Operations-Focused Optimized Theater Weather Sensing Strategies Using Preemptive Binary Integer Programming

This thesis describes a method that optimally deploys weather sensors of all types in a battlefield environment. Gridded climatology models are used to determine an estimate for the weighted frequency of occurrence of operationally significant inclement weather events. That data is used to formulate a series of preemptive Binary Integer Linear Programs that maximize detection of expected operationally significant inclement weather occurrences within the constraints of feasibility of sensor deployment, sensor operational lifespan and the sensor’s ability to detect the operationally significant inclement weather elements. The preemptive Binary Integer Linear Programs are combined into a single objective function that maintains the preemptive nature of the original objective functions. The BILP solutions are described as a meteorology and oceanographic collection plan supporting a particular military campaign. A method for sensitivity analysis of differing BILP optimal solutions is provided. Various realistic instances of the problem are solved to optimality and analyzed to demonstrate that the problem formulation accurately captures all aspects of the problem. This type of analysis was not possible before this methodology was developed

Different Formulations of the Orthogonal Array Problem and Their Symmetries

Modern statistical experiments routinely feature a large number of input variables that can each be set to a variety of different levels. In these experiments, output response changes as a result of changes in the individual factor level settings. Often, an individual experimental run can be costly in time, money or both. Therefore, experimenters generally want to gain the desired information on factor effects from the smallest possible number of experimental runs. Orthogonal arrays provide the most desirable designs. However, finding orthogonal arrays is a very challenging problem. There are numerous integer linear programming formulations (ILP) in the literature whose solutions are orthogonal arrays. Because of the nature of orthogonal arrays, these ILP formulations contain symmetries where some portion of the variables in the formulation can be swapped without changing the ILP. These symmetries make it possible to eliminate large numbers of infeasible or equivalent solutions quickly, thereby greatly reducing the time required to find all non-equivalent solutions to the ILPs. In this dissertation, a new method for identifying symmetries is developed and tested using several existing and new ILP formulations for enumerating orthogonal arrays

Lightning forecast from chaotic and incomplete time series using wavelet de-noising and spatiotemporal kriging

Purpose – Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts. Design/methodology/approach – Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction. Findings – The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction. Research limitations/implications – The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force. Practical implications – These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology. Social implications – Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions. Originality/value – Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods

Final Report of the AFIT Quality Initiative External Discovery Committee

This report summarizes the findings of the Air Force Institute of Technology’s (AFIT’s) Quality Initiative - External Discovery Team. The overarching purpose of the Quality Initiative is to create a detailed, executable investment strategy for modernizing AFIT’s instructional capabilities across five thrust areas. These activities were completed over the course of one year, beginning in June of 2016 and concluding in June of 2017. The data gathered were evaluated and several recommendations for further review were decided upon by the External Discovery Team. The following report briefly covers those recommendations and provides sources from which the recommendations were gleaned. These recommendations are meant to serve as a baseline for ways in which AFIT could begin to program resources to help improve teaching and instruction across the institution as a whole. The data presented here are meant to serve as a compliment to the Internal Discovery Team’s report that focuses on data and feedback gathered from institutions internal to AFIT

Probing Sub-Micron Forces by Interferometry of Bose-Einstein Condensed Atoms

We propose a technique, using interferometry of Bose-Einstein condensed alkali atoms, for the detection of sub-micron-range forces. It may extend present searches at 1 micron by 6 to 9 orders of magnitude, deep into the theoretically interesting regime of 1000 times gravity. We give several examples of both four-dimensional particles (moduli), as well as higher-dimensional particles -- vectors and scalars in a large bulk-- that could mediate forces accessible by this technique.Comment: 32 pages, 5 figures, RevTeX4, expanded discussion of interactions, references added, to appear in PR

A Specific CNOT1 Mutation Results in a Novel Syndrome of Pancreatic Agenesis and Holoprosencephaly through Impaired Pancreatic and Neurological Development.

We report a recurrent CNOT1 de novo missense mutation, GenBank: NM_016284.4; c.1603C>T (p.Arg535Cys), resulting in a syndrome of pancreatic agenesis and abnormal forebrain development in three individuals and a similar phenotype in mice. CNOT1 is a transcriptional repressor that has been suggested as being critical for maintaining embryonic stem cells in a pluripotent state. These findings suggest that CNOT1 plays a critical role in pancreatic and neurological development and describe a novel genetic syndrome of pancreatic agenesis and holoprosencephaly.IB is funded by Wellcome (WT206194). ATH and SE are the recipients of a Wellcome Trust Senior Investigator award and ATH is employed as a core member of staff within the NIHR funded Exeter Clinical Research Facility and is an NIHR senior investigator. EDF was a Naomi Berrie Fellow in Diabetes Research during the study. SEF has a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number: 105636/Z/14/Z). CCW holds a Wellcome Trust Intermediate Clinical Fellowship (Grant Number: 105914/Z/14/Z). HH is funded by the Research Foundation-Flanders (FWO), the VUB Research Council and Stichting Diabetes Onderzoek Nederland

Validation of a serum ELISA test for cyathostomin infection in equines

Cyathostomins are ubiquitous equine nematodes. Infection can result in larval cyathostominosis due to mass larval emergence. Although faecal egg count (FEC) tests provide estimates of egg shedding, these correlate poorly with burden and provide no information on mucosal/luminal larvae. Previous studies describe a serum IgG(T)-based ELISA (CT3) that exhibits utility for detection of mucosal/luminal cyathostomins. Here, this ELISA is optimised/validated for commercial application using sera from horses for which burden data were available. Optimisation included addition of total IgG-based calibrators to provide standard curves for quantification of antigen-specific IgG(T) used to generate a CT3-specific 'serum score' for each horse. Validation dataset results were then used to assess the optimised test's performance and select serum score cut-off values for diagnosis of burdens above 1,000, 5,000 and 10,000 cyathostomins. The test demonstrated excellent performance (Receiver Operating Characteristic Area Under the Curve values >0.9) in diagnosing infection, with >90% sensitivity and >70% specificity at the selected serum score cut-off values. CT3-specific serum IgG(T) profiles in equines in different settings were assessed to provide information for commercial test use. These studies demonstrated maternal transfer of CT3-specific IgG(T) in colostrum to newborns, levels of which declined before increasing as foals consumed contaminated pasture. Studies in geographically distinct populations demonstrated that the proportion of horses that reported as test positive at a 14.37 CT3 serum score (1,000-cyathostomin threshold) was associated with parasite transmission risk. Based on the results, inclusion criteria for commercial use were developed. Logistic regression models were developed to predict probabilities that burdens of individuals are above defined thresholds based on the reported serum score. The models performed at a similar level to the serum score cut-off approach. In conclusion, the CT3 test provides an option for veterinarians to obtain evidence of low cyathostomin burdens that do not require anthelmintic treatment and to support diagnosis of infection

Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe

A detailed analysis is carried out to assess the HadGEM3-A global atmospheric model skill in simulating extreme temperatures, precipitation and storm surges in Europe in the view of their attribution to human influence. The analysis is performed based on an ensemble of 15 atmospheric simulations forced with observed Sea Surface Temperature of the 54 year period 1960-2013. These simulations, together with dual simulations without human influence in the forcing, are intended to be used in weather and climate event attribution. The analysis investigates the main processes leading to extreme events, including atmospheric circulation patterns, their links with temperature extremes, land-atmosphere and troposphere-stratosphere interactions. It also compares observed and simulated variability, trends and generalized extreme value theory parameters for temperature and precipitation. One of the most striking findings is the ability of the model to capture North Atlantic atmospheric weather regimes as obtained from a cluster analysis of sea level pressure fields. The model also reproduces the main observed weather patterns responsible for temperature and precipitation extreme events. However, biases are found in many physical processes. Slightly excessive drying may be the cause of an overestimated summer interannual variability and too intense heat waves, especially in central/northern Europe. However, this does not seem to hinder proper simulation of summer temperature trends. Cold extremes appear well simulated, as well as the underlying blocking frequency and stratosphere-troposphere interactions. Extreme precipitation amounts are overestimated and too variable. The atmospheric conditions leading to storm surges were also examined in the Baltics region. There, simulated weather conditions appear not to be leading to strong enough storm surges, but winds were found in very good agreement with reanalyses. The performance in reproducing atmospheric weather patterns indicates that biases mainly originate from local and regional physical processes. This makes local bias adjustment meaningful for climate change attribution