Chiral structures of lander molecules on Cu(100)

Supramolecular assemblies of lander molecules (C$_{90}$H$_{98}$) on Cu(100) are investigated with low-temperature scanning tunneling microscopy. The energetically most favourable conformation of the adsorbed molecule is found to exist in two mirror symmetric enantiomers or conformers. At low coverage, the molecules align in enantiomerically pure chains along the chiral directions $[01\bar{2}],[02\bar{1}],[012]$ and $[021]$. The arrangement is proposed to be mainly governed by intermolecular van-der-Waals interaction. At higher coverages, the molecular chains arrange into chiral domains, for which a structural model is presented.Comment: to appear in Nanotechnology vol. 15 (2004

Spin polarization of Auger- and of photoelectrons from barium atoms exposed to circularly polarized radiation and their cross comparison

Kuntze R, Salzmann M, Böwering N, Heinzmann U. Spin polarization of Auger- and of photoelectrons from barium atoms exposed to circularly polarized radiation and their cross comparison. Zeitschrift für Physik D: Atoms, Molecules and Clusters. 1994;30(2-3):235-237.New results of spin polarization of both photoelectrons and Auger electrons are reported after 5[Rho] photoionization of free Ba atoms with circularly polarized light. A substantial polarization transfer from the spin polarized photons to the spin polarized photoelectrons and via the hole state orientation to the spin polarized Auger-electrons is observed. The cross comparison of the results for photoelectrons and Auger-electrons allows a quantitative test of the assumed two step model where both electron-emission processes occur in sequence

Analysis of Agglomerative Clustering

The diameter k-clustering problem is the problem of partitioning a finite subset of R^d into k subsets called clusters such that the maximum diameter of the clusters is minimized. One early clustering algorithm that computes a hierarchy of approximate solutions to this problem for all values of k is the agglomerative clustering algorithm with the complete linkage strategy. For decades this algorithm has been widely used by practitioners. However, it is not well studied theoretically. In this paper we analyze the agglomerative complete linkage clustering algorithm. Assuming that the dimension dis a constant, we show that for any k the solution computed by this algorithm is an O(log k)-approximation to the diameter k-clustering problem. Moreover, our analysis does not only hold for the Euclidean distance but for any metric that is based on a norm

Medicines adherence: Involving patients in decisions about prescribed medicines and supporting adherence

It is thought that between a third and a half of all medicines1 There are many causes of non-adherence but they fall into two overlapping categories: intentional and unintentional. Unintentional non-adherence occurs when the patient wants to follow the agreed treatment but is prevented from doing so by barriers that are beyond their control. Examples include poor recall or difficulties in understanding the instructions, problems with using the treatment, inability to pay for the treatment, or simply forgetting to take it. prescribed for long-term conditions are not taken as recommended. If the prescription is appropriate, then this may represent a loss to patients, the healthcare system and society. The costs are both personal and economic. Adherence presumes an agreement between prescriber and patient about the prescriber’s recommendations. Adherence to medicines is defined as the extent to which the patient’s action matches the agreed recommendations. Non-adherence may limit the benefits of medicines, resulting in lack of improvement, or deterioration, in health. The economic costs are not limited to wasted medicines but also include the knock-on costs arising from increased demands for healthcare if health deteriorates. Non-adherence should not be seen as the patient’s problem. It represents a fundamental limitation in the delivery of healthcare, often because of a failure to fully agree the prescription in the first place or to identify and provide the support that patients need later on. Addressing non-adherence is not about getting patients to take more medicines per se. Rather, it starts with an exploration of patients’ perspectives of medicines and the reasons why they may not want or are unable to use them. Healthcare professionals have a duty to help patients make informed decisions about treatment and use appropriately prescribed medicines to best effec

Building an Assessment Use Argument for sign language: the BSL Nonsense Sign Repetition Test

In this article, we adapt a concept designed to structure language testing more effectively, the Assessment Use Argument (AUA), as a framework for the development and/or use of sign language assessments for deaf children who are taught in a sign bilingual education setting. By drawing on data from a recent investigation of deaf children's nonsense sign repetition skills in British Sign Language, we demonstrate the steps of implementing the AUA in practical test design, development and use. This approach provides us with a framework which clearly states the competing values and which stakeholders hold these values. As such, it offers a useful foundation for test-designers, as well as for practitioners in sign bilingual education, for the interpretation of test scores and the consequences of their use

Analysis of Agglomerative Clustering

The diameter $k$-clustering problem is the problem of partitioning a finite subset of $\mathbb{R}^d$ into $k$ subsets called clusters such that the maximum diameter of the clusters is minimized. One early clustering algorithm that computes a hierarchy of approximate solutions to this problem (for all values of $k$) is the agglomerative clustering algorithm with the complete linkage strategy. For decades, this algorithm has been widely used by practitioners. However, it is not well studied theoretically. In this paper, we analyze the agglomerative complete linkage clustering algorithm. Assuming that the dimension $d$ is a constant, we show that for any $k$ the solution computed by this algorithm is an $O(\log k)$-approximation to the diameter $k$-clustering problem. Our analysis does not only hold for the Euclidean distance but for any metric that is based on a norm. Furthermore, we analyze the closely related $k$-center and discrete $k$-center problem. For the corresponding agglomerative algorithms, we deduce an approximation factor of $O(\log k)$ as well.Comment: A preliminary version of this article appeared in Proceedings of the 28th International Symposium on Theoretical Aspects of Computer Science (STACS '11), March 2011, pp. 308-319. This article also appeared in Algorithmica. The final publication is available at http://link.springer.com/article/10.1007/s00453-012-9717-