Do hybrid electric vehicles really work?

Peer Reviewe

Do hybrid electric vehicles really work?

Peer Reviewe

Cuff-less continuous blood pressure monitoring system using pulse transit time techniques

This paper describes the development of a continuous cuff-less blood pressure system based on the pulse transit time (PTT) technique. In this study, PTT is defined by two different approaches denoted as PTT1 and PTT2. PTT1 is the time difference between the R-wave peak of the Electrocardiogram (ECG) and the peak of the Photoplethysmogram (PPG). PTT2 is the time difference between two peak PPG signals on same cardiac cycle at different positions on the body. The ECG is acquired on the chest using 3 lead electrodes and a reflection mode optical sensor is deployed on brachial artery and fingertip to monitor the PPGs. These data were synchronized using a National Instruments data acquisition card along with Matlab software for subsequent analysis. A wrist-type cuff-based blood pressure device was used to measure blood pressure on the right hand. Brachial blood pressure was measured on the upper left arm using oscillometric blood pressure monitor. Experiments were conducted by elevating the right hand at different position to investigate variability of PTT under the effects of hydrostatic pressure. Next the variability of PTT due to blood pressure changes during a Valsalva maneuver was investigated. The result shows that the PTT1 is inversely proportional to blood pressure in both experiments. Meanwhile, there is weak correlation between PTT2 and blood pressure measurement which suggests that by excluding the pre-ejection period (PEP) time in PTT calculation may reduce the accuracy of PTT for blood pressure measurement. In conclusion, PTT measurement between ECG and PPG signals has potential to be a reliable technique for cuff-less blood pressure measurement

Development of tubular cardiovascular phantom system for pulse transit time simulation

This paper presents on the development of a tubular cardiovascular phantom system to simulate pulse transit time (PTT). The PTT defined as the delay time between two pulses in one cardiac cycle has been shown to be promising method for cuffless continuous blood pressure (BP) measurement. However most of the PTT measurement was performed on human subjects, thus giving a difficulty in validating sensor performance due to variability of BP. Therefore, a cardiovascular phantom system was proposed for simulate the PTT measurement. An electronic controlled module was developed to control pump operation for pulse generation. Plastic optical fibre (POF) sensors were used to measure the pulse signal on the flexible tube and the results were compared with an in-line pressure sensor. In this experiment, the delay time between two pulses were calculated offline using Matlab software and correlated with pulse pressure. The result demonstrate that the pulse delay time recorded by both sensors decreased with increase of pulse rate and pulse pressure. These results on the phantom study showed similar pattern to the human model, thus indicating that the system is able to simulate PTT for sensor validation purposes

Computer Program Generation of Extreme Value Distribution Data

The application of the Monte Carlo method on the estimation in Gumbel extreme value distribution was studied. The Gumbel extreme value distribution is used to estimate the flood flow of specific return period for the design of flood mitigation project. This paper is a programming effort (1) to estimate the parameters of Gumbel distribution using the observed data and (2) to provide a random variate generating subroutine to generate random samples and order statistics of a Gumbel distribution random variable. The mean squared error is used to measure the accuracy of the estimation method. Finally, an example of the use of these programs is given to illustrate application in the analysis of a hydrologic system

Detecting suicidality on Twitter

Twitter is increasingly investigated as a means of detecting mental health status, including depression and suicidality, in the population. However, validated and reliable methods are not yet fully established. This study aimed to examine whether the level of concern for a suicide-related post on Twitter could be determined based solely on the content of the post, as judged by human coders and then replicated by machine learning. From 18th February 2014 to 23rd April 2014, Twitter was monitored for a series of suicide-related phrases and terms using the public Application Program Interface (API). Matching tweets were stored in a data annotation tool developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). During this time, 14,701 suicide-related tweets were collected: 14% were randomly (n = 2000) selected and divided into two equal sets (Set A and B) for coding by human researchers. Overall, 14% of suicide-related tweets were classified as ‘strongly concerning’, with the majority coded as ‘possibly concerning’ (56%) and the remainder (29%) considered ‘safe to ignore’. The overall agreement rate among the human coders was 76% (average κ = 0.55). Machine learning processes were subsequently applied to assess whether a ‘strongly concerning’ tweet could be identified automatically. The computer classifier correctly identified 80% of ‘strongly concerning’ tweets and showed increasing gains in accuracy; however, future improvements are necessary as a plateau was not reached as the amount of data increased. The current study demonstrated that it is possible to distinguish the level of concern among suicide-related tweets, using both human coders and an automatic machine classifier. Importantly, the machine classifier replicated the accuracy of the human coders. The findings confirmed that Twitter is used by individuals to express suicidality and that such posts evoked a level of concern that warranted further investigation. However, the predictive power for actual suicidal behaviour is not yet known and the findings do not directly identify targets for intervention.This project was supported in part by funding from the NSW Mental Health Commission and the NHMRC John Cade Fellowship 1056964. PJB and ALC are supported by the NHMRC Early Career Fellowships 1035262 and 1013199