Interactions between energy intake and expenditure in the development and treatment of obesity.

Summary There are four interactions to consider between energy intake (EI) and energy expenditure (EE) in the development and treatment of obesity. (1) Does sedentariness alter levels of EI or subsequent EE? and (2) Do high levels of EI alter physical activity or exercise? (3) Do exercise-induced increases in EE drive EI upwards and undermine dietary approaches to weight management and (4) Do low levels of EI elevate or decrease EE? There is little evidence that sedentariness alters levels of EI. This lack of cross-talk between altered EE and EI appears to promote a positive EB. Lifestyle studies also suggest that a sedentary routine actually offers the opportunity for over-consumption. Substantive changes in non exercise activity thermogenesis are feasible, but not clearly demonstrated. Cross talk between elevated EE and EI is initially too weak and takes too long to activate, to seriously threaten dietary approaches to weight management. It appears that substantial fat loss is possible before intake begins to track a sustained elevation of EE. There is more evidence that low levels of EI does lower physical activity levels, in relatively lean men under conditions of acute or prolonged semi-starvation and in dieting obese subjects. During altered EB there are a number of small but significant changes in the components of EE, including (i) sleeping and basal metabolic rate, (ii) energy cost of weight change alters as weight is gained or lost, (iii) exercise efficiency, (iv) energy cost of weight bearing activities, (v) during substantive overfeeding diet composition (fat versus carbohydrate) will influence the energy cost of nutrient storage by ~ 15%. The responses (i-v) above are all “obligatory” responses. Altered EB can also stimulate facultative behavioural responses, as a consequence of cross-talk between EI and EE. Altered EB will lead to changes in the mode duration and intensity of physical activities. Feeding behaviour can also change. The degree of inter-individual variability in these responses will define the scope within which various mechanisms of EB compensation can operate. The relative importance of “obligatory” versus facultative, behavioural responses -as components of EB control- need to be defined.

Modelling and execution of asset management process via workflow automation

An Asset Management (AM) life-cycle constitutes a set of processes that align with the development, operation and maintenance of assets, in order to meet the desired requirements and objectives of the stake holders of the business. The scope of AM is often broad within an organization due to the interactions between its internal elements such as human resources, finance, technology, engineering operation, information technology and management, as well as external elements such as governance and environment. Due to the complexity of the AM processes, it has been proposed that in order to optimize asset management activities, process modelling initiatives should be adopted. Although organisations adopt AM principles and carry out AM initiatives, most do not document or model their AM processes, let alone enacting their processes (semi-) automatically using a computer-supported system. There is currently a lack of knowledge describing how to model AM processes through a methodical and suitable manner so that the processes are streamlines and optimized and are ready for deployment in a computerised way. This research aims to overcome this deficiency by developing an approach that will aid organisations in constructing AM process models quickly and systematically whilst using the most appropriate techniques, such as workflow technology. Currently, there is a wealth of information within the individual domains of AM and workflow. Both fields are gaining significant popularity in many industries thus fuelling the need for research in exploring the possible benefits of their cross-disciplinary applications. This research is thus inspired to investigate these two domains to exploit the application of workflow to modelling and execution of AM processes. Specifically, it will investigate appropriate methodologies in applying workflow techniques to AM frameworks. One of the benefits of applying workflow models to AM processes is to adapt and enable both ad-hoc and evolutionary changes over time. In addition, this can automate an AM process as well as to support the coordination and collaboration of people that are involved in carrying out the process. A workflow management system (WFMS) can be used to support the design and enactment (i.e. execution) of processes and cope with changes that occur to the process during the enactment. So far few literatures can be found in documenting a systematic approach to modelling the characteristics of AM processes. In order to obtain a workflow model for AM processes commonalities and differences between different AM processes need to be identified. This is the fundamental step in developing a conscientious workflow model for AM processes. Therefore, the first stage of this research focuses on identifying the characteristics of AM processes, especially AM decision making processes. The second stage is to review a number of contemporary workflow techniques and choose a suitable technique for application to AM decision making processes. The third stage is to develop an intermediate ameliorated AM decision process definition that improves the current process description and is ready for modelling using the workflow language selected in the previous stage. All these lead to the fourth stage where a workflow model for an AM decision making process is developed. The process model is then deployed (semi-) automatically in a state-of-the-art WFMS demonstrating the benefits of applying workflow technology to the domain of AM. Given that the information in the AM decision making process is captured at an abstract level within the scope of this work, the deployed process model can be used as an executable guideline for carrying out an AM decision process in practice. Moreover, it can be used as a vanilla system that, once being incorporated with rich information from a specific AM decision making process (e.g. in the case of a building construction or a power plant maintenance), is able to support the automation of such a process in a more elaborated way.