The evolution of the 1952 nursing practice act in the state of Rhode Island and Providence plantations.

Thesis (M.S.)--Boston Universit

High levels of gene flow and genetic diversity in Irish populations of Salix caprea L. inferred from chloroplast and nuclear SSR markers

peer-reviewedBackground Salix caprea is a cold-tolerant pioneer species that is ecologically important in Europe and western and central Asia. However, little data is available on its population genetic structure and molecular ecology. We describe the levels of geographic population genetic structure in natural Irish populations of S. caprea and determine the extent of gene flow and sexual reproduction using both chloroplast and nuclear simple sequence repeats (SSRs). Results A total of 183 individuals from 21 semi-natural woodlands were collected and genotyped. Gene diversity across populations was high for the chloroplast SSRs (H T  = 0.21-0.58) and 79 different haplotypes were discovered, among them 48% were unique to a single individual. Genetic differentiation of populations was found to be between moderate and high (mean G ST  = 0.38). For the nuclear SSRs, G ST was low at 0.07 and observed heterozygosity across populations was high (H O  = 0.32-0.51); only 9.8% of the genotypes discovered were present in two or more individuals. For both types of markers, AMOVA showed that most of the variation was within populations. Minor geographic pattern was confirmed by a Bayesian clustering analysis. Gene flow via pollen was found to be approximately 7 times more important than via seeds. Conclusions The data are consistent with outbreeding and indicate that there are no significant barriers for gene flow within Ireland over large geographic distances. Both pollen-mediated and seed-mediated gene flow were found to be high, with some of the populations being more than 200 km apart from each other. These findings could simply be due to human intervention through seed trade or accidental transportation of both seeds and pollen. These results are of value to breeders wishing to exploit natural genetic variation and foresters having to choose planting material.Teagasc Walsh Fellowship Programm

The effect of certain environmental factors and the influence of the separate answer sheet upon the results of the Terman-McNemar test of mental ability

Thesis (Ed.M.)--Boston Universit

Deep Residual Policy Reinforcement Learning as a Corrective Term in Process Control for Alarm Reduction: A Preliminary Report

Conventional process controllers (such as proportional integral derivative controllers and model predictive controllers) are simple and effective once they have been calibrated for a given system. However, it is difficult and costly to re-tune these controllers if the system deviates from its normal conditions and starts to deteriorate. Recently, reinforcement learning has shown a significant improvement in learning process control policies through direct interaction with a system, without the need of a process model or the system characteristics, as it learns the optimal control by interacting with the environment directly. However, developing such a black-box system is a challenge when the system is complex and it may not be possible to capture the complete dynamics of the system with just a single reinforcement learning agent. Therefore, in this paper, we propose a simple architecture that does not replace the conventional proportional integral derivative controllers but instead augments the control input to the system with a reinforcement learning agent. The agent adds a correction factor to the output provided by the conventional controller to maintain optimal process control even when the system is not operating under its normal condition

Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines

An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes on combining the advantages of input-output hidden Markov models and reinforcement learning. We propose a novel hierarchical modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while at a low level, provides the optimal replacement policy. This approach outperforms baseline deep reinforcement learning (DRL) models and has performance comparable to that of a state-of-the-art reinforcement learning system while being more interpretable

Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

Distributed catchment models are widely used tools for predicting hydrologic behavior. While distributed models require many parameters to describe a system, they are expected to simulate behavior that is more consistent with observed processes. However, obtaining a single set of acceptable parameters can be problematic, as parameter equifinality often results in several behavioral sets that fit observations (typically streamflow). In this study, we investigate the extent to which equifinality impacts a typical distributed modeling application. We outline a hierarchical approach to reduce the number of behavioral sets based on regional, observation-driven, and expert-knowledge-based constraints. For our application, we explore how each of these constraint classes reduced the number of behavioral parameter sets and altered distributions of spatiotemporal simulations, simulating a well-studied headwater catchment, Stringer Creek, Montana, using the distributed hydrology–soil–vegetation model (DHSVM). As a demonstrative exercise, we investigated model performance across 10 000 parameter sets. Constraints on regional signatures, the hydrograph, and two internal measurements of snow water equivalent time series reduced the number of behavioral parameter sets but still left a small number with similar goodness of fit. This subset was ultimately further reduced by incorporating pattern expectations of groundwater table depth across the catchment. Our results suggest that utilizing a hierarchical approach based on regional datasets, observations, and expert knowledge to identify behavioral parameter sets can reduce equifinality and bolster more careful application and simulation of spatiotemporal processes via distributed modeling at the catchment scale

Blending PEG-based polymers and their use in surface micro-patterning by the FIMIC method to obtain topographically smooth patterns of elasticity

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We have designed and fabricated a library of polyethylene glycol (PEG)-based polymer blends, including blends of two PEG-based polymers that are liquid at room temperature where the optimisation of the blending method allows for the incorporation of higher molecular-weight PEG-based polymers which are solid at room temperature. The absence of a solvent in these blends makes them perfect candidates for use in our recently developed Fill-Molding in Capillaries (FIMIC) patterning method. As our FIMIC samples have shown to be not completely smooth (a small topography up to several nanometers has been seen previously), and this is likely to affect the cellular behaviour, we have improved our technique in order to obtain virtually smooth samples that exhibit a pattern of elasticity only. It is demonstrated that, by taking advantage of the differential swelling of the pattern components, we can level out the undesired topographic difference. In particular, by employing blends of materials, (1) the swelling degree of each component can be fine-tuned to even out any topography and (2) the use of the same blends in the sample, yet with varying cross-linker amounts, ensures the swelling degree and elasticity change without changing the surface chemistry significantly. Genuine, binary patterns of elasticity can thus be fabricated, which are a great asset to study cell migration phenomena in systematic detail.DFG, EXC 314, Unifying Concepts in Catalysi

Surfactant-aided exfoliation of molydenum disulphide for ultrafast pulse generation through edge-state saturable absorption

We use liquid phase exfoliation to produce dispersions of molybdenum disulphide (MoS2) nanoflakes in aqueous surfactant solutions. The chemical structures of the bile salt surfactants play a crucial role in the exfoliation and stabilization of MoS2. The resultant MoS2 dispersions are heavily enriched in single and few (<6) layer flakes with large edge to surface area ratio. We use the dispersions to fabricate free-standing polymer composite wide-band saturable absorbers to develop mode-locked and Q- switched fibre lasers, tunable from 1535-1565 and 1030-1070 nm, respectively. We attribute this sub-bandgap optical absorption and its nonlinear saturation behaviour to edge-mediated states introduced within the material band-gap of the exfoliated MoS2 nanoflakes.Comment: 6 pages, 5 figure