Mutual enlightenment : augmenting human factors research in surgical robotics

This discussion has outlined a theoretical and pragmatic framework to demonstrate that future research involving the analysis of human performance in surgical should encourage the use of phenomenology to enhance the knowledge base of this area of study. Merging experiential (first-person) and experimental (third-person) methods may possibly help improve research designs and analyses in the investigation of robotics in surgical performance. By relying solely on third-person techniques, the current methodology and interpretation used to analyze human performance in surgical robotics is limited. Recent advances in cognitive science and psychology have also recognized this limitation and have now begun to shift to neurophenomenology. Finally, discussion on recent robotics research presented here demonstrates the potential phenomenology holds for augmenting the methodological and analysis techniques currently used by researchers of human performance in surgical robotics.

A framework of promoting stakeholder mutual benefits for sustainable housing implementation

Strong regulatory pressure and rising public awareness on environmental issues will continue to influence the market demand for sustainable housing for years to come. Despite this potential, the voluntary uptake rate of sustainable practices is not as high as expected within the new built housing industry. This is in contrast to the influx of emerging building technologies, new materials and innovative designs as showcased in office buildings and exemplar homes worldwide. One of the possible reasons for this under-performance is that key stakeholders such as developers, builders and consumers do not fully understand and appreciate the related challenges, risks and opportunities of pursuing sustainability. Therefore, in their professional and business activities, they may not be able to see the tangible and mutual benefits that sustainable housing may bring. This research investigates the multiple challenges to achieving benefits (CABs) from sustainable housing development, and links these factors to the characteristics of key stakeholders in the housing supply chain. It begins with a comparative survey study among seven stakeholder groups in the Australian housing industry, in order to examine the importance and interrelationships of CABs. In-depth interviews then further explore the survey findings with a focus on stakeholder diversity, which leads to the identification of 12 critical mutual-benefit factors and their interrelationship. Based on such a platform, a mutual-benefit framework is developed with the aid of Interpretive Structure Modelling, to identify the patterns of stakeholder benefit materialisation, suggest the priority of critical factors and provide related stakeholder-specific action guidelines for sustainable housing implementation. The study concludes with a case study of two real-life housing projects to test the application of the mutual-benefit framework for improvement. This framework will lead to a shared value of sustainability among stakeholders and improved stakeholder collaboration, which in turn help to break the "circle of blame" for the current under-performance of sustainable housing implementation.

THE quest for alpha : can artificial neural networks help?

The application of artificial neural networks (ANN) in finance is relatively new area of research. We employed ANNs that used both fundamental and technical inputs to predict future prices of widely held Australian stocks and used these predicted prices for stock portfolio selection over a 10-year period (2001-2011). We found that the ANNs generally do well in predicting the direction of stock price movements. The stock portfolios selected by the ANNs with median accuracy are able to generate positive alpha over the 10-year period. More importantly, we found that a portfolio based on randomly selected network configuration had zero chance of resulting in a significantly negative alpha but a 27% chance of yielding a significantly positive alpha. This is in stark contrast to the findings of the research on mutual fund performance where active fund managers with negative alphas outnumber those with positive alphas.

Recent developments in expressed emotion and schizophrenia.

Reviews outcome studies on the course of schizophrenia as predicted by expressed emotion (EE) and considers methodological issues. The nature of EE and the mechanism for the predictive results are explored. EE probably determines relapse through its effect on emotions and symptom control. A stress-vulnerability model of relapse is advanced that incorporates biological factors and cycles of mutual influence between symptomatic behavior, life events, and EE. A social interaction model of schizophrenia may help to alleviate concerns that EE represents an attempt to blame families for schizophrenic relapse. Aversive types of behavior in patients and their relatives are seen as understandable reactions to stress that are moderated by social perceptions and coping skills. Families have made positive achievements, including the provision of noninvasive support.

Word Segmentation for Chinese Wikipedia Using N-Gram Mutual Information

In this paper, we propose an unsupervised segmentation approach, named "n-gram mutual information", or NGMI, which is used to segment Chinese documents into n-character words or phrases, using language statistics drawn from the Chinese Wikipedia corpus. The approach alleviates the tremendous effort that is required in preparing and maintaining the manually segmented Chinese text for training purposes, and manually maintaining ever expanding lexicons. Previously, mutual information was used to achieve automated segmentation into 2-character words. The NGMI approach extends the approach to handle longer n-character words. Experiments with heterogeneous documents from the Chinese Wikipedia collection show good results.