Recognition of false alarms in fall detection systems

Falls are a major cause of hospitalization and injury-related deaths among the elderly population. The detrimental effects of falls, as well as the negative impact on health services costs, have led to a great interest on fall detection systems by the health-care industry. The most promising approaches are those based on a wearable device that monitors the movements of the patient, recognizes a fall and triggers an alarm. Unfortunately such techniques suffer from the problem of false alarms: some activities of daily living are erroneously reported as falls, thus reducing the confidence of the user. This paper presents a novel approach for improving the detection accuracy which is based on the idea of identifying specific movement patterns into the acceleration data. Using a single accelerometer, our system can recognize these patterns and use them to distinguish activities of daily living from real falls; thus the number of false alarms is reduced

Hybrid 1D Plasmonic/Photonic Crystals are Responsive to Escherichia Coli

Photonic crystal-based biosensors hold great promise as valid and low-cost devices for real-time monitoring of a variety of biotargets. Given the high processability and easiness of read-out even for unskilled operators, these systems can be highly appealing for the detection of bacterial contaminants in food and water. Here, we propose a novel hybrid plasmonic/photonic device that is responsive to Escherichia coli, which is one of the most hazardous pathogenic bacterium. Our system consists of a thin layer of silver, a metal that exhibits both a plasmonic behavior and a well-known biocidal activity, on top of a solution processed 1D photonic crystal. We attribute the bio-responsivity to the modification of the dielectric properties of the silver film upon bacterial contamination, an effect that likely stems from the formation of polarization charges at the Ag/bacterium interface within a sort of bio-doping mechanism. Interestingly, this triggers a blue-shift in the photonic response. This work demonstrates that our hybrid plasmonic/photonic device can be a low-cost and portable platform for the detection of common contaminants in food and water

Development of a Reduced Order Model for Fuel Burnup Analysis

Fuel burnup analysis requires a high computational cost for full core calculations, due to the amount of the information processed for the total reaction rates in many burnup regions. Indeed, they reach the order of millions or more by a subdivision into radial and axial regions in a pin-by-pin description. In addition, if multi-physics approaches are adopted to consider the effects of temperature and density fields on fuel consumption, the computational load grows further. In this way, the need to find a compromise between computational cost and solution accuracy is a crucial issue in burnup analysis. To overcome this problem, the present work aims to develop a methodological approach to implement a Reduced Order Model (ROM), based on Proper Orthogonal Decomposition (POD), in fuel burnup analysis. We verify the approach on 4 years of burnup of the TMI-1 unit cell benchmark, by reconstructing fuel materials and burnup matrices over time with different levels of approximation. The results show that the modeling approach is able to reproduce reactivity and nuclide densities over time, where the accuracy increases with the number of basis functions employed

DAMEWARE - Data Mining & Exploration Web Application Resource

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms.Comment: User Manual of the DAMEWARE Web Application, 51 page