Forecasting fire development with sensor-linked simulation

In fire, any information about the actual condition within the building could be essential for quick and safe response of both fire–fighters and occupants. In most cases, however, the emergency responders will rarely be aware of the actual conditions within a building and they will have to make critical decisions based on limited information. Recent buildings are equipped with numbers of sensors which may potentially contain useful information about the fire; however, most buildings do not have capability of exploiting these sensors to provide any useful information beyond the initial stage of warning about the possible existence of a fire. A sensor–linked modelling tool for live prediction of uncontrolled compartment fires, K– CRISP, has therefore been developed. The modelling strategy is an extension of the Monte– Carlo fire model, CRISP, linking simulations to sensor inputs which controls evolution of the parametric space in which new scenarios are generated, thereby representing real–time “learning” about the fire. CRISP itself is based on a zone model representation of the fire, with linked capabilities for egress modelling and failure prediction for structural members, thus providing a major advantage over more detailed approaches in terms of flexibility and practicality, though with the conventional limitations of zone models. Large numbers of scenarios are required, but computational demands are mitigated to some extent by various procedures to limit the parameters which need to be varied. HPC (high performance computing) resources are exploited in “urgent computing” mode. K–CRISP was demonstrated in conjunction with measurements obtained from two sets of full–scale fire experiments. In one case, model execution was performed live. The thesis further investigates the predictive capability of the model by running it in pseudo real–time. The approach adopted for steering is shown to be effective in directing the evolution of the fire parameters, thereby driving the fire predictions towards the measurements. Moreover, the availability of probabilistic information in the output assists in providing potential end users with an indication of the likelihood of various hazard scenarios. The best forecasts are those for the immediate future, or for relatively simple fires, with progressively less confidence at longer lead times and in more complex scenarios. Given the uncertainties in real fire development the benefits of more detailed model representations may be marginal and the system developed thus far is considered to be an appropriate engineering approach to the problem, providing information of potential benefit in emergency response. Thus, the sensor–linked model proved to be capable of forecasting the fire development super–real– time and it was also able to predict critical events such as flashover and structural collapse. Finally, the prediction results are assessed and the limitations of the model were further discussed. This enabled careful assessment of how the model should be applied, what sensors are required, and how reliable the model can be, etc

Probe card-Type multizone electrostatic chuck inspection system

Electrostatic chucks (ESCs) are major components of the equipment used to improve the production yield of wafers and temperature uniformity across wafer surfaces by controlling the wafer temperature precisely. However, ESCs are directly exposed to harsh environments, such as plasma, chemical gases, and high temperature fluctuations. Therefore, ESCs may malfunction if used for a certain period. Therefore, repair and performance verification of failed ESCs are required. In this study, we developed a multizone probe card system suitable for electrical testing of the heating electrodes embedded in ESC control modules to correlate the failure mode factors of ESCs. This system has the advantages of examining the resistance of the internal heating electrode of a 144-zone ESC in a short time and detecting an abnormality in this component based on the measured data. The heating electrode resistance measurement error rate of the developed system was 1%, and the maintenance time was reduced by approximately 66% compared with that of existing ESC maintenance methods

Nanotechnology in Meat Processing and Packaging: Potential Applications — A Review

Growing demand for sustainable production, increasing competition and consideration of health concerns have led the meat industries on a path to innovation. Meat industries across the world are focusing on the development of novel meat products and processes to meet consumer demand. Hence, a process innovation, like nanotechnology, can have a significant impact on the meat processing industry through the development of not only novel functional meat products, but also novel packaging for the products. The potential benefits of utilizing nanomaterials in food are improved bioavailability, antimicrobial effects, enhanced sensory acceptance and targeted delivery of bioactive compounds. However, challenges exist in the application of nanomaterials due to knowledge gaps in the production of ingredients such as nanopowders, stability of delivery systems in meat products and health risks caused by the same properties which also offer the benefits. For the success of nanotechnology in meat products, challenges in public acceptance, economics and the regulation of food processed with nanomaterials which may have the potential to persist, accumulate and lead to toxicity need to be addressed. So far, the most promising area for nanotechnology application seems to be in meat packaging, but the long term effects on human health and environment due to migration of the nanomaterials from the packaging needs to be studied further. The future of nanotechnology in meat products depends on the roles played by governments, regulatory agencies and manufacturers in addressing the challenges related to the application of nanomaterials in food

Sensor-Linked Simulation for Emergency Response

The FireGrid project defines a vision whereby computational fire simulation tools are linked to real-time information derived from sensors in a building, providing potentially valuable information to an end user in terms of the current fire conditions, and, via steered models, their possible evolution. This paper outlines the “steering” of the simulation by the sensor data, and demonstrates the potential to provide responders with more information than that available solely from the sensor measurements. A hypothetical example is described, with a coupled model of fire development and human evacuation behaviour used to predict the locations in the building where casualties are most likely to occur

Clinical Characteristics and Risk Factors for Nosocomial Candidemia in Medical Intensive Care Units: Experience in a Single Hospital in Korea for 6.6 Years

The aim of this study was to determine candidemia incidence among patients in a medical intensive-care unit (MICU) and the associated mortality rate and to identify risk factors associated with candidemia. We retrospectively performed a 1:3 matched case-control study of MICU patients with candidemia. Controls were matched for sex, age, and Acute Physiology and Chronic Health Evaluation (APACHE) II score. Candidemia incidence was 9.1 per 1,000 admissions. The most common pathogen was Candida albicans. Crude mortality was 96% among candidemia patients and 52% among controls (P<0.001). Mortality differed significantly between the groups according to Kaplan-Meier survival analysis (P=0.024). Multivariate analysis identified the following independent risk factors for candidemia: central venous catheterization (odds ratio [OR] = 3.2, 95% confidence interval [CI]=1.2-9.0), previous steroid therapy (OR=4.7, 95% CI=1.8-12.1), blood transfusion during the same admission period (OR=6.3, 95% CI=2.4-16.7), and hepatic failure upon MICU admission (OR=6.9, 95% CI=1.7-28.4). In conclusion, we identify an additional independent risk factor for candidemia, the presence of hepatic failure on MICU admission. Therefore, increased awareness of risk factors, including hepatic failure, is necessary for the management of candidemia

Sensor-linked fire simulation using a Monte-Carlo approach

Peer-reviewed article published in the Proceedings of the 9th International Symposium on Fire Safety Science, Karlsruhe, 2008.This study is aimed at developing a predictive capability for uncontrolled compartment fires which can be “steered” by real-time measurements. This capability is an essential step towards facilitating emergency response via systems such as FireGrid, which seek to provide fire and rescue services with information on the possible evolution of fire incidents on the scene. The strategy proposed to achieve this is a novel coupled simulation tool, based on the Monte-Carlo-based fire model, CRISP, with scenario selection achieved via comparison with (pseudo) sensor inputs. Here, some key aspects of such a system are illustrated and discussed in the context of the detailed measurements obtained in the full-scale fire test undertaken in a furnished apartment at Dalmarnock. The capability of CRISP in reproducing the fire conditions – given knowledge of the approximate heat release rate in the fire – was first verified. It is then shown that continuous selection from amongst a multiplicity of scenarios generated in Monte-Carlo fashion can be achieved, so that the predictions evolve in a way that closely follows the real fire conditions. Whilst the benefits of sensor-steering are already clearly apparent, further improvements will be possible by establishing an appropriate feedback loop between the results assessment and the parametric space in which new fires are generated, perhaps using Bayesian methods. Nevertheless, true predictive capability remains crucially dependent on the sufficient representation in the model of the mechanisms of fire growth, and this must be the focus in achieving better forecasting ability

Using Simulation for Decision Support: Lessons Learned from FireGrid

This paper describes some of the lessons learned from the FireGrid project. It starts with a brief overview of the project. The discussion of the lessons learned that follows is intended for others attempting to develop a similar system, where sensor data is used to steer a super-real time simulation in order to generate predictions that will provide decision support for emergency responders