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Foundational basis for optimal climate change detection from energy-balance and cointegration models This thesis has critically examined the validity of optimal fingerprinting methods for the detection and attribution (D&A) of climate change trends. The validity is called into question because optimal fingerprinting involves a linear regression of non-stationary time series. Such non-stationary regressions are in general statistically inconsistent, meaning they are liable to produce spurious results. This thesis has investigated, using an idealized linear-response-model framework motivated by energy-balance considerations, whether the standard assumptions of optimal fingerprinting are sufficient to guarantee consistency, and hence whether detected climate trends are likely to be genuine or artefacts of spurious correlation. The principal reasoning tool in the thesis is the linear impulse-response model, familiar to many climatologists when parameterized as an energy-balance model (EBM), a simplified representation of global climate. A rigorous and efficient maximum likelihood method has been developed for estimating parameters of EBMs with any k > 0 number of boxes from CO2-quadrupling general circulation model (GCM) experiments and the method implemented as a free software package. It has been found that a three-box ocean is optimal for emulating the global mean surface temperature (GMST) impulse responses of GCMs in the Coupled Model Intercomparison Project Phase 5 (CMIP5). A new linear-filtering method has also been developed for estimating historical effective radiative forcing (ERF) from time series of GMST. It has been shown that the response of any k-box EBM can be represented as an ARMA(k, k-1) autoregressive moving-average filter and that, by inverting the ARMA filter, time series of surface temperature may be converted into radiative forcing. A comparison with an established method ("ERF_trans"), using historical simulations from HadGEM3-GC31-LL, found that the new method gives an ERF time series that closely matches published results (correlation of 0.83). Applying the new method to historical temperature observations, in combination with HadGEM3, produces evidence of a significant increase in ERF over the historical period with an estimated forcing in 2018 of 1.45 +- 0.504 Watts per square metre. It has been proved, using an idealized linear-response-model framework where forcing is represented as an integrated process, that if standard assumptions hold then the optimal fingerprinting estimator is consistent, and hence robust against spurious regression. Hypothesis tests, conducted using historical GMST observations and simulation output from 13 GCMs of the CMIP6 generation, have produced no evidence that these assumptions are violated in practice. The historical trends in GMST which are detected and attributed using these GCMs are therefore very likely not spurious. Consistency of the fingerprinting estimator was found to depend on "cointegration" between historical observations and GCM output. Detection of such a cointegration for the GMST variable indicates that the least-squares estimator is "superconsistent", with better convergence properties than might previously have been assumed. Furthermore, a new method has been developed for quantifying D&A uncertainty, which exploits the connection between cointegration and error-correction time series models to eliminate the need for pre-industrial control simulations.

Evidence Based Medicine (EBM) is the process of systematically finding, judging, and using research findings as the basis for clinical decisions and has become the standard of medical practice. There are countless new studies and research being published daily. Keeping track of each of them is impossible, not to mention needing to read and comprehend them. While search engines can help healthcare professionals search for a topic with suggesting relevant papers on the topic, healthcare professionals still need to go through the papers and extract relevant information themselves. This is a very time-consuming task as one study on Information Retrieval (IR) practices of healthcare information professionals that it takes on average 4 hours for healthcare information professionals to finish a search task. Moreover, a systematic review study on the barriers to medical residents' practicing of evidence-based medicine revealed that two of the most frequently mentioned barriers for residents were limitations in available time, knowledge, and skills. In this project, we address both problems by building a Medical Question Answering (QA) system that employees semi-supervised information extraction methods in Natural Language Processing (NLP) to construct a large scale Knowledge Graph (KG) from the extracted facts from a large repository of medical research publications. Then, the system translates a given user’s question in a natural language to the KG efficiently to extract relevant answers based on evidences to present in a user-friendly manner. The system returns a compilation of summaries for the related evidences with one sentence summary for each evidence relevant to the user’s question and the reference to the full publication. The system can help address the barriers of knowledge and skills by providing comprehensive summary of the evidences for a given question in a natural language that eliminates the need to formulate complex structured queries. The system was evaluated on a real-life test dataset collected from healthcare professionals, and the evaluation results showed performances comparable to the state-of-the-art Biomedical QA systems in the literature.

A holistic, integrated management approach called ecosystem-based management (EBM) has been adopted in many large aquatic ecosystems. A survey-based approach was used to assess characteristics of implementation of EBM in Lake Erie and four reference aquatic systems (Chesapeake Bay, Puget Sound, Tampa Bay and the Baltic Sea) to identify important factors characteristic of successful implementation of EBM in these large aquatic ecosystems to sustain them in a healthy, productive and resilient condition so they can provide the services humans want and need. To understand differences in perceptions of diverse stakeholders, survey respondents were stratified in two ways: by area of focus (Aquatic, Fisheries, Watershed and Ecosystem) and by type of organization (Government/Regulatory, Business/Industry, Academic and NGO). Three analyses were performed on the data using ANOVA, PCA and crosstabs methodology: comparison across the five ecosystems (Analysis A), stratification and comparison of Lake Erie stakeholders by area of focus and type of organization (Analysis B), and stratification and comparison of all ecosystem stakeholders by area of focus and type of organization (Analysis C). Stakeholder perceptions versus realities in Lake Erie and reference aquatic ecosystems were also analyzed in detail regarding EBM implementation, voluntary versus legislatively mandated implementation, ecosystem condition, perspectives of diverse stakeholders, public engagement and leadership organizations present. EBM implementation was positively correlated with positive ecosystem outcomes when the ecosystems were viewed both individually and collectively. This view was shared by all classes of respondents (by area of focus and type of organization) from the five ecosystems collectively. However, this was not true for the Lake Erie ecosystem. Only Watershed respondents (by area of focus) and Government/Regulatory and Academic respondents (by type of organization) showed this result. Ecosystem size was negatively correlated with EBM success. For all ecosystems collectively, the correlation for EBM, positive outcomes and ecosystem condition with increasing ecosystem size was negative. There was no significance seen between the presence of a legislative mandate to implement collaborative ecosystem management and positive ecosystem outcomes at either the individual or collective ecosystem level. Contrasting perspectives on EBM implementation by various categories of stakeholders were also shown. Ten principles for successful EBM implementation in large aquatic ecosystems are proposed, and the required attributes of a successful EBM model for large aquatic ecosystems are defined. A new EBM model for Lake Erie is recommended for effective protection and restoration of the ecosystem including key characteristics of binational leadership by the International Joint Commission (or similar binational organization), implementation of key EBM parameters on a combination of voluntary and mandated bases and a strengthened, coordinated binational public engagement program. The Lake Erie EBM model is then applied to a current initiative: implementation of wind power in the lake. Recommendations are made on how to minimize negative impacts on the ecosystem while maximizing synergy for all parties involved from utilizing an EBM strategy. Thoughts on further research to enhance the efficiency and effectiveness of EBM implementation in large aquatic ecosystems are then offered.

Arsenic is one of the toxic substances introduced in groundwater by various anthropogenic and natural sources. Understanding fate and transport of arsenic in groundwater and wetlands is crucial for remediation. Previously fate and transport of arsenic have been modeled using various equation based methods (EBM) such as ordinary differential equations (ODE) and partial differential equations (PDE), which encompass rigorous mathematics and assume that only one species of arsenic is present. But in reality, various forms of arsenic are present in groundwater. Based on the availability of oxygen, arsenic transforms from one form to another, thus creating a heterogeneous mix. Therefore, the equations used to describe the relation among parameters of interest become non-linear. The agent-based method (ABM) has emerged as a potential tool to model multidisciplinary and highly complex environmental problems. The goal of this research was to develop an ABM for the transport of arsenate (H2ASO4-) in water and soil. First, the diffusion of arsenate from contaminated water into the overlying uncontaminated water was modeled and second, the diffusion of arsenate from contaminated soil to the overlying uncontaminated water was modeled. Since this is the first time the model was developed using ABM, the results obtained from both models were compared with results from HYDRUS 1-D for verification. Although HYDRUS can model diffusion process, it is unable to model processes such as reduction and oxidation of arsenic, which is an important process for arsenic remediation. Therefore, HYDRUS is used for initial comparison purpose. The results obtained from ABM and HYDRUS-1D for diffusion in water showed good agreement with each other. However, the results obtained for diffusion in soil using ABM and HYDRUS 1-D were not in complete agreement with each other. The difference in the results obtained was due to the relation on which each model focused upon. Specifically, HYDRUS results were obtained by assigning diffusivity coefficient value and monitoring variability over time by using partial differential equations. However, ABM results were obtained by allowing each individual contaminant to move freely in the porous soil. Another reason for the difference was due to tortuosity. In HYDRUS, tortuosity depends on porosity (i.e. τ = ε-1/3), but ABM model does not have a specific relation between tortuosity and porosity, therefore, the results obtained differed.

The purpose of the research has been to develop mesh structures with tailored properties for hip stems. Stress shielding is one of the crucial problems with current hip implants due to the modulus mismatch between the bone and the hip stem. Solid titanium or cobalt-chromium stems are changing the natural stress patterns in the femur. Stresses are transferred through the hip stem and are concentrated more at the distal end of the stem, which weakens the top portion of the femur and overloads the distal portion of the femur. Stress shielding in the long term may result in implant failure due to bone loss, which is costly and painful for the patient. Naturally, biomaterials have tailored structures that display optimal behaviors under tensile, bending and other applied loads. Bones are also tailored to the loading conditions and show stress patterns accordingly. This thesis reports on the development of new hip implants where the mechanical properties of the stems are tailored to match the bone’s properties using open cell structures and mesh structures. Solid free form fabrication techniques are used in this thesis to manufacture parts in the Electron Beam Melting (EBM) by Arcam AB, Sweden. This processing method offers a unique way of making hip stems with mesh and solid sections together in one build. Different designs of hip stems have been manufactured and tested. Their affects on the bones have been analyzed and demonstrated using the Finite Element Analysis (ABAQUS). Hip implants were tested on cadaver bones to measure the difference between mesh stems and solid commercial hip stems. This thesis also includes studies about the mesh quality under different processing conditions, and the applications of structural optimization.