Within Lake Clustering of High Resolution Satellite Retrievals: A Functional Data and Clustering Approach

No abstract available

An investigation of the relationships between interdisciplinary communications and patient care.

Thesis (M.S.)--Boston Universit

A rapid protocol for generating arthropod DNA barcodes suitable for use with undergraduate students

We provide a protocol for rapid DNA extraction from spiders suitable for undergraduate practical sessions. Students who were previously naïve to the theory and laboratory technique of DNA barcoding were successfully able to extract and recover 29 DNA sequences from 16 species of small spiders in the family Linyphiidae. We anticipate that with careful selection of specimens, undergraduate students could participate in sessions which both benefit their professional development and further taxonomic understanding across a variety of organisms.PostprintPostprintPeer reviewe

A Multifidelity Framework for Wind Speed Data

Monitoring wind speed is essential to develop offshore wind farms. However, recorded wind data often lack the necessary accuracy for understanding the profitability of the wind farm, and even when they exist, they are scarce in time or space. Intuitively, using multiple data sources could balance the trade-off between scarcity and accuracy. A multi-fidelity framework in the form of the autoregressive Gaussian process is introduced to analyze wind speed reanalysis data fusing datasets of different reliability and resolution to provide a more accurate wind speed data product

State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality

Satellite remote sensing can provide indicative measures of environmental variables that are crucial to understanding the environment. The spatial and temporal coverage of satellite images allows scientists to investigate the changes in environmental variables in an unprecedented scale. However, identifying spatiotemporal patterns from such images is challenging due to the complexity of the data, which can be large in volume yet sparse within individual images. This paper proposes a new approach, state space functional principal components analysis (SS-FPCA), to identify the spatiotemporal patterns in processed satellite retrievals and simultaneously reduce the dimensionality of the data, through the use of functional principal components. Furthermore our approach can be used to produce interpolations over the sparse areas. An algorithm based on the alternating expectation–conditional maximisation framework is proposed to estimate the model. The uncertainty of the estimated parameters is investigated through a parametric bootstrap procedure. Lake chlorophyll-a data hold key information on water quality status. Such information is usually only available from limited in situ sampling locations or not at all for remote inaccessible lakes. In this paper, the SS-FPCA is used to investigate the spatiotemporal patterns in chlorophyll-a data of Taruo Lake on the Tibetan Plateau, observed by the European Space Agency MEdium Resolution Imaging Spectrometer

Cultivating support during COVID ‐19 through clinical supervision: A discussion article

Aim: This article aims to discuss how clinical supervision is an important approach in supporting frontline nurses and students during and post COVID‐19 through the lens of the nursing metaparadigms. Design: Discussion article. Methods: Discourse of the literature considering the importance of working collaboratively with healthcare and educational organisations in operationalising clinical supervision. Results: The evidence base supporting clinical supervision as an effective support strategy for nurses exists, however, its implementation and practice has become sporadic. A resurgence is required to support student's and nurse's during this pandemic. It is timely for nurse educators to creatively engage with clinical partners in supporting clinical supervision to enhance both nurses and students pandemic practice experiences. Clinical supervision is proposed as one strategy to support and guide both nurses and students to develop, strengthen and challenge the effectiveness of their care during COVID‐19

Nonparametric statistical downscaling for the fusion of data of different spatiotemporal support

Statistical downscaling has been developed for the fusion of data of different spatial support. However, environmental data often have different temporal support, which must also be accounted for. This paper presents a novel method of nonparametric statistical downscaling, which enables the fusion of data of different spatiotemporal support through treating the data at each location as observations of smooth functions over time. This is incorporated within a Bayesian hierarchical model with smoothly spatially varying coefficients, which provides predictions at any location or time, with associated estimates of uncertainty. The method is motivated by an application for the fusion of in situ and satellite remote sensing log(chlorophyll-a) data from Lake Balaton, in order to improve the understanding of water quality patterns over space and time