Fiscal autonomy for Scotland? A rejoinder

In their paper „A restatement of the case for fiscal autonomy‟ Hallwood and MacDonald (2006b) claim that Barnett is a formula for a rake‟s progress and that fiscal autonomy, as outlined in their previous paper „The economic case for Scottish fiscal autonomy: with or without independence‟ (Hallwood and MacDonald, 2006a), offers a superior financial settlement for Scotland. We here restate our continued disagreements with their argument. We start with corrections of their interpretation of our paper „Flaws and myths in the case for Scottish fiscal autonomy‟ (Ashcroft, Christie and Swales, 2006) before highlighting where we believe their latest paper fails to provide answers to important questions we posed

Fast-field cycling NMR is sensitive to the method of cross-linking in BSA gels

This work was supported by ARUK (grant number 19689).Non peer reviewedPublisher PD

Digital flashcard L2 Vocabulary learning out-performs traditional flashcards at lower proficiency levels: A mixed-methods study of 139 Japanese university students

[EN] This study investigates the effect of using digital flashcards on L2 vocabulary learning compared to using paper flashcards, at different levels of English proficiency. Although flashcards are generally believed to be one of the most efficient vocabulary study techniques available, little empirical data is available in terms of the comparative effectiveness of digital flashcards, and at different levels of student English proficiency. This study used a mixed-methods experimental design. The between-subjects factor was English Proficiency consisting of three groups: basic, intermediate and advanced. All participants underwent both a digital flashcards treatment and paper flashcards treatment using words from the Academic Words List. For each study mode, the two dependent variables were Immediate, and Delayed Relative Vocabulary Gain. The results of this study indicated that Japanese university students of lower levels of English proficiency have significantly higher vocabulary learning gains when using digital flashcards than when using paper flashcards. Students at higher levels of proficiency performed equally well using both study modes. It appears that by compensating for the gap in metacognitive awareness and effective learning strategies between students of lower and higher levels of language proficiency, digital flashcards may provide the additional support lower-level learners need to match their advanced-level peers in terms of their rate of deliberate vocabulary acquisition.Ashcroft, RJ.; Cvitkovic, R.; Praver, M. (2018). Digital flashcard L2 Vocabulary learning out-performs traditional flashcards at lower proficiency levels: A mixed-methods study of 139 Japanese university students. The EuroCALL Review. 26(1):14-28. doi:10.4995/eurocall.2018.7881SWORD1428261Ashcroft, R. J., & Imrie, A. C. (2014). Learning vocabulary with digital flashcards. JALT2013 Conference Proceedings, 639-646. Retrieved from http://jalt-publications.org/proceedings/issues/2014-08_2013.1Baddeley, A. D. (1990). Human memory: theory and practice. Hove: Erlbaum.Cohen, A. D. (1993). Language learning: insights for learners, teachers, and researchers. Boston, MA: Heinle & Heinle.Coxhead, A. (2000). A New Academic Word List. TESOL Quarterly,34(2), 213. doi: 10.2307/3587951Cross, D., & James, C. V. (2001). A practical handbook of language teaching. London: Longman.Elgort, I. (2010). Deliberate Learning and Vocabulary Acquisition in a Second Language. Language Learning,61(2), 367-413. doi: 10.1111/j.1467-9922.2010.00613.xGartner Your Source for Technology Research and Insight. (n.d.). Retrieved March 07, 2017, from http://www.gartner.com/technology/home.jsp.Hirschel, R., & Fritz, E. (2013). Learning vocabulary: CALL program versus vocabulary notebook. System,41(3), 639-653.Horst, M., Cobb, T., & Meara, P. (1998). Beyond A Clockwork Orange: Acquiring second language vocabulary through reading. Reading in a Foreign Language,11, 207-223.Hughes, A. (2013). Testing for language teachers. Cambridge: Cambridge University Press.Hulstijn, J. (2001). Intentional and incidental second language vocabulary learning: A reappraisal of elaboration, rehearsal, and automaticity. In P. J. Robinson (Ed.), Cognition and second language instruction (pp. 258-286). Cambridge: Cambridge University Press.Laufer, B., & Shmueli, K. (1997). Memorizing New Words: Does Teaching Have Anything To Do With It? RELC Journal,28(1), 89-108. doi:10.1177/003368829702800106Lees, D. (2013). A Brief Comparison Of Digital- And Self-Made Word Cards For Vocabulary Learning. Kwansei Gakuin University Humanities Review,18, 59-71. Retrieved June 2, 2017, from kwansei.repo.nii.ac.jp.Nakata, T. (2008). English vocabulary learning with word lists, word cards and computers: implications from cognitive psychology research for optimal spaced learning. ReCALL,20(1), 3-20.Nation, I. S., & Webb, S. A. (2011). Researching and analyzing vocabulary. Boston, MA: Heinle, Cengage Learning.Nation, I. (2003). Effective ways of building vocabulary knowledge. ESL Magazine, 14-15.Nation, I. (2005). Language education: Vocabulary. In I. C. Brown (Ed.), Encyclopaedia of language and linguistics (2nd ed., Vol. 6, pp. 494-499). Oxford: Elsevier.Nation, I. (1995). Best practice in vocabulary teaching and learning. EA Journal, 7-15. Retrieved March 8, 2017, from http://www.victoria.ac.nz/lals/about/staff/publications/paul-nation/1995-Best-practice.pdf.Nikoopour, J., & Kazemi, A. (2014). Vocabulary Learning through Digitized & Non-digitized Flashcards Delivery. Procedia - Social and Behavioral Sciences, 98, 1366-1373.Puentedura, R. R. (2012, August 23). The SAMR Model: Background and Exemplars. Retrieved March 07, 2017, from http://www.hippasus.com/rrpweblog/archives/2012/08/23/SAMR_BackgroundExemplars.pdf.Quizlet. (2017). Retrieved March 07, 2017, from https://quizlet.com.Reinders, H., & White, C. (2011). The theory and practice of technology in materials development and task design. In N. Harwood (Ed.), English language teaching materials: theory and practice (pp. 58-80). Cambridge: Cambridge University.Richards, J. C., & Schmidt, R. W. (2002). Dictionary of language teaching & applied linguistics. Harlow: Longman.Soanes, C. (2010). The paperback Oxford English dictionary. Oxford: Oxford Univ. Press.Suppes, P., & Crothers, E. J. (1967). Experiments in second-language learning. New York: Academic Press.Webb, S. (2007). The Effects of Repetition on Vocabulary Knowledge. Applied Linguistics,28(1), 46-65. doi:10.1093/applin/aml04

Eigenstates of the Atom-Field Interaction and the Binding of Light in Photonic Crystals

We solve for the exact atom-field eigenstates of a single atom in a three dimensional spherical cavity, by mapping the problem onto the anisotropic Kondo model. The spectrum has a rich bound state structure in comparison with models where the rotating wave approximation is made. It is shown how to obtain the Jaynes-Cummings model states in the limit of weak coupling. Non-perturbative Lamb shifts and decay rates are computed. The massive Kondo model is introduced to model light localization in the form of photon-atom bound states in photonic crystals.Comment: 22 pages, one figur

Creating vegetation density profiles for a diverse range of ecological habitats using terrestrial laser scanning

Vegetation structure is an important determinant of species habitats and diversity. It is often represented by simple metrics, such as canopy cover, height and leaf area index, which do not fully capture three-dimensional variations in density. Terrestrial laser scanning (TLS) is a technology that can better capture vegetation structure, but methods developed to process scans have been biased towards forestry applications. The aim of this study was to develop a methodology for processing TLS data to produce vegetation density profiles across a broader range of habitats. We performed low-resolution and medium-resolution TLS scans using a Leica C5 Scanstation at four locations within eight sites near Wollongong, NSW, Australia (34·38-34·41°S, 150·84-150·91°E). The raw point clouds were converted to density profiles using a method that corrected for uneven ground surfaces, varying point density due to beam divergence and occlusion, the non-vertical nature of most beams and for beams that passed through gaps in the vegetation without generating a point. Density profiles were evaluated against visual estimates from three independent observers using coarse height classes (e.g. 5-10 m). TLS produced density profiles that captured the three-dimensional vegetation structure. Although sites were selected to differ in structure, each was relatively homogeneous, yet we still found a high spatial variation in density profiles. There was also large variation between observers, with the RMS error of the three observers relative to the TLS varying from 16·2% to 32·1%. Part of this error appeared to be due to misjudging the height of vegetation, which caused an overestimation in one height class and an underestimation in another. Our method for generating density profiles using TLS can capture three-dimensional vegetation structure in a manner that is more detailed and less subjective than traditional methods. The method can be applied to a broad range of habitats - not just forests with open understoreys. However, it cannot accurately estimate near-surface vegetation density when there are uneven surfaces or dense vegetation prevents sufficient ground returns. Nonetheless, TLS density profiles will be an important input for research on species habitats, microclimates and nutrient cycles