211,938 research outputs found

    Defining and Measuring High Technology in Georgia

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    This report defines and measures the high technology sector in Georgia

    Learning from our place in the global economy

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    A study undertaken for Greater Lincolnshire Local Enterprise Partnership into Greater Lincolnshire's economy and its relationship with similar regions in the UK and worldwide

    Probabilistic Anomaly Detection in Natural Gas Time Series Data

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    This paper introduces a probabilistic approach to anomaly detection, specifically in natural gas time series data. In the natural gas field, there are various types of anomalies, each of which is induced by a range of causes and sources. The causes of a set of anomalies are examined and categorized, and a Bayesian maximum likelihood classifier learns the temporal structures of known anomalies. Given previously unseen time series data, the system detects anomalies using a linear regression model with weather inputs, after which the anomalies are tested for false positives and classified using a Bayesian classifier. The method can also identify anomalies of an unknown origin. Thus, the likelihood of a data point being anomalous is given for anomalies of both known and unknown origins. This probabilistic anomaly detection method is tested on a reported natural gas consumption data set

    A Proposed Framework of Kansei Engineering Application in Dealing with Customer Emotional Needs in Services

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    Many studies on product designs have been widely conducted with a focus on functionality rather than human emotions. However, customers today are very dynamic and no longer focus only on functionality needs. Emotions increasingly, play an important role in purchasing decision. In dealing with customer emotional needs, Kansei Engineering is proposed. This approach captures customers’ desires and feelings (emotions/kansei) concerning products and translates these emotional needs into concrete product design. Kansei Engineering has been applied extensively in product design, but not in services. A service is an intangible product. It is the fastest growing sector in today’s businesses. Some prominent tools such as Quality Function Deployment (QFD) and Kano’s Model are often used in services, but not incorporating customer emotional needs. In addition, some attentions have been widely used in investigating customer emotional satisfaction in services. However, there is not a formal methodology that can account for customer emotional needs. Therefore, to fill in these niches, this paper provides a proposed framework of Kansei Engineering in services. The proposed framework incorporates QFD and Kano’s Model as methods which focus on customer satisfaction. By applying Kano’s Model, customer needs are exploited through a questionnaire, and, service attributes are classified. QFD is then used to transform customer emotional needs into engineering characteristics. In addition, other models such as Bayesian Network (BN) and Markov Chain are utilized as well. The latter two models are useful to promote prediction and diagnostic inference with a probability view point due to the dynamics of customer emotional needs. The use of such supporting models will enhance the ability of the Kansei Engineering methodology to meet a sudden change or trend of customers’ emotional needs. Essentially, the proposed framework will start and end with customers to achieve customer emotional satisfaction. In order to demonstrate the applicability of the Kansei Engineering approach in service design, this paper provides an illustration. A simple literature survey was conducted at a university field. A majority of the students spend most of their time on campus. Why do students spend so much time at the university when they can better spend their time elsewhere, such as at home? How can a university be made a convenient second home for students? In this research, these two questions will be answered and tackled by introducing a modified Kansei Engineering method. The university does not only provide an academic service, but also an emotional experience for students. It is hoped that by introducing an improved innovative framework of Kansei Engineering, it could increase the level of customer satisfaction in pursuit of customer loyalty and a long-term relationship eventually

    Winning and losing in the creative industries: an analysis of creative graduates' career opportunities across creative disciplines

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    Following earlier work looking at overall career difficulties and low economic rewards faced by graduates in creative disciplines, the paper takes a closer look into the different career patterns and economic performance of “Bohemian” graduates across different creative disciplines. While it is widely acknowledged in the literature that careers in the creative field tend to be unstructured, often relying on part-time work and low wages, our knowledge of how these characteristics differ across the creative industries and occupational sectors is very limited. The paper explores the different trajectory and career patterns experienced by graduates in different creative disciplinary fields and their ability to enter creative occupations. Data from the Higher Education Statistical Agency (HESA) are presented, articulating a complex picture of the reality of finding a creative occupation for creative graduates. While students of some disciplines struggle to find full-time work in the creative economy, for others full-time occupation is the norm. Geography plays a crucial role also in offering graduates opportunities in creative occupations and higher salaries. The findings are contextualised in the New Labour cultural policy framework and conclusions are drawn on whether the creative industries policy construct has hidden a very problematic reality of winners and losers in the creative economy

    Jobs and Income Growth of Top Earners and the Causes of Changing Income Inequality: Evidence from U.S. Tax Return Data

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    This paper presents summary statistics on the occupations of taxpayers in the top percentile of the national income distribution and fractiles thereof, as well as the patterns of real income growth between 1979 and 2005 for top earners in each occupation, based on information reported on U.S. individual income tax returns. The data demonstrate that executives, managers, supervisors, and financial professionals account for about 60 percent of the top 0.1 percent of income earners in recent years, and can account for 70 percent of the increase in the share of national income going to the top 0.1 percent of the income distribution between 1979 and 2005. During 1979-2005 there was substantial heterogeneity in growth rates of income for top earners across occupations, and significant divergence in incomes within occupations among people in the top 1 percent. We consider the implications for various competing explanations for the substantial changes in income inequality that have occurred in the U.S. in recent times. We then use panel data on U.S. tax returns spanning the years 1987 through 2005, to estimate the elasticity of gross income with respect to net-of-tax share (that is, one minus the marginal tax rate). Information on occupation allows us to control for other influences on income in a flexible way using interactions among occupation, position in the income distribution, stock prices, housing prices, and the business cycle. We also allow for income shifting across years in response to anticipated tax changes, for the long-run effect of a tax reform to differ from the short-run effects, for heterogeneous mean-reversion across incomes, and for heterogeneous elasticities across income classes. In a specification that does all this, we estimate a significant elasticity of 0.7 among taxpayers in the top 0.1 percent of the income distribution. Outside of the top 0.1 percent of the income distribution, we find no conclusive evidence of a positive elasticity of income with respect to net-of-tax shares. We find that the estimate for the top 0.1 percent is not robust to controlling for a spline in lagged income that is very flexible at the upper reaches of the income distribution, suggesting that the method used to allow for income dynamics is very important. Allowing for income shifting across years in response to anticipated tax changes has important consequences for the estimates.income distribution, behavioral response to taxation

    Detecting Slow Wave Sleep Using a Single EEG Signal Channel

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    Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

    Classification of Human Retinal Microaneurysms Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography

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    Purpose. Microaneurysms (MAs) are considered a hallmark of retinal vascular disease, yet what little is known about them is mostly based upon histology, not clinical observation. Here, we use the recently developed adaptive optics scanning light ophthalmoscope (AOSLO) fluorescein angiography (FA) to image human MAs in vivo and to expand on previously described MA morphologic classification schemes. Methods. Patients with vascular retinopathies (diabetic, hypertensive, and branch and central retinal vein occlusion) were imaged with reflectance AOSLO and AOSLO FA. Ninety-three MAs, from 14 eyes, were imaged and classified according to appearance into six morphologic groups: focal bulge, saccular, fusiform, mixed, pedunculated, and irregular. The MA perimeter, area, and feret maximum and minimum were correlated to morphology and retinal pathology. Select MAs were imaged longitudinally in two eyes. Results. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging revealed microscopic features of MAs not appreciated on conventional images. Saccular MAs were most prevalent (47%). No association was found between the type of retinal pathology and MA morphology (P = 0.44). Pedunculated and irregular MAs were among the largest MAs with average areas of 4188 and 4116 μm2, respectively. Focal hypofluorescent regions were noted in 30% of MAs and were more likely to be associated with larger MAs (3086 vs. 1448 μm2, P = 0.0001). Conclusions. Retinal MAs can be classified in vivo into six different morphologic types, according to the geometry of their two-dimensional (2D) en face view. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging of MAs offers the possibility of studying microvascular change on a histologic scale, which may help our understanding of disease progression and treatment response

    Bioaugmentation for Improved Recovery of Anaerobic Digesters After Toxicant Exposure

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    Bioaugmentation was investigated as a method to decrease the recovery period of anaerobic digesters exposed to a transient toxic event. Two sets of laboratory-scale digesters (SRT = 10 days, OLR = 2 g COD/L-day), started with inoculum from a digester stabilizing synthetic municipal wastewater solids (MW) and synthetic industrial wastewater (WW), respectively, were transiently exposed to the model toxicant, oxygen. Bioaugmented digesters received 1.2 g VSS/L-day of an H2-utilizing culture for which the archaeal community was analyzed. Soon after oxygen exposure, the bioaugmented digesters produced 25–60% more methane than non-bioaugmented controls (p \u3c 0.05). One set of digesters produced lingering high propionate concentrations, and bioaugmentation resulted in significantly shorter recovery periods. The second set of digesters did not display lingering propionate, and bioaugmented digesters recovered at the same time as non-bioaugmented controls. The difference in the effect of bioaugmentation on recovery may be due to differences between microbial communities of the digester inocula originally employed. In conclusion, bioaugmentation with an H2-utilizing culture is a potential tool to decrease the recovery period, decrease propionate concentration, and increase biogas production of some anaerobic digesters after a toxic event. Digesters already containing rapidly adaptable microbial communities may not benefit from bioaugmentation, whereas other digesters with poorly adaptable microbial communities may benefit greatly
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