Complex dynamics of evaporation-driven convection in liquid layers

The spontaneous convective patterns induced by evaporation of a pure liquid layer are studied experimentally. A volatile liquid layer placed in a cylindrical container is left free to evaporate into air at rest under ambient conditions. The liquid/gas interface of the evaporating liquid layer is visualized using an infrared (IR) camera. The phenomenology of the observed convective patterns is qualitatively analysed, showing in particular that the latter can be quite complex especially at moderate liquid thicknesses. Attention is also paid to the influence of the container diameter on the observed patterns sequence.Comment: videos include

How In-Silico Experiments Can Help Drug-Discovery: The Glutamatergic Synapse as an Example of Application

This work aims to show on a very concrete example that simulations (In-Silico experiments) can help drug discovery process and therapeutic strategies search. Such an approach must be based, to reflect the complexity of physiological systems, on a modeling methodology taking into account several organization levels and time scales, and focused on physiological functions and their interactions. First, we present shortly a modeling framework built on top of a physiological systems theory. Then, we apply this approach to model the memory induction at synaptic level where the described system includes some cellular and molecular mechanisms. Finally we propose an application of \u27in silico\u27 experiments in order to exhibit some synergistic effects of biochemical mechanisms and to suggest new combinatorial therapeutics

Integrated monitoring system for fall detection in elderly

Falling and its resulting injuries are an important public health problem for older adults. The National Safety Council estimates that persons over the age of 65 have the highest mortality rate (death rate) from injuries. The risk of falling increases with age; one of three adults 65 or older falls every year. Demographic predictions of population aged 65 and over suggest the need for telemedicine applications in the eldercare domain. This paper presents an integrated monitoring system for the detection of people falls in home environment. The system consist of combining low level features extracted from a video and heart rate tracking in order to classify the fall event. The extracted data will be processed by a neural network for classifying the events in two classes: fall and not fall. Reliable recognition rate of experimental results underlines satisfactory performance of our system

Forest Fires Prediction: A Proposal for a new hybrid index

All over the world, statistics show that forest fires rate has been increasing in the recent decades although many studies and various indices were proposed to predict fire occurrence and then take in advance the necessary actions. These indices use only weather data to make their decision of prediction. In this paper, a new proposal for a fire detection index is presented that combines between meteorological and topographic parameters. The reason is to reduce errors due to inaccuracies in weather prediction. The parameters of slope, aspect and elevation are introduced, and a comparison is held between the proposed index and other existing indices that reveals the distinction of the new combination over the present models: Angstrom, Nesterov, KBDI and Canadian Fire Weather Index. The implementation of the proposed hybrid index using data from Lebanon demonstrated its ability to accurately predict the hazard of fire occurrence

Automated Monitoring System for Fall Detection in the Elderly

Falls are a major problem for the elderly people living independently. Accordingto the World Health Organization, falls and sustained injuries are the third cause of chronic disability. In the last years there have been many commercial solutions aimed at automatic and non automatic detection of falls like the social alarm (wrist watch with a button that is activated by the subject in case of a fall event), and the wearable fall detectors that are based on combinations of accelerometers and tilt sensors. Critical problems are associated with those solutions like button is often unreachable after the fall, wearable devices produce many false alarms and old people tend to forget wearing them frequently. To solve these problems, we propose an automated monitoring that will detects the face of the person, extract features such as speed and determines if a human fall has occurred. An alarm is triggered immediately upon detection of a fall

The use of Artificial Neural Networks to adjust and robustness study of experience tables of maintenance in disability

Pricing and, more important, reserving "life / death" and "disability" risks are strictly defined by the regulation, which imposes particular constraints on the technical rate and the laws of occurrence or maintenance. However, the assessment of portfolios reserving differs from the standard one proposed by the BCAC. Insurance companies are increasingly forced toseek the construction of experience tables to manage these risks, especially since it is unrealistic today to expect offset losses by financial products. Traditional adjustment methods, in actuarial literature, usually used to smooth the recovery curve rate estimated usually by the robust Adjusted Kaplan‐Meier estimator, induce a model error due a boundary bias. The available data are usually sparse and poor quality on the border. Thus a boundary bias is due to weight allocation by the fixed symmetric argument outside the support of the gross curve, when smoothing close to the boundary is carried out. The objective of this work is the use of Artificial Neural Networks (ANN) for adjustment and smoothing experience tables of maintenance in disability applied to a two cycles real set data. The artificial neural networks are parametric nonlinear models able to play an "universal approximator" role achieving a local and global approximation. Two architectures networks are particularly suited to model and smooth gross output rates: Feedforward Neural Networks (FNN) and Radial Basis Functions (RBF) Networks. The robustness of the ANN globally and especially at the edge of curve can be also studied. Graphical tests are used to compare output surfaces rates obtained by neural networks with those obtained by Whittaker‐Henderson framework

Real Time Recognition of Elderly Daily Activity using Fuzzy Logic through Fusion of Motion and Location Data

One of the major problems that may encounter old people at home is falling. Approximately, one of three adults of the age of 65 or older falls every year. The World Health Organization reports that injuries due to falls are the third most common cause of chronic disability. In this paper, we proposed an approach to indoor human daily activity recognition, which combines motion and location data by using a webcam system, with a particular interest to the problem of fall detection. The proposed system identifies the face and the body in a given area, collects motion data such as face and body speeds and location data such as center of mass and aspect ratio; then the extracted parameters will be fed to a Fuzzy logic classifier that classify the fall event in two classes: fall and not fall