Raman spectroscopic study of the mixed anion sulphate-arsenate mineral parnauite Cu9[(OH)10|SO4|(AsO4)2].7H2O

The mixed anion mineral parnauite Cu9[(OH)10|SO4|(AsO4)2].7H2O has been studied by Raman spectroscopy. Characteristic bands associated with arsenate, sulphate, hydroxyl units are identified. Broad bands are observed and are resolved into component bands. Two intense bands at 859 and 830 cm-1 are assigned to the 1 (AsO4)3- symmetric stretching and 3 (AsO4)3- antisymmetric stretching modes. The comparatively sharp band at 976 cm-1 is assigned to the ν1 (SO4)2- symmetric stretching mode and a broad spectral profile centered upon 1097 cm-1 is attributed to the ν3 (SO4)2- antisymmetric stretching mode. A comparison of the Raman spectra is made with other arsenate bearing minerals such as carminite, clinotyrolite, kankite, tilasite and pharmacosiderite.

A multiple-control fuzzy vault

We introduce multiple-control fuzzy vaults allowing generalised threshold, compartmented and multilevel access structure. The presented schemes enable many useful applications employing multiple users and/or multiple locking sets. Introducing the original single control fuzzy vault of Juels and Sudan we identify several similarities and differences between their vault and secret sharing schemes which influence how best to obtain working generalisations. We design multiple-control fuzzy vaults suggesting applications using biometric credentials as locking and unlocking values. Furthermore we assess the security of our obtained generalisations for insider/ outsider attacks and examine the access-complexity for legitimate vault owners.

The learning climate of chartered quantity surveying practices

This paper reports on a fully structured interview survey investigating the relationship between the learning climate of chartered quantity surveying practices and individual learning styles, approaches to learning, ability, measures of length of service and the size of the quantity surveying organisation. The results indicate that the learning environment is generally supportive in terms of human support, but less supportive in terms of staff development systems; as individuals rise in the hierarchy of an organisation, their perception of its ability to provide an appropriate learning environment increases. Likewise, perceptions of human support and working practices within organisations increase significantly with length of time in the profession; larger organisations have more advanced staff development systems but provide less human support; and the learning environment both overall and in terms of working practices correlates positively with learning styles and approaches to learning.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

On application of structural decomposition for process model abstraction

Real world business process models may consist of hundreds of elements and have sophisticated structure. Although there are tasks where such models are valuable and appreciated, in general complexity has a negative influence on model comprehension and analysis. Thus, means for managing the complexity of process models are needed. One approach is abstraction of business process models-creation of a process model which preserves the main features of the initial elaborate process model, but leaves out insignificant details. In this paper we study the structural aspects of process model abstraction and introduce an abstraction approach based on process structure trees (PST). The developed approach assures that the abstracted process model preserves the ordering constraints of the initial model. It surpasses pattern-based process model abstraction approaches, allowing to handle graph-structured process models of arbitrary structure. We also provide an evaluation of the proposed approach.

Scale adaptive tracking using mean shift and efficient feature matching

The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency.