The effects of prompts-based argumentation scaffolds on peer-led interactive argumentation

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 23, 2010).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. David H. Jonassen.VitaIncludes bibliographical references.Ph. D. University of Missouri--Columbia 2009.The main focus of this study was to determine whether prompts-based argumentation scaffolds (PAS) would result in improvement of students' argumentation in a peer-led argumentation context. The study also examined the effects of PAS on students' reasoning performance and their feelings of group community. Thirty-two participants were randomly assigned to one of three conditions: a) no prompts, b) cognitive prompts, and c) socio-cognitive prompts. As designed, the sociocognitive prompts resulted in a significantly greater amount of socio-emotionally enhanced strategy use. With regard to argument behaviors, students in all three conditions made a considerable number of opposing arguments, which could be attributed to the task design of the study. More important, the socio-cognitive prompts condition resulted in a statistically significant greater number of substantial agreeing arguments. As expected, students in the scaffolded conditions performed better in terms of overall argumentation than students in the control condition. This difference, however, was not statistically significant. Contrary to expectation, students in the socio-cognitive prompts condition did not successfully justify their positions within the framework of others' views in the individual reasoning performance test. Lastly, the socio-cognitive prompts did not result in significantly stronger feelings of group community, although students in this condition reported slightly stronger feelings of group community than their counterparts

Two-stage combinatorial optimization framework for air traffic flow management under constrained capacity

Air traffic flow management is a critical component of air transport operations because at some point in time, often very frequently, one of more of the critical resources in the air transportation network has significantly reduced capacity, resulting in congestion and delay for airlines and other entities and individuals who use the network. Typically, these “bottlenecks” are noticed at a given airport or terminal area, but they also occur in en route airspace. The two-stage combinatorial optimization framework for air traffic flow management under constrained capacity that is presented in this thesis, represents a important step towards the full consideration of the combinatorial nature of air traffic flow management decision that is often ignored or dealt with via priority-based schemes. It also illustrates the similarities between two traffic flow management problems that heretofore were considered to be quite distinct. The runway systems at major airports are highly constrained resources. From the perspective of arrivals, unnecessary delays and emissions may occur during peak periods when one or more runways at an airport are in great demand while other runways at the same airport are operating under their capacity. The primary cause of this imbalance in runway utilization is that the traffic flow into and out of the terminal areas is asymmetric (as a result of airline scheduling practices), and arrivals are typically assigned to the runway nearest the fix through which they enter the terminal areas. From the perspective of departures, delays and emissions occur because arrivals take precedence over departures with regard to the utilization of runways (despite the absence of binding safety constraints), and because arrival trajectories often include level segments that ensure “procedural separation” from arriving traffic while planes are not allowed to climb unrestricted along the most direct path to their destination. Similar to the runway systems, the terminal radar approach control facilities (TRACON) boundary fixes are also constrained resources of the terminal airspace. Because some arrival traffic from different airports merges at an arrival fix, a queue for the terminal areas generally starts to form at the arrival fix, which are caused by delays due to heavy arriving traffic streams. The arrivals must then absorb these delays by path stretching and adjusting their speed, resulting in unplanned fuel consumption. However, these delays are often not distributed evenly. As a result, some arrival fixes experience severe delays while, similar to the runway systems, the other arrival fixes might experience no delays at all. The goal of this thesis is to develop a combined optimization approach for terminal airspace flow management that assigns a TRACON boundary fix and a runway to each flight while minimizing the required fuel burn and emissions. The approach lessens the severity of terminal capacity shortage caused by and imbalance of traffic demand by shunting flights from current positions to alternate runways. This is done by considering every possible path combination. To attempt to solve the congestion of the terminal airspace at both runways and arrival fixes, this research focuses on two sequential optimizations. The fix assignments are dealt with by considering, simultaneously, the capacity constraints of fixes and runways as well as the fuel consumption and emissions of each flight. The research also develops runway assignments with runway scheduling such that the total emissions produced in the terminal area and on the airport surface are minimized. The two-stage sequential framework is also extended to en route airspace. When en route airspace loses its capacity for any reason, e.g. severe weather condition, air traffic controllers and flight operators plan flight schedules together based on the given capacity limit, thereby maximizing en route throughput and minimizing flight operators' costs. However, the current methods have limitations due to the lacks of consideration of the combinatorial nature of air traffic flow management decision. One of the initial attempts to overcome these limitations is the Collaborative Trajectory Options Program (CTOP), which will be initiated soon by the Federal Aviation Administration (FAA). The developed two-stage combinatorial optimization framework fits this CTOP perfectly from the flight operator's perspective. The first stage is used to find an optimal slot allocation for flights under satisfying the ration by schedule (RBS) algorithm of the FAA. To solve the formulated first stage problem efficiently, two different solution methodologies, a heuristic algorithm and a modified branch and bound algorithm, are presented. Then, flights are assigned to the resulting optimized slots in the second stage so as to minimize the flight operator's costs.Ph.D

Abbott Lab Instrumentation Validation

Over the past 40 years, since the initial catheter ablation procedure was completed, cardiac mapping has become an essential part of electrophysiological procedures. Cardiac mapping is an electrophysiological study that allows physicians to analyze the electrical activity of the heart in order to diagnose and treat cardiovascular disease. Institutions like Cal Poly can utilize professional instrumentation systems with an appropriate wet lab to develop new devices and study various phenomena in an in-vitro physiological environment. Cardiac mapping systems used in tandem with catheter ablation procedures are essential to ensuring that the trend of deaths due to cardiovascular disease continues to decline. This project thesis will signify the importance of the development of a wet lab integrated with Abbott’s Ensite Precision Cardiac Mapping System for future product development and in-vitro studies

A Note on Location Parameter Estimation using the Weighted Hodges-Lehmann Estimator

Robust design is one of the main tools employed by engineers for the facilitation of the design of high-quality processes. However, most real-world processes invariably contend with external uncontrollable factors, often denoted as outliers or contaminated data, which exert a substantial distorting effect upon the computed sample mean. In pursuit of mitigating the inherent bias entailed by outliers within the dataset, the concept of weight adjustment emerges as a prudent recourse, to make the sample more representative of the statistical population. In this sense, the intricate challenge lies in the judicious application of these diverse weights toward the estimation of an alternative to the robust location estimator. Different from the previous studies, this study proposes two categories of new weighted Hodges-Lehmann (WHL) estimators that incorporate weight factors in the location parameter estimation. To evaluate their robust performances in estimating the location parameter, this study constructs a set of comprehensive simulations to compare various location estimators including mean, weighted mean, weighted median, Hodges-Lehmann estimator, and the proposed WHL estimators. The findings unequivocally manifest that the proposed WHL estimators clearly outperform the traditional methods in terms of their breakdown points, biases, and relative efficiencies

Zero-shot Triplet Extraction by Template Infilling

The task of triplet extraction aims to extract pairs of entities and their corresponding relations from unstructured text. Most existing methods train an extraction model on training data involving specific target relations, and are incapable of extracting new relations that were not observed at training time. Generalizing the model to unseen relations typically requires fine-tuning on synthetic training data which is often noisy and unreliable. We show that by reducing triplet extraction to a template infilling task over a pre-trained language model (LM), we can equip the extraction model with zero-shot learning capabilities and eliminate the need for additional training data. We propose a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that aligns the task objective to the pre-training objective of generative transformers to generalize to unseen relations. Experiments on FewRel and Wiki-ZSL datasets demonstrate that ZETT shows consistent and stable performance, outperforming previous state-of-the-art methods, even when using automatically generated templates. https://github.com/megagonlabs/zett/Comment: IJCNLP-AACL 2023 (main

A Case of Placenta Increta Presenting as Delayed Postabortal Intraperitoneal Bleeding in the First Trimester

Placenta increta is an uncommon and life-threatening complication of pregnancy characterized by complete or partial absence of the decidua basalis. Placenta increta usually presents with vaginal bleeding during difficult placental removal in the third-trimester. Although placenta increta may complicate first and early second-trimester pregnancy loss, the diagnosis can be very difficult during early pregnancy and thus the lesion is difficult to identify. We encountered with a woman who was diagnosed with placenta increta after receiving emergency hysterectomy due to intraperitoneal bleeding 2 months after an uncomplicated dilatation and curettage in the first trimester. Therefore, we report this case with a brief review of the literature