1,724,575 research outputs found
Diesel exhaust particles alter endothelial tube permeability:
No full textEpidemiological studies suggest that an increase of diesel exhaust particles (DEP) in ambient air corresponds to in an increase in hospital recorded myocardial infarctions within 48 hr after exposure. Among the many theories to explain this data are endothelial dysfunction and translocation of DEP into the vasculature. We hypothesized that translocation of DEP occurs because endothelial cells become permeable after exposure. To support this hypothesis, in vitro-assembled endothelial tubes were used to evaluate how DEP affected parameters influencing permeability, i.e., cell-cell junction integrity, and proinflammatory and oxidative stress-induced upregulation of Vascular Endothelial Growth Factor (VEGF, also known as Vascular Permeability Factor). Our first experiments demonstrated that the adherens junction molecule, VE-Cadherin, becomes redistributed from the membrane at cell-cell borders to the cytoplasm in response to DEP, separating the plasma membranes of adjacent cells. DEP were occasionally found in the endothelial cell cytoplasm and in the tube lumen. A second set of experiments demonstrated that DEP induced the generation of ROS, such as H2O2 in the HUVEC tube cells. Transcription factor Nrf2 was translocated to the cell nucleus and activated
transcription of the antioxidative enzyme HO-1. ELISA assays determined that DEP increased secretion of pro-inflammatory cytokines IL-6 and TNF-α. The oxidative and pro-inflammatory responses both induced secretion of VEGF, a factor known to enhance permeability. Usually, vascular permeability is associated with activation of the Akt pathway leading to increased cell survival. A third set of experiments found that DEP-induced permeability was instead associated with increased apoptosis. This was the associated with deactivation of the Akt pathway. These results suggest mechanisms for how DEP may affect in vivo capillaries.Ph.D.Includes bibliographical references (p. 148-167)by Ming-Wei Cha
Scholarly Program Notes
Get PDFAN ABSTRACT OF THE RESEARCH PAPER OF
MING WEI NEO, for the Master of Music degree in COLLABORATIVE PIANO, presented on APRIL 11, 2012, at Southern Illinois University Carbondale.
TITLE: SCHOLARLY PROGRAM NOTES
MAJOR PROFESSOR: Dr. Paul Transue
This document is a compilation of biographical and musical information that serves to inform the audience about the music presented at the graduate recital of Ms. Ming Wei Neo. The works discussed will include an opera scene from Act I and the arias, “Che gelida manina” and “Mi chiamano Mimì” from La Bohème by Giacomo Puccini; a song cycle, A Charm of Lullabies Op.41 by Benjamin Britten; four songs, “Il pleure dans mon cœur”, “L’ombre des arbres”, “Green” and “Spleen” from Ariette oubliées by Claude Debussy; Meine Liebe ist grün, Der Tod, das ist die kühle Nacht, Botschaft, O kühler Wald, and Von ewiger Liebe by Johannes Brahms; and Siete canciones populares españolas by Manuel de Falla
Learning to be smart: can humans learn to improve profitability and risk control in financial trading?
No full textRecently, the efficient market hypothesis has faced strong challenges from various fields, and the purpose of this thesis is to provide empirical evidence for the challenges to the efficient market hypothesis from two perspectives. The first one is from the field of machine learning. While an increasing number of machine learning studies report the high accuracy of stock market prediction, this is not consistent with the efficient market hypothesis which suggests that current stock prices discount available information and that it is not possible to obtain systematic returns by exploiting any predictability of prices. As most of the machine learning studies choose relatively simple test settings, I suspect that the reported high accuracy might result from biased performance measurement. That is, the selection of methodological factors is influential on prediction performance in stock markets. To test my conjecture, I run the benchmark with a comprehensive combination of the methodological factors to collect the performance measures under various settings. Next, I analyze the relationship between the prediction performance and the methodological factors. I find the significant influence of the selection of methodological factor on prediction performance, which means that the reported high prediction performance might be biased and my results are not against the prediction of the efficient market hypothesis.The second challenge is that there is increasing evidence of anomalies in financial markets. This suggests that the underlying rationality principle of the efficient market hypothesis may be flawed. The manner in which individuals learn from experience also remains a matter of debate. The rationality assumption would be justified if individuals follow Bayesian learning, i.e., individuals learn from experience to appropriately adjust their probability estimates and finally make rational and appropriate decisions. To examine the relationship between experience and performance measures, I use linear mixed models to analyze spread trading data. I find that, as individuals gain experience, they increase their degree of risk-taking and realize higher returns. However, thesereturns are subject to greater volatility and, as a result, they achieve lower risk-adjusted returns. Since the individuals following Bayesian learning should be able to appropriately update probability estimates conditioned on new information, their decision choices and their risk-adjusted performance should be improved. My results show that individuals fail to follow Bayesian learning. On the other hand, my results can be explained by reinforcement learning, wherein individuals repeat behavior that was rewarding in the past. Traders may try several trading strategies with different levels of risk. Since higher risk generally brings both higher profits and greater losses, traders who undertake riskier strategies will either make higher profits or suffer greater losses. Those traders making a higher profit are reinforced by the riskier strategies and overlook the underlying risk, which leads to lower risk-adjusted performance. Hence, my results cast doubt on the validity of the rationality assumption.To further explore the degree of rationality with trading data, I propose a method to estimate the degree to which an individual behave like a rational agent, and other behavioral characteristics. The experience weighted attraction (EWA) can be used to estimate the degree of rationality in psychological experiments, but cannot be used with trading data. The reason is that the number of strategies available to decision makers was limited in psychological experiments, but in real-world trading environments, traders have no limits in terms of the strategies they can adopt. We propose a decision-based strategy mapping framework (DSM) to resolve this problem. The DSM is designed to artificially limit the strategy space associated with real-world trading data, by using scenarios. In each scenario, individuals are assumed to have only one decision to make. This allows us to estimate, using data associated with an individual’s real-world trading, their behavioral characteristics associated with EWA. Subsequently, we examine the relationship between the estimated behavioral characteristics of traders and their trading behavior and performance. My results suggest that those traders who behave like rational agents tend to trade more actively. However, surprisingly, those traders who are more rational do not achieve superior trading performance.In conclusion, the findings of this thesis support the efficient market hypothesis that the markets are efficient, at least to the extent to which excess returns cannot be earned with state of the art machine learning techniques. However, the results of learning behavior from individual-level analysis suggest that the rational agent assumption of the efficient market hypothesis is likely to over-simplify individual behavior in the real world.<br/
Spatial Transformation in Shanghai: the strategy, institutional arrangement and planning procedures - the case of EXPO 2010
Get PDFAs the economic center of China, Shanghai has achieved 8.2% of GDP increasing in 2011, and the GDP per capital reached $ 12 784, which is close to the level of some developed countries. Meanwhile, its urbanization rate has been 89% in 2009, the whole city is going through severe economic and spatial transformation and requalification. This paper aims to take EXPO 2010 as a case to interpret the strategic logic, institutional arrangement and planning procedures in Shanghai in recent years. The EXPO 2010 might be an extreme case not only because it is a public project in a very big scale, but also because it is the first time of a developing country holding EXPO, which gives this project political meaning - a successful EXPO is required for its international reputation. But it is exactly such a project that could reveal its real motivation, its institutional arrangement which the city considered as the most efficient, and the innovation of planning procedures which could be the paradigm of the future practice. The first part of the paper will introduce the identity card and the chronology of EXPO 2010, also the economic situation and spatial planning documents will be presented to help us understand the strategic purpose of EXPO 2010: it is a good opportunity for the city to transfer it economic structure from industrialization to post-industrialization, to revive the inner city, to integrate both sides of the important River crossing the city - Huangpu River, and to redefine Huangpu River as a symbol of post industrialization. In the succeeding parts, the leading strategy of pre-post strategy will be introduced: the planner stretched the planning effective date to 2020 when a world city is expected, and made the design backwards. In this way, the planning structure, the infrastructure and a big percentage of building which are constructed for EXPO 2010 will be directly put into operation after EXPO. In the institutional aspect, the urban government seems to play a role of developer: they turn the degraded industrial and residential land into prepared culture, business and top-class residential land, and release it to private developer again. In the planning procedure aspect, the chief planners, the decision makers and the implemental planner for the first time work closely, to make sure the plan could instruct the projects. Lastly, the theoretical base, breakthrough and criticisms will be discussed based on this case
Identifying inter-project relationships with recurrent neural networks: towards an AI framework of project success prediction
No full textA growing number of emerging studies have been undertaken to examine the mediating dynamics between intelligent agents, activities, and cost within allocated budgets to predict the outcomes of complex projects in dint of their significant uncertain nature in achieving a successful outcome. Emerging studies have used machine learning models to perform predictions, and artificial neural networks are the most frequently used machine learning model. However, most machine learning algorithms used in prior studies generally assume that input features, such as project complexity, team size and strategic importance, and prediction outputs, are independent. That is, a project’s success is assumed to be independent of other projects. As the datasets used to train in prior studies often contain projects from different clients across industries, this theoretical assumption remains tenable. However, in practice, projects are often interrelated across several dimensions, such as distributed overlapping teams. Therefore, we argue that the inter-project relationships should be taken into consideration to improve prediction performance. Furthermore, an ongoing ethnographic study at a leading project management artificial intelligence consultancy, referred to in this research as Company Alpha, suggests that projects within the same portfolio frequently share overlapping characteristics. To capture the emergent inter-project relationships, this study aims to compare two specific types of artificial neural network prediction performances; (i) multilayer perceptron and; (ii) recurrent neural networks. The multilayer perceptron is one of the most widely used artificial neural networks in the project management literature, and recurrent networks are distinguished by the memory they take from prior inputs to influence input and output. Through this comparison, this research will examine whether recurrent neural networks can capture the potential inter-project relationship towards achieving improved performance in contrast to multilayer perceptron. Our empirical investigation using ethnographic practice-based exploration at Company Alpha will contribute to project management knowledge and support developing an intelligent project prediction AI framework with future applications for project practice
The Effect of Crystallinity and Aging Enthalpy on the Mechanical Properties of Gelatin Films
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Applied algorithmic machine learning for intelligent project prediction: towards an AI framework of project success
No full textA growing number of emerging studies have been undertaken to examine the mediating dynamics between intelligent agents, activities, and cost within allocated budgets, in order to predict the outcomes of complex projects in dint of their significant uncertain nature in achieving a successful outcome. For example, prior studies have used machine learning models to calculate and perform predictions. Artificial neural networks are the most frequently used machine learning model with support vector machine, and genetic algorithm and decision trees are sometimes used in several related studies. Furthermore, most machine learning algorithms used in prior studies generally assume that inputs and outputs are independent of each other, which suggests that a project's success is expected to be independent of other projects. As the datasets used to train in prior studies often contain projects from different clients across industries, this theoretical assumption remains tenable. However, in practice projects are often interrelated across several different dimensions, for example through distributed overlapping teams. An ongoing ethnographic study at a leading project management artificial intelligence consultancy, referred to in this research as Company Alpha, suggests that projects within the same portfolio frequently share overlapping characteristics. To capture the emergent inter-project relationships, this study aims to compare two specific types of artificial neural network prediction performances; (i) multilayer perceptron and; (ii) recurrent neural networks. The multilayer perceptron has been found to be one of the most widely used artificial neural networks in the project management literature, and recurrent networks are distinguished by the memory they take from prior inputs to influence input and output. Through this comparison, this research will examine whether recurrent neural networks can capture the potential inter-project relationship towards achieving improved performance in contrast to multilayer perceptron. Our empirical investigation using ethnographic practice-based exploration at Company Alpha will contribute to project management knowledge and support developing an intelligent project prediction AI framework with future applications for project practice
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Trust in AI systems for project and risk management: evaluating the role of transparency, reputation, technical competence, and reliability
No full textMachine learning algorithms are often perceived as opaque, undermining user trust in AI systems. Explainable AI (XAI) seeks to mitigate this by enhancing transparency through clear explanations of algorithmic predictions. Furthermore, the reliability of these systems can be improved by integrating predictions from multiple algorithms. This study developed a hybrid project prediction system that merges XAI with several machine learning algorithms, thus fostering trust among project professionals and decision-makers. The theoretical model, grounded in literature on technology adoption, inter-organisational relationships, and XAI, examines four key trust factors: transparency, reputation, technical competence, and reliability. Employing a survey experiment and structural equation modelling, the research provides a nuanced understanding of how these factors influence trust in AI applications within project and risk management, contributing significantly to both academic literature and practical implementations
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