Uncertainty complicates early requirements and architecture decisions and may expose a software project to significant risk. Yet software architects lack support for evaluating uncertainty, its impact on risk, and the value of reducing uncertainty before making critical decisions. We propose to apply decision analysis and multi-objective optimisation techniques to provide such support. We present a systematic method allowing software architects to describe uncertainty about the impact of alternatives on stakeholders' goals; to calculate the consequences of uncertainty through Monte-Carlo simulation; to shortlist candidate architectures based on expected costs, benefits and risks; and to assess the value of obtaining additional information before deciding. We demonstrate our method on the design of a system for coordinating emergency response teams. Our approach highlights the need for requirements engineering and software cost estimation methods to disclose uncertainty instead of hiding it.
Abstract-Sustainability management systems are important technologies used by large organisations to monitor and control their CO2 emissions and other environmental impacts. So far, these systems have been designed to support data collection and reporting mandated by regulations, but they provide little support for decision making in order to improve an organisation's environmental sustainability. In this paper, we present a systematic method to help organisations make such decisions. Our method builds on goal-oriented requirements engineering for modelling an organisation's sustainability goals and on statistical decision analysis for guiding decisions under uncertainty. By being explicit about uncertainty, our method provides a sound assessment of the risks associated with alternative options (including the "business as usual" option), and, through the concept of information value, informs decision makers whether reducing uncertainty through additional data collection will be useful for their decisions. We illustrate our approach on real sustainability decision questions at UCL's Department of Computer Science.
Body Mass Index is an aging public health indicator obesity measurement, based solely on height and weight. Despite its shortcomings, it has gained acceptance over time primarily because of its simplicity, and it has been applied not only to individuals, but also to various population groups. Calls to replace BMI with a more modern indicator within the medical community have been growing louder, but the medical community moves very slow and acceptance of a multidimensional health indicator appears to most to be far off. However, by using multinational 3D sizing studies done over the years by the clothing industry, the introduction of a 3D public health indicator as well as a multidimensional population measurement reference set can be implemented in the very near future.
Current techniques to evaluate the physical characteristics of the preoperative bariatric subject are generally limited to height, weight and perhaps anthropometric measurements such as waist and hips. By keeping the height and weight values, and using a commercial 3D whole body scanner and associated measurement software, one can provide a complete spacial evaluation of the subject, including excess volume, excess fat and fat density. Background Identifying the morbidly obese, at first glance, appears straightforward. From an observer's perspective, one perceives the morbidly obese as being extremely overweight, abnormal in both shape and size. Categorizing and assessing the morbidly obese, however, has always been a challenge. Morbid obesity is defined as: 1). Being 100 pounds or more above your ideal body weight. 2). Or, having a Body Mass Index (BMI) of 40 or greater. 3). Or, having an excess body weight twice that of the norm for the height of the subject.
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