Experimental study of non-Newtonian fluid flow in microchannels

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.Non-Newtonian fluid flow in microchannels has significant applications in science and engineering. The effects of temperature and PAM solution concentrations on rheological parameters are analyzed by measuring them with rotating cylinder viscometer. Flow characteristics for deionized water and PAM solutions in fused silica microtubes with diameters ranging from 50 to 320μm, fused silica square microchannels with diameters 75 and 100μm, and stainless steel microtubes with diameters from 120 to 362μm, are studied experimentally. The test results for deionized water in microchannels are in good agreement with theoretical predictions for conventional-size channels. Friction factors of PAM solutions are much higher than theoretical predictions. With the PAM concentration reduced, the deviation is more, which is possibly caused by the significant electroviscous effect on PAM solutions flow in microchannels

Fault Detection Filter Design for LTI System with Time Delays

This paper deals with the fault detection filter design problem for linear time invariant time-delay systems with unknown input. The core of our study is to a) take the behavior of delayed state and measurement into consideration when the observer-based fault detection filter is constructed; b) solve the formulated fault detection filter design problem by combining of using the left eigenstmcture assignment approach and H ∞ optimization technique. Through a suitable choice of the filter gain matrices and residual weighting matrix, the residual can be completely decoupled from the delay-free unknown input, while the influence of the delayed unknown input on residual is mimmized in the sense of H ∞ norm. Numerical simulation is used to illustrate the efficiency of the proposed method.published_or_final_versio

Investigation of multilingual deep neural networks for spoken term detection

The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (∼10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance. © 2013 IEEE

Articulatory-feature based sequence kernel for high-level speaker verification

Research has shown that articulatory feature-based phonetic-class pronunciation models (AFCPMs) can capture the pronunciation characteristics of speakers. However, the scoring method used in AFCPMs does not explicitly use the discriminative information available in the training data. To harness this information, this paper proposes converting speaker models to supervectors by stacking the discrete densities in AFCPMs. An AF-kernel is constructed from the supervectors of target speakers, background speakers, and claimants. An AF-kernel based SVM is then trained to classify the super-vectors. Results show that AF-kernel scoring is complementary to likelihood-ratio scoring, leading to better performance when the two scoring methods are combined.Department of Electronic and Information EngineeringRefereed conference pape

Effects of ferroelectric-poling-induced strain on the quantum correction to low-temperature resistivity of manganite thin films

Author name used in this publication: H. L. W. ChanAuthor name used in this publication: H. S. Luo2010-2011 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

A hierarchical EMS for aggregated BESSs in energy and performance-based regulation markets

The battery energy storage systems (BESSs) have been increasingly installed in the power system, especially with the growing penetration rate of the renewable energy sources. However, it is difficult for BESSs to be profitable due to high capital costs. In order to boost the economic value of BESSs, this paper proposes a hierarchical energy management system (HiEMS) to aggregate multiple BESSs, and to achieve multi-market business operations. The proposed HiEMS optimizes the multi-market bids considering a realistic BESS performance model, and coordinates the BESSs and manages their state of charge (SOC) values, according to their price penalties based on dynamically generated annualized cost. By taking part in the energy market and regulation market at the same time, the cost-performance index (CPI) of the BESS aggregation is greatly improved. The impact of photovoltaic generation (PV) on system performance and CPI is also studied.This work is supported in part by DNV GL Energy (formerly KEMA) Technology Centre, Nanyang Technological University, and the Energy Innovation Research Programme (EIRP, Award No. NRF2014EWT-EIRP002-005), administrated by the Energy Market Authority (EMA). The EIRP is a competitive grant call initiative driven by the Energy Innovation Programme Office, and funded by the National Research Foundation (NRF) Singapore

Balancing the yin and yang: TMT gender diversity, psychological safety, and firm ambidextrous strategic orientation in Chinese high-tech SMEs

This paper offers a novel theoretical account of why and when top management team (TMT) gender diversity lends strategic advantage. Building on social role theory, we develop a moderated-mediation model showing: a) TMT psychological safety mediates the effect of TMT gender diversity on firm ambidextrous strategic orientation (ASO) (why) and b) firm slack moderates this mediated effect (when). We tested our model in the context of Chinese high-tech small- and medium-sized enterprises. After confirming gender differences in social role-based proclivities at the TMT level, a multi-wave survey study of 373 members from 120 TMTs showed that TMT gender diversity positively affects ASO via TMT psychological safety, and this mediated effect is stronger when firm slack is lower than higher. We further interviewed 23 top managers to supplement key quantitative results. Our study advances upper echelons research on TMT gender diversity in two ways. First, it highlights the gender-specific interpersonal benefit of TMT gender diversity, which is markedly distinct from the cognitive-variety argument associated generically with TMT demographic diversity. Second, it considers both men and women in TMTs in a more balanced manner, thereby offering an alternative account to the female-focused theorization of the positive strategic implications of TMT gender diversity

Meta-Regression on the Heterogenous Factors Contributing to the Prevalence of Mental Health Symptoms During the COVID-19 Crisis Among Healthcare Workers.

Objective: This paper used meta-regression to analyze the heterogenous factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) in China under the COVID-19 crisis. Method: We systematically searched PubMed, Embase, Web of Science, and Medrxiv and pooled data using random-effects meta-analyses to estimate the prevalence rates, and ran meta-regression to tease out the key sources of the heterogeneity. Results: The meta-regression results uncovered several predictors of the heterogeneity in prevalence rates among published studies, including severity (e.g., above severe vs. above moderate, p < 0.01; above moderate vs. above mild, p < 0.01), type of mental symptoms (PTSD vs. anxiety, p = 0.04), population (frontline vs. general HCWs, p < 0.01), sampling location (Wuhan vs. Non-Wuhan, p = 0.04), and study quality (p = 0.04). Conclusion: The meta-regression findings provide evidence on the factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) to guide future research and evidence-based medicine in several specific directions. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=220592, identifier: CRD42020220592

The association of pulmonary function with carotid atherosclerosis in older Chinese: Guangzhou Biobank Cohort Study-CVD Subcohort

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