Introduction

Tan, Lena

doi:10.1057/9781137548887_1

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…In order to calculate the hexatic (short-range) order of the cluster crystal the first step is to distinguish between different clusters. Here, we used a hierarchical clustering technique [56] that associates each particle to a single cluster in an unambiguous way ( Fig. 7 and text below).…”

Section: Discussionmentioning

confidence: 99%

Monodisperse cluster crystals: Classical and quantum dynamics

et al. 2015

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We study the phases and dynamics of a gas of monodisperse particles interacting via soft-core potentials in two spatial dimensions, which is of interest for soft-matter colloidal systems and quantum atomic gases. Using exact theoretical methods, we demonstrate that the equilibrium lowtemperature classical phase simultaneously breaks continuous translational symmetry and dynamic space-time homogeneity, whose absence is usually associated with out-of-equilibrium glassy phenomena. This results in an exotic self-assembled cluster crystal with coexisting liquid-like long-time dynamical properties, which corresponds to a classical analog of supersolid behavior. We demonstrate that the effects of quantum fluctuations and bosonic statistics on cluster-glassy crystals are separate and competing: zero-point motion tends to destabilize crystalline order, which can be restored by bosonic statistics.

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Section: Discussionmentioning

confidence: 99%

Monodisperse cluster crystals: Classical and quantum dynamics

et al. 2015

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“…The reader can consult [2,23,24] for a comprehensive introduction to classification and classifiers. The data contains variables related to students' pre-university marks used to select the students' prior entrance to university and examination marks of the courses that are taught in the first and second academic years of their study.…”

Section: Data Mining Techniques For Classificationmentioning

confidence: 99%

Predicting Student Academic Performance at Degree Level: A Case Study

Asif¹,

Merceron²,

Pathan³

2014

IJISA

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Abstract-Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socio-economic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.

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Introduction

Cited by 2 publications

References 40 publications

Monodisperse cluster crystals: Classical and quantum dynamics

Monodisperse cluster crystals: Classical and quantum dynamics

Predicting Student Academic Performance at Degree Level: A Case Study

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