Towards a spectroscopically accurate set of potentials for heavy hydride laser cooling candidates: effective core potential calculations of BaH

BaH (and its isotopomers) is an attractive molecular candidate for laser cooling to ultracold temperatures and a potential precursor for the production of ultracold gases of hydrogen and deuterium. The theoretical challenge is to simulate the laser cooling cycle as reliably as possible and this paper addresses the generation of a highly accurate ab initio $^{2}\Sigma^+$ potential for such studies. The performance of various basis sets within the multi-reference configuration-interaction (MRCI) approximation with the Davidson correction (MRCI+Q)is tested and taken to the Complete Basis Set (CBS) limit. It is shown that the calculated molecular constants using a 46 electron Effective Core-Potential (ECP) and even-tempered augmented polarized core-valence basis sets (aug-pCV$n$Z-PP, n= 4 and 5) but only including three active electrons in the MRCI calculation are in excellent agreement with the available experimental values. The predicted dissociation energy De for the X$^2\Sigma^+$ state (extrapolated to the CBS limit) is 16895.12 cm$^{-1}$ (2.094 eV), which agrees within 0.1$\%$ of a revised experimental value of <16910.6 cm$^{-1}$, while the calculated re is within 0.03 pm of the experimental result.

Counter-Mappings: Cartography and Difference

Introduction to the co-edited book 'Cartographies of Differences: Interdisciplinary Perspectives

How do clinicians become teachers? A communities of practice perspective.

There is widespread acceptance that clinical educators should be trained to teach, but faculty development for clinicians is undermined by poor attendance and inadequate learning transfer. As a result there has been growing interest in situating teacher development initiatives in clinical workplaces. The relationship between becoming a teacher and clinical workplace contexts is under theorised. In response, this qualitative research set out to explore how clinicians become teachers in relation to clinical communities and institutions. Using communities of practice (CoP) as a conceptual framework this research employed the sensitising concepts of regimes of competence and vertical (managerial) and horizontal (professional) planes of accountability to elucidate structural influences on teacher development. Fourteen hospital physicians completed developmental timelines and underwent semi-structured interviews, exploring their development as teachers. Despite having very different developmental pathways, participants’ descriptions of their teacher identities and practice that were remarkably congruent. Two types of CoP occupied the horizontal plane of accountability i.e. clinical teams (Firms) and communities of junior doctors (Fraternities). Participants reproduced teacher identities and practice that were congruent with CoPs’ regimes of competence in order to gain recognition and legitimacy. Participants also constructed their teacher identities in relation to institutions in the vertical plane of accountability (i.e. hospitals and medical schools). Institutions that valued teaching supported the development of teacher identities along institutionally defined lines. Where teaching was less valued, clinicians adapted their teacher identities and practices to suit institutional norms. Becoming a clinical educator entails continually negotiating one’s identity and practice between two potentially conflicting planes of accountability. Clinical CoPs are largely conservative and reproductive of teaching practice whereas accountability to institutions is potentially disruptive of teacher identity and practice.

Detecting obfuscated malware using reduced opcode set and optimised runtime trace

The research presented, investigates the optimal set of operational codes (opcodes) that create a robust indicator of malicious software (malware) and also determines a program’s execution duration for accurate classification of benign and malicious software. The features extracted from the dataset are opcode density histograms, extracted during the program execution. The classifier used is a support vector machine and is configured to select those features to produce the optimal classification of malware over different program run lengths. The findings demonstrate that malware can be detected using dynamic analysis with relatively few opcodes.