Reexamining Black-Body Shifts for Hydrogenlike Ions

We investigate black-body induced energy shifts for low-lying levels of atomic systems, with a special emphasis on transitions used in current and planned high-precision experiments on atomic hydrogen and ionized helium. Fine-structure and Lamb-shift induced black-body shifts are found to increase with the square of the nuclear charge number, whereas black-body shifts due to virtual transitions decrease with increasing nuclear charge as the fourth power of the nuclear charge. We also investigate the decay width acquired by the ground state of atomic hydrogen, due to interaction with black-body photons. The corresponding width is due to an instability against excitation to higher excited atomic levels, and due to black-body induced ionization. These effects limit the lifetime of even the most fundamental, a priori absolutely stable, "asymptotic" state of atomic theory, namely the ground state of atomic hydrogen.Comment: 11 pages; LaTe

Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer

Atmos. Meas. Tech., 10, 695-708, 2017 http://www.atmos-meas-tech.net/10/695/2017/ doi:10.5194/amt-10-695-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License.Characterisation of bioaerosols has important implications within environment and public health sectors. Recent developments in ultraviolet light-induced fluorescence (UV-LIF) detectors such as the Wideband Integrated Bioaerosol Spectrometer (WIBS) and the newly introduced Multiparameter Bioaerosol Spectrometer (MBS) have allowed for the real-time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal spores and pollen. This new generation of instruments has enabled ever larger data sets to be compiled with the aim of studying more complex environments. In real world data sets, particularly those from an urban environment, the population may be dominated by non-biological fluorescent interferents, bringing into question the accuracy of measurements of quantities such as concentrations. It is therefore imperative that we validate the performance of different algorithms which can be used for the task of classification. For unsupervised learning we tested hierarchical agglomerative clustering with various different linkages. For supervised learning, 11 methods were tested, including decision trees, ensemble methods (random forests, gradient boosting and AdaBoost), two implementations for support vector machines (libsvm and liblinear) and Gaussian methods (Gaussian naïve Bayesian, quadratic and linear discriminant analysis, the k-nearest neighbours algorithm and artificial neural networks). The methods were applied to two different data sets produced using the new MBS, which provides multichannel UV-LIF fluorescence signatures for single airborne biological particles. The first data set contained mixed PSLs and the second contained a variety of laboratory-generated aerosol. Clustering in general performs slightly worse than the supervised learning methods, correctly classifying, at best, only 67. 6 and 91. 1 % for the two data sets respectively. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 82. 8 and 98. 27 % of the testing data, respectively, across the two data sets. A possible alternative to gradient boosting is neural networks. We do however note that this method requires much more user input than the other methods, and we suggest that further research should be conducted using this method, especially using parallelised hardware such as the GPU, which would allow for larger networks to be trained, which could possibly yield better results. We also saw that some methods, such as clustering, failed to utilise the additional shape information provided by the instrument, whilst for others, such as the decision trees, ensemble methods and neural networks, improved performance could be attained with the inclusion of such information.Peer reviewe

Exploring women's sensory experiences of undergoing colposcopy and related procedures: implications for preparatory sensory information provision

INTRODUCTION: Some women experience distress during colposcopy examinations which is partly related to women's fear, or experience, of pain during the procedure. However, little is known about women's sensory experiences of colposcopy (other than pain) or what might impact on these experiences. The aim of this study was to explore women's sensory experiences of colposcopy and related procedures and identify factors which influenced negative sensory experiences. METHODS: In-depth interviews were conducted with 23 women who had undergone, for the first time, a colposcopy (some with related procedures, including punch biopsies and loop excision) as part of follow-up for abnormal cervical cytology. Interviews were analysed thematically using the Framework Approach to organise the data and identify emerging higher-order themes. RESULTS: Women described a range of sensory experiences including pain or discomfort, cramping, stinging and cold sensations (due to the application of acetic acid to the cervix). Four key themes emerged as important aspects of the overall sensory experience: levels of pain, treatment-specific sensations, anaesthetic-specific sensations and solution-specific sensations. Factors that may influence women having a negative sensory experience were sensory expectations of the procedure(s) and lack of preparatory sensory information. DISCUSSION: Our study provides unique in-depth insight into women's sensory experiences of colposcopy and related procedures and suggests women require more preparatory sensory information. The issues identified as contributing to women having a negative sensory experience may help inform the development of pre-colposcopy information which may better prepare women with abnormal cervical cytology for follow-up examinations

Evaluation of Machine Learning Algorithms for Classification of Primary Biological Aerosol using a new UV-LIF spectrometer

© Author(s) 2016. This work is distributed under the Creative Commons Attribution 3.0 License.Characterisation of bio-aerosols has important implications within Environment and Public Health sectors. Recent developments in Ultra-Violet Light Induced Fluorescence (UV-LIF) detectors such as the Wideband Integrated bio-aerosol Spectrometer (WIBS) and the newly introduced Multiparameter bio-aerosol Spectrometer (MBS) has allowed for the real time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal Spores and pollen. This new generation of instruments has enabled ever larger data sets to be compiled with the aim of studying more complex environments. In real world data sets, particularly those from an urban environment, the population may be dominated by non- biological fluorescent interferents bringing into question the accuracy of measurements of quantities such as concentrations. It is therefore imperative that we validate the performance of different algorithms which can be used for the task of classification. For unsupervised learning we test Hierarchical Agglomerative Clustering with various different linkages. For supervised learning, ten methods were tested; including decision trees, ensemble methods: Random Forests, Gradient Boosting and Ad-aBoost; two implementations for support vector machines: libsvm and liblinear; Gaussian methods: Gaussian naïve Bayesian, quadratic and linear discriminant analysis and finally the k-nearest neighbours algorithm. The methods were applied to two different data sets measured using a new Multiparameter bio-aerosol Spectrometer which provides multichannel UV-LIF fluorescence signatures for single airborne biological particles. Clustering, in general performs slightly worse than the supervised learning methods correctly classifying, at best, only 72.7 and 91.1 percent for the two data sets respectively. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 88.1 and 97.8 percent of the testing data respectively across the two data sets.Peer reviewe

Absolute frequency measurements of 85Rb nF7/2 Rydberg states using purely optical detection

A three-step laser excitation scheme is used to make absolute frequency measurements of highly excited nF7/2 Rydberg states in 85Rb for principal quantum numbers n=33-100. This work demonstrates the first absolute frequency measurements of rubidium Rydberg levels using a purely optical detection scheme. The Rydberg states are excited in a heated Rb vapour cell and Doppler free signals are detected via purely optical means. All of the frequency measurements are made using a wavemeter which is calibrated against a GPS disciplined self-referenced optical frequency comb. We find that the measured levels have a very high frequency stability, and are especially robust to electric fields. The apparatus has allowed measurements of the states to an accuracy of 8.0MHz. The new measurements are analysed by extracting the modified Rydberg-Ritz series parameters.Comment: 12 pages, 5 figures, submitted to New. J. Phy

Comparison of Fencing Designs for Excluding Deer from Roadways

We evaluated the efficacy of several fencing designs for restricting movements of 18 captive, female white-tailed deer (Odocoelus virginianus), including standard wovenwire fencing (1.2-m, 1.5-m, 1.8-m, 2.1-m, and 2.4-m tall), opaque fencing (1.2-m, 1.5-m, and 1.8-m tall), and an outrigger fence (i.e., 0.6-m outriggers attached to a 1.2-m-tall wire fence angled at 45º). We recorded the number of successful fence crossings for each deer and characterized behaviors associated with each failed crossing attempt. No deer crossed the 2.4-m fence, whereas all deer crossed the 1.2-m fence. We observed no differences in crossing success between woven-wire and opaque fencing at height

Machine learning for improved data analysis of biological aerosol using the WIBS

Abstract. Primary biological aerosol including bacteria, fungal spores and pollen have important implications for public health and the environment. Such particles may have different concentrations of chemical fluorophores and will provide different responses in the presence of ultraviolet light which potentially could be used to discriminate between different types of biological aerosol. Development of ultraviolet light induced fluorescence (UV-LIF) instruments such as the Wideband Integrated Bioaerosol Sensor (WIBS) has made is possible to collect size, morphology and fluorescence measurements in real-time. However, it is unclear without studying responses from the instrument in the laboratory, the extent to which we can discriminate between different types of particles. Collection of laboratory data is vital to validate any approach used to analyse the data and to ensure that the data available is utilised as effectively as possible. In this manuscript we test a variety of methodologies on traditional reference particles and a range of laboratory generated aerosols. Hierarchical Agglomerative Clustering (HAC) has been previously applied to UV-LIF data in a number of studies and is tested alongside other algorithms that could be used to solve the classification problem: Density Based Spectral Clustering and Noise (DBSCAN), k-means and gradient boosting. Whilst HAC was able to effectively discriminate between the reference particles, yielding a classification error of only 1.8 %, similar results were not obtained when testing on laboratory generated aerosol where the classification error was found to be between 11.5 % and 24.2 %. Furthermore, there is a worryingly large uncertainty in this approach in terms of the data preparation and the cluster index used, and we were unable attain consistent results across the different sets of laboratory generated aerosol tested. The best results were obtained using gradient boosting, where the misclassification rate was between 4.38 % and 5.42 %. The largest contribution to this error was the pollen samples where 28.5 % of the samples were misclassified as fungal spores. The technique was also robust to changes in data preparation provided a fluorescent threshold was applied to the data. Where laboratory training data is unavailable, DBSCAN was found to be an potential alternative to HAC. In the case of one of the data sets where 22.9 % of the data was left unclassified we were able to produce three distinct clusters obtaining a classification error of only 1.42 % on the classified data. These results could not be replicated however for the other data set where 26.8 % of the data was not classified and a classification error of 13.8 % was obtained. This method, like HAC, also appeared to be heavily dependent on data preparation, requiring different selection of parameters dependent on the preparation used. Further analysis will also be required to confirm our selection of parameters when using this method on ambient data. There is a clear need for the collection of additional laboratory generated aerosol to improve interpretation of current databases and to aid in the analysis of data collected from an ambient environment. New instruments with a greater resolution are likely improve on current discrimination between pollen, bacteria and fungal spores and even between their different types, however the need for extensive laboratory training data sets will grow as a result. </jats:p

Trends in, and predictors of, anxiety and specific worries following colposcopy: a 12-month longitudinal study

Objective Little is known about which women are at greatest risk of adverse psychological after-effects following colposcopy. This study examined time trends in, and identified predictors of, anxiety and specific worries over 12 months. Methods Women attending two hospital-based colposcopy clinics for abnormal cervical cytology were invited to complete psychosocial questionnaires at 4, 8 and 12 months following colposcopy. General anxiety and screening-specific worries (about cervical cancer, having sex and future fertility) were measured. Generalised estimating equations were used to assess associations between socio-demographic, lifestyle and clinical variables and risk of psychological outcomes. Results Of 584 women initially recruited, 429, 343 and 303 completed questionnaires at 4, 8 and 12 months, respectively. Screening-specific worries declined significantly over time but were still relatively high at 12 months: 23%, 39% and 18% for worries about cervical cancer, fertility and having sex, respectively. Anxiety remained stable (20%) over time. Risks of cervical cancer worry and anxiety were both almost double in women without private health insurance (cervical cancer worry: OR = 1.80, 95% CI 1.25–2.61; anxiety: OR = 1.84, 95% CI 1.20–2.84). Younger women (<40 years) had higher risk of fertility worries. Non-Irish women had higher risk of anxiety (OR = 2.13, 95% CI 1.13–4.01). Conclusions Screening-specific worries declined over time but anxiety remained stable. Notable proportions of women still reported adverse outcomes 12 months following colposcopy, with predictors varying between outcomes. Women in socio-demographically vulnerable groups were at greatest risk of adverse psychological outcomes. This information could inform development of interventions to alleviate psychological distress post-colposcopy. Copyright © 2015 John Wiley & Sons, Ltd