178 research outputs found
Female board directorships and related party transactions
No full textUsing a sample of Chinese firms from 2005 to 2018, we show that firms with female directors (either executive or independent) are characterised by fewer related party transactions (RPTs), particularly in state-owned enterprises (SOEs). Fewer RPTs are associated with improved subsequent operating performance and, in contrast, RPTs are associated with decreased performance for firms with no or fewer female directors, suggesting that female directors engage or allow only efficient but not opportunistic RPTs to facilitate the long-term strategic objectives of their firms. Our findings are robust for using an alternative measure of RPTs, female board directorships and methods to mitigate potential endogeneity issues
Intracavity optogalvanic spectroscopy applied to radiocarbon detection
No full textRadiocarbon is extremely useful for archeological dating as well as for clinical, laboratory and atmospheric tracer applications. In this thesis, we report a new physical description of the ICOGS system process of radiocarbon optogalvanic signal extraction and analysis. To be specific, we first describe the fundamental theory of the Optogalvanic effect. Based on a set of 4-energy level rate equations for N2 buffer gas and a set of 2-energy level for CO2 sample gas, we simulate the N2 and CO2 ICOGS OG signals. Moreover, according to the phase difference from our simulation and experiment observation, we propose three methods (1.Vector Phase and Amplitude fitting, 2.Differential Method and 3.Vector Decomposition Method) to separate CO2 OG signal from N2 OG signal. Experimental results demonstrate our quantitative radiocarbon detection near 10 zeptomole14C levels in 10 µg samples.M.S.Includes bibliographical referencesby Junming Li
Magnetoelectric study in Terfenol-D/Tb₂(MoO₄)₃ bilayer composite
No full textAuthor name used in this publication: Junming LiuAuthor name used in this publication: Jiyang Wang2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishedVoR allowe
Tunable filters based on an SOI nano-wire waveguide micro ring resonator
No full textMicro ring resonator(MRR) filters based on a silicon on insulator(SOI) nanowire waveguide are fabricated by electron beam photolithography(EBL) and inductive coupled plasma(ICP) etching technology. The cross-section size of the strip waveguides is450×220 nm2, and the bending radius of the micro ring is around5μm. The test results from the tunable filter based on a single ring show that the free spectral range(FSR) is16.8 nm and the extinction ratio(ER) around the wavelength1550 nm is18.1 dB. After thermal tuning, the filter's tuning bandwidth reaches4.8 nm with a tuning efficiency of0.12 nm/°C Meanwhile, we fabricated and studied multi-channel filters based on a single ring and a double ring. After measurement, we drew the following conclusions: during the signal transmission of multi-channel filters, crosstalk exists mainly among different transmission channels and are fairly distinct when there are signals input to add ports.?2011 Chinese Institute of Electronics
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Analyzing Unstructured Data for Marketing Insights
No full textIn this dissertation, I am focused on analyzing the effects of information embedded in unstructured data on consumer decisions and firm strategies. Mining unstructured data (such as natural language and visual imagery) for insights and implications has become a key area in business research. Methodologically, I employ cutting-edge techniques in machine learning and deep learning to construct structural and sentiment measures for large-scale data and employ econometric methods to analyze their impact.
The dissertation includes three projects on the effects of information that exists in different formats (text vs. image, virtual vs. reality) and on different platforms (crowdfunding, online reviews, and video games). The data techniques I employed in the analysis include convolutional neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), attention model, transfer learning, support vector machine (SVM), and latent Dirichlet allocation (LDA).
In the first chapter, I examine the differentiation of the content of online reviews, and the strategic motivations behind the differentiation. In the second chapter, I investigate the impact of text and image in project description on the likelihood of crowdfunding success, as well as their joint effects. Finally, the third chapter investigates the impact of real-world events on consumers’ likelihood of playing video games and making virtual purchases
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Probabilistic Graphical Models for Crowdsourcing and Turbulence
No full textGraphical models provide a useful framework and formalism from which to modeland solve problems involving random processes. We demonstrate the versatility and
usefulness of graphical models on two problems, one involving crowdsourcing and one
involving turbulence.
In crowdsourcing, we consider the problem of inferring true labels from a set
of crowdsourced annotations. We design generative models for the crowdsourced
annotations involving as latent variables the worker reliability, the structure of the
labels, and the ground truth labels. Furthermore, we design an effective inference
algorithm to infer the latent variables.
In turbulence, we consider the problem of modeling the mixing distribution of
homogeneous isotropic passive scalar turbulence. We consider models specifying the
conditional distribution of a coarse grained node given its adjacent coarse grained
nodes. In particular, we demonstrate the effectiveness of a higher order moments
based extension of the Gaussian distribution
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Natural Language Processing and Machine Learning for Chronic Disease Management and Prevention: Focus on Asthma
No full textChronic diseases are the leading causes of decreasing quality of life, hospitalization, disability and death in the United States (US) and all over the world. For a long time, researchers have been seeking ways that promote management and prevention of chronic conditions, (i) to save time, money and energy, (ii) to support evidence-based health care decisions and (iii) to customize individual patients’ disease management plans. Healthcare related Big Data Analytical tools have the potential to leverage data from large-scale longitudinal sources for population-level chronic disease prevention, as well as to capture trends and propose models for individual-level proactive self-management. Nonetheless, the exact role of Big Data Analytical tools in the area of chronic disease management has not been fully studied. To take full advantage of Big Data, there is an urgent need to enrich our understanding of Big Data and use it to provide insights for researchers, patients, and health providers. By choosing asthma, one of the most serious chronic diseases in the US, as a research case, this dissertation addresses three research questions:
(I) How can we use Big Data for asthma surveillance to enable health providers to respond promptly?
(II) How can we apply Big Data for asthma risk factors analysis and enhance chronic disease self-management and population-level interventions?
(III) By identifying smoking as one of the highest population-attributable risk factors, can we use Big Data in evaluating possible substitutes like e-cigarettes?
The dissertation comprises of four essays. The first essay seeks to provide an efficient framework to extract signals from social media and make social media data available to answer these questions. The second essay focuses on building robust Big Data based population-level surveillance models that enable health providers to respond to chronic conditions, like asthma, in real time. In the third essay, a framework for comprehensive asthma risk factors analysis is proposed. In the fourth essay, we examine behaviors for alleviating chronic disease risks such as smoking. Models, frameworks, and design principles proposed in these essays advance not only healthcare research, but also more broadly contribute to design science and predictive modeling research domains
The Accounting Professional Skill Need and the Reform of Undergraduate Accounting Education: A Survey Study from the Stakeholders
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