Knowledge data discovery and data mining in a design environment

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development

Process performance measurement support : a critical analysis

Design development processes, within engineering disciplines, lack the necessary mechanisms in identifying the specific areas where improved design development performance may be obtained. In addition, they lack the means to consider and align the goals and respective performance levels of related development activities with an organisation's overall goals and performance levels. Current research in organisational performance behaviour, formalised through performance frameworks and methodologies, has attempted to identify and focus upon those critical factors which impinge upon a wealth creation system while attempting to, simultaneously, remain representative of organisational functions, processes, people, decisions and goals. Effective process improvements remain conditional upon: the ability to measure the potential performance gains which may result from an improvement initiative; the ability to understand existing process dynamics and in turn understand the subsequent impact of some change to a system/process; and, the ability to identify potential areas for improvement. The objective of this paper is to discuss some of the management techniques, which are purported to support various process performance concerns and perspectives, and present the major factors that remain unsupported in identifying, measuring and understanding design process performance

A new model for high value meetings

The purpose of this research is to consider how organisations can increase competiveness by maximising the value of meetings whilst minimising their cost. This involves the development of a model which considers both the scheduling and management of meetings, whilst taking into account importance, value and cost where previously there has been no measure of these elements. This work will provide not only academic research within this under-represented area, but through a case study, a practical application. As time lost through unproductive meetings is estimated to cost billions, the potential saving through the application of this research is significant

Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States

Increasing fire severity and warmer, drier postfire conditions are making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here, we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a dataset of postfire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Postfire regeneration is sensitive to high-severity fire, which limits seed availability, and postfire climate, which influences seedling establishment. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected climate change impacts for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in postfire regeneration. Across 40 to 42% of the study area, we project postfire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031 to 2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981 to 2000 to 26 to 31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support postfire conifer regeneration. © 2023 the Author(s)

An approach, insights and methodology for performance improvement through process activity management

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The impact of acute general hospital reconfiguration in North East London

SIGLEAvailable from British Library Document Supply Centre-DSC:6966.200(97) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A decision support framework for proactive maintenance of water and wastewater systems

Proactive maintenance of assets is a much sought after goal in the water and wastewater industry, where substantial savings could be made by identifying impending failures in pumps and other essential components of the system. A detailed analysis of the operational behaviour of the monitored assets can be used as the foundation to generate estimations on the likelihood of a failure or malfunction in a particular component based on knowledge of previous behavioural patterns. Preventative maintenance or component replacement can then be optimally scheduled based on need, as opposed to traditional reactive maintenance strategies. In most current condition monitoring software, an alarm is normally raised once a fault has occurred, therefore often requiring immediate action. On the other hand, combining the condition monitoring and fault log data that is normally acquired with expert knowledge of the meaning and causes of faults embedded in the software allows predictive maintenance to be implemented. The paper reports on a number of advanced machine learning techniques that have been applied to operational data acquired over a significant period of water pump operation. Results from a representative site within Scottish Water's water network will be presented that demonstrate the application of such software techniques can indeed surface changes in parameters, for example flow and pump power drawn, forming the basis to infer the state of components and the onset of changes in the health of the asset