Computational Studies of Light-Matter Interactions in Two- and Three-Dimensional Systems

A computational approach is taken to studying a range of light-matter interactions which are interesting in terms of their potential applications as well as from a fundamental point of view. Two different types of polariton, part-light, part-matter quasiparticles, namely exciton-polaritons and Tamm plasmon-polaritons (a type of surface plasmon-polariton) are considered. The conditions required for the strong coupling of optical whispering gallery modes and bulk excitons in submicron spheres are ascertained for the materials gallium arsenide, gallium nitride and zinc oxide. It is shown that the strong coupling regime may be accessed by optical modes with a low decay constant, typically exhibited by those modes with higher angular momentum quantum numbers. Tamm plasmon-polaritons have previously been shown to exist at the boundary between a metal and a planar Bragg reflector structure. The conditions required for the formation of Tamm plasmon-polaritons in cylindrical multilayer structures with a metal core, cladding or metal in both of these locations are determined. The cylindrical Tamm plasmon-polaritons are shown to have low effective masses and low group velocities. It is also shown that it is possible to obtain split polariton modes in structures containing metal in both the core and the cladding. The effect of disorder on a two-dimensional photonic crystal structure consisting of air holes in a slab of dielectric material is studied. It is shown that the defined threshold disorder is not signicantly affected by the dierent relative band widths of the ideal crystal structures considered

The Definitive Guide to the Rise and Fall of British Steam [An Illustrated History of British Steam Locomotion]

BRITISH STEAM is a copiously illustrated guide, which explores the rise of the steam locomotive, the engine which dominated British railways, and changed the way that people travelled during the nineteenth century. Steam railways were once the most efficient way to travel, before the invention of electric and diesel locomotives, which heralded the demise of the steam engine. This book documents the development of steam, the pioneers in engineering who made it all possible, and the memorable age when steam dominated our railways and transport systems. The book features scores of historical images plus eleven historical videos - accessible via its easy-to-use "interactive app" feature. ISBN-10: 1-7819-7284-9; ISBN-13: 978-1-78197-384-4. 304pp. with 250 illustrations (78 in full colour) and 11 interactive videos. Hardback

Meta-analysis of the Cepheid Xpert® CT/NG assay for extragenital detection of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) infections.

Background Most studies evaluating extragenital testing performance for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) detection by the Xpert® CT/NG show high per cent agreement with comparison assays; however, the precision around positive per cent agreement is low and thus the values that have been reported are not highly informative. Therefore, a systematic review was conducted and data from five studies were combined to better assess positive per cent agreement.MethodsThe literature indexed on PubMed.gov was searched. Included studies were those that were an evaluation of the Xpert CT/NG assay with rectal and/or pharyngeal specimen types compared with another nucleic acid amplification test (NAAT), the Aptima transcription mediated amplification assay. A full Bayesian method was used for bivariate fixed-effect meta-analysis of positive and negative per cent agreement and pooled estimates (and 95% confidence intervals (CI)) were presented for each.ResultsThe pooled positive and negative per cent agreement for detection of CT in rectal specimens was 89.72% (95% CI: 84.97%, 93.64%) and 99.23% (95% CI: 98.74%, 99.60%), and in pharyngeal specimens, they were 89.96% (95% CI: 66.38%, 99.72%) and 99.62% (95% CI: 98.95%, 99.95%) respectively. For NG detection in rectal specimens, the pooled positive and negative per cent agreement was 92.75% (95% CI: 87.91%, 96.46%) and 99.75% (95% CI: 99.46%, 99.93%), and in pharyngeal specimens, they were 92.51% (95% CI: 85.84%, 97.18%) and 98.56% (95% CI: 97.69%, 99.23%) respectively.ConclusionsIt was found that the Xpert CT/NG assay performed similarly to the Aptima transcription mediated amplification assay for the detection of CT and NG in extragenital specimens. The Xpert assay has the benefit of providing faster results at the point-of-care, thus reducing the turnaround time for results, potentially enabling same-day treatment

Machine learning for understanding complex, interlinked social data

With the growing availability of ‘big’ data, increasing computer power, and improved data storage capacities, machine learning techniques are now frequently employed in order to make sense of data. Yet, the social sciences have been slow to adopt these techniques, and there is little evidence of their use in some academic fields. This thesis explores the methods most commonly utilised in social science research, that is, linear regression and null hypothesis significance testing, in order to identify how machine learning methods might complement these more established methods. A case study exploring the Troubled Families programme provides a practical example of how machine learning techniques can be utilised on complex, interlinked social data in order to provide deeper understanding and more insight into the data. Eleven different types of families were identified using cluster analysis, and analysis was performed in order to understand how the family’s lives changed after joining the TF programme when compared to before. The analysis provided insight into the various types of families that existed and the problems that they had. It also highlighted that, had the data been analysed on an overall global level, it would have been prone to an averaging effect whereby many of the changes that occurred were not apparent; analysis on the cluster-level resulted in identification of cluster-level patterns, and a greater understanding of the data. This thesis demonstrated that machine learning techniques, such as cluster analysis and decision tree learning, can be effectively utilised on complex ‘real-life’ social science datasets. These methods can identify hidden groups and relationships, and important predictors in a dataset, provide a better understanding of the structure of the data, and aid in generating research questions and hypotheses

A Semantic and Syntactic Similarity Measure for Political Tweets

Measurement of the semantic and syntactic similarity of human utterances is essential in allowing machines to understand dialogue with users. However, human language is complex, and the semantic meaning of an utterance is usually dependent upon the context at a given time and learnt experience of the meaning of the words that are used. This is particularly challenging when automatically understanding the meaning of social media, such as tweets, which can contain non-standard language. Short Text Semantic Similarity measures can be adapted to measure the degree of similarity of a pair of tweets. This work presents a new Semantic and Syntactic Similarity Measure (TSSSM) for political tweets. The approach uses word embeddings to determine semantic similarity and extracts syntactic features to overcome the limitations of current measures which may miss identical sequences of words. A large dataset of tweets focusing on the political domain were collected, pre-processed and used to train the word embedding model, with various experiments performed to determine the optimal model and parameters. A selection of tweet pairs were evaluated by humans for semantic equivalence and correlated against the measure. The new measure can be used in a variety of applications, including for identifying and analyzing political narratives. Experiments on three diverse human-labelled test datasets demonstrate that the measure outperforms an existing measure, performs well on tweets from the political domain and may also generalize outside the political domain

Immune genes undergo more adaptive evolution than non-immune system genes in Daphnia pulex

<p>Abstract</p> <p>Background</p> <p>Understanding which parts of the genome have been most influenced by adaptive evolution remains an unsolved puzzle. Some evidence suggests that selection has the greatest impact on regions of the genome that interact with other evolving genomes, including loci that are involved in host-parasite co-evolutionary processes. In this study, we used a population genetic approach to test this hypothesis by comparing DNA sequences of 30 putative immune system genes in the crustacean <it>Daphnia pulex</it> with 24 non-immune system genes.</p> <p>Results</p> <p>In support of the hypothesis, results from a multilocus extension of the McDonald-Kreitman (MK) test indicate that immune system genes as a class have experienced more adaptive evolution than non-immune system genes. However, not all immune system genes show evidence of adaptive evolution. Additionally, we apply single locus MK tests and calculate population genetic parameters at all loci in order to characterize the mode of selection (directional versus balancing) in the genes that show the greatest deviation from neutral evolution.</p> <p>Conclusions</p> <p>Our data are consistent with the hypothesis that immune system genes undergo more adaptive evolution than non-immune system genes, possibly as a result of host-parasite arms races. The results of these analyses highlight several candidate loci undergoing adaptive evolution that could be targeted in future studies.</p

Growing a Culture of Assessment at the Drake Memorial Library

The Drake Memorial Library is 1 of 75 libraries across North America to participate in ACRL’s Assessment in Action program. The 14-month program entails the development and implementation of an action learning project examining the library’s impact on student success and contribution to assessment activities on campus. Brockport’s four person team includes members from outside of the library to foster cross-campus collaboration. This poster describes the program and the goals, methods, results and conclusions of the Drake Memorial Library\u27s action learning project

Landscape controls on fuel moisture variability in fire-prone heathland and peatland landscapes

Background: Cross-landscape fuel moisture content is highly variable but not considered in existing fire danger assessments. Capturing fuel moisture complexity and its associated controls is critical for understanding wildfire behavior and danger in emerging fire-prone environments that are influenced by local heterogeneity. This is particularly true for temperate heathland and peatland landscapes that exhibit spatial differences in the vulnerability of their globally important carbon stores to wildfire. Here we quantified the range of variability in the live and dead fuel moisture of Calluna vulgaris across a temperate fire-prone landscape through an intensive fuel moisture sampling campaign conducted in the North Yorkshire Moors, UK. We also evaluated the landscape (soil texture, canopy age, aspect, and slope) and micrometeorological (temperature, relative humidity, vapor pressure deficit, and windspeed) drivers of landscape fuel moisture variability for temperate heathlands and peatlands for the first time. Results: We observed high cross-landscape fuel moisture variation, which created a spatial discontinuity in the availability of live fuels for wildfire spread (fuel moisture &lt; 65%) and vulnerability of the organic layer to smoldering combustion (fuel moisture &lt; 250%). This heterogeneity was most important in spring, which is also the peak wildfire season in these temperate ecosystems. Landscape and micrometeorological factors explained up to 72% of spatial fuel moisture variation and were season- and fuel-layer-dependent. Landscape factors predominantly controlled spatial fuel moisture content beyond modifying local micrometeorology. Accounting for direct landscape–fuel moisture relationships could improve fuel moisture estimates, as existing estimates derived solely from micrometeorological observations will exclude the underlying influence of landscape characteristics. We hypothesize that differences in soil texture, canopy age, and aspect play important roles across the fuel layers examined, with the main differences in processes arising between live, dead, and surface/ground fuels. We also highlight the critical role of fuel phenology in assessing landscape fuel moisture variations in temperate environments. Conclusions: Understanding the mechanisms driving fuel moisture variability opens opportunities to develop locally robust fuel models for input into wildfire danger rating systems, adding versatility to wildfire danger assessments as a management tool

Biopsy and selective recall compared with immediate large loop excision in management of women with low grade abnormal cervical cytology referred for colposcopy : multicentre randomised controlled trial

Peer reviewedPublisher PD

Federated learning for generating synthetic data: a scoping review

Introduction Federated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global model. Since the training data remains with each local client and is not shared or exchanged with other clients the use of FL may reduce privacy and security risks (compared to methods where multiple data sources are pooled) and can also address data access and heterogeneity problems. Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of the original data records, therefore minimising disclosure risk. Using FL to produce synthetic data (which we refer to as "federated synthesis") has the potential to combine data from multiple clients without compromising privacy, allowing access to data that may otherwise be inaccessible in its raw format. Objectives The objective was to review current research and practices for using FL to generate synthetic data and determine the extent to which research has been undertaken, the methods and evaluation practices used, and any research gaps. Methods A scoping review was conducted to systematically map and describe the published literature on the use of FL to generate synthetic data. Relevant studies were identified through online databases and the findings are described, grouped, and summarised. Information extracted included article characteristics, documenting the type of data that is synthesised, the model architecture and the methods (if any) used to evaluate utility and privacy risk. Results A total of 69 articles were included in the scoping review; all were published between 2018 and 2023 with two thirds (46) in 2022. 30% (21) were focussed on synthetic data generation as the main model output (with 6 of these generating tabular data), whereas 59% (41) focussed on data augmentation. Of the 21 performing federated synthesis, all used deep learning methods (predominantly Generative Adversarial Networks) to generate the synthetic data. Conclusions Federated synthesis is in its early days but shows promise as a method that can construct a global synthetic dataset without sharing any of the local client data. As a field in its infancy there are areas to explore in terms of the privacy risk associated with the various methods proposed, and more generally in how we measure those risks