RANK-BASED TEMPO-SPATIAL CLUSTERING: A FRAMEWORK FOR RAPID OUTBREAK DETECTION USING SINGLE OR MULTIPLE DATA STREAMS

In the recent decades, algorithms for disease outbreak detection have become one of the main interests of public health practitioners to identify and localize an outbreak as early as possible in order to warrant further public health response before a pandemic develops. Today’s increased threat of biological warfare and terrorism provide an even stronger impetus to develop methods for outbreak detection based on symptoms as well as definitive laboratory diagnoses. In this dissertation work, I explore the problems of rapid disease outbreak detection using both spatial and temporal information. I develop a framework of non-parameterized algorithms which search for patterns of disease outbreak in spatial sub-regions of the monitored region within a certain period. Compared to the current existing spatial or tempo-spatial algorithm, the algorithms in this framework provide a methodology for fast searching of either univariate data set or multivariate data set. It first measures which study area is more likely to have an outbreak occurring given the baseline data and currently observed data. Then it applies a greedy searching mechanism to look for clusters with high posterior probabilities given the risk measurement for each unit area as heuristic. I also explore the performance of the proposed algorithms. From the perspective of predictive modeling, I adopt a Gamma-Poisson (GP) model to compute the probability of having an outbreak in each cluster when analyzing univariate data. I build a multinomial generalized Dirichlet (MGD) model to identify outbreak clusters from multivariate data which include the OTC data streams collected by the national retail data monitor (NRDM) and the ED data streams collected by the RODS system. Key contributions of this dissertation include 1) it introduces a rank-based tempo-spatial clustering algorithm, RSC, by utilizing greedy searching and Bayesian GP model for disease outbreak detection with comparable detection timeliness, cluster positive prediction value (PPV) and improved running time; 2) it proposes a multivariate extension of RSC (MRSC) which applies MGD model. The evaluation demonstrated the advantage that MGD model can effectively suppress the false alarms caused by elevated signals that are non-disease relevant and occur in all the monitored data streams

The Impact of a Nuclear Disturbance on a Space-Based Quantum Network

Quantum communications tap into the potential of quantum mechanics to go beyond the limitations of classical communications. Currently, the greatest challenge facing quantum networks is the limited transmission range of encoded quantum information. Space-based quantum networks offer a means to overcome this limitation, however the performance of such a network operating in harsh conditions is unknown. This dissertation analyzes the capabilities of a space-based quantum network operating in a nuclear disturbed environment. First, performance during normal operating conditions is presented using Gaussian beam modeling and atmospheric modeling to establish a baseline to compare against a perturbed environment. Then, the DEfense Land Fallout Interpretive Code software and computational fluid dynamics study the effect of a nuclear explosion on the surrounding environment. Finally, these sources of noise are combined to estimate the degradation of quantum states being transmitted through a nuclear disturbed environment. It is found that the effects of a nuclear environment on a quantum network is a function of the height of blast, the explosive yield, and the network design. Debris lofted into the atmosphere during a surface blast dissipate after a couple of hours, yet the concentration is initially high and results in heavy signal loss. The nuclear fireball produced additional background light interference that scatters into the receiver\u27s detector from tens of seconds to a couple of minutes, causing excessive noise in the detector. All these effects are likely to impede a quantum network’s ability to distribute quantum information between a ground station and low Earth orbit satellite for approximately one transmission period. Afterwards, by the next satellite pass, normal operation is expected to resume. These results provide the operational capabilities of space-based optical quantum networks following a nuclear explosion. The model can be expanded to model satellite-based quantum networks in other harsh atmospheric environments

Radon

Radon isotopes (222Rn, 220Rn) are noble, naturally occurring radioactive gases. They originate from the alpha decay of radium isotopes (226Ra, 224Ra), which occur in most materials in the environment, i.e. soil, rocks, raw and building materials. Radon is also found in ground and tap water. The two radon isotopes are chemically identical, but they have very different halflives: 3.82 days for radon (222Rn) and 56 seconds for thoron (220Rn). Thus, they behave very differently in the environment. Both isotopes are alpha-emitters; their decay products are polonium, bismuth and lead isotopes.Peer ReviewedObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaPostprint (published version

The effect of short-term changes in air pollution on respiratory and cardiovascular morbidity in Nicosia, Cyprus.

Presented at the 6th International Conference on Urban Air Quality, Limassol, March, 2007. Short-paper was submitted for peer-review and appears in proceedings of the conference.This study investigates the effect of daily changes in levels of PM10 on the daily volume of respiratory and cardiovascular admissions in Nicosia, Cyprus during 1995-2004. After controlling for long- (year and month) and short-term (day of the week) patterns as well as the effect of weather in Generalized Additive Poisson models, some positive associations were observed with all-cause and cause-specific admissions. Risk of hospitalization increased stepwise across quartiles of days with increasing levels of PM10 by 1.3% (-0.3, 2.8), 4.9% (3.3, 6.6), 5.6% (3.9, 7.3) as compared to days with the lowest concentrations. For every 10μg/m3 increase in daily average PM10 concentration, there was a 1.2% (-0.1%, 2.4%) increase in cardiovascular admissions. With respects to respiratory admissions, an effect was observed only in the warm season with a 1.8% (-0.22, 3.85) increase in admissions per 10μg/m3 increase in PM10. The effect on respiratory admissions seemed to be much stronger in women and, surprisingly, restricted to people of adult age

Dimension reduction approaches for atmospheric remote sensing of greenhouse gases

Hiilidioksidi (CO2) ja metaani (CH4) ovat merkittävimmät ihmisperäiset kasvihuonekaasut, joilla molemmilla on huomattava vaikutus ilmastonmuutokseen ja ilmakehän lämpenemiseen. Näiden kaasujen pitoisuuksien epäsuorat kaukokartoitusmittaukset ovat oleellinen osa ihmisperäisten päästöjen kehityksen seurannassa. Näitä mittauksia tarvitaan myös arvioitaessa kasvihuonekaasujen vaikutusta ilmakehän prosesseihin. Edellämainitun tutkimuksen luotettavuus perustuu suurilta osin mittausten epävarmuuden arvioinnin paikkansapitävyyteen, minkä takaamiseksi käytetään korkeatasoista epävarmuusanalyysiä. Tämän väitöskirjatyön tavoitteena on kehittää ja ottaa käyttöön luotettavia ja laskennallisesti tehokkaita epävarmuusanalyysimenetelmiä sovellettuna kasvihuonekaasujen kaukokartoitukseen. Kehitetyt menetelmät perustuvat matemaattisesti käänteisongelmien teoriaan ja todennäköisyysteorian sovelluksiin. Käytämme erityisesti informaatioteoreettisia työkaluja pienentääksemme käänteisongelman ulottuvuutta. Tämä johtaa laskennalliseen ongelmaan, joka on huomattavasti nopeampi ratkaista. Työn sovelluskohteita ovat Nasan Orbiting Carbon Observatory 2 -satelliitin hiilidioksidipitoisuusmittaukset sekä Sodankylän Arktisessa Avaruuskeskuksessa sijaitsevan spektrometrin mittaamat metaanipitoisuudet. Jälkimmäisessä keskitymme sekä yksittäisiin mittauksiin että koko aikasarjan tutkimiseen ajalta 2009–2018. Kehitetyt menetelmät toimivat erittäin hyvin käsitellyissä sovelluksissa luoden pohjaa uusille operatiivisille epävarmuusanalyysialgoritmeille.Carbon dioxide (CO2) and methane (CH4) are two most significant anthropogenic greenhouse gases contributing to climate change and global warming. Indirect remote sensing measurements of atmospheric concentrations of these gases are crucial for monitoring manmade emissions and understanding their effects and related atmospheric processes. The reliability of these studies depends largely on robust uncertainty quantification of the measurements, which provides rigorous error estimates and confidence intervals for all results. The main goal of this work is to develop and implement rigorous, robust and computationally efficient means of uncertainty quantification for atmospheric remote sensing of greenhouse gases. We consider both CO2 measurements by NASA’s Orbiting Carbon Observatory 2 (OCO-2) and CH4 measurements by Sodankylä Arctic Space Center’s Fourier Transform Spectrometer (FTS), the latter being studied from the perspectives of both individual measurements, and the entire time series from 2009-2018. Our approach leverages recent mathematical results on dimension reduction to produce novel algorithms that are a step towards thorough and efficient operational uncertainty quantification in the field of atmospheric remote sensing. Mathematically, the process of inferring gas concentrations from indirect measurements is an ill-posed inverse problem, meaning that a well-defined solution doesn't exist without proper regularization. Bayesian approach utilizes probability theory to provide a regularized solution to the inverse problem as a posterior probability distribution. The posterior distribution is conventionally approximated using a Gaussian distribution, and results are reported as the mean of the distribution as a point estimate, and the corresponding variance as a measure of uncertainty. In reality, due to non-linear physics models used in the computations, the posterior is not well approximated by a Gaussian distribution, and ignoring its actual shape can lead to unpredictable errors and inaccuracies in the retrieval. Markov chain Monte Carlo (MCMC) methods offer a robust way to explore the actual properties of posterior distributions, but they tend to be computationally infeasible as the dimension of the state vector increases. In this work, the low intrinsic information content of remote sensing measurements is exploited to implement the Likelihood-Informed Subspace (LIS) dimension reduction method, which increases the computational efficiency of MCMC. Novel algorithms using LIS are implemented to abovementioned atmospheric CH4 profile and column-averaged CO2 concentration inverse problems

Investigation related to multispectral imaging systems

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community

Advances in the Modeling of Time-Resolved Laser-Induced Incandescence

Aerosolized nanoparticles represent both great potential for the development of emerging technologies and one of the biggest challenges currently facing our planet. In the former case, aerosol-based synthesis techniques represent one of the most cost-effective approaches to generating engineered nanoparticles having applications that range from medicine to energy. In the latter case, aerosolized soot is the second largest forcing factor after carbon dioxide in climate change models and contributes significantly to asthma, bronchitis, and various other respiratory illnesses. The increased predominance of engineered nanoparticles also presents significant environmental and health risks due to various toxicological effects. In any of these cases, robust characterization is critical to the function and regulation of these nanoaerosols. Time-resolved laser-induced incandescence (TiRe-LII) is well-suited to meeting this challenge. Since its inception in the 1980s, TiRe-LII has matured into a standard diagnostic for characterizing soot in combustion applications and, increasingly, engineered nanoparticles synthesized as an aerosol. The in situ nature of the technique makes it well-suited to probe in-flame soot formation and the fundamentals of nanoparticle formation. Moreover, its cost-effectiveness and real-time capabilities make TiRe-LII particularly well-suited as an avenue for online control of nanoparticle synthesis. TiRe-LII involves heating nanoparticles within a sample volume of aerosol to incandescent temperatures using a short laser-pulse. Following the laser pulse, the nanoparticles return to the ambient gas temperature via conductive and evaporative cooling. The magnitude of the peak spectral incandescence signal can be used to derive the particle volume fraction, while the temperature decay of the nanoparticles can be used to infer thermophysical properties, including the nanoparticle size, thermal accommodation coefficient (TAC), and latent heat of vaporization. Data analysis requires the use of spectroscopic models, used to convert the observed incandescence to a volume fraction or nanoparticle temperature, and heat transfer models, used to model the changes in the nanoparticle temperature over the duration of a signal. These models have evolved considerably over the past two decades, increasing the interpretive power of TiRe-LII. Nevertheless, there are several factors that impede further improvements to the reliability of TiRe-LII derived quantities. Several anomalies have been observed in measured signals collected from both engineered nanoparticle and soot, ranging from faster-than-expected temperature decays to inconsistencies in measurements between laboratories and experimental conditions. Resolving these differences is crucial to improving the robustness of TiRe-LII both as a combustion and engineered nanoparticle diagnostic. However, this first requires the development of advanced analysis tools that allow for a better understanding of nanoscale physics and the uncertainties associated with model development. This thesis presents several advances in the modeling and interpretation of TiRe-LII signals. The current state-of-the-art in TiRe-LII models is first established and the process of model inversion is discussed, with particular reference to uncertainty quantification within the Bayesian perspective. This lays the foundation for analysis of the measurement errors associated with TiRe-LII signals, providing practitioners with another source of information to characterize measurement devices and fluctuations in observed processes. Next, a novel approach to describe the relationship between the peak nanoparticle temperature and the laser fluence is derived. This allows the first comparison of fluence curves obtained using different instrumentation and under different measurement conditions. This dissertation proceeds by examining inversion of the spectroscopic model to determine both the nanoparticle temperature decay and the factor that scales emission from the nanoparticles to the observed signal. Unexpected temporal effects in the latter quantity are examined as an additional source of information that TiRe-LII practitioners can use for nanoparticle characterization and for diagnosing problems with measurement devices. Molecular dynamics simulations are employed to calculate the thermal accommodation coefficient, a parameter fundamental to the heat transfer model used in interpreting the inferred nanoparticle temperature decay, using the results are used in an analysis of TiRe-LII collected from iron, silver, and molybdenum nanoparticles. The cross-comparison of these materials highlights the utility of the developed analysis tools and provides fundamental insights into both nanoscale physics and bulk thermophysical properties. This dissertation concludes with a critical discussion of model development, emphasizing the importance of complexity and uncertainty in model selection. This is particularly important in the context of the context of the increasingly divergent set of TiRe-LII models available in the literature, indicative of model tuning. In summary, this dissertation not only presents direct improvements to the spectroscopic and heat transfer models used in traditional TiRe-LII analysis but also presents a set of new approaches by which the remaining challenges in TiRe-LII analysis can be resolved