A system approach to nuclear facility monitoring

Sensor technology for use in nuclear facility monitoring has reached and advanced stage of development. Research on where to place these sensors in a facility and how to combine their outputs in a meaningful fashion does not appear to be keeping pace. In this paper, we take a global view of the problem where sensor technology is viewed as only one piece of a large puzzle. Other pieces of this puzzle include the optimal location and type of sensors used in a specific facility, the rate at which sensors record information, and the risk associated with the materials/processes at a facility. If the data are analyzed off-site, how will they be transmitted? Is real-time analysis necessary? Are we monitoring only the facility itself, or might we also monitor the processing that occurs there? How are we going to combine the output from the various sensors to give us an accurate picture of the state of the facility? This paper will not try to answer all these questions, but rather it will attempt to stimulate thought in this area by formulating a systems approach to the problem demonstrated by a prototype system and a systems proposed for an actual facility. Our focus will be on the data analysis aspect of the problem

Blast furnace stove control

This paper outlines the process model and model-based control techniques implemented on the hot blast stoves for the No. 7 Blast Furnace at the Inland Steel facility in East Chicago, Indiana. A detailed heat transfer model of the stoves is developed. It is then used as part of a predictive control scheme to determine the minimum amount of fuel necessary to achieve the blast air requirements. The controller also considers maximum and minimum temperature constraints within the stove

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Data resources and sample applications for information analysis in CW and BW defense

This is the final report of a one-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The objective of this project was to identify representative problems in chemical warfare (CW) and biological warfare (BW) defense to which LANL`s information analysis capabilities can be applied. In particular, the focus was on methods for detecting the outbreak of diseases and predicting their evolution. The authors found that a number of disease models can be expressed in a common form, and that these models can be transformed in a way that allows their parameters to be estimated on-line in a computationally efficient manner. Using this technique, diseases could be tracked in an automated and cost-effective manner. However, the greatest needs in this area are epidemiological models that account for spatial and transport aspects of disease spread. Also, there is great potential for the mining of morbidity and disease data in order to identify similar cases that may indicate an emerging disease or an attack

The DESCARTES-Nantes survey of kidney transplant recipients displaying clinical operational tolerance identifies 35 new tolerant patients and 34 almost tolerant patients

Nephrolog