Enhancing the information content of geophysical data for nuclear site characterisation

Our knowledge and understanding to the heterogeneous structure and processes occurring in the Earth’s subsurface is limited and uncertain. The above is true even for the upper 100m of the subsurface, yet many processes occur within it (e.g. migration of solutes, landslides, crop water uptake, etc.) are important to human activities. Geophysical methods such as electrical resistivity tomography (ERT) greatly improve our ability to observe the subsurface due to their higher sampling frequency (especially with autonomous time-lapse systems), larger spatial coverage and less invasive operation, in addition to being more cost-effective than traditional point-based sampling. However, the process of using geophysical data for inference is prone to uncertainty. There is a need to better understand the uncertainties embedded in geophysical data and how they translate themselves when they are subsequently used, for example, for hydrological or site management interpretations and decisions. This understanding is critical to maximize the extraction of information in geophysical data. To this end, in this thesis, I examine various aspects of uncertainty in ERT and develop new methods to better use geophysical data quantitatively. The core of the thesis is based on two literature reviews and three papers. In the first review, I provide a comprehensive overview of the use of geophysical data for nuclear site characterization, especially in the context of site clean-up and leak detection. In the second review, I survey the various sources of uncertainties in ERT studies and the existing work to better quantify or reduce them. I propose that the various steps in the general workflow of an ERT study can be viewed as a pipeline for information and uncertainty propagation and suggested some areas have been understudied. One of these areas is measurement errors. In paper 1, I compare various methods to estimate and model ERT measurement errors using two long-term ERT monitoring datasets. I also develop a new error model that considers the fact that each electrode is used to make multiple measurements. In paper 2, I discuss the development and implementation of a new method for geoelectrical leak detection. While existing methods rely on obtaining resistivity images through inversion of ERT data first, the approach described here estimates leak parameters directly from raw ERT data. This is achieved by constructing hydrological models from prior site information and couple it with an ERT forward model, and then update the leak (and other hydrological) parameters through data assimilation. The approach shows promising results and is applied to data from a controlled injection experiment in Yorkshire, UK. The approach complements ERT imaging and provides a new way to utilize ERT data to inform site characterisation. In addition to leak detection, ERT is also commonly used for monitoring soil moisture in the vadose zone, and increasingly so in a quantitative manner. Though both the petrophysical relationships (i.e., choices of appropriate model and parameterization) and the derived moisture content are known to be subject to uncertainty, they are commonly treated as exact and error‐free. In paper 3, I examine the impact of uncertain petrophysical relationships on the moisture content estimates derived from electrical geophysics. Data from a collection of core samples show that the variability in such relationships can be large, and they in turn can lead to high uncertainty in moisture content estimates, and they appear to be the dominating source of uncertainty in many cases. In the closing chapters, I discuss and synthesize the findings in the thesis within the larger context of enhancing the information content of geophysical data, and provide an outlook on further research in this topic

The maximization of submodular functions : old and new proofs for the correctness of the dichotomy algorithm

The first purpose of this paper is to make an old (Russian) theoretical results about the structure of local and global maxima of submodular functions, Cherenin’s excluding rules and his Dichotomy Algorithm more accessible for Western community. The second purpose of this paper is to present our main result which can be stated as follows. For any pair of embedded subsets, the difference of their function values is a lower bound for the difference between the unknown(!) optimal values of the corresponding partition defined by these subsets. A simple justification of Cherenin’s rules, the Dichotomy Algorithmand its generalization with the new branching rules from our main result are presented. The usefulness of our new branching rules is illustrated by means of a numerical example.

Neurosyphilis in a Non-HIV Patient: More than a Psychiatric Concern

Neurosyphilis is a form of tertiary syphilis infection caused by the spirochete bacterium Treponema pallidum. Patients suffering from this illness can present with neurological manifestations such as headaches, seizures, hearing loss, and ataxia. However, the typical presentation of neurosyphilis is the insidious onset of psychiatric symptoms including personality changes. A good history and clinical work-up is essential in the diagnostic process. There has been a recent increase in the incidence of infectious syphilis in Canada (1). However, in other parts of the world including China, infectious syphilis rates have remained high due to limited access to primary care and affordable treatments (2 Here, we present a case of neurosyphilis in a 40 year old Chinese male residing in China who presents with an 18 month history of personality changes as well as neurological and physical manifestations of the infection

Access Control of Web- and Java-Based Applications

Cybersecurity has become a great concern as threats of service interruption, unauthorized access, stealing and altering of information, and spreading of viruses have become more prevalent and serious. Application layer access control of applications is a critical component in the overall security solution that also includes encryption, firewalls, virtual private networks, antivirus, and intrusion detection. An access control solution, based on an open-source access manager augmented with custom software components, was developed to provide protection to both Web-based and Javabased client and server applications. The DISA Security Service (DISA-SS) provides common access control capabilities for AMMOS software applications through a set of application programming interfaces (APIs) and network- accessible security services for authentication, single sign-on, authorization checking, and authorization policy management. The OpenAM access management technology designed for Web applications can be extended to meet the needs of Java thick clients and stand alone servers that are commonly used in the JPL AMMOS environment. The DISA-SS reusable components have greatly reduced the effort for each AMMOS subsystem to develop its own access control strategy. The novelty of this work is that it leverages an open-source access management product that was designed for Webbased applications to provide access control for Java thick clients and Java standalone servers. Thick clients and standalone servers are still commonly used in businesses and government, especially for applications that require rich graphical user interfaces and high-performance visualization that cannot be met by thin clients running on Web browser

State tagging for improved Earth and environmental data quality assurance

Environmental data allows us to monitor the constantly changing environment that we live in. It allows us to study trends and helps us to develop better models to describe processes in our environment and they, in turn, can provide information to improve management practices. To ensure that the data are reliable for analysis and interpretation, they must undergo quality assurance procedures. Such procedures generally include standard operating procedures during sampling and laboratory measurement (if applicable), as well as data validation upon entry to databases. The latter usually involves compliance (i.e., format) and conformity (i.e., value) checks that are most likely to be in the form of single parameter range tests. Such tests take no consideration of the system state at which each measurement is made, and provide the user with little contextual information on the probable cause for a measurement to be flagged out of range. We propose the use of data science techniques to tag each measurement with an identified system state. The term “state” here is defined loosely and they are identified using k-means clustering, an unsupervised machine learning method. The meaning of the states is open to specialist interpretation. Once the states are identified, state-dependent prediction intervals can be calculated for each observational variable. This approach provides the user with more contextual information to resolve out-of-range flags and derive prediction intervals for observational variables that considers the changes in system states. The users can then apply further analysis and filtering as they see fit. We illustrate our approach with two well-established long-term monitoring datasets in the UK: moth and butterfly data from the UK Environmental Change Network (ECN), and the UK CEH Cumbrian Lakes monitoring scheme. Our work contributes to the ongoing development of a better data science framework that allows researchers and other stakeholders to find and use the data they need more readily

On the field estimation of moisture content using electrical geophysics‐the impact of petrophysical model uncertainty

The spatiotemporal distribution of pore water in the vadose zone can have a critical control on many processes in the near-surface Earth, such as the onset of landslides, crop yield, groundwater recharge, and runoff generation. Electrical geophysics has been widely used to monitor the moisture content (θ) distribution in the vadose zone at field sites, and often resistivity (ρ) or conductivity (σ) is converted to moisture contents through petrophysical relationships (e.g., Archie's law). Though both the petrophysical relationships (i.e., choices of appropriate model and parameterization) and the derived moisture content are known to be subject to uncertainty, they are commonly treated as exact and error-free. This study examines the impact of uncertain petrophysical relationships on the moisture content estimates derived from electrical geophysics. We show from a collection of data from multiple core samples that significant variability in the θ(ρ) relationship can exist. Using rules of error propagation, we demonstrate the combined effect of inversion and uncertain petrophysical parameterization on moisture content estimates and derive their uncertainty bounds. Through investigation of a water injection experiment, we observe that the petrophysical uncertainty yields a large range of estimated total moisture volume within the water plume. The estimates of changes in water volume, however, generally agree within (large) uncertainty bounds. Our results caution against solely relying on electrical geophysics to estimate moisture content in the field. The uncertainty propagation approach is transferrable to other field studies of moisture content estimation

Assessment of Feeding Behavior in Laboratory Mice

The global obesity epidemic has heightened the need for an improved understanding of how body weight is controlled, and research using mouse models is critical to this effort. In this perspective, we provide a conceptual framework for investigation of feeding behavior in this species, with an emphasis on factors that influence study design, data interpretation, and relevance to feeding behavior in humans. Although we focus on the mouse, the principles presented can be applied to most other animal models. This document represents the current consensus view of investigators from the National Institutes of Health (NIH)-funded Mouse Metabolic Phenotyping Centers (MMPCs)

River reach-level machine learning estimation of nutrient concentrations in Great Britain

Nitrogen (N) and phosphorus (P) are essential nutrients necessary for plant growth and support life in aquatic ecosystems. However, excessive N and P can lead to algal blooms that deplete oxygen and lead to fish death and the release of toxins that are harmful to humans. Estimates of N and P levels in rivers are typically calculated at station or grid (>1 km) scale; therefore, it is difficult to visualise the evolution of water quality as water travels downstream. Using a high-resolution reach-scale river network and associating each reach with land cover fractions and catchment descriptors, we trained random forest models on aggregated data (2010–2020) from the Environmental Agency Open Water Quality Data Archive for 2,343 stations to predict long-term nitrate and orthophosphate concentrations at each river reach in Great Britain (GB). We separated the model training and predictions for different seasons to investigate the potential difference in feature importance. Our model predicted concentrations with an average testing coefficient of determination (R2) of 0.71 for nitrate and 0.58 for orthophosphate using 5-fold cross-validation. Our model showed slightly better performance for higher Strahler stream orders, highlighting the challenges of making predictions in small streams. Our results revealed that arable and horticultural land use is the strongest and most reliable predictor for nitrate, while floodplain extents and standard percentage runoff are stronger predictors for orthophosphate. Nationally, higher orthophosphate concentrations were observed in urbanised areas. This study shows how combining a river network model with machine learning can easily provide a river network understanding of the spatial distribution of water quality levels