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The effect of treatment dose on recovery of post-stroke aphasia is not well understood. Inconsistent conceptualisation, measurement, and reporting of the multiple dimensions of dose hinders efforts to evaluate dose-response relationships in aphasia rehabilitation research. We review the state of dose conceptualisation in aphasia rehabilitation and compare the applicability of three existing dose frameworks to aphasia rehabilitation research - the Frequency, Intensity, Time, and Type principle (FITT), the Cumulative Intervention Intensity (CII) framework, and the Multidimensional Dose Articulation Framework (MDAF). The MDAF specifies dose in greater detail than the CII framework and the FITT principle. On this basis we selected the MDAF to be applied to three diverse examples of aphasia rehabilitation research. We next critically examined applicability of the MDAF to aphasia rehabilitation research and identified the next steps needed to systematically conceptualise, measure, and report the multiple dimensions of dose, which together can progress understanding of the effect of treatment dose on outcomes for people with aphasia following stroke. Further consideration is required to enable application of this framework to aphasia interventions that focus on participation, personal, and environmental interventions and to understand how the construct of episode difficulty applies across therapeutic activities used in aphasia interventions.

The Electric Power Research Institute (EPRI) with Électricité de France (EDF) developed the Integrated Life Cycle Management (ILCM) computer code to provide a standard methodology to support effective decision making for the long-term management of selected nuclear station components. In 2016, a Likelihood of Replacement (LoR) expert elicitation was developed to provide reliability curves for determination of replacement options for components that were not initially included in ILCM. The LoR methodology required expert’s to estimate future replacement probabilities which were then combined with historical failures using Bayesian analysis. Although this methodology was effective, parts of the industry were accustomed to providing a High/Medium/Low (HML) probability categorization for selected periods of operation. This paper presents an approach for calculating Weibull replacement probability curves from HML categorical replacement probability estimates. Additional questions beyond the initial HML categorization were developed. These focused on the timing of category transitions to refine parameter likelihood functions, reduce parameter uncertainty, and offset the significant Weibull parameter uncertainty introduced by using categorical estimates.

Électricité de France (EDF) has developed the Investment Portfolio Optimal Planning (IPOP) software tool [1] to be released with the Integrated Life Cycle Management (ILCM) software tool developed by the Electric Power Research Institute (EPRI) [2]. IPOP is an extremely powerful tool that uses genetic algorithms to provide an optimal strategy for investment in spare components and preventive replacements of multiple components at multiple power plant stations across an entire fleet. A drawback of IPOP is that it requires an extensive amount of user information to run even a single component. In response, Component Optimization Analysis Tools (COATs) was developed to simplify the process of deriving an optimal strategy for purchasing spares and replacements for a single component. This paper describes a two-layer algorithm used in the replacement strategy optimization in COATs. The inner layer consists of a Monte Carlo simulation that estimates the Expected Net Present Value (ENPV) of a given replacement strategy. A strategy consists of: the age of a component at which it needs to be replaced, the age of a component at which a spare should be purchased, years left in the plant at which to skip a scheduled replacement, and the end of life at which the scheduled replacement is skipped; and the years left in the plant at which no more spares are purchased. The Monte Carlo analysis uses these four strategy inputs with component costs, acquisition times, and reliability curves with plant downtime costs to calculate an ENPV for that strategy. The outer layer of the algorithm is an optimization layer that can use either Bayesian optimization or genetic algorithms to maximize the ENPV. These optimization algorithms are routinely available in various software packages and effectively treat the ENPV Monte Carlo as a black box function. An efficiency comparison is given between the two optimization algorithms to demonstrate under which conditions each algorithm out performs the other.

The EPRI Preventive Maintenance Basis Database (PMBD) has become a standard in the industry to develop, validate, or examine the impact of custom changes to maintenance strategies for common power plant equipment. The PMBD provides failure modes and an indication of frequency of occurrence. Recent feedback from PMBD users has made it clear that including a “Cost Module” to work with PMBD data would be a useful addition to the PMBD program and allow users to view the cost impacts associated with alternate custom maintenance strategies. This paper presents a methodology for the merging of maintenance information extracted from PMBD with cost estimates and additional expert-provided reliability data to estimate a maintenance cost distribution. Additional expert information includes missing data and PM type: monitoring, wear-rate reducing (e.g. oil change), or life-restoring (e.g. refurbishment). The cost distribution is calculated via Monte Carlo simulation and is dependent on the PM plan currently considered. Value-based optimization of the PM plan is performed through Bayesian optimization of the mean PM cost by varying the various PM frequencies. Bayesian optimization iteratively uses Gaussian Process Regression (GPR) to fit a non-parametric meta-model to a noisy objective function. As a part of GPR it is necessary to fit a covariance function that describes the spatial correlation or smoothness of the objective cost function. The meta-model with the covariance function effectively produces a built-in sensitivity analysis for the optimization as well.

Much of the literature on Augmentative and Alternative Communication (AAC) revolves around teaching communication skills or including students who use AAC in the classroom. There has been less focus on AAC use by teachers. Though there are, in fact, teachers who use AAC, disabled teachers face assumptions that speech is necessary to teach. In his text, Motor Speech Disorders, Duffy specifically mentions teaching as a career that could be ended by a motor speech disorder. In the introduction to Enhancing Diversity: Educators with Disabilities, C. E. Keller admits both that he had assumed a student of his with an acquired speech disorder could teach and that he did not know how to react when presented with the question of reading aloud to students. Sam Harvey has ongoing coursework on pedagogy with their teaching supervisor while teaching composition courses. Alyssa Hillary is officially considered a teaching assistant, both while teaching their own face-to-face classes and assisting in electrical engineering laboratories.

In 2014, the Integrated Life Cycle Management (ILCM) computer code was developed through collaboration between the Electric Power Research Institute (EPRI) and Électricité de France (EDF) to provide a standard methodology to support effective decision making for the long-term management of selected station assets. In order for the ILCM program to become a standard tool in the industry, additional work was needed in the development of a Likelihood of Replacement (LoR) calculator. The LoR calculator estimates the likelihood that a component will have to be replaced due to failure or reasons other than failure, such as high maintenance cost, inability to maintain, obsolescence, and other similar reasons. Expert elicitation was chosen as the method of gathering data and opinions on component replacement probabilities. The majority of expert elicitation techniques consist of experts giving opinions on the probability of replacement at various points in time, from which a reliability curve can be calculated. Furthermore, any failure or replacement data is subjectively incorporated in to the expert’s opinion. The present work uses Bayesian analysis to provide an objective method for statistically combining expert opinion with failure and replacement data. This paper also describes the process of extracting a Weibull LoR curve from expert’s opinions and reported failures and replacements. The expert’s work history and answer confidence is used to assign uncertainty in their answers and calculate 5th, 50th, and 95th percentile credibility Weibull curves for the probability of replacement.

In response to the technical challenges faced by aging plant systems and components at nuclear power plants (NPP), the Electric Power Research Institute (EPRI) has a product entitled Integrated Life Cycle Management (ILCM). The ILCM software is a quantitative tool that supports capital asset and component replacement decision-making at NPPs. ILCM is comprised of models that predict the probability of failure (PoF) over time for various high-value components such as steam generators, turbines, generators, etc. The PoF models allow the user to schedule replacements at the optimum time, thereby reducing unplanned equipment shutdowns and costs. This paper describes a mathematical model that was developed for critical heat exchangers in a power plant. The heat exchanger model calculates the probability of the tubes, shell, or internals failing individually, and then accumulates the failures across the heat exchanger sub-components. The dominant degradation mechanisms addressed by the model include stress corrosion cracking, wear, microbiologically influenced corrosion, flow accelerated corrosion, and particle-induced erosion. The heat exchanger model combines physics-based algorithms and operating experience distributions to predict the cumulative PoF over time. The model is applicable to shell and tube heat exchangers and air-to-water heat exchangers. Many different types of fluids including open cycle fresh water, closed cycle fresh water, sea water, brackish water, air, closed cooling water, steam, oil, primary water, and condensate are included. Examples of PoF over time plots are also provided for different fluid types and operating conditions.

Breast cancer is the most frequently diagnosed cancer in women. To date, the exact causes of breast cancer still remains unknown. The most effective way to tackle the disease is early detection through breast screening programmes. Breast density is a well established image based risk factor. An accurate dense breast tissue segmentation can play a vital role in precise identification of women at risk, and determining appropriate measures for disease prevention. Fractal techniques have been used in many biomedical image processing applications with varying degrees of success. This paper describes a fractal inspired approach to mammographic tissue segmentation. A multiresolution stack representation and 3D histogram features extended from 2D are proposed. Quantitative and qualitative evaluation was performed including mammographic tissue segmentation and density classification. Results showed that the developed methodology was able to differentiate between breast tissue variations. The achieved density classification accuracy for 360 digital mammograms is 78i¾?% based on the BI-RADS scheme. The developed fractal inspired approach in conjunction with the stack representation and 3D histogram features has demonstrated an ability to produce quality mammographic tissue segmentation. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

The corrosion mechanism for secondary side systems of pressurized water reactors has always been difficult to monitor and quantify. Ion chromatography may be the method to evaluate the corrosion phenomena using on-line systems. Two separate ion chromatography methods have been developed to monitor manganese, a component of carbon steel and stainless steel used in secondary system piping. The first method employs step gradient cation-exchange separation with chemically suppressed conductometric detection. The second method employs traditional transition metal analysis using an analytical column followed by post-column reagent addition and mixing with visible detection at 520 nm. The presence of manganese was verified using corrosion product monitors. Each monitor has a 0.45-μm particulate filter followed by three cation-exchange membrane filters for the determination of soluble metallic species. Atomic absorption spectrophotometry was employed to analyze the processed filters. Results for the corrosion monitors and the ion chromatography methods were compared to attempt validation of all methods and to establish practical methods for on-line corrosion monitoring.

Gradient elution separations were developed for four pellicular and two macroporous resin based columns for the anlysis of high-purity water. These elution techniques are applicable to both concentrator and loop injections and used sodium hydroxide and sodium tetraborate eluents with suppressed conductivity detection. The analytes of interest are flouride, acetate, formate, pyruvate, chloride, nitrite, nitrate, bromide, sulfate and oxalate. These comparisons allow for the proper selection of column and eluent for part-per-billion (109) and sub-part-per-billion analysis of high purity water.

It is possible to determine anions and silica in a single injection. However, there are interactions that occur between the anions and silica that are not completely understood. This work provides the data for the hypothesis that the hydronium ion occuring at a suppressor promotes a catalytic formation of silica complexes. Silica forms halogen complexes and uses sulfur as a bridge to form other compounds thereby reducing the amount of free silica, flouride, chloride and sulfate. This in turn is reflected in the response at the conductivity and visible detectors. This work also provides the data that even when the systems not coupled there still are anion-silica complexes being formed which inhibit complete detection of either the anions or silica separately. The interactions between silica and the anions occur either precolumn or on column with the possible exception of fluoride which coelutes with silica.