Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion

Atmospheric trace-gas inversion is the procedure by which the sources and sinks of a trace gas are identified from observations of its mole fraction at isolated locations in space and time. This is inherently a spatio-temporal bivariate inversion problem, since the mole-fraction field evolves in space and time and the flux is also spatio-temporally distributed. Further, the bivariate model is likely to be non-Gaussian since the flux field is rarely Gaussian. Here, we use conditioning to construct a non-Gaussian bivariate model, and we describe some of its properties through auto- and cross-cumulant functions. A bivariate non-Gaussian, specifically trans-Gaussian, model is then achieved through the use of Box--Cox transformations, and we facilitate Bayesian inference by approximating the likelihood in a hierarchical framework. Trace-gas inversion, especially at high spatial resolution, is frequently highly sensitive to prior specification. Therefore, unlike conventional approaches, we assimilate trace-gas inventory information with the observational data at the parameter layer, thus shifting prior sensitivity from the inventory itself to its spatial characteristics (e.g., its spatial length scale). We demonstrate the approach in controlled-experiment studies of methane inversion, using fluxes extracted from inventories of the UK and Ireland and of Northern Australia.Comment: 45 pages, 7 figure

Development of Android Based Real Time Monitoring System for Fire-fighters

Fire-fighters are trained rescuers especially in extinguishing hazardous fires and saving lives from dangerous situations however they tend to put their lives at risk while on the job. Statistic shows the number of deaths of fire-fighters while on the job are still high up to this year and a higher percentage of rapid fire progress and exertion are dedicated to the cause of death of fire-fighters while on the job. Therefore, a real-time monitoring on the physiological state for fire-fighters is something crucial to be done. However, the Fire and Rescue Department of Malaysia practices the traditional communication method which is by communicating via walkie-talkie. The practice of real-time assessment should be carried out by Fire and Rescue Department of Malaysia in order to avoid having a fire-fighter’s live at risk. This could be achieved by using the ARMOR (Android Based Real Time Monitoring System) whereby it can transmit voice data and physiological data such as heart rate, respirator rate, peak acceleration and posture. Based on the research and critical analysis, an android platform have been found to be a suitable selection as it supports Bluetooth Wi-Fi and Radio Frequency, accessible from any android devices and it is user friendly. As a result, a real-time intelligent monitoring system was successfully developed on an android platform. The physiological data for heart rate, respiration rate, posture, and peak acceleration was successfully transmitted and monitored on an android device at real-time

Enhanced Critical parameters of nano-Carbon doped MgB2 Superconductor

The high field magnetization and magneto transport measurements are carried out to determine the critical superconducting parameters of MgB2-xCx system. The synthesized samples are pure phase and the lattice parameters evaluation is carried out using the Rietveld refinement. The R-T(H) measurements are done up to a field of 140 kOe. The upper critical field values, Hc2 are obtained from this data based upon the criterion of 90% of normal resistivity i.e. Hc2=H at which Rho=90%Rho; where RhoN is the normal resistivity i.e., resistivity at about 40 K in our case. The Werthamer-Helfand-Hohenberg (WHH) prediction of Hc(0) underestimates the critical field value even below than the field up to which measurement is carried out. After this the model, the Ginzburg Landau theory (GL equation) is applied to the R-T(H) data which not only calculates the Hc2(0) value but also determines the dependence of Hc2 on temperature in the low temperature high field region. The estimated Hc(0)=157.2 kOe for pure MgB2 is profoundly enhanced to 297.5 kOe for the x=0.15 sample in MgB2-xCx series. Magnetization measurements are done up to 120 kOe at different temperatures and the other parameters like irreversibility field, Hirr and critical current density Jc(H) are also calculated. The nano carbon doping results in substantial enhancement of critical parameters like Hc2, Hirr and Jc(H) in comparison to the pure MgB2 sample.Comment: 25 pages with 9 Figs: comments/suggestions([email protected]

Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion

Atmospheric trace-gas inversion refers to any technique used to predict spatial and temporal fluxes using mole-fraction measurements and atmospheric simulations obtained from computer models. Studies to date are most often of a data-assimilation flavour, which implicitly consider univariate statistical models with the flux as the variate of interest. This univariate approach typically assumes that the flux field is either a spatially correlated Gaussian process or a spatially uncorrelated non-Gaussian process with prior expectation fixed using flux inventories (e.g., NAEI or EDGAR in Europe). Here, we extend this approach in three ways. First, we develop a bivariate model for the mole-fraction field and the flux field. The bivariate approach allows optimal prediction of both the flux field and the mole-fraction field, and it leads to significant computational savings over the univariate approach. Second, we employ a lognormal spatial process for the flux field that captures both the lognormal characteristics of the flux field (when appropriate) and its spatial dependence. Third, we propose a new, geostatistical approach to incorporate the flux inventories in our updates, such that the posterior spatial distribution of the flux field is predominantly data-driven. The approach is illustrated on a case study of methane (CH$_4$) emissions in the United Kingdom and Ireland.Comment: 39 pages, 8 figure

Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network

The UK is one of several countries around the world that has enacted legislation to reduce its greenhouse gas emissions. In this study, we present top-down emissions of methane (CH4) and nitrous oxide (N2O) for the UK and Ireland over the period August 2012 to August 2014. These emissions were inferred using measurements from a network of four sites around the two countries. We used a hierarchical Bayesian inverse framework to infer fluxes as well as a set of covariance parameters that describe uncertainties in the system. We inferred average UK total emissions of 2.09 (1.65–2.67) Tg yr−1 CH4 and 0.101 (0.068–0.150) Tg yr−1 N2O and found our derived UK estimates to be generally lower than the a priori emissions, which consisted primarily of anthropogenic sources and with a smaller contribution from natural sources. We used sectoral distributions from the UK National Atmospheric Emissions Inventory (NAEI) to determine whether these discrepancies can be attributed to specific source sectors. Because of the distinct distributions of the two dominant CH4 emissions sectors in the UK, agriculture and waste, we found that the inventory may be overestimated in agricultural CH4 emissions. We found that annual mean N2O emissions were consistent with both the prior and the anthropogenic inventory but we derived a significant seasonal cycle in emissions. This seasonality is likely due to seasonality in fertilizer application and in environmental drivers such as temperature and rainfall, which are not reflected in the annual resolution inventory. Through the hierarchical Bayesian inverse framework, we quantified uncertainty covariance parameters and emphasized their importance for high-resolution emissions estimation. We inferred average model errors of approximately 20 and 0.4 ppb and correlation timescales of 1.0 (0.72–1.43) and 2.6 (1.9–20 3.9) days for CH4 and N2O, respectively. These errors are a combination of transport model errors as well as errors due to unresolved emissions processes in the inventory. We found the largest CH4 errors at the Tacolneston station in eastern England, which may be due to sporadic emissions from landfills and offshore gas in the North Sea

Optical and electronic properties of sub-surface conducting layers in diamond created by MeV B-implantation at elevated temperatures

Boron implantation with in-situ dynamic annealing is used to produce highly conductive sub-surface layers in type IIa (100) diamond plates for the search of a superconducting phase transition. Here we demonstrate that high-fluence MeV ion-implantation, at elevated temperatures avoids graphitization and can be used to achieve doping densities of 6 at.%. In order to quantify the diamond crystal damage associated with implantation Raman spectroscopy was performed, demonstrating high temperature annealing recovers the lattice. Additionally, low-temperature electronic transport measurements show evidence of charge carrier densities close to the metal-insulator-transition. After electronic characterization, secondary ion mass spectrometry was performed to map out the ion profile of the implanted plates. The analysis shows close agreement with the simulated ion-profile assuming scaling factors that take into account an average change in diamond density due to device fabrication. Finally, the data show that boron diffusion is negligible during the high temperature annealing process.Comment: 22 pages, 6 figures, submitted to JA

Intracranial vascular pathology in two further patients with Floating-Harbor syndrome: Proposals for cerebrovascular disease risk management

Floating-Harbor syndrome (FHS) is a rare, heritable disorder caused by variants in the SRCAP gene. Most individuals with FHS have characteristic facial features, short stature, and speech and language impairment. Although FHS has been likely under-diagnosed due to a combination of lack of recognition of the clinical phenotype and limited access to genomic testing, it is a rare condition with around 100 individuals reported in the medical literature. Case series have been biased towards younger individuals (vast majority <20 years of age) meaning that it has been challenging to provide accurate medical advice for affected individuals in adulthood. We report two young adults with FHS who presented with intracranial haemorrhage likely secondary to cerebrovascular aneurysms, with devastating consequences, making a total of four FHS patients reported with significant cerebrovascular abnormalities. Three of four patients had hypertension, at least one in conjunction with normal renal structure. We consider possible relationships between hypertension, renal pathology and aneurysms in the context of FHS, and consider mechanisms through which disruption of the SRCAP protein may lead to vascular pathology. We recommend that clinicians should have a low threshold to investigate symptoms suggestive of cerebrovascular disease in FHS. We advise that patients with FHS should have annual blood pressure monitoring from adolescence, renal ultrasound at diagnosis repeated in adulthood, and timely investigation of any neurological symptoms. For patients with FHS, particularly with hypertension, we advise that clinicians should consider at least one MRA (Magnetic Resonance Imaging with Angiography) to check for cerebral aneurysms