Cathodoluminescence and Raman Spectroscopic Characterization of Experimentally Shocked Plagioclase

Cathodoluminescence (CL) spectrum of plagioclase shows four emission bands at around 350, 420, 570 and 750 nm, which can be assigned to Ce3+, Al[Single Bond]O−[Single Bond]Al or Ti4+, Mn2+ and Fe3+ centers, respectively. Their CL intensities decrease with an increase in experimentally shock pressure. The peak wavelength of the emission band related to Mn2+ shifts from 570 nm for unshocked plagioclase to 630 nm for plagioclase shocked above 20 GPa. The Raman spectrum of unshocked plagioclase has pronounced peaks at around 170, 280, 480 and 510 cm−1, whereas Raman intensities of all peaks decrease with an increase in shock pressure. This result suggests that shock pressure causes destruction of the framework structure in various extents depending on the pressure applied to plagioclase. This destruction is responsible for a decrease in CL intensity and a peak shift of yellow emission related to Mn2+. An emission band at around 380 nm in the UV-blue region is observed in only plagioclase shocked above 30 GPa, whereas it has not been recognized in the unshocked plagioclase. Raman spectroscopy reveals that shock pressure above 30 GPa converts plagioclase into maskelynite. It implies that an emission band at around 380 nm is regarded as a characteristic CL signal for maskelynite. CL images of plagioclase shocked above 30 GPa show a dark linear stripe pattern superimposed on bright background, suggesting planer deformation features (PDFs) observed under an optical microscope. Similar pattern can be identified in Raman spectral maps. CL and Raman spectroscopy can be expected as a useful tool to evaluate shock pressure induced on the plagioclase in terrestrial and meteoritic samples

Luminescence Properties of Experimentally Grown Forsterite Chondrule: Implication for Astromineralogy.

第2回極域科学シンポジウム/第34回南極隕石シンポジウム 11月17日（木） 国立国語研究所 2階講

How is Learning Fluctuating? FutureLearn MOOCs Fine-Grained Temporal Analysis and Feedback to Teachers

Data-intensive analysis of massive open online courses (MOOCs) is popular. Researchers have been proposing various parameters conducive to analysis and prediction of student behaviour and outcomes in MOOCs, as well as different methods to analyse and use these parameters, ranging from statistics, to NLP, to ML, and even graph analysis. In this paper, we focus on patterns to be extracted, and apply systematic data analysis methods in one of the few genuinely large-scale data collection of 5 MOOCs, spread over 21 runs, on FutureLearn, a UK-based MOOCs provider, that, whilst offering a broad range of courses from many universities, NGOs and other institutions, has been less evaluated, in comparison to, e.g., its American counterparts. We analyse temporal quiz solving patterns; specifically, the less explored issue on how the first number of weeks of data predicts activities in the last weeks; we also address the classical MOOC question on the completion chance. Finally, we discuss the type of feedback a teacher or designer could receive on their MOOCs, in terms of fine-grained analysis of their material, and what personalisation could be provided to a student

Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn courses

Whilst a high dropout rate is a well-known problem in MOOCs, few studies take a data-driven approach to understand the reasons of such a phenomenon, and to thus be in the position to recommend and design possible adaptive solutions to alleviate it. In this study, we are particularly interested in finding a novel early detection mechanism of potential dropout, and thus be able to intervene at an as early time as possible. Additionally, unlike previous studies, we explore a light-weight approach, based on as little data as possible – since different MOOCs store different data on their users – and thus strive to create a truly generalisable method. Therefore, we focus here specifically on the generally available registration date and its relation to the course start date, via a comprehensive, larger than average, longitudinal study of several runs of all MOOC courses at the University of Warwick between 2014-1017, on the less explored European FutureLearn platform. We identify specific periods where different interventions are necessary, and propose, based on statistically significant results, specific pseudo-rules for adaptive feedback

Angiotensin-converting enzyme gene and retinal arteriolar narrowing: The Funagata Study

The purpose of this study is to determine whether the angiotensin-converting enzyme (ACE) gene polymorphism is associated with retinal arteriolar narrowing, a subclinical marker of chronic hypertension. The Funagata Study examined a population-based sample of Japanese aged 35+ years; 368 participants had both retinal vessel diameter measurements and ACE insertion/deletion (ACE I/D) polymorphism analyses performed. Assessment of retinal vessel diameter and retinal vessel wall signs followed the protocols used in the Blue Mountains Eye Study. ACE gene polymorphisms D/D, I/D and I/I were present in 34 (9.2%), 170 (46.2%) and 164 (44.5%) participants, respectively, distributed in Hardy–Weinberg equilibrium. After multivariable adjustment, retinal arteriolar diameter was significantly narrower in subjects with the D/D genotype compared to subjects with I/D and I/I genotypes (mean difference −6.49 μm, 95% confidence interval (CI): −12.86 μm, −0.11 μm). Our study suggests that the ACE I/D polymorphism may be associated with subclinical structural arteriolar changes related to chronic hypertension