BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

Treatment and valorization plants in materials recovery supply chain

Aim of industrial symbiosis is to create synergies between industries in order to exchange resources (by-products, water and energy) through geographic proximity and collaboration [1]. By optimizing resource flows in a “whole-system approach”, a minimization of dangerous emissions and of supply needs can be achieved. Resources exchanges are established to facilitate recycling and re-use of industrial waste using a commercial vehicle. Several paths can be identified in order to establish an industrial symbiosis network (Figure 1, left), in relation (i) to the life cycle phase (raw material, component, product) and (ii) to the nature (material, water, energy) of the resource flows to be exchanged. Sometimes by-products and/or waste of an industrial process have to be treated and valorized in order to become the raw materials for others. In particular, two main treatment processes can be identified: refurbishment/upgrade for re-use (Figure 1, center) and recycling for material recovery (Figure 1, right). A brief overview of technological and economic aspects is given, together with their relevance to industrial symbiosis

A Decision Technology System To Advance the Diagnosis and Treatment of Breast Cancer

Geographical variations in cancer rates have been observed for decades. Described spatial patterns and trends have provided clues for generating hypotheses about the etiology of cancer. For breast cancer, investigators have demonstrated that some variation can be explained by differences in the population distribution of known breast cancer risk factors such as menstrual and reproductive variables (Laden, Spiegelman, and Neas, 1997; Robbins, Bescianini, and Kelsey, 1997; Sturgeon, Schairer, and Gail, 1995). However, regional patterns also may reflect the effects of Workshop on Hormones, Hormone Metabolism, Environment, and Breast Cancer (1995): (a) environmental hazards (such as air and water pollution), (b) demographics and the lifestyle of a mobile population, (c) subgroup susceptibility, (d) changes and advances in medical practice and healthcare management, and (e) other factors. To accurately measure breast cancer risk in individuals and population groups, it is necessary to singly and jointly assess the association between such risk and the hypothesized factors. Various statistical models will be needed to determine the potential relationships between breast cancer development and estimated exposures to environmental contamination. To apply the models, data must be assembled from a variety of sources, converted into the statistical models’ parameters, and delivered effectively to researchers and policy makers. A Web-enabled decision technology system can be developed to provide the needed functionality. This chapter will present a conceptual architecture for such a decision technology system. First, there will be a brief overview of a typical geographical analysis. Next, the chapter will present the conceptual Web-based decision technology system and illustrate how the system can assist users in diagnosing and treating breast cancer. The chapter will conclude with an examination of the potential benefits from system use and the implications for breast cancer research and practice

Updated version of final design and of the architecture of SEAMLESS-IF

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

Assessing the Benefits of Public Research Within an Economic Framework: The Case of USDA's Agricultural Research Service

Evaluation of publicly funded research can help provide accountability and prioritize programs. In addition, Federal intramural research planning generally involves an institutional assessment of the appropriate Federal role, if any, and whether the research should be left to others, such as universities or the private sector. Many methods of evaluation are available, peer review—used primarily for establishing scientific merit—being the most common. Economic analysis focuses on quantifying ultimate research outcomes, whether measured in goods with market prices or in nonmarket goods such as environmental quality or human health. However, standard economic techniques may not be amenable for evaluating some important public research priorities or for institutional assessments. This report reviews quantitative methods and applies qualitative economic reasoning and stakeholder interviewing methods to the evaluation of economic benefits of Federal intramural research using three case studies of research conducted by USDA’s Agricultural Research Service (ARS). Differences among the case studies highlight the need to select suitable assessment techniques from available methodologies, the limited scope for comparing assessment results across programs, and the inherent difficulty in quantifying benefits in some research areas. When measurement and attribution issues make it difficult to quantify these benefits, the report discusses how qualitative insights based on economic concepts can help research prioritization.Agricultural Research Service, Federal intramural research, publicly funded research, Environmental Economics and Policy, Food Consumption/Nutrition/Food Safety, Livestock Production/Industries, Productivity Analysis,

Integration of all FSSIM components within SEAMLESS-IF and a stand alone Graphical User Interface for FSSIM

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use,