Electron Spin Resonance (ESR) detection of active oxygen species and organic phases in Martian soils

The presence of active oxygen species (O(-), O2(-), O3(-)) and other strong oxidants (Fe2O3 and Fe3O4) was invoked in interpretations of the Viking biological experiments and a model was also suggested for Martian surface chemistry. The non-biological interpretations of the biological results gain futher support as no organic compounds were detected in the Viking pyrolysis-gas chromatography mass spectrometer (GCSM) experiments at concentrations as low as 10 ppb. Electron spin resonance (ESR) measures the absorption of microwaves by a paramagnetic and/or ferromagnetic center in the presence of an external field. In many instances, ESR has the advantage of detailed submicroscopic identification of the transient species and/or unstable reaction intermediates in their environments. Since the higly active oxygen species (O(-), O2(-), O3(-), and R-O-O(-)) are all paramagnetic in nature, they can be readily detected in native form by the ESR method. Active oxygen species likely to occur in the Martian surface samples were detected by ESR in UV-irradiated samples containing MgO. A miniaturized ESR spectrometer system can be developed for the Mars Rover Sample Return Mission. The instrument can perform the following in situ Martian samples analyses: detection of active oxygen species; characterization of Martian surface chemistry and photooxidation processes; and searching for organic compounds in the form of free radicals preserved in subsoils, and detection of microfossils with Martian carbonate sediments

Electron Spin Resonance (ESR) studies of returned comet nucleus samples

The most important objective of the Comet Nucleus Sample Returm Mission is to return samples which could reflect formation conditions and evolutionary processes in the early solar nebula. It is expected that the returned samples will consist of fine-grained silicate materials mixed with ices composed of simple molecules such as H2O, NH3, CH4 as well as organics and/or more complex compounds. Because of the exposure to ionizing radiation from cosmic-ray, gamma-ray, and solar wind protons at low temperature, free radicals are expected to be formed and trapped in the solid ice matrices. The kind of trapped radical species together with their concentration and thermal stability can be used as a dosimeter as well as a geothermometer to determine thermal and radiation histories as well as outgassing and other possible alternation effects since the nucleus material was formed. Since free radicals that are known to contain unpaired electrons are all paramagnetic in nature, they can be readily detected and characterized in their native form by the Electron Spin Resonance (ESR) method. In fact, ESR has been shown to be a non-destructive, highly sensitive tool for the detection and characterization of paramagnetic, ferromagnetic, and radiation damage centers in terrestrial and extraterrestrial geological samples. The potential use of ESR as an effective method in the study of returned comet nucleus samples, in particular, in the analysis of fine-grained solid state icy samples is discussed

Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable selection

We introduce a framework for efficient Markov chain Monte Carlo algorithms targeting discrete-valued high-dimensional distributions, such as posterior distributions in Bayesian variable selection problems. We show that many recently introduced algorithms, such as the locally informed sampler of Zanella (J Am Stat Assoc 115(530):852–865, 2020), the locally informed with thresholded proposal of Zhou et al. (Dimension-free mixing for high-dimensional Bayesian variable selection, 2021) and the adaptively scaled individual adaptation sampler of Griffin et al. (Biometrika 108(1):53–69, 2021), can be viewed as particular cases within the framework. We then describe a novel algorithm, the adaptive random neighbourhood informed sampler, which combines ideas from these existing approaches. We show using several examples of both real and simulated data-sets that a computationally efficient point-wise implementation (PARNI) provides more reliable inferences on a range of variable selection problems, particularly in the very large p setting

Adaptive MCMC for Bayesian variable selection in generalised linear models and survival models

Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions to the marginal likelihood. The Reversible Jump Markov Chain Monte Carlo (RJMCMC) approach can be employed to jointly sample models and coefficients, but the effective design of the trans-dimensional jumps of RJMCMC can be challenging, making it hard to implement. Alternatively, the marginal likelihood can be derived conditional on latent variables using a data-augmentation scheme (e.g., Pólya-gamma data augmentation for logistic regression) or using other estimation methods. However, suitable data-augmentation schemes are not available for every generalised linear model and survival model, and estimating the marginal likelihood using a Laplace approximation or a correlated pseudo-marginal method can be computationally expensive. In this paper, three main contributions are presented. Firstly, we present an extended Point-wise implementation of Adaptive Random Neighbourhood Informed proposal (PARNI) to efficiently sample models directly from the marginal posterior distributions of generalised linear models and survival models. Secondly, in light of the recently proposed approximate Laplace approximation, we describe an efficient and accurate estimation method for marginal likelihood that involves adaptive parameters. Additionally, we describe a new method to adapt the algorithmic tuning parameters of the PARNI proposal by replacing Rao-Blackwellised estimates with the combination of a warm-start estimate and the ergodic average. We present numerous numerical results from simulated data and eight high-dimensional genetic mapping data-sets to showcase the efficiency of the novel PARNI proposal compared with the baseline add–delete–swap proposal

Improved Object Detection in an Image by Correcting Regions with Distortion

This publication describes techniques and processes for correcting distortion in an image in order to improve object detection by an object detector on an imaging device. In order to avoid missing target objects (e.g., faces) in an image during object detection due to distortion, the object detector performs object detection on the image using a low-threshold value. A low-threshold value is associated with a lesser chance of missing target objects. The detection results are compared against regions of the image that are known to have distortion due to factors, such as a wide field of view (WFOV) lens of the camera. The overlapping areas of the detection results and the distorted regions are identified as candidate areas that would benefit from distortion correction. Using an algorithm, the candidate regions are corrected (e.g., undistorted, cropped, down-sampled, rotated, and/or frontalized) to reduce distortion. Object detection is performed again over the corrected candidate regions, resulting in improved confidence. The object detection results can then be used by the imaging device to provide a high-quality image and a positive user experience with the imaging device

Probing Parity Violation in the Stochastic Gravitational Wave Background with Astrometry

Astrometry holds the potential for testing fundamental physics through the effects of the Stochastic Gravitational Wave Background (SGWB) in the $\sim 1-100$ nHz frequency band on precision measurements of stellar positions. Such measurements are complementary to tests made possible by the detection of the SGWB using Pulsar Timing Arrays. Here, the feasibility of using astrometry for the identification of parity-violating signals within the SGWB is investigated. This is achieved by defining and quantifying a non-vanishing $EB$ correlation function within astrometric correlation functions, and investigating how one might estimate the detectability of such signals.Comment: 7 pages, 2 figure

A Simple Route towards High-Concentration Surfactant-Free Graphene Dispersions

A simple solvent exchange method is introduced to prepare high-concentration and surfactant-free graphene liquid dispersion. Natural graphite flakes are first exfoliated into graphene in dimethylformamide (DMF). DMF is then exchanged by terpineol through distillation, relying on their large difference in boiling points. Graphene can then be concentrated thanks to the volume difference between DMF and terpineol. The concentrated graphene dispersions are used to fabricate transparent conductive thin films, which possess comparable properties to those prepared by more complex methods.Comment: 9 pages, 3 figure

Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

The prediction of gas well performance is crucial for estimating the ultimate recovery rate of natural gas reservoirs. However, physics-based numerical simulation methods require a significant effort to build a robust model, while the decline curve analysis method used in this field is based on certain assumptions, hence its applications are limited due to the strict working conditions. In this work, a deep learning model driven jointly by the decline curve analysis model and production data is proposed for the production performance prediction of gas wells. Due to the time-series characteristics of gas well production data, the long short-term memory neural network is selected to establish the architecture of artificial intelligence. The existing decline curve analysis model is first implicitly incorporated into the training process of the neural network and then used to drive the neural network construction along with the actual gas well production historical data. By applying the proposed innovative model to analyze the conventional and tight gas well performance predictions based on field data, it is demonstrated that the proposed long short-term memory neural network deep learning model driven jointly by the decline curve analysis model and production data can effectively improve the interpretability and predictive ability of the traditional long short-term memory neural network model driven by production data alone. Compared with the data-driven model, the jointly driven model can reduce the mean absolute error by 42.90% and 13.65% for a tight gas well and a carbonate gas well, respectively.Document Type: Original articleCited as: Xue, L., Wang, J., Han, J., Yang, M., Mwasmwasa, M. S., Nanguka, F. Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data. Advances in Geo-Energy Research, 2023, 8(3): 159-169. https://doi.org/10.46690/ager.2023.06.0

Aging and loss decision making: increased risk aversion and decreased use of maximizing information, with correlated rationality and value maximization

Frontiers in Human Neuroscience9May1-1