in increasing student retention rates. An introduction to learning analyticsAlthough data analytics capabilities have been developing over the last 10-20 years, there has broadly been a disconnect between business intelligence and the use of data for supporting learning-based hypotheses. For example, although data have been gathering in educational databases, the capability and know-how for using it to advance learning and improve the student experience has barely begun and only rarely been investigated (Ferguson, 2012a(Ferguson, , 2012b. With the build-up of data from learning management systems, customer relationship management systems and student-based systems, some universities have begun to investigate how to increase student retention, improve student-centred services and to develop more interactive learning experiences.
Scattering of laser light by stimulated Brillouin scattering (SBS) and stimulated Raman scattering (SRS) is a concern for indirect drive inertial confinement fusion (ICF). The hohlraum designs for the National Ignition Facility (NIF) raise particular concerns due to the large scale and homogeneity of the plasmas within them. Experiments at Nova have studied laser–plasma interactions within large scale length plasmas that mimic many of the characteristics of the NIF hohlraum plasmas. Filamentation and scattering of laser light by SBS and SRS have been investigated as a function of beam smoothing and plasma conditions. Narrowly collimated SRS backscatter has been observed from low density, low-Z, plasmas, which are representative of the plasma filling most of the NIF hohlraum. SBS backscatter is found to occur in the high-Z plasma of gold ablated from the wall. Both SBS and SRS are observed to be at acceptable levels in experiments using smoothing by spectral dispersion (SSD).
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