New Releases by Dan Wang

A riveting, firsthand investigation of China’s seismic progress, its human costs, and what it means for America. For close to a decade, technology analyst Dan Wang—“a gifted observer of contemporary China” (Ross Douthat)—has been living through the country’s astonishing, messy progress. China’s towering bridges, gleaming railways, and sprawling factories have improved economic outcomes in record time. But rapid change has also sent ripples of pain throughout the society. This reality—political repression and astonishing growth—is not a paradox, but rather a feature of China’s engineering mindset. In Breakneck, Wang blends political, economic, and philosophical analysis with reportage to reveal a provocative new framework for understanding China—one that helps us see America more clearly, too. While China is an engineering state, relentlessly pursuing megaprojects, the United States has stalled. America has transformed into a lawyerly society, reflexively blocking everything, good and bad Blending razor-sharp analysis with immersive storytelling, Wang offers a gripping portrait of a nation in flux. Breakneck traverses metropolises like Shanghai, Chongqing, and Shenzhen, where the engineering state has created not only dazzling infrastructure but also a sense of optimism. The book also exposes the downsides of social engineering, including the surveillance of ethnic minorities, political suppression, and the traumas of the one-child policy and zero-Covid. In an era of animosity and mistrust, Wang unmasks the shocking similarities between the United States and China. Breakneck reveals how each country points toward a better path for the other: Chinese citizens would be better off if their government could learn to value individual liberties, while Americans would be better off if their government could learn to embrace engineering—and to produce better outcomes for the many, not just the few.

"What does it mean to be a person? In the West, we might identify traits like possessing a "voice," having the capacity for love, and being self-determined. But if words like "person," "human," or "love" seem to carry an internal meaning, where does this meaningfulness come from? Dan Wang turns to romantic comedies, action-thrillers, queer melodramas, and opera for answers. Lyric Personhood explores the low-brow aesthetics of the sentimental and the melodramatic in these genres, arguing that a "person" has always been an aesthetic concept, and not just a legal, moral, political, or philosophical one, in the history of European culture since the Enlightenment. Part of the reason this unspoken register of personhood has such enduring force, Wang asserts, is that they do not only exist as language or as ideas but are also encoded into the audiovisual fabric of Western storytelling. In other words, when someone watches a romantic comedy, they are absorbing not only arguments or ideas about love and its place in a life worth living, but also configurations of space and time, sonic phrasings of foreground and background, and other aesthetic patterns that produce a felt sense of what it means to be a fully realized person. Lyric Personhood traces a formal imagination of personhood that threads through opera and film aesthetics, allowing a longer story to be told about the conditions that make personhood imaginable in the West"-- Provided by publisher.

Digital learning environments are becoming increasingly ubiquitous as a wide range of EdTech products and services enter classrooms and households across the globe. One salient attribute of these environments is their capacity to generate large amounts of data as students interact with the technology. These data logs can help construct a detailed picture of how students work on a task and provide valuable insights into their underlying competencies. At the same time, the sheer volume of interaction data poses challenges, such as how to extract meaningful behavioral patterns from the raw data and model them to assess specific constructs. This dissertation contributes to the efforts of educational researchers and practitioners in harnessing the data generated by digital technology to support teaching and learning, with an emphasis on using interactive tasks to assess and teach problem-solving practices.

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.

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.

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.

"In spite of the fact that Iceland is frequently regarded as the archetypal example of mantle plumes, the existence, depth extent, origin, dimension and excess temperature of the hypothesized plume remain enigmatic and hotly debated. The controversy mostly originates from the limited vertical resolution of seismic tomography techniques and the associated uncertainty in the depth and lateral extents of the lower wavespeed anomaly. Here we utilize a robust receiver-function-based technique to image the topography of the 410 and 660 km discontinuities bordering the mantle transition zone beneath Iceland and surrounding oceanic regions, and construct thermal and seismic wavespeed models of the upper mantle and mantle transition zone based on the observations. The preferred model invokes a broad plume laterally extending ~1000 km originated from the lower mantle. The dominant phase transition across the 660-km discontinuity is the post-spinel transition in the peripheral area, but becomes the post-garnet transition in the central portion of the plume stem due to the excessive temperature anomaly. This phase transition variation significantly enlarges the plume dimension and enhances upwelling of plume material"--Abstract, page iv.

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.

Government revenue forecasting errors have become larger, especially in exceptional times such as the periods surrounding economic recessions. Inaccurate revenue estimates stem from unanticipated revenue increases or decreases from a previous trend. Unfortunately, current forecasting methods relying primarily on trend analysis do not incorporate these kinds of sudden changes easily. When revenue punctuations occur, the revenue forecasting errors increase.To reduce forecasting errors caused by revenue punctuations in government revenue collections, I argued that analysts must not dismiss outliers as extraneous or useless phenomena. My research revealed an approach to incorporate outliers or punctuations into revenue forecasting. First, this research studied the criterion for judging the appearance of revenue punctuations using state governments’ quarterly collections of the five largest taxes from 1977 to 2016. Second, the research explored the patterns of these revenue punctuations, specifically the relationship between the changes in dollar amount and the amount of time from one revenue punctuation to another. Inspired by the few statistical techniques for identifying outliers, this research applied the studentized residuals method to detect the revenue punctuations. The result revealed that all five tax categories for each state have revenue punctuations, except Motor Fuels Tax in the state of Tennessee. Furthermore, this research disclosed that while not all the states and all the tax categories have statistically significant relationships between the depth and length of revenue punctuations, some states still have valid relationships. For the states that have statistically significant relationships, a forecaster, knowing depth, could calculate length and vice versa. Thus, the forecasting errors caused by revenue punctuations could be reduced when the protocols my research identified are used.

This research involved randomly choosing 155 sets of experimental data that were collected from building companies. These sets of data were the result of lime analyses by sucrose titration done within 12 months by the laboratory attendants. The results of this experimental data were then gathered to be analyzed using the linear fitting formula. The results indicate that the analysis can accurately determine quicklime''s effective calcium content by regression analysis using the digestion temperatures. In addition, the X-ray diffraction method was used to test the quicklime''s calcium content, which verified that the method of linear fitting has good representativeness and practicability. This can greatly optimize the traditional process and has the prospect of being used widely.

The advent of machine learning (ML) tools presents researchers with the possibility of using large and new datasets related to text and image repositories. In this paper, we make a methodological contribution to strategy research by documenting a novel synthesis of two machine learning methods—the unsupervised topic modeling of textual data and the supervised ML coding of facial images with a neural network algorithm. We employ these novel methods to study CEO oral communication, using videos and corresponding transcripts of emerging market CEO interviews to conduct our analysis. Building on Helfat and Peteraf (2015) who document the importance of “oral language” as an important managerial cognitive capability, we code the topics and sentiments expressed in the text of what the CEOs say (verbal language) and separately code the facial expressions of the CEOs (non-verbal communication). Using the interview text sentiment scores as well as our video-based facial expression sentiment variables, we conducted factor analysis to construct four distinct CEO oral communication “styles,” which we label Expressive, Stern, Dour, and Contented. We also reveal that CEOs who communicate with certain styles also tend to focus on specific topics, even controlling for their country of origin and gender. For example, CEOs who tend to be more expressive devote more attention to topics related to society at large and avoid topics related to the government. By contrast, dour CEOs are more likely to dwell on topics related to both the government and society. These results suggest that a CEO’s communication style reveals a substantial amount of information about their attention to certain aspects of their businesses.

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 unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics.

Older adults with cognitive impairment have significantly burdened their families and the society due to costly caring and waste of labors. Developing intelligent assistant agents (IAAs) in smart homes that can help those people accomplishing activities of daily living (ADLs) independently has attracted tremendous attention, from both academia and industry. Ideally, IAAs should recognize older adults'' goals and reason about further steps needed for the goals. This paper proposed a goal recognition and planning algorithm to support an IAA in smart home. The algorithm addresses several important issues. First it can deal with partial observability by Bayesian inference for step recognition. Even advanced sensors are not guaranteed to be 100% reliable. Besides, due to limited accessibility or privacy, not all attributes of physical objects can be measured by sensors. The proposed algorithm can reason about ongoing goals with some sensors missing or unreliable. Second, the algorithm reasons about concurrent goals. For everyday life, a person is typically involved in multi-tasks by switching back and forth. Based on the context, the proposed algorithm can assign a step to the correct goal and keep tracks of the goal''s ongoing status. The context involves status of ongoing goals inferred from a recognition procedure, and desired next steps and tasks, which are obtained through a planning procedure. Last but not least, the algorithm can handle incorrectly executed steps. For older adults with cognitive impairment, executing unrelated or wrong steps towards certain goals is common in their daily life. A module is designed to hand wrong steps by detecting and then prompt the person with correct steps. The algorithm is based on Hierarchical Task Network (HTN), of which the knowledge base is composed of methods (for tasks) and operators (for steps). Such hierarchical modeling of tasks and steps enables the algorithm to deal with partially ordered subtasks and alternative plans. Furthermore, the preconditions of methods and operators enable to generate feasible hints of next steps and tasks by considering uncertainties in belief states. In the experiment, a simulator is designed to simulate the virtual sensors and a virtual human executing a sequence of steps predefined in a test case. The algorithm is tested on many simulated easy or difficult cases. For example single goal and correct steps are easy test cases. Having multiple goals with wrong steps makes the problem more difficult. Also cases of sensors missing are experimented. The results shows that the algorithm works very well on simple cases, achieving nearly 100% accuracy. Even for the hardest cases, the performance is acceptable when sensor reliabilities are above 0.95. Test cases with missing sensors also provide meaningful guideline for setting up sensors for an intelligent assistant agent.

Chapter 1 considers how feeling and expression acquired the normative burden of representing the truth of the self; it does so by tracing developments in operatic form and culminates in a reading of The Marriage of Figaro ''s closing scene. Then, each of the subsequent chapters extends the analysis toward the present while also revisiting different facets of the Figaro case: Chapter 2 considers the relation between political knowledge and epiphany in two operas by Richard Wagner; Chapter 3 explores the epistemology of the soundtrack in Hollywood romantic comedy; and Chapter 4 addresses, in three films in different genres that star Colin Firth, the generality of the liberal scene in which a lyrical voice establishes the sound of a repaired social collectivity.

This research provides a theoretical foundation for our previous empirical finding that leverage effect has a role in estimating and forecasting volatility. This empirics is also related to earlier econometric studies of news impact curves (Engle and Ng, Chen and Ghysels). Our new theoretical development is based on the concept of projection on stable subspaces of semi-martingales. We show that this projection provides a framework for forecasting (across time periods) that is internally consistent with the semi-martingale model which is used for the intra-day high frequency asymptotics. The paper shows that the approach provides improved estimation and forecasting both theoretically, in simulation, and in data.

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.

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.

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.

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.

The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.