"Teacher training institutions, prospective principals and supervisors interested in the educational system of the State of Florida are in need of some statistical indication of the employment trends occurring in the administration and supervision area of the school in the state. Although the picture of the number employed is changing rapidly, because of the increasing growth of facilities within the state, it may be concluded that a fairly stable trend has been established in employment practices. Those interested may want to know what counties seem to have a larger turnover than others. They may want to know what the trends are in regard to transfers to and from counties. These and similar questions may find their answers, to a certain degree, in the survey that is serving as a basis for this study. It is hoped that this survey will not only meet this need up to the point of its limitations, but that it will also serve to point up the need for further study in the area. Perhaps it will serve as a stimulus for someone to look into the various reasons for the dropping of principals from administration and into what qualifications seem to be in demand in addition to certification, experience, etc."--Introduction. / "August, 1956." / Typescript. / "Submitted to the Graduate Council of Florida State University in partial fulfillment of the requirements for the degree of Master of Arts." / Advisor: H. A. Curtis, Professor Directing Paper. / Includes bibliographical references (leaf 24).

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Meteorology. / Microfiche copy available in Archives and Science. / Bibliography: leaf 38. / by Alan L. Adams. / M.S.

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Meteorology. / Microfiche copy available in Archives and Science. / Bibliography : leaves 64-65. / M.S.

The goal of this study is to improve seasonal tornado outbreak forecasting by creating a statistical model that forecasts tornado outbreak frequency in the US using teleconnection indices as predictors. For this study, a tornado outbreak is defined as more than 6 tornado reports associated with a single synoptic system and an event N15 rating index of 0.5 or higher. The tornado outbreak season is confined to all months after February for a given calendar year. Monthly teleconnection indices are derived from a rotated principal component analysis (RPCA) of the geopotential height fields. Various regression techniques were trained with a sample of monthly teleconnection indices, tested on new data, and optimized to achieve the highest predictive skill. The outcome of this study could potentially allow forecasters the ability to predict tornado outbreak potential on a climatological scale with months of lead-time, allowing for better preparation strategies for tornado outbreak seasons.

With significant growth in modern forms of communication such as social media and micro- blogs we are able to gain a real-time understanding into events happening in many parts of the world. In addition, these modern forms of communication have helped shed light into the increasing instabilities across the world via the design of anticipatory intelligence systems [45, 43, 20] that can forecast population level events like civil unrest, disease occurrences with reasonable accuracy. Event forecasting systems are generally prone to become outdated (model drift) as they fail to keep-up with constantly changing patterns and thus require regular re-training in order to sustain their accuracy and reliability. In this dissertation we try to address some of the issues associated with design and maintenance of event forecasting systems in general. We propose and showcase performance results for a drift adaptation technique in event forecasting systems and also build a hybrid system for event coding which is cognizant of and seeks human intervention in uncertain prediction contexts to maintain a good balance between prediction-fidelity and cost of human effort. Specifically we identify several micro-tasks for event coding and build separate pipelines for each with uncertainty estimation capabilities and thereby be able to seek human feedback whenever required for each micro-task independent of the rest. / Doctor of Philosophy / Event forecasting systems help reduce violence, loss/damage to humans and property. They find applicability in supply chain management, prioritizing citizen grievances, designing mea- sures to control violence and minimize disruptions and also in applications like health/tourism by providing timely travel alerts. Several issues exist with the design and maintenance of such event forecasting systems in general. Predictions from such systems may drift away from ground reality over time if not adapted to various shifts (or changes) in event occurrence patterns in real-time. A continuous source of ground-truth events is of paramount necessity for the continuous maintenance of forecasting systems. However ground-truth events used for training may not be reliable but often information about their uncertainty is not reflected in the systems that are used to build the ground truth. This dissertation focuses on addressing such issues pertaining to design and maintenance of event forecasting systems. We propose a framework for online drift-adaptation and also build machine learning methods capable of modeling and capturing uncertainty in event detection systems. Finally we propose and built a hybrid event coding system that can capture the best of both automated and manual event coders. We breakdown the overall event coding pipeline into several micro-tasks and propose individual methods for each micro-task. Each method is built with the capability to know what it doesn't know and thus is capable of balancing quality vs throughput based on available human resources.

Forecasting tools play an important role in public response to epidemics. Despite this, limited work has been done in comparing best-in-class techniques across the broad spectrum of time series forecasting methodologies. Forecasting frameworks were developed that utilised three methods designed to work with nonlinear dynamics: Iterated Filtering (IF) 2, Hamiltonian MCMC (HMC), and S-mapping. These were compared in several forecasting scenarios including a seasonal epidemic and a spatiotemporal epidemic. IF2 combined with parametric bootstrapping produced superior predictions in all scenarios. S-mapping combined with Dewdrop Regression produced forecasts slightly less-accurate than IF2 and HMC, but demonstrated vastly reduced running times. Hence, S-mapping with or without Dewdrop Regression should be used to glean initial insight into future epidemic behaviour, while IF2 and parametric bootstrapping should be used to refine forecast estimates in time. / Thesis / Master of Science (MSc)

A majority of tornado research focuses on the Great Plains region of the United States. Knowledge of tornado environments outside of the Great Plains is limited; this is especially true for the Southeast U.S. In addition, little is known about the role of the lower troposphere on tornadogenesis. Therefore, this study examines low-level thermodynamic parameters associated with tornadoes in the Southeast U.S between 1960 and 2002. Previous studies have shown that higher values of CAPE, 0-3 km CAPE, 0-1 and 0-3 km SRH, and 0-1 and 0-1 km EHI are more likely associated with significant tornadoes. Similar studies have shown that decreasing LCL, LFC heights, and CIN are common with significant tornadoes. This study supports that higher values of shear and instability/shear and lower CIN values are associated with significant tornadoes in the Southeast. However, this study indicates that instability, LCL, and LFC heights may be poor tornado strength discriminators.

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