Most Popular Books by Dan Wang

This thesis focuses on the impact of media convergence on the Chinese newspaper organization in the global text of advanced technology, through a rare ethnographic case study of a Chinese Communist Party media organization. Based on online and offline data obtained through seven months'' ethnographic research carried out in 2016 at Party newspaper organization PaperX, including a special focus on the newspaper''s police beat, it seeks to understand how the impact of media convergence is manifested in news routines and to discuss the implications of the impact on Chinese journalists. The research was designed during a time of change in China''s media environment. On August 18, 2014, the country''s leader, President Xi Jinping, also General Secretary of the Communist Party of China, gave a speech highlighting the "Directives on Boosting Integrated Development of Traditional and New Media" , a new set of guidelines to assist traditional media in handling the economic downturn that started to affect the Chinese newspaper industry from 2012 as the penetration of digital technologies deepened. In a similar way to Western media, Chinese newsrooms have needed to adapt to Internet structures in news production. Unlike Western media, Chinese newsroom development is subject to strong political guidance. Media convergence in China thus represents a culturally specific phenomenon within the global landscape of newspaper industry digitalization. The sociology of newsroom studies and the labor lens are the two main research approaches adopted to address the purposes of this thesis. The thesis uses newsroom studies to examine the impact of media convergence as a logic institutionalized into the newsroom structure, journalists'' routines and practices, and the identity construction of journalists. Through the labor lens approach, the research explores individual journalists'' praxis in the changing media environment in China and the constant shift between alienation, de-alienation and enlightened alienation. Two tensions continuously emerge throughout the research: old versus new values and practices; and individual versus structural needs of the profession. Findings indicates that media convergence logic has had multiple impacts reaching to the core of journalistic practice at PaperX. At the structural level, the re-centralization of media control closed down local support for PaperX, particularly the limited latitude previously granted to the newspaper by local government departments to "supervise" (serve as a watchdog), while financial support from the government (both local and central levels) saw the newspaper adopt an administratization of advertising operational strategy focused on soliciting and making government information service reports and announcements. Such structural changes appeared to have a paradoxical effect on journalists'' perceptions of their job, with staffers being both insecure and apathetic about their current work and proud to be connected to government sources. The principles directing the organization and journalists in their routines and practices changed to become "official" oriented. Yet, journalists were found to project informal and invisible practices to reconcile their paradoxical feelings about their work. Meanwhile, at the identity level, journalists actively reconstructed their professional identity on social media to showcase the products of their work to government officials and managers on the WeChat Moments social media platform, and to display their close connections with the police, using these contacts as resources to boost the social authority derived from their identity. Overall, this study''s contribution lies in its insights into Chinese newsroom production in the global context of advanced technology, in its deployment of ethnographic data gathering, and the use of the labor lens perspective to analyze journalists'' relationship with their organization and media institutions. The documenting of PaperX''s experiences in adapting to the new era of newsgathering in China could also shed significant light on the future development of Chinese Communist Party newspapers at the local level.

This paper investigates the impact of early relationships on entrepreneurial firm innovation. Prior research has largely focused on the benefits of network ties, documenting the many advantages that accrue to firms embedded in a rich network of inter-organizational relationships. In contrast, we build on competitive interaction research to consider potential drawbacks and emphasize how competitive exposure, enabled by powerful intermediaries, can inhibit innovation. We develop a conceptualization of information leakage that occurs when firms are indirectly tied to their competitors through these shared intermediary organizations. To test our theory, we examine every relationship between entrepreneurial firms and their venture capital investors in the minimally invasive surgical segment of the medical device industry over a 22 year period. The theory and evidence provide novel insights for entrepreneurship research while contributing to the literatures on innovation and competition through networks.

For decades, magnetism is widely applied in the industry as technologies such as sensors, memories, motors, generators, and others. Since the invention of the giant magnetoresistance effect (GMR) effect and the resulting magnetic read head, which was awarded the 2007 Nobel Prize in Physics to Albert Fert and Peter Grunberg, the study of magnetic-based technology has developed rapidly. There are many advantages to using magnetic-based devices such as high storage capacity, high reliability, cheaper cost, and non-volatility. Thanks to those advantages, magnetic-based devices for example hard disk drives (HDDs) is now widely used in computer memories even compared with solid state disk drives (SSDs) [1]. However, different from SSDs which store data in microchips, HDDs use a fixed read/write head to read information from the mechanically moved magnetic disk, which is slow and energetically inefficient. Such kind of low speed and high power needed consumption is preventing magnetic based devices from further applications. In my thesis, I will illustrate my study towards resolving these disadvantages, using a newly discovered phenomenon called spintronics. Due to the spin transfer torque between electron spins and lattice in materials such as ferromagnets, the magnetic domains can be driven by injecting a current, via a domain wall (DW) motion. Such property enables the potential applications of DWs in high-speed memory or logic devices. I will first give a summary of the magnetic energy terms which relevant to understanding thin film domain wall behavior. Next, I will give a brief introduction to magnetic energy terms and the motivation and background of my study on magnetic domain walls (DWs). There are two types of transverse DWs, a 180° domain wall (180DW) and a 360° domain wall (360DW). My research will mainly focus on the study of fast and in-situ formation of these two types of DWs, especially 360DWs which have not been well understood previously. In my method, these two types of DWs will be generated by using an external Oersted field, then injecting a current pulse in the transverse current line, and the chirality of DWs is based on the design and control of nanowire geometry. By using this method, not only the reliability is high for application purposes, but also the chirality of the formed 180DW and 360 DW can be well controlled, which is critical in applications as devices. After discussing the results of 180/360DWs formation, I will then talk about their dynamics property under the magnetic field or spin current, and further on how the chirality of 180/360DWs will response to geometry effects of the nanowire. Finally, with a combination of DW chirality and topological effects, I have discovered that the trajectory of the DWs can be controlled by the DWs chirality in a well-controlled Y-shape nanowire, which allows us to design a chirality sorter of 180/360DWs using such devices. My research is implemented mainly by micromagnetic simulations using finite element differentiation methods. The dynamics of magnetization is based on the one-dimensional Landau-Lifshitz-Gilbert (LLG) equations where both magnetic field and spin current will exert torques to magnetic moments. Two different tool kits are used for my simulations, OOMMF and Mumax3. Both of the two tools have their respective advantages and disadvantages and are more appropriate in respective studies, which will be discussed in further detail. I have also compared the results of the two tools. In the last, I will talk about the experimental study of DW behaviors. I have built a magnetoresistance system that can apply a magnetic field and spin current pulses into the samples and detect the change of sample magnetization by measuring the change of sample resistance. I will show the preliminary results for experimental measurements in the thesis and present my plans for future work.

Aimsu2028To explore the effect of FGM on blood glucose control and QOL of Type 2 diabetes in outpatients.u2028u2028Methodu2028A total of 60 patients Blood Glucose control and QOL of Type 2 diabetes in outpatients were assigned randomly to the FGM intervention (n = 30) or the standard care (SMBG) group (n = 30) and followed for 12 weeks. Both groups were instructed to adjust their insulin doses in face-to-face visits at 1week, 2 weeks, 4 weeks and 12 weeks. Quality of life (DDS), HbA1c, and frequency of hypoglycemic events were evaluated.u2028u2028Resultsu202812 weeks later, compared to the baseline, SMBG group and FGM group had a significant declination in HbA1c, from 8.5u00b11.4% to 7.1u00b11.6%uff0cfrom 8.4u00b11.2% to 6.9u00b11.6%uff08Pu30080.05u3009, but there was no significant difference in both groups,uff08Puff090.05uff09. The incidence of hyperglycemia was significantly less in FGM group (Pu30080.05u3009. Similarly, DDS was significantly improved in FGM group than in SMBG groupuff08Pu30080.05uff09.u2028Discussionu2028FGM was associated with an enormous increase in treatment satisfaction , less hypoglycemia and similar glucose control in outpatients with insulin injection.

In order to accurately identify potential urban green technology innovation influencing factors, a three-dimensional framework of "market-institution-technology" was constructed based on asymmetric innovation theory. Based on the asymmetric innovation theory, a three-dimensional framework of "market-institution-technology" was constructed, and a combination of necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis (fsQCA) was used to investigate the paths of improving green technology innovation in 296 cities in China from a histological perspective.

My dissertation explores how skilled return migrants adapt the expertise they gained working abroad to workplaces in their home countries with data from an original survey of 4,183 skilled returnees from 81 countries, who had worked abroad in the U.S. First, I find that whether returnees are successful at transferring knowledge depends less on their individual ability and more on features of the professional environments in their home countries. Also, returnees who are effective brokers of knowledge bring back ideas about management practices more than they do technical knowledge. Third, returnees are more likely to become entrepreneurs if they have strong local ties to their home countries, the effect of which is stronger in countries with already high rates of self-employment. Finally, the very returnees who are most successful as knowledge brokers are also most likely to venture abroad again. These findings challenge the conventional notion of return migrants as agents of economic transformation, suggesting that many institutional challenges stand in the way of their ability to broker resources across borders.

To alleviate the spectrum scarcity problem, the FCC has been suggested a brand new paradigm for dynamically accessing the allocated spectrum. Cognitive radio (CR) technology has emerged as a promising solution to realize dynamic spectrum access (DSA). With the capability of sensing the frequency bands in a time and locationvarying spectrum environment and adjusting the operating parameters based on the sensing outcome, CR technology allows an unlicensed user to exploit the licensed channels which are not used by licensed users in an opportunistic manner. In this research, an intelligent spectrum mobility and resource management frame-work in CR ad hoc networks is explored. In particular, five spectrum mobility and resource management issues in CR ad hoc networks are investigated: 1) global time synchronization in CR ad hoc networks; 2) spectrum sensing scheduling scheme to dif-ferentiate the signals from primary users (PUs) and secondary users (SUs); 3) power control scheme for concurrent transmissions of location-aware mobile cognitive radio ad hoc networks; 4) jointly power adaptation and spectrum handoff scheme in mobile cognitive radio networks; and 5) end-to-end congestion control scheme in multi-hop cognitive radio ad hoc networks. The contributions of the research include: 1) fundamentally solving the global time synchronization problem for the CR ad hoc network in a time and location-varying spectrum environment. 2) Joint power control, frequency selection, and spectrum handoff is proposed in mobile CR ad hoc network. 3) Upper layer spectrum sensing scheme can differentiate the signals from PUs and SUs. 4) The proposed end-to-end congestion control protocol can significantly enhance the transport layer of the CR ad hoc networks. To the best of our knowledge, this is the first work that comprehensively investigate the spectrum mobility and resource management issues from the physical layer to the transport layer in mobile CR ad hoc networks.

Leverage effect has become an extensively studied phenomenon which describes the negative relation between the stock return and its volatility. Although this characteristic of stock returns is well acknowledged, most studies about it are based on cross-sectional calibration with parametric models. Other than that, most previous work are over daily or longer return horizons and usually do not specify the quantitative measure of it. This paper provides nonparametric estimation of a class of stochastic measures of leverage effect for both cases with and without microstructure noise, and studies the statistical properties of the estimators when the log price process is a quite general continuous semimartingale, in the stochastic volatility context and for high frequency data. The consistency and limit distribution of the estimators are derived, and simulation results present the properties accordingly. This estimator also provides the opportunity to study the empirical relation between skewness and leverage effect, which further leads to the prediction of skewness. Furthermore, adopting similar ideas to these in this paper, it is easy to extend the study to other important aspects of the stock returns, e.g. volatility of volatility.

ACM e-Energy has become the reference conference for researchers working in the areas of computing and communication for smart energy systems and energy-efficient computing and communication systems. In line with the global effort towards sustainability and in contrast to climate change, ACM e-Energy is looking with particular attention at analysis and solutions for carbon emission reduction and a sustainable development of infrastructures, services and technologies in the information and communications fields.

Reservoir tank modeling has traditionally been employed to simplify complicated reservoir simulation models and to reduce computational time whilst maintaining model accuracy. In this thesis, we refine this concept by replacing a simple tank model with a system of ordinary differential equations (ODEs) to model the dynamic changes of well inflow, aquifer influx, fluid compressibility, and pore volume. A dual time step method is used to solve the system of equations, which is not included in the existing model. Well transmissibility and aquifer sizes are kept constant during small time steps in which pressures and flow rates are solved. The new pressure is then used to update the well indices and aquifer size over larger time steps. This new model is transient during a single large time step calculation and hence represents an enhancement over standard finite difference method formulations. The reservoir is subdivided into a number of subvolumes representing individual reservoir compartments and aquifers, which may or may not be in communication. Using the concepts of transmissibility and compressibility, the complex 3D reservoir system is converted into a model that establishes flow into wells and between compartments. Pressure loss due to friction along the well is also fully integrated in the model. The multiple reservoir compartments and flowing wellbore are coupled to provide influx and inter-compartment fluid transfer. Employing the fourth-order Runge-Kutta Method, the ordinary differential equations generated by the system of reservoir units, are solved accurately and efficiently. The new method is verified by comparing it with a standard reservoir simulation launcher (Eclipse Trademark of Schlumberger Technology Corporation). Case studies are utilized to illustrate the results of the method which predict oil/gas production with water encroachment from an aquifer. Sensitivity analysis is performed to understand the relationships between input variables and output results in the model. For black oil reservoirs, this model incorporates wellbore friction and up to fifty reservoir compartments, which allows us to more accurately predict the reservoir performance. In addition, this model incorporates and compares the effects of compressibility for gas reservoirs, the results show that for those gas reservoirs with high rock compressibility, the gas reservoir model with water compressibility and pore volume term considered must be used in order to obtain more realistic simulation results.

Fecal pollution is a primary concern for recreational water bodies in US. The current water quality criteria are based on the cultivability of fecal indicator bacteria like E. coli and enterococci. These indicators are ubiquitous in the digestive tract of warm-blooded animals but are also known to persist and even multiply in the environment given favorable conditions. Hence their presence does not provide information on the sources of fecal contamination. In the past decade microbial source tracking (MST), the research area that uses host-specific microbes to identify the sources of fecal pollution, has undergone rapid development. A brief historical overview is provided in Chapter 1 and state-of-the art methods are presented and analyzed in Chapter 2. Quantitative PCR (qPCR)-based MST methods have been widely used for two reasons: they can detect both cultivable and uncultivable microbes and their process time is shorter comparing to other MST methods. The results from qPCR reactions need to be further processed to achieve quantitative MST because of analytical complications caused by the environmental water matrix. Two of the most important aspects will be addressed in Chapters 3 and 4. Chapter 3 attempts to solve the imperfect specificity and sensitivity of the qPCR assays with a statistical procedure that evaluates the impact of false positive/negative information. Chapter 4 addresses the inaccuracy in qPCR data analysis caused by the high noise signal and inconsistent amplification efficiency frequently seen in environmental water samples. Corrective measures are proposed to improve the accuracy of estimated DNA concentrations.

Recent breakthroughs in deep learning have made possible the learning of deep layered hierarchical representations of sensory input. Stacked restricted Boltzmann machines (RBMs), also called deep belief networks (DBNs), and stacked autoencoders are two representative deep learning methods. The key idea is greedy layer-wise unsupervised pre-training followed by supervised fine-tuning, which can be done efficiently and overcomes the difficulty of local minima when training all layers of a deep neural network at once. Deep learning has been shown to achieve outstanding performance in a number of challenging real-world applications. Existing deep learning methods involve a large number of meta-parameters, such as the number of hidden layers, the number of hidden nodes, the sparsity target, the initial values of weights, the type of units, the learning rate, etc. Existing applications usually do not explain why the decisions were made and how changes would affect performance. Thus, it is difficult for a novice user to make good decisions for a new application in order to achieve good performance. In addition, most of the existing works are done on simple and clean datasets and assume a fixed set of labeled data, which is not necessarily true for real-world applications. The main objectives of this dissertation are to investigate the optimal meta-parameters of deep learning networks as well as the effects of various data pre-processing techniques, propose a new active labeling framework for cost-effective selection of labeled data, and apply deep learning to a real-world application--emotion prediction via physiological sensor data, based on real-world, complex, noisy, and heterogeneous sensor data. For meta-parameters and data pre-processing techniques, this study uses the benchmark MNIST digit recognition image dataset and a sleep-stage-recognition sensor dataset and empirically compares the deep network''s performance with a number of different meta-parameters and decisions, including raw data vs. pre-processed data by Principal Component Analysis (PCA) with or without whitening, various structures in terms of the number of layers and the number of nodes in each layer, stacked RBMs vs. stacked autoencoders. For active labeling, a new framework for both stacked RBMs and stacked autoencoders is proposed based on three metrics: least confidence, margin sampling, and entropy. On the MINIST dataset, the methods outperform random labeling consistently by a significant margin. On the other hand, the proposed active labeling methods perform similarly to random labeling on the sleep-stage-recognition dataset due to the noisiness and inconsistency in the data. For the application of deep learning to emotion prediction via physiological sensor data, a software pipeline has been developed. The system first extracts features from the raw data of four channels in an unsupervised fashion and then builds three classifiers to classify the levels of arousal, valence, and liking based on the learned features. The classification accuracy is 0.609, 0.512, and 0.684, respectively, which is comparable with existing methods based on expert designed features.

Sialic acids (SAs) are a diverse family of naturally occurring 2-keto-3-deoxy-nononic acids that are involved in a wide range of biological processes, including early fetal development, cellular recognition, and utilization by microbes. While it is clear that cell surface SAs are highly involved in the immune system, the sialylation status of individual immune cells and functions are still unknown. In this study, I combined the newly developed LC-MS/MS methods with flow cytometry and confocal microscopy to systematically study the sialylation and desialylation dynamics of macrophages at different conditions. First, I developed an accurate LC-MS/MS method to quantify free SA in human plasma with isotope-labeled standard calibration and 3,4-diaminotoluene derivatization. This method is capable to distinguish SA analogus in complex biological samples, which paves the path for dynamic SAs research. Menwhile, another LC-MS/MS method with direct SAs quantification was developed for high throughtput analysis. This method does not require complicated sample preparation and can quantify SA at 2 ng/mL. Next, I performed globally profiling of sialylation status of Raw 264.7 macrophages by flow cytometry, confocal microscopy, and LC-MS/MS. Both flow cytometry and confocal microscopy showed the predominat of a-2,3 linked SAs on the cell surface, and increase of a-2,6 linked SAs after atorvastatin treatment. Moreover, LC-MS/MS showed total SA increased 3 times upon treatment. Further experiment indicated the correlation of a-2,6 linked SAs with cell apoptosis. Finally, I systematically examined the sialylation and desialylation profiles of THP-1 monocytes after differentiation and polarization. Both a-2,3 and a-2,6 linked SAs on the cell surface were decreased during diffrentiation, which was in accordance with the increased free SA in the medium and elevated activity of NEU1 sialidase. Meanwhile, the increase of SA expression during differentiation was evidenced by siaoglycoconjugates inside the cells and total SA in the cell lysate. Overall, the combined approach has bee successfully applied to profile SAs in the cell culture system. LC-MS/MS can accurately quantify SA in a high throughtput fashion. The SA linkages can be distinguished by flow cytometry and confocal microscopy with specific lectin labelings. The SA levels and linkages provide markers of cells at different status.

Informal care is a primary source of support for older adults with cognitive impairment but is less available to those who live alone. We leverage the U.S. Health and Retirement Survey 2000-2018 to examine trends in the prevalence of physical disability and social support among older adults with cognitive impairment living alone, and their gender and racial/ethnic disparities. Information on physical disability and social support was collected through measures of basic and instrumental activities of daily living (BADLs, IADLs). Logistic and Poisson regression were adopted to estimate linear trends over time for binary and integer outcomes, respectively. Among those who reported BADL/IADL disability, the proportion unsupported for BADLs decreased significantly over time, while the proportion unsupported for IADLs increased significantly over time. Among those who received IADL support, the number of unmet IADL support needs increased significantly over time. Over time, Black respondents had a relatively increasing trend of being BADL-unsupported, and Hispanic and Black respondents had a relatively increasing trend in the number of unmet BADL needs, compared to the corresponding trends in White respondents. Among US older adults with cognitive impairment living alone, fewer people received IADL support over time, and the extent of unmet IADL support needs increased. Racial/ethnic disparities were seen both in the prevalence of reported BADL/IADL disability and unmet needs for BADL/IADL support; some but not all were compatible with a reduction in disparity over time. Our findings may prompt customized interventions to reduce disparities and unmet support needs.