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International audience; This contribution is focused on the Mullins effect in polyethylene. An ultra-low-density polyethylene with 0.15 crystal content, a low-density polyethylene with 0.3 crystal content and a high-density polyethylene with 0.72 crystal content are subjected to cyclic stretching over a large strain range. Experimental observations are first reported to examine how the crystal content influences the Mullins effect in polyethylene. It is found that the cyclic stretching is characterized by a stress-softening, a hysteresis and a residual strain, whose amounts depends on the crystal content and the applied strain. A unified viscohyperelastic-viscoelastic-viscoplastic constitutive model is proposed to capture the polyethylene response over a large strain range and its crystal-dependency. The macro-scale polyethylene response is decomposed into two physically distinct sources, a viscoelastic-viscoplastic intermolecular part and a viscohyperelastic network part. The local inelastic deformations of the rubbery amorphous and crystalline phases are considered by means of a micromechanical treatment using the volume fraction concept. Experimentally-based material kinetics are designed by considering the Mullins effect crystal-dependency and are introduced into the constitutive equations to capture the experimental observations. It is shown that the model is able to accurately reproduce the Mullins effect in polyethylene over a large strain range. The inherent deformation mechanisms are finally presented guided by the proposed constitutive model.

This paper presents a telemetry system to acquire the Electrocardiogram (ECG) waveform and analyze it using an algorithm developed to detect cardiac abnormalities in patients. The ECG data is sent in real-time to the patient's smartphone from a Bluetooth sensor. Two approaches are being developed to process the data. The Web server approach is to send the data from the phone to a Web server where the data will be analyzed and processed and the results will be sent back; this can be done through Wi-fi or a 3G connection. The Phone Processing approach is to run the algorithm within the application on the smartphone in which the data can be processed locally, and the results can be displayed in case of no network. The results will be stored on a Web server in both approaches for further clinical diagnosis. The necessary hardware to acquire the ECG and transmit it to the smartphone has been developed. Also, a robust diagnostic algorithm and an interactive smartphone application have been developed. On integration, the system will be capable of analyzing the ECG in real time and providing early warning of myocardial infarction (MI) to patients saving valuable time and effort in their treatment.

Cardiovascular diseases (CVD) are the leading cause of death globally. According to the World Health Organization, around 17.3 million people died from CVD in 2008, which is estimated to increase to 23 million annually by 2030. This calls for the exploitation of the latest technology to analyze and detect heart diseases in a timely fashion. Electrocardiogram (ECG) records the electrical activity of heart, and any irregularities in the heart rhythm are used as indicators of possible heart diseases. However, the process of acquiring an ECG and visually inspecting it is laboursome and time consuming. It is in this context that developing a mobile ECG monitoring system based on smart phones and automated diagnostic algorithms prove significant. The ECG acquiring system being developed by our team transmits ECG from the human body to the patient's smart phone in real-time via Bluetooth. This is completed by an application running on the phone. Two approaches are being developed to process the data received. The first is to send the data from the phone to a Webserver where the data will be analyzed and processed and lastly send the results back; this can be done through Wi-fi or a 3G connection. The second approach is to run the algorithm within the application on the smart phone itself. This way, the data can be processed locally, and the results can be displayed in case of no network. Both approaches are being compared for pros and cons. In the first approach, the Webserver can be updated frequently without modifying the application. Moreover, the usage of RAM memory will be efficient, and the code can be preserved from corruption. Nevertheless, this process will not succeed if the server fails or if the network interrupts. The second approach can process the data without network connectivity. However, it is more demanding on the phone, more susceptible to bugs and more difficult to update and debug the C code. The necessary hardware to acquire ECG and transmit it to the smart phone has been developed. Also, a robust diagnostic algorithm and an interactive smart phone application are being built. On integration, the system will be capable of analyzing ECG in real time and providing early warning to patients under cardiac risk saving valuable time and effort in their treatment.