An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: a case study of nitrogen dioxide concentrations in Scotland

The long-term health effects of air pollution can be estimated using a spatio-temporal ecological study, where the disease data are counts of hospital admissions from populations in small areal units at yearly intervals. Spatially representative pollution concentrations for each areal unit are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over grid level concentrations from an atmospheric dispersion model. We propose a novel fusion model for estimating spatially aggregated pollution concentrations using both the modelled and monitored data, and relate these concentrations to respiratory disease in a new study in Scotland between 2007 and 2011

Translation across time in East and West encounters: an overview

Preprint Version (SherpaRomeoGreen)Introduction to the edited special issue of Translation Studies entitled: East and West Encounters: Translation across Time.info:eu-repo/semantics/publishedVersio

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research

Arran Stibbe

Arran Stibbe is Professor of Ecological Linguistics at the University of Gloucestershire. He has an academic background in both linguistics and human ecology and combines the two in his research and teaching. He is the founder of the International Ecolinguistics Association, and author of Animals Erased: Discourse, Ecology and Reconnection with Nature.&nbsp

Plasmonic hot electrons for sensing, photodetection, and solar energy applications: A perspective

In plasmonic metals, surface plasmon resonance decays and generates hot electrons and hot holes through non-radiative Landau damping. These hot carriers are highly energetic, which can be modulated by the plasmonic material, size, shape, and surrounding dielectric medium. A plasmonic metal nanostructure, which can absorb incident light in an extended spectral range and transfer the absorbed light energy to adjacent molecules or semiconductors, functions as a “plasmonic photosensitizer.” This article deals with the generation, emission, transfer, and energetics of plasmonic hot carriers. It also describes the mechanisms of hot electron transfer from the plasmonic metal to the surface adsorbates or to the adjacent semiconductors. In addition, this article highlights the applications of plasmonic hot electrons in photodetectors, photocatalysts, photoelectrochemical cells, photovoltaics, biosensors, and chemical sensors. It discusses the applications and the design principles of plasmonic materials and devices

SIMC 2.0: Improved Secure ML Inference Against Malicious Clients

In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem is first formulated by Lehmkuhl \textit{et al.} with a solution (MUSE, Usenix Security'21), whose performance is then substantially improved by Chandran et al.'s work (SIMC, USENIX Security'22). However, there still exists a nontrivial gap in these efforts towards practicality, giving the challenges of overhead reduction and secure inference acceleration in an all-round way. We propose SIMC 2.0, which complies with the underlying structure of SIMC, but significantly optimizes both the linear and non-linear layers of the model. Specifically, (1) we design a new coding method for homomorphic parallel computation between matrices and vectors. It is custom-built through the insight into the complementarity between cryptographic primitives in SIMC. As a result, it can minimize the number of rotation operations incurred in the calculation process, which is very computationally expensive compared to other homomorphic operations e.g., addition, multiplication). (2) We reduce the size of the garbled circuit (GC) (used to calculate nonlinear activation functions, e.g., ReLU) in SIMC by about two thirds. Then, we design an alternative lightweight protocol to perform tasks that are originally allocated to the expensive GCs. Compared with SIMC, our experiments show that SIMC 2.0 achieves a significant speedup by up to $17.4\times$ for linear layer computation, and at least $1.3\times$ reduction of both the computation and communication overheads in the implementation of non-linear layers under different data dimensions. Meanwhile, SIMC 2.0 demonstrates an encouraging runtime boost by $2.3\sim 4.3\times$ over SIMC on different state-of-the-art ML models

A Surface-Enhanced Raman Scattering Sensor Integrated with Battery-Controlled Fluidic Device for Capture and Detection of Trace Small Molecules

For surface-enhanced Raman scattering (SERS) sensors, one of the important issues is the development of substrates not only with high SERS-activity but also with strong ability to capture analytes. However, it is difficult to achieve the two goals simultaneously especially when detecting small molecules. Herein a compact battery-controlled nanostructure-assembled SERS system has been demonstrated for capture and detection of trace small molecule pollutants in water. In this SERS fluidic system, an electrical heating constantan wire covered with the vertically aligned ZnO nanotapers decorated with Ag-nanoparticles is inserted into a glass capillary. A mixture of thermo-responsive microgels, Au-nanorods colloids and analyte solution is then filled into the remnant space of the capillary. When the system is heated by switching on the battery, the thermo-responsive microgels shrink, which immobilizes the analyte and drives the Au-nanorod close to each other and close to the Ag-ZnO nanotapers. This process has also created high-density “hot spots” due to multi-type plasmonic couplings in three-dimensional space, amplifying the SERS signal. This integrated device has been successfully used to measure methyl parathion in lake water, showing a great potential in detection of aquatic pollutants

Impact of endoscopic thoracic R4 sympathicotomy combined with R3 ramicotomy for primary palmar hyperhidrosis

BackgroundEndoscopic thoracoscopic sympathectomy (ETS) is the preferred method for treating primary palmar hyperhidrosis (PPH) that bears the risk of compensatory hyperhidrosis (CH) following surgery. The current study aims to evaluate the effectiveness and safety of an innovative surgical procedure of ETS.MethodsA survey of the clinical data of 109 patients with PPH who underwent ETS in our department from May 2018 to August 2021 was retrospectively conducted. The patients were organized into two groups. Group A underwent R4 sympathicotomy combined with R3 ramicotomy. Group B underwent R3 sympathicotomy. Patients were followed up to evaluate the safety, effectiveness and the incidence of postoperative CH of the modified surgical approach.ResultsA total of 102 patients completed follow-up, and seven of the total enrolled patients were lost to follow-up, with a loss rate of 6% (7/109). Among these, Group A constitutes 54 cases, group B constitutes 48 cases, and the mean follow-up was 14 months (interquartile range 12–23 months). There was no statistically difference in surgical safety, postoperative efficacy, and postoperative quality of life (QoL) score between group A and group B (p > 0.05). The score of the psychological assessment was higher (p = 0.004) in group A (14.15 ± 2.06) compared to group B (13.30 ± 1.86). The incidence of CH in group A was lower than in group B (p = 0.019).ConclusionR4 sympathicotomy combined with R3 ramicotomy is safe and effective for PPH treatment, along with a reduced incidence of postoperative CH rate and improved postoperative psychological satisfaction

An experimental study of cathodic protection for chloride contaminated reinforced concrete

Cathodic protection (CP) is being increasingly used on reinforced concrete structures to protect steel reinforcing bars from corrosion in aggressive conditions. Due to the complexity of environmental conditions, the design specifications in national and international standards are still open to discussion to achieve both sufficient and efficient protection for reinforced concrete structures in engineering practices. This paper reports an experimental research to investigate the influence of chloride content on concrete resistivity, rebar corrosion rate and the performance of CP operation using different current densities. It aims to understand the correlation between the chloride content and concrete resistivity together with the CP current requirement, and to investigate the precision of the CP design criteria in standards