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

Sequencing three crocodilian genomes to illuminate the evolution of archosaurs and amniotes

The International Crocodilian Genomes Working Group (ICGWG) will sequence and assemble the American alligator (Alligator mississippiensis), saltwater crocodile (Crocodylus porosus) and Indian gharial (Gavialis gangeticus) genomes. The status of these projects and our planned analyses are described

The impact of immediate breast reconstruction on the time to delivery of adjuvant therapy: the iBRA-2 study

Background: Immediate breast reconstruction (IBR) is routinely offered to improve quality-of-life for women requiring mastectomy, but there are concerns that more complex surgery may delay adjuvant oncological treatments and compromise long-term outcomes. High-quality evidence is lacking. The iBRA-2 study aimed to investigate the impact of IBR on time to adjuvant therapy. Methods: Consecutive women undergoing mastectomy ± IBR for breast cancer July–December, 2016 were included. Patient demographics, operative, oncological and complication data were collected. Time from last definitive cancer surgery to first adjuvant treatment for patients undergoing mastectomy ± IBR were compared and risk factors associated with delays explored. Results: A total of 2540 patients were recruited from 76 centres; 1008 (39.7%) underwent IBR (implant-only [n = 675, 26.6%]; pedicled flaps [n = 105,4.1%] and free-flaps [n = 228, 8.9%]). Complications requiring re-admission or re-operation were significantly more common in patients undergoing IBR than those receiving mastectomy. Adjuvant chemotherapy or radiotherapy was required by 1235 (48.6%) patients. No clinically significant differences were seen in time to adjuvant therapy between patient groups but major complications irrespective of surgery received were significantly associated with treatment delays. Conclusions: IBR does not result in clinically significant delays to adjuvant therapy, but post-operative complications are associated with treatment delays. Strategies to minimise complications, including careful patient selection, are required to improve outcomes for patients

Nonparametric regression and density estimation in Besov spaces via wavelets

For density estimation and nonparametric regression, block thresholding is very adaptive and efficient over a variety of general function spaces. By using block thresholding on kernel density estimators, the optimal minimax rates of convergence of the estimator to the true distribution are attained. This rate holds for large classes of densities residing in Besov spaces, including discontinuous functions with the number of discontinuities growing with sample size as well as functions with other types of irregularities. The results hold for both convolution and wavelet kernel methods. Additionally, the proposed wavelet estimator is an improvement on previous estimators in that it simultaneously achieves both local and global optimal rates through careful choice of block length and a truncation parameter for the estimate\u27s orthogonal series expansion. The estimator is examined via simulations and compared against other kernel density estimators. In the case of nonparametric regression, most previous work has focused on data where the sampling points of the unknown function are equally spaced apart. If the design points occur as a Poisson process or have a uniform distribution rather than being equally spaced apart, the wavelet method of block thresholding can be applied directly to the data as though it was equispaced without sacrificing adaptivity or rates of convergence. When the underlying true function is in certain Besov and Hölder spaces, the resulting estimator achieves the optimal minimax rate of convergence. Simulations are run on this estimator and compared to previous estimators used for the same purpose

1.2 Dimension Reduction Techniques Used in Image Analysis......... 4

The Office of Graduate Studies has verified and approved the above named committee members. ii To my parents. iii ACKNOWLEDGEMENTS I am very grateful to my major professor, Dr. Anuj Srivastava, for his knowledge, guidance, patience, and open door policy, in helping me from the beginning to the end of this work. His contribution in the completion of my dissertation is critical. I am also grateful to my committee members: Dr. Fred Huffer, for his valuable remarks, for his time, and for his teaching and willingness to help students. I am grateful to Dr. Liu, for allowing me to use his code for the fast ICA algorithm, and for his expertise in image analysis and ICA. I am grateful to Dr. McGee for his great help in resolving administrative problems, and to Dr. Chicken for his time and knowledge. I would like to thank my parents in Russia for their moral support and love throughout my studies