Developing a rating scale for projected stories

The 6-Part Story Method (6PSM) is a projective tool in wide use by dramatherapists in the UK, USA and Israel (Lahad & Ayalon, 1993). In contrast to projective tests used by psychotherapists and psychologists, the 6PSM has never been the subject of any validation or reliability studies. This paper reports on the identification of scale items to describe the manifest content of 6-part stories. 26 statements with acceptable inter-rater reliability have been identified. These statements were used to rate stories produced by clinicians (n=24), mainstream community mental health patients (n=21) and patients with a Borderline Personality Disorder (n=19). Some features that were expected to be indicators of an author with a BPD diagnosis proved to be as common in stories from other authors. However a scale of eight items was identified that differentiated well between authors with a BPD diagnosis and others, with adequate test-retest and inter-rater reliability. Concurrent validity was tested against the Structured Clinical Interview for DSM-IV Axis II (SCID-II), the Clinical Outcomes in Routine Evaluation Outcome Measure (CORE-OM) and the Inventory of Interpersonal Problems short form (IIP-32)

The employer's perspective on retirement

In this chapter we discuss the literature with respect to the role of employers in retirement processes of older workers and provide suggestions for future research. In the first part of this chapter we will review existing theoretical insights regarding the employers’ actions and attitudes toward older workers and retirement. In the next section we will discuss empirical findings with regard to age related stereotypes in the workplace and age norms with respect to retirement and present some results form an international comparative employer study. We conclude with a section on the management of retirement processes, focussing on the exit and hiring of older workers.

Magnesium and magnesium alloys as degradable metallic biomaterials

Drawbacks associated with permanent metallic implants lead to the search for degradable metallic biomaterials. Magnesium has been considered as it is essential to bodies and has a high biodegradation potential. For magnesium and its alloys to be used as biodegradable implant materials, their degradation rates should be consistent with the rate of healing of the affected tissue, and the release of the degradation products should be within the body's acceptable absorption levels. Conventional magnesium degrades rapidly, which is undesirable. In this study, biodegradation behaviours of high purity magnesium and commercial purity magnesium alloy AZ31 in both static and dynamic Hank's solution have been systematically investigated. The results show that magnesium purification and selective alloying are effective approaches to reduce the degradation rate of magnesium. In the static condition, the corrosion products accumulate on the materials surface as a protective layer, which results in a lower degradation rate than the dynamic condition. Anodised coating can significantly further reduce the degradation rate of magnesium. This study indicates that magnesium can be used as degradable implant materials as long as the degradation is controlled at a low rate. Magnesium purification, selective alloying and anodised coating are three effective approaches to reduce the rate of degradation

Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images

Current CNN-based algorithms for recovering the 3D pose of an object in an image assume knowledge about both the object category and its 2D localization in the image. In this paper, we relax one of these constraints and propose to solve the task of joint object category and 3D pose estimation from an image assuming known 2D localization. We design a new architecture for this task composed of a feature network that is shared between subtasks, an object categorization network built on top of the feature network, and a collection of category dependent pose regression networks. We also introduce suitable loss functions and a training method for the new architecture. Experiments on the challenging PASCAL3D+ dataset show state-of-the-art performance in the joint categorization and pose estimation task. Moreover, our performance on the joint task is comparable to the performance of state-of-the-art methods on the simpler 3D pose estimation with known object category task

A metal–organic framework/α-alumina composite with a novel geometry for enhanced adsorptive separation

The development of a metal–organic framework/α-alumina composite leads to a novel concept: efficient adsorption occurs within a plurality of radial micro-channels with no loss of the active adsorbents during the process. This composite can effectively remediate arsenic contaminated water producing potable water recovery, whereas the conventional fixed bed requires eight times the amount of active adsorbents to achieve a similar performance