Dynamic Knowledge Creation Through Value Management Teams

With the intention of introducing unique and value-added products to the market, organizations have become more conscious of how to best create knowledge as reported by Ganesh Bhatt in 2000 in 'Information dynamics, learning and knowledge creation in organizations'. Knowledge creation is recognized as having an important role in generating and sustaining a competitive advantage as well as in meeting organizational goals, as reported by Aleda Roth and her colleagues in 1994 in 'The knowledge factory for accelerated learning practices.' One of the successful ingredients of value management (VM) is its utilization of diverse knowledge resources, drawing upon different organizational functions, professional disciplines, and stakeholders, in a facilitated team process. Multidisciplinary VM study teams are viewed as having high potential to innovate due to their heterogeneous nature. This paper looks at one of the VM workshop's major benefits, namely, knowledge creation. A case study approach was used to explore the nature, processes, and issues associated with fostering a dynamic knowledge creation capability within VM teams. The results indicate that the dynamic knowledge creating process is embedded in and influenced by managing team constellation, creating shared awareness, developing shared understanding, and producing aligned action. The catalysts that can speed up the processes are open dialogue and discussion among participants. This process is enhanced by the use of facilitators, skilled at extracting knowledge.

A self-learning algorithm for biased molecular dynamics

A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.

An accelerated method of developing DBT projects using CDIO partner institutions as parallel processors

The cycle of the academic year impacts on efforts to refine and improve major group design-build-test (DBT) projects since the time to run and evaluate projects is generally a full calendar year. By definition these major projects have a high degree of complexity since they act as the vehicle for the application of a range of technical knowledge and skills. There is also often an extensive list of desired learning outcomes which extends to include professional skills and attributes such as communication and team working. It is contended that student project definition and operation, like any other designed product, requires a number of iterations to achieve optimisation. The problem however is that if this cycle takes four or more years then by the time a project’s operational structure is fine tuned it is quite possible that the project theme is no longer relevant. The majority of the students will also inevitably experience a sub-optimal project experience over the 5 year development period. It would be much better if the ratio were flipped so that in 1 year an optimised project definition could be achieved which had sufficient longevity that it could run in the same efficient manner for 4 further years. An increased number of parallel investigators would also enable more varied and adventurous project concepts to be examined than a single institution could undertake alone in the same time frame.
This work-in-progress paper describes a parallel processing methodology for the accelerated definition of new student DBT project concepts. This methodology has been devised and implemented by a number of CDIO partner institutions in the UK & Ireland region. An agreed project theme was operated in parallel in one academic year with the objective of replacing a multi-year iterative cycle. Additionally the close collaboration and peer learning derived from the interaction between the coordinating academics facilitated the development of faculty teaching skills in line with CDIO standard 10.

Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments

Abstract
Publicly available, outdoor webcams continuously view the world and share images. These cameras include traffic cams, campus cams, ski-resort cams, etc. The Archive of Many Outdoor Scenes (AMOS) is a project aiming to geolocate, annotate, archive, and visualize these cameras and images to serve as a resource for a wide variety of scientific applications. The AMOS dataset has archived over 750 million images of outdoor environments from 27,000 webcams since 2006. Our goal is to utilize the AMOS image dataset and crowdsourcing to develop reliable and valid tools to improve physical activity assessment via online, outdoor webcam capture of global physical activity patterns and urban built environment characteristics.
This project’s grand scale-up of capturing physical activity patterns and built environments is a methodological step forward in advancing a real-time, non-labor intensive assessment using webcams, crowdsourcing, and eventually machine learning. The combined use of webcams capturing outdoor scenes every 30 min and crowdsources providing the labor of annotating the scenes allows for accelerated public health surveillance related to physical activity across numerous built environments. The ultimate goal of this public health and computer vision collaboration is to develop machine learning algorithms that will automatically identify and calculate physical activity patterns.