16 research outputs found

    Few-body semiclassical approach to nucleon transfer and emission reactions

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    A three-body semiclassical model is proposed to describe the nucleon transfer and emission reactions in a heavy-ion collision. In this model the two heavy particles, i.e. nuclear cores A1(ZA1,MA1)_1(Z_{A_1}, M_{A_1}) and A2(ZA2,MA2)_2(Z_{A_2}, M_{A_2}), move along classical trajectories R1(t)\vec R_1(t) and R2(t)\vec R_2(t) respectively, while the dynamics of the lighter neutron, n, is considered from a quantum mechanical point of view. Here, MiM_i are the nucleon masses and ZiZ_i are the Coulomb charges of the heavy nuclei (i=1,2i=1,2). A Faddeev-type semiclassical formulation using realistic paired nuclear-nuclear potentials is applied so that all three channels (elastic, rearrangement and break-up) are described in an unified manner. In order to solve these time-dependent equations the Faddeev components of the total three-body wave-function are expanded in terms of the input and output channel target eigenfunctions. In the special case when the nuclear cores are identical (A1_1 \equiv A2_2) and the two-level approximation in the expansion over target functions the time-dependent semiclassical Faddeev equations are resolved in an explicit way. To determine the realistic R1(t)\vec R_1(t) and R2(t)\vec R_2(t) trajectories of the nuclear cores a self-consistent approach based on the Feynman path integral theory is applied.Comment: 15 pages, 1 figur

    Quenching of parapara-H2_2 with an ultra-cold anti-hydrogen atom Hˉ1s\bar{H}_{1s}

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    In this work we report the results concerning calculations for quantum-mechanical rotational transitions in molecular hydrogen, H2_2, induced by an ultra-cold ground state anti-hydrogen atom Hˉ1s\bar{H}_{1s}. The calculations are accomplished using a non-reactive close-coupling quantum-mechanical approach. The H2_2 molecule is treated as a rigid rotor. The total elastic scattering cross section σel(ϵ)\sigma_{el}(\epsilon) at energy ϵ\epsilon, state-resolved rotational transition cross sections σjj(ϵ)\sigma_{jj'}(\epsilon) between states jj and jj' and corresponding thermal rate coefficients kjj(T)k_{jj'}(T) are computed in the temperature range 0.004 K T \lesssim T \lesssim 4 K. Satisfactory agreement with other calculations (variational) has been obtained for σel(ϵ)\sigma_{el}(\epsilon).Comment: 24 pages, 3 tables, 8 figure

    The influence of individual cognitive style on performance in management education

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    This paper reports the outcomes of an empirical study undertaken to explore the possibility that cognitive style may be an important factor influencing performance on certain types of task in management education. Four hundred and twelve final-year undergraduate degree students studying management and business administration were tested using the Allinson-Hayes Cognitive Style Index. Their cognitive styles were then compared with assessment grades achieved for academic modules, the task categories of which were deemed to be consonant with either the wholist/intuitive or the analytic style of working. Overall ability defined by final degree grades was also tested against individuals’ cognitive styles. As expected, students whose dominant cognitive styles were analytic attained higher grades for long term solitary tasks involving careful planning and analysis of information. However, contrary to expectations, performance on tasks believed to be more suited to the wholist/intuitive style was also higher for analytic individuals, as was overall ability defined by final degree grades. The results were discussed in terms of the nature of the tasks and the need for methods of performance assessment that are independent of an orientation bias. Without this, it is argued, employment selection criteria may favour the wrong type of candidate in some circumstances

    The hidden architecture of higher education:Building a big data infrastructure for the ‘smarter university’

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    Universities are increasingly organized and managed through digital data. The collection, processing and dissemination of Higher Education data is enabled by complex new data infrastructures that include both human and nonhuman actors, all framed by political, economic and social contingencies. HE data infrastructures need to be seen not just as technical programs but as practical relays of political objectives to reform the sector. This article focuses on a major active data infrastructure project in Higher Education in the United Kingdom. It examines the sociotechnical networks of organizations, software programs, standards, dashboards and visual analytics technologies that constitute the infrastructure, and how these technologies are fused to governmental imperatives of market reform. The analysis foregrounds how HE is being reimagined through the utopian ideal of the ‘smarter university’ while simultaneously being reformed through the political project of marketization

    Data governance support for business intelligence in higher education: a systematic literature review

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    Business Intelligence (BI) is important for achieving effective decision-making in higher education. This study, however, advocated the need to support BI with data governance in higher education. The systematic literature review was conducted using a qualitative approach. The study cover 2005– 2019. A total of 483 papers were retrieved and after exclusion and inclusion criteria, two hundred and three were removed due to lack of relevance. Some of the removed papers were those written in other languages other than the English language. Finally, one hundred and eighty were analyzed for this study. Some of those sources used for the study include Scopus, Springer, science direct, IEEE explore, Web of science. The results were arranged under word cloud, word frequency, Year-source by attribute, matrix coding by methodology, business intelligence, and its benefits, critical success factor, data governance, and its benefits, an overview of higher education and need to support business intelligence with data governance. The study provides information to higher education business intelligence experts on the need to support their BI with data governance
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