Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

We used historical data to build two types of model that predict Ground Delay Program implementation decisions and also produce insights into how and why those decisions are made. More specifically, we built behavioral cloning and inverse reinforcement learning models that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. We found that the random forest behavioral cloning models we developed are substantially better at predicting hourly Ground Delay Program implementation for these airports than the inverse reinforcement learning models we developed. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. We also investigated the structure of the models in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that GDP implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours

Clinical presentation of abdominal tuberculosis in HIV seronegative adults

BACKGROUND: The accurate diagnosis of abdominal tuberculosis usually takes a long time and requires a high index of suspicion in clinic practice. Eighty-eight immune-competent patients with abdominal tuberculosis were grouped according to symptoms at presentation and followed prospectively in order to investigate the effect of symptomatic presentation on clinical diagnosis and prognosis. METHODS: Based upon the clinical presentation, the patients were divided into groups such as non-specific abdominal pain & less prominent in bowel habit, ascites, alteration in bowel habit, acute abdomen and others. Demographic, clinical and laboratory features, coexistence of pulmonary tuberculosis, diagnostic procedures, definitive diagnostic tests, need for surgical therapy, and response to treatment were assessed in each group. RESULTS: According to clinical presentation, five groups were constituted as non-specific abdominal pain (n = 24), ascites (n = 24), bowel habit alteration (n = 22), acute abdomen (n = 9) and others (n = 9). Patients presenting with acute abdomen had significantly higher white blood cell counts (p = 0.002) and abnormalities in abdominal plain radiographs (p = 0.014). Patients presenting with alteration in bowel habit were younger (p = 0.048). The frequency of colonoscopic abnormalities (7.5%), and need for therapeutic surgery (12.5%) were lower in patients with ascites, (p = 0.04) and (p = 0.001), respectively. There was no difference in gender, disease duration, diagnostic modalities, response to treatment, period to initial response, and mortality between groups (p > 0.05). Gastrointestinal tract alone was the most frequently involved part (38.5%), and this was associated with acid-fast bacteria in the sputum (p = 0.003). CONCLUSION: Gastrointestinal tract involvement is frequent in patients with active pulmonary tuberculosis. Although different clinical presentations of patients with abdominal tuberculosis determine diagnostic work up and need for therapeutic surgery, evidence based diagnosis and consequences of the disease does not change

Generating day-of-operation probabilistic capacity scenarios from weather forecasts

Airport arrival capacity, referred to here as the airport acceptance rate (AAR), is strongly influenced by the weather in the vicinity of the airport and thus AAR prediction necessitates an airport-specific weather forecast. Weather forecasts, however, are seldom accurate in predicting the actual weather conditions. Strategic decisions, for example arrival rates in a ground delay program (GDP), must be made ahead of time, usually more than two hours, when there is an uncertainty about the future capacity. This research uses probabilistic capacity scenarios to represent the uncertainty in the future arrival capacity. A probabilistic capacity scenario is defined as a time series of AAR values with which a certain probability of realization is associated. A set of probabilistic capacity scenarios may be used to represent the uncertainty in arrival capacity at an airport over the course of the day. There has been considerable research in developing GDP models that determine efficient ground delay decisions and require probabilistic capacity scenarios as inputs. It is assumed that the capacity scenarios can be developed from weather forecasts or can be obtained from the expertise of the air traffic managers. There is, however, considerably less literature on the development of specific day-of-operation probabilistic capacity scenarios from weather forecasts. This limits the use of these GDP models in real- world application. This thesis fills that gap and presents methodologies to generate probabilistic capacity scenarios from weather forecasts. In this thesis we develop methodologies for generating probabilistic capacity scenarios using a widely available airport-specific weather forecast called the Terminal Aerodrome Forecast (TAF). These methodologies require the issued TAF forecast and the realized capacity for days in the past. We apply and assess the performance of these methodologies on four US airports: San Francisco International Airport, Boston Logan International Airport, Chicago O'Hare International Airport and Los Angeles International Airports. Though we have focused on these airports as case studies, the TAF-based scenario generation techniques can be applied to any airport.In the first methodology, TAF Clustering, the scenarios are representative capacity profiles for days having similar TAFs. Groups of similar TAFs are found using K-means clustering and the number is verified using Silhouette value. In the second methodology, Dynamic Time Warping (DTW) Scenarios, the scenarios are the actual realized capacity profiles for days that have similar TAFs. The similarity between TAFs is determined using a statistical technique for comparing multidimensional time series called DTW. DTW Scenarios uses three airport specific input parameters. These parameters control the numbers and the probabilities of the scenarios. We determine the values of the parameters through optimization to maximize the performance of the scenarios through minimizing average delay costs. The optimal values are determined through a specialized algorithm designed for situations where evaluating the objective function is computationally expensive.For San Francisco International Airport we also use another forecast: the San Francisco Marine Initiative forecast (STRATUS) to develop the scenarios. In this methodology called, Fog burn-off clustering, the scenarios are representative capacity profiles for days that have the fog burn-off time in the same quarter hour. We measure the efficacy of the various scenario generation methodologies in a real world setting based on 45 historic days for each of the four case-study airports. For each day, the generated scenarios are provided as inputs to a static stochastic ground delay model (SSGDM) that determines the series of planned arrival rates that minimize the sum of ground delay costs and expected air delay costs, assuming that the plan is not adjusted to evolving information. The ground delay is determined directly from the SSGDM whereas the realized air delay is determined from a queuing diagram based on the planned arrival rate and the realized arrival capacity. The realized delay costs are averaged over 45 days for each airport, and is the metric used to compare the different scenario generation methodologies. Employing this approach, we compare the different methods for capacity scenario generation against each other and against two other reference cases. Under the first reference case, Naïve Clustering, the scenarios are developed from historical capacity data without the use of the weather forecast. Groups of similar arrival profiles are determined though K-means clustering. In the second reference case, Perfect Information, we assume that the GDP is planned based on perfect information about the future arrival capacity. Our results show that, on average, scenarios generated using the TAF-based DTW method results in the lowest delay cost amongst all scenario based methodologies. It is shown that capacity scenarios generated using day-of-operation weather forecasts can reduce the cost of delays by 5%-30% compared to scenarios that do not make use of weather forecast. The benefit of the TAF based approach is more pronounced on days that have a greater capacity-demand imbalance when compared to Naïve Clustering

Fasting as Dissent: Examining the Body Discourses and Publicity of Mahatma Gandhi and Irom Sharmila

ABSTRACT\ud FASTING AS DISSENT: EXAMINING THE BODY DISCOURSES\ud AND PUBLICITY OF MAHATMA GANDHI AND\ud IROM SHARMILA\ud by\ud Arjun Buxi\ud Master of Arts in Communication Studies\ud California State University, Chico\ud Summer 2011\ud In this thesis, I explore the efficacy of hunger striking as a method of exposing\ud the unjustness of a powerful, repressive State. I confront the exclusion of oppressed\ud minorities from the arena of public debate based on the attributes that make them ???different.???\ud As recourse, I argue that the minority dissident???s last resort for resisting the\ud State is the creation of bodily discourses and a spectacle of his/her suffering. However,\ud what are the effects of the minority hunger striker???s attributes of ???difference??? on his/her\ud fast? Conversely, what are the effects of fasting on behalf of a minority group? I attempt\ud to advance some answers to those questions through a comparative analysis between\ud Mahatma Gandhi and Irom Sharmila, who respectively fasted on behalf of, and as a\ud member of, an oppressed minority. Through analysis of news media discourses,\ud documentary footage, and biographies, this thesis examines the implications of a hunger\ud striker???s battle for autonomy while imprisoned and subverted by the State, and his/her\ud efforts to gain publicity and sympathy from an audience.\ud I demonstrate the limitations Sharmila faced as a female, ethnic minority\ud hunger striker, and the implications of the State???s counter-discourses upon a fast-untodeath.\ud It is shown that by a problematic application of the law, force-feeding, and hospitalization,\ud the State weakened Sharmila???s spectacle of suffering, and framed her humanitarian\ud crisis as a problem of ???national security???. As contrast, I present how Gandhi\ud successfully framed his fast as confronting first and foremost a social problem, using a\ud dual argument of political and social reform to successfully implicate both State and\ud society for moral transgression. Though Gandhi enjoyed a more well known persona\ud and achieved more tangible reform, Sharmila has gained some public support and spoke\ud up against injustice. The sheer longevity of Sharmila???s fast-unto-death (10 years and\ud still going) demonstrates the efficacy of hunger striking in enabling a desperate, forgotten,\ud minority citizen to continually and determinedly resist a State with his/her last resort,\ud the body.CSU, Chic