Advanced sensing technologies and systems for lung function assessment

Chest X-rays and computed tomography scans are highly accurate lung assessment tools, but their hazardous nature and high cost remain a barrier for many patients. Acoustic imaging is an alternative to lung function assessment that is non-hazardous, less costly, and has a patient-to-equipment approach. In this thesis, the suitability of acoustic imaging for lung health assessment is proven via systematic review and numerical airway modelling. An acoustic lung sound acquisition system, consisting of an optimal denoising filter translated into imaging for continual and reliable lung function assessment, is then developed. To the author’s best knowledge, locating obstructed airways via an acoustic lung model andthe resulting acoustic lung imaging have yet to be investigated in the open literature; hence,a novel acoustic lung spatial model was first developed in this research, which links acousticlung sounds and acoustic images with pathologic changes. About 89% structural similaritybetween an acoustic reference image based on actual lung sound and the developed modelacoustic image based on the computation of airway impedance was achieved. External interference is inevitable in lung sound recordings; thus, an indirect unifying of wavelet-based total variation (WATV) and empirical Wiener denoising filter is proposed to enhance recorded lung sound signals. To the author’s best knowledge, the integration of WATV and Wiener filters has not been investigated for lung sound signals. Selection and analysis of optimal parameters for the denoising filter were performed through a case study. The optimal parameters achieved through simulation studies led to an average 12.69 ± 5.05 dB improvement in signal-to-noise ratio (SNR), and the average SNR was improved by 16.92 ± 8.51 dB in the experimental studies. The hybrid denoising filter significantly enhances the signal quality of the captured lung sounds while preserving the characteristics of a lung sound signal and is less sensitive to the variation of SNR values of the input signal. A robust system was developed based on the established lung spatial model and denoising filter through hardware redesign and signal processing, which outperformed commercial digital stethoscopes regarding SNR and root mean square error by about 8 dB and 0.15, respectively. Regarding sensing sensitivity power spectrum mapping, the developed system sensors’ position is neutral, as opposed to digital stethoscopes, when representing lung signals, with a signal power loss ratio of around 5 dB compared to 10 dB from digital stethoscopes. The developed system obtains better detection by about 10% in the obstructed airway region compared to digital stethoscopes in the experimental studies

Participant Network Patterns in Enhancing Online Community Interactivity

Social media is having an increasing impact on businesses. In particular, the explosive growth of online brand communities has attracted organizations and marketers’ attentions. However, despite the increasing importance of online community for marketing, it is noticed that relatively few of them are successful in attracting community members and enhancing interactivity. In this study, we argue that it is necessary to have a comprehensive understanding regarding how the community members participate in the communal context and interact with each other, and thereby the community interactivity can be continued. To this end, we collected a large amount of data from an online discussion forum where we found that the participants were highly interactive across the discussion topics, thus forming robust communities. Currently, the data analysis pertaining to this study is work in progress, but we will be in a position to offer more in-depth analysis of the rich findings that the research has generated by the time of the conference

A program for the Bayesian Neural Network in the ROOT framework

We present a Bayesian Neural Network algorithm implemented in the TMVA package, within the ROOT framework. Comparing to the conventional utilization of Neural Network as discriminator, this new implementation has more advantages as a non-parametric regression tool, particularly for fitting probabilities. It provides functionalities including cost function selection, complexity control and uncertainty estimation. An example of such application in High Energy Physics is shown. The algorithm is available with ROOT release later than 5.29.Comment: 12 pages, 6 figure

Comorbid Mental Disorders in Anxiety Disorders: Genetic Aspects of Bipolar Disorders and of Ethnicity

Anxiety disorder (AD) is commonly comorbid with other mental illness. It could be a state or trait, controversially. Evidence for an association between alcoholism and anxiety has emerged from clinical studies of patients with alcoholism, and those of patients with anxiety disorders. Alcohol dependence (or abuse) as well as bipolar disorder (BP) is usually comorbid with anxiety disorder and/or depressive disorder, which often coexist and are difficult to distinguish from one another. However, in Han Chinese population, the comorbidity rate either with alcoholism or bipolar disorder was not reported as much high as reported in Caucasians, this finding of comorbidity between anxiety/depressive disorders and alcohol dependence (or abuse) or/and bipolar disorders, possibly at the genetic level, makes the differentiation of their categorical diagnoses in the association study vitally important

Feasibility of Combined UASB-MBR System in Treating PTA Wastewater and Polyimide Membrane for Biogas Purification

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive