The Effect of pH and Pulsed Ultraviolet Light Emitting Diode Duty Cycles on the First Order Rate Constant and Byproduct Profile of the Advanced Oxidation of Tartrazine

Water treatment capability is required for military operations, production of drinking water, and industrial wastewater treatment. The Advanced Oxidation Process (AOP) is one of many viable steps for treating water; however, it is important to understand the byproducts of the treatment process to avoid creating other constituents more severe than the first. Tartrazine (TAR) was oxidized with pulsed Ultra- Violet (UV) Light Emitting Diodes (LEDs) in combination with hydrogen peroxide (H2O2) in an AOP at the laboratory scale. The relative concentration of TAR was reduced from 1 to between 1 – 0.75 over pH values between 6 and 9, and at Duty Cycles (DCs) ranging from 0% to100%. The first order rate constant for TAR removal was statistically and positively correlated with DC, was statistically and negatively correlated with pH, and was typically greatest at pH6. DC and pH were variables in a regression model of the first order rate constant with adjusted R2 of 0.85. Chromatographic contrast angle determinations revealed that the byproduct profile was most significantly influenced by pH 7 under both positive and negative ionization, by the 70% DC for the positive ionization, and 50% DC for the negative ionization. DC and pH were variables in regression models

Choosing Your Neighbors: Networks of Diffusion in International Relations

In examining the discussion of social and political phenomena like regime transition, conflict, and policy change, scholars routinely make choices about how proximity is defined and which neighbors should be considered more important than others. Since each specification offers an alternative view of the networks through which discussion can take place, one's decision can exert a significant influence on the magnitude and scope of estimated discussion effects. This problem is widely recognized, but is rarely the subject of direct analysis. In international relations research, connectivity choices are usually ad hoc, driven more by data availability than by theoretically informed decision criteria. We take a closer look at the assumptions behind these choices, and propose a more systematic method to asses the structural similarity of two or more alternative networks, and select one that most plausibly relates theory to empirics. We apply this method to the spread of democratic regime change, and offer an illustrative example of how neighbor choices might impact predictions and inferences in the case of the 2011 Arab Spring.GovernmentSociolog

stm: An R Package for Structural Topic Models

This paper demonstrates how to use the R package stm for structural topic modeling. The structural topic model allows researchers to flexibly estimate a topic model that includes document-level metadata. Estimation is accomplished through a fast variational approximation. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest

In vivo microdialysis reveals age-dependent decrease of brain interstitial fluid tau levels in P301S human tau transgenic mice

Although tau is a cytoplasmic protein, it is also found in brain extracellular fluids, e.g., CSF. Recent findings suggest that aggregated tau can be transferred between cells and extracellular tau aggregates might mediate spread of tau pathology. Despite these data, details of whether tau is normally released into the brain interstitial fluid (ISF), its concentration in ISF in relation to CSF, and whether ISF tau is influenced by its aggregation are unknown. To address these issues, we developed a microdialysis technique to analyze monomeric ISF tau levels within the hippocampus of awake, freely moving mice. We detected tau in ISF of wild-type mice, suggesting that tau is released in the absence of neurodegeneration. ISF tau was significantly higher than CSF tau and their concentrations were not significantly correlated. Using P301S human tau transgenic mice (P301S tg mice), we found that ISF tau is fivefold higher than endogenous murine tau, consistent with its elevated levels of expression. However, following the onset of tau aggregation, monomeric ISF tau decreased markedly. Biochemical analysis demonstrated that soluble tau in brain homogenates decreased along with the deposition of insoluble tau. Tau fibrils injected into the hippocampus decreased ISF tau, suggesting that extracellular tau is in equilibrium with extracellular or intracellular tau aggregates. This technique should facilitate further studies of tau secretion, spread of tau pathology, the effects of different disease states on ISF tau, and the efficacy of experimental treatments

Structural Topic Models for Open-Ended Survey Responses

Collection and especially analysis of open-ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author's gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments

Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%

Determining appropriate data analytics for transformer health monitoring

Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring