Simulation Study of Day-Night Variations in Emissions Impacts and Network Augmentation Schemes: An Application to PierPASS Policy for Port Trucks in California

Bhagat, Ankoor University of California, Irvine 2014 해외박사(DDOD)

Freight operations are critical to our prosperity, but they also generate substantial external costs in the form of additional congestion, air pollution, and health impacts. Unfortunately these external costs are not well understood. In this dissertation, I focus on the drayage trucks that serve the San Pedro Bay Ports (or SPBP, i.e. the Ports of Los Angeles and Long Beach in Southern California), which is the largest port complex in the country. This research focuses on the PierPASS program, which shifts drayage trucks traffic from mid-day and peak hours to the evening and night hours. External costs from drayage trucks remain a major concern for communities adjacent to the ports because they bear a disproportionate fraction of the health impacts (respiratory and cardiovascular illnesses, cancers, and premature deaths) associated with the pollution generated by ports operations. In this context, the purpose of my dissertation is analyze the impacts of shifting freight traffic to off-peak periods with an emphasis on congestion, air pollution (NOx, and PM) and related health impacts. This impact analysis was conducted using a framework that integrates microscopic traffic simulation with emission estimation, air dispersion, and a health impact assessment. The research also developed a new approach for origin-destination demand estimation on large microscopic simulation network that is made by augmenting an existing simulation network. Thus the research makes both policy analysis and methodological contributions, and is expected to help enable policy makers to craft cleaner logistics policies. I found that PierPASS had little impact on traffic congestion and on overall emissions of various pollutants. However, PierPASS had a significant impact on the distribution of these emissions between day and night. During night-time, total port truck emissions increased by 71% for NOx and 72% for PM, while day-time emissions decreased by 9% for both NOx and PM. My dispersion analysis shows that PierPASS increased air pollutant concentrations during both day time and night time because of boundary layer effects. Finally, my health impact analyses using EPA's BenMAP model show that the annual social costs due to PierPASS are $438 million.

The relationship between factors that influence college choice and persistence in Longhorn Opportunity Scholarship recipients at the University of Texas at Austin

Bhagat, Geeta Srinivasan The University of Texas at Austin 2004 해외박사(DDOD)

Although students choose their colleges for a variety of reasons, low-income, first-generation, and minority students are often limited in their choices. Furthermore, even when those students do attend college, their persistence rates are generally lower than those of traditional students. This study examines the relationship between factors that influence choice and persistence in recipients of the Longhorn Opportunity Scholarship (LOS), a scholarship given to students from Texas high schools that are under-represented at the University of Texas at Austin (U.T. Austin), the flagship university of the University of Texas System. LOS students are usually low-income, thus, the scholarship offers these students an opportunity to attend a university they otherwise might not be able to attend. Furthermore, the academic and social support offered by U.T. Austin's Longhorn Scholars Program increases the likelihood these students will graduate. In this study, a qualitative methodology called Interactive Qualitative Analysis (IQA), developed by Norvell Northcutt at U.T. Austin, was used to study the relationship between choice and persistence in LOS recipients. One focus group of LOS freshmen addressed the question of why they chose U.T. Austin, while another focus group of LOS seniors addressed the question of what factors helped them persist. Understanding the relationship between choice and persistence for these students should assist university administrators in understanding what qualities attract talented low-income, first-generation and/or minority students to an institution, and if those same factors or others play a role in their persistence. If the students' pre-matriculation expectations are met by the university and these choice factors are similar to those factors that ensure persistence, then administrators can assume that programs such as the Longhorn Scholars Program are instrumental in the persistence of quality low-income, first-generation, and/or minority students.

Nogo Receptor 1 Restricts Plasticity in the Adult Brain

Bhagat, Sarah Maneck Yale University 2015 해외박사(DDOD)

Early in development the brain is extremely plastic and synapses are easily formed, modified, or eliminated. Following adulthood, remodeling of neural circuits and synapses becomes much more limited. Identifying molecular mechanisms that regulate plasticity in adulthood is important for targeting disease states in which a return to an enhanced plasticity state would be beneficial. The Strittmatter laboratory has identified Nogo Receptor 1 (NgR1) as a receptor that interacts with myelin associated inhibitors (MAIs) that inhibit axonal sprouting and recovery after spinal cord injury. The work presented here demonstrates a role for NgR1 in regulating plasticity in the healthy, intact adult brain. In Chapter 1, the research identifying NgR1, its ligands, and their role in the brain and central nervous system (CNS) is discussed. Included is a discussion concerning the nature of developmentally regulated plasticity in the adult brain in paradigms such as ocular dominance plasticity and fear erasure. Chapter 2 presents work done to explore the cellular mechanisms through which NgR1 restricts neuronal plasticity. Genetically deleting NgR1 resulted in enhanced structural plasticity, including increased gains and losses of spines in sensory and motor areas of cortex. Chapter 3 describes experiments showing genetic or pharmacological blockade of NgR1 enhances fear extinction learning. Chapter 4 describes structural and cell-specific mechanisms underlying the ability of NgR1 to regulate fear extinction. The final chapter expands these findings to explore the role of NgR1 in depressive-like behaviors following chronic stress. Genetic deletion of NgR1 in mice resulted in resilience to depressive-like behavior following chronic stress. Future experiments discussed in Chapter 6, are proposed to continue to explore basic cellular mechanisms of NgRI-mediated plasticity and further understand the translational impact of NgR1 in human disease.

Modeling critical factors to optimize the treatment of selected fruits and vegetables with chlorine dioxide gas using a miniaturized industrial-size tunnel system

Bhagat, Arpan R Purdue University 2010 해외박사(DDOD)

There has been an increase in the number of foodborne illness outbreaks associated with fresh produce in recent years. This indicates that the disinfection procedure employed in lowering the number of microorganisms on ready-to-eat produce is inadequate. These facts have prompted industry and regulatory agencies to seek alternative microbial intervention treatments. Our research has shown that chlorine dioxide gas (ClO2) is an effective surface antimicrobial agent. Use of ozone and traditional sanitation technologies such as chlorinated water are unable to cause a greater than three log reduction of microorganisms on the fresh produce surface. Gaseous ClO2, on the other hand, showed a greater than a 5 log reduction of microorganisms on selected fresh produce with diverse surface characteristics. This is consistent with the recommendations of the National Advisory Committee on Microbiological Criteria for Foods (NACMCF). However, in order to apply ClO2 gas technology on an industrial scale on contaminated fresh produce, the technology must be evaluated for its impact on the quality parameters such as nutritional content and visual rating of the fresh produce being treated. Thus to study the feasibility of the ClO2 gas technology as a surface disinfectant for the fresh produce, the long term objective of the study was to evaluate the inactivation of pathogenic bacteria and a non-pathogenic bacterial surrogate on the surface of 3 fresh produce surfaces and to integrate quality data with microbiological data to obtain equivalent/optimum processing parameters for ClO2 gas. The specific objectives were to: (1) Obtain the inactivation kinetics of bacteria [pathogens (Listeria monocytogenes and Salmonella enterica) and surrogate (Hafnia alvei)] on fresh produce surface (tomatoes, oranges, sprouts) using ClO2 gas. (2) Evaluate the effects of ClO2 gas treatments (0.1 mg/l to 5 mg/l) on the quality attributes of produce. The overall objective of this research was to develop a mathematical model and a rubric that takes into account various parameters (type of microorganisms, type of fruit/vegetable, ClO2 gas concentration and time) and correlates them with shelf life data for the decontamination of whole fresh produce surface.

Learning paraphrases from text

Bhagat, Rahul University of Southern California 2009 해외공개박사

Paraphrases are textual expressions that convey the same meaning using different surface forms. Capturing the variability of language, they play an important role in many natural language applications including question answering, machine translation, and multi-document summarization. In linguistics, paraphrases are characterized by approximate conceptual equivalence. Since no automated semantic interpretation systems available today can identify conceptual equivalence, paraphrases are difficult to acquire without human effort. The aim of this thesis is to develop methods for automatically acquiring and filtering phrase-level paraphrases using a monolingual corpus. Noting that the real world uses far more quasi-paraphrases than the logically equivalent ones, we first present a general typology of quasi-paraphrases together with their relative frequencies. To our knowledge the first one ever. We then present a method for automatically learning the contexts in which quasi-paraphrases obtained from a corpus are mutually replaceable. For this purpose, we use Relational Selectional Preferences (RSPs) that specify the selectional preferences of the syntactic arguments of phrases (usually verbs or verb phrases). From the RSPs of individual phrases, we learn Inferential Selectional Preferences (ISPs), which specify the selectional preferences of a pair of quasi-paraphrases. We then apply the learned ISPs to the task of filtering incorrect inferences. We achieve an accuracy of 59% for this task, which is a statistically significant improvement over several baselines. Knowing that quasi-paraphrases are often inexact because they contain semantic implications which can be directional, we present an algorithm called LEDIR to learn the directionality of quasi-paraphrases using the (syntactic argument based) RSPs for phrases. Learning directionality allows us to differentiate the strong (bidirectional) from the weak (unidirectional) paraphrases. We show that the directionality of the quasi-paraphrases can be learned with 48% accuracy. This is again a significant improvement over several baselines. In learning the context and directionality of quasi-paraphrases, we have encountered the need for semantic concepts: Both RSPs and ISPs are defined in terms of semantic concepts. For learning these semantic concepts from text, we use a semi-supervised clustering algorithm HMRF-KMeans. We show that compared to the commonly used unsupervised clustering approach, this algorithm performs much better. Applying the semi-supervised clustering algorithm to the task of discovering verb classes, we obtain precision scores of 54% and 37% and corresponding recall scores of 53% and 38% for our two test sets. These are large improvements over the baseline. We next investigate the task of learning surface paraphrases, i.e., paraphrases that do not require the use of a syntactic interpretation. Since one would need a very large corpus to find enough surface variations, we start with a really large but unprocessed corpus of 150GB (25 billion words) obtained from Google News. We rely only on distributional similarity to learn paraphrases from this corpus. To scale paraphrase acquisition to this large corpus, we apply only simple POS tagging and randomized algorithms. We build a paraphrase resource containing more than 2.5 million phrases. In the resource, 71% of the quasi-paraphrases are correct. Having learned the surface paraphrases, we investigate their utility for the task of relation extraction. We show that these paraphrases can be used to learn surface patterns for relation extraction. The extraction patterns obtained by using the paraphrases are not only more precise (more than 80% precision for both our test relations), but also have higher relative recall compared to a state-of-the-art baseline. This method also delivers more extraction patterns than the baseline. Applying the learned extraction patterns to the task of extracting relation instances from a test corpus, our system takes a hit in relative recall as compared to the baseline, but results in a much higher precision (more than 85% precision for both our test relations). Finally, we use paraphrases to learn patterns for domain-specific information extraction (IE). Since the paraphrases are learned from a large broad-coverage corpus, our patterns are domain-independent, making the task of moving to new domains very easy. We empirically show that patterns learned using (broad-coverage corpus based) paraphrases are comparable in performance to several state-of-the-art domain-specific IE engines. Thus, in this thesis we define quasi-paraphrases, present methods to learn them from a corpus, and show that quasi-paraphrases are useful for information extraction.