Data and process requirements for product recall coordination

When an organisation becomes aware that one of its products may pose a safety risk to customers, it must take appropriate action as soon as possible or it can be held liable. The ability to automatically trace potentially dangerous goods through the supply chain would thus help organisations fulfill their legal obligations in a timely and effective manner. Furthermore, product recall legislation requires manufacturers to separately notify various government agencies, the health department and the public about recall incidents. This duplication of effort and paperwork can introduce errors and data inconsistencies. In this paper, we examine traceability and notification requirements in the product recall domain from two perspectives: the activities carried out during the manufacturing and recall processes and the data collected during the enactment of these processes. We then propose a workflow-based coordination framework to support these data and process requirements.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

Knowledge integration within Japanese firms : the Fujitsu way

The paper analyses knowledge integration processes at Fujitsu from a multi-level and systemic perspective. The focus is on team-building capability, capturing and utilising individual tacit knowledge, and communication networks for integrating dispersed specialist knowledge required in the development of new products and services. The analysis shows how knowledge integration is performed by Fujitsu at different layers of the company.

Maintenance chain integration using Petri-net enabled multiagent system modeling and implementation approach

Engineering asset management (EAM) is a broad discipline and the EAM functions and processes are characterized by its distributed nature. However, engineering asset nowadays mostly relies on self-maintained experiential rule bases and periodic maintenance, which is lacking a collaborative engineering approach. This research proposes a collaborative environment integrated by a service center with domain expertise such as diagnosis, prognosis, and asset operations. The collaborative maintenance chain combines asset operation sites, service center (i.e., maintenance operation coordinator), system provider, first tier collaborators, and maintenance part suppliers. Meanwhile, to realize the automation of communication and negotiation among organizations, multiagent system (MAS) technique is applied to enhance the entire service level. During the MAS design processes, this research combines Prometheus MAS modeling approach with Petri-net modeling methodology and unified modeling language to visualize and rationalize the design processes of MAS. The major contributions of this research include developing a Petri-net enabled Prometheus MAS modeling methodology and constructing a collaborative agent-based maintenance chain framework for integrated EAM.

Changing personal epistemologies in early childhood pre-service teachers using an integrated teaching program

This study investigated changes in pre-service teachers’ personal epistemologies as they engaged in an integrated teaching program. Personal epistemology refers to individual beliefs about the nature of knowing and knowledge and has been shown to influence teaching practice. An integrated approach to teaching, based on both an implicit and explicit focus on personal epistemology, was developed by an academic team within a Bachelor of Education (Early Childhood). The teaching program integrated content across four units of study, modelling personal epistemologies implicitly through collaborative reflexive practice. The students were also required to engage in explicit reflections on their personal epistemologies. Quantitative measures of personal epistemology were collected at the beginning and end of the semester using the Epistemological Beliefs Survey (EBS) to assess changes across the teaching period. Results indicated that pre-service teachers’ epistemological beliefs about the integration of knowledge became more sophisticated over the course of the teaching period. Qualitative data included pre-service teachers’ responses to open ended questions and field experience journal reflections about their perceptions of the teaching program and were collected at the end of the semester. These data showed that pre-service teachers held different conceptions about learning as integration, which provided a more nuanced understanding of the EBS data. Understanding pre-service teachers’ epistemological beliefs provides promising directions for teacher preparation and professional enrichment.

Using reputation management in participatory sensing for data classification

Participatory sensing enables collection, processing, dissemination and analysis of environmental sensory data by ordinary citizens, through mobile devices. Researchers have recognized the potential of participatory sensing and attempted applying it to many areas. However, participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data quality has become a significant issue. This study proposes using reputation management to classify the gathered data and provide useful information for campaign organizers and data analysts to facilitate their decisions.

The validity and structure of culture-level personality scores: Data from ratings of young adolescents

We examined properties of culture-level personality traits in ratings of targets (N=5,109) ages 12 to 17 in 24 cultures. Aggregate scores were generalizable across gender, age, and relationship groups and showed convergence with culture-level scores from previous studies of self-reports and observer ratings of adults, but they were unrelated to national character stereotypes. Trait profiles also showed cross-study agreement within most cultures, 8 of which had not previously been studied. Multidimensional scaling showed that Western and non-Western cultures clustered along a dimension related to Extraversion. A culture-level factor analysis replicated earlier findings of a broad Extraversion factor but generally resembled the factor structure found in individuals. Continued analysis of aggregate personality scores is warranted.