Clarifying the occurrence and morphological characteristics of petroleum hydrocarbons (PHs) in soil can facilitate a comprehensive understanding of their migration and transformation patterns in soil/sediment. Additionally, by establishing the dynamic transformation process of each occurrence state, the ecological impact and environmental risk associated with PHs in soil/sediment can be assessed more precisely. The adsorption experiments and closed static incubation experiments was carried out to explore the PHs degradation and fraction distribution in aged contaminated soil under two remediation scenarios of natural attenuation (NA) and bioaugmentation (BA) by exogenous bacteria through a new sequential extraction method based on Tenax-TA, Hydroxypropyl-β-cyclodextrin and Rhamnolipid (HPCD/RL), accelerated solvent extractor (ASE) unit and alkaline hydrolysis extraction. The adsorption experiment results illustrated that bioaugmentation could promote the desorption of PHs in the adsorption phase, and the soil-water partition coefficient Kd decreased from 0.153 L/g to 0.092 L/g. The incubation experiment results showed that compared with natural attenuation, bioaugmentation could improve the utilization of PHs in aged soil and promote the generation of non-extractable hydrocarbons. On the 90th day of the experiment, the concentrations of weakly adsorbed hydrocarbons in the natural attenuation and bioaugmentation experimental groups decreased by 46.44% and 87.07%, respectively, while the concentrations of strongly adsorbed hydrocarbons and non-extractable hydrocarbons increased by 77.93%, 182.14%, and 80.91%, and 501.19%, respectively, compared their initial values. We developed a novel dynamic model and inverted the kinetic parameters of the model by the parameter scanning function and the Markov Chain Monte Carlo (MCMC) method based on the Bayesian approach in COMSOL Multiphysics® finite element software combined with experimental data. There was a good linear relationship between experimental interpolation data and model prediction data. The R2 for the concentrations of weakly adsorbed hydrocarbons ranged from 0.9953 to 0.9974, for strongly adsorbed hydrocarbons from 0.9063 to 0.9756, and for non-extractable hydrocarbons from 0.9931 to 0.9982. These extremely high correlation coefficients demonstrate the high accuracy of the parameters calculated using the Bayesian inversion method.

Camera calibration is necessary for many machine vision applications. The calibration methods are based on linear or non-linear optimization techniques that aim to find the best estimate of the camera parameters. One of the most commonly used methods in computer vision for the calibration of intrinsic camera parameters and lens distortion (interior orientation) is Zhang\'s method. Additionally, the uncertainty of the camera parameters is normally estimated by assuming that their variability can be explained by the images of the different poses of a checkerboard. However, the degree of reliability for both the best parameter values and their associated uncertainties has not yet been verified. Inaccurate estimates of intrinsic and extrinsic parameters during camera calibration may introduce additional biases in post-processing. This is why we propose a novel Bayesian inference-based approach that has allowed us to evaluate the degree of certainty of Zhang\'s camera calibration procedure. For this purpose, the a prioriprobability was assumed to be the one estimated by Zhang, and the intrinsic parameters were recalibrated by Bayesian inversion. The uncertainty of the intrinsic parameters was found to differ from the ones estimated with Zhang\'s method. However, the major source of inaccuracy is caused by the procedure for calculating the extrinsic parameters. The procedure used in the novel Bayesian inference-based approach significantly improves the reliability of the predictions of the image points, as it optimizes the extrinsic parameters.

For the past few years, short-lived unsaturated halocarbons have been marketed as environmentally friendly replacements for long-lived halogenated greenhouse gases and ozone-depleting substances. The phase-in of unsaturated halocarbons for various applications, such as refrigeration and foam blowing, can be tracked by their emergence and increase in the atmosphere. We present the first atmospheric measurements of the hydrofluoroolefin (HFO) HFO-1336mzz(Z) ((Z)-1,1,1,4,4,4-hexafluoro-2-butene, cis-CF3CH═CHCF3), a newly used unsaturated hydrofluorocarbon. HFO-1336mzz(Z) has been detected in >90% of all measurements since 2018 during multi-month campaigns at three Swiss and one Dutch location. Since 2019, it is found in ∼30% of all measurements that run continuously at the Swiss high-altitude Jungfraujoch station. During pollution events, mole fractions of up to ∼10 ppt were observed. Based on our measurements, Swiss and Dutch emissions were estimated at 2-7 Mg yr-1 (2019-2021) and 30 Mg yr-1 (2022), respectively. Modeled spatial emission distributions only partly conform to population density in both countries. Monitoring the presence of new unsaturated halocarbons in the atmosphere is crucial since long-term effects of their degradation products are still debated. Furthermore, the production of HFOs involves climate-active substances, which may leak to the atmosphere─in the case of HFO-1336mzz(Z), for example, the ozone-depleting CFC-113a (CF3CCl3).

Magnetic resonance elastography (MRE) is an MRI-based diagnostic method for measuring mechanical properties of biological tissues. MRE measurements are processed by an inversion algorithm to produce a map of the biomechanical properties. In this paper a new and powerful method (ensemble Kalman inversion with level sets (EKI)) of MRE inversion is proposed and tested. The method has critical advantages: material property variation at disease boundaries can be accurately identified, and uncertainty of the reconstructed material properties can be evaluated by consequence of the probabilistic nature of the method. EKI is tested in 2D and 3D experiments with synthetic MRE data of the human kidney. It is demonstrated that the proposed inversion method is accurate and fast.

Accurate renditions of country-scale methane (CH4) emissions are critical in understanding the regional CH4 budget and essential for adapting national climate mitigation policies to curtail the atmospheric build-up of this greenhouse gas with high warming potential. India housing 30% of the Asian population is currently appraised as a region of CH4 source based on the inventories. To date, there have not been many reported efforts to estimate the regional CH4 emissions using direct measurements of boundary layer CH4 concentrations at multiple locations over India. Here, 2 years (2017-2018) of in situ CH4 observations from three distantly placed stations over the peninsular India is combined with state-of-the-art inversion using a Lagrangian particle dispersion model for the estimation of CH4 emission. This study updates CH4 emission over the peninsular India (land area south of 21.5°N) as ~ 10.63 Terra gram (Tg) CH4 year-1, which is 0.13 Tg CH4 year-1 higher than the existing inventory-based emission. On seasonal scale, the changes from the existing CH4 emission inventories are 0.12, 0.05, 0.055 and 0.28 Tg CH4 year-1 during winter, pre-monsoon, monsoon and post-monsoon seasons respectively. Spatial distributions of seasonal variability of posterior emissions suggest an enhancement over the eastern region of peninsular India compared to the western part. The study with observations from three stations over the peninsular India provides an update on the inventory-based estimation of CH4 emissions and urges the importance of more observations over the Indian region for the accurate estimation of fluxes.

Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules. Furthermore, we introduce a machine learning algorithm according to multilayer feed-forward neural networks. The trained model makes it possible to predict the sensor behavior based on the given parameters.

Strict air pollution control measures were conducted during the Youth Olympic Games (YOG) period at Nanjing city and surrounding areas in August 2014. This event provides a unique chance to evaluate the effect of government control measures on regional atmospheric pollution and greenhouse gas emissions. Many previous studies have observed significant reductions of atmospheric pollution species and improvement in air quality, while no study has quantified its synergism on anthropogenic CO2 emissions, which can be co-reduced with air pollutants. To better understand to what extent these pollution control measures have reduced anthropogenic CO2 emissions, we conducted atmospheric CO2 measurements at the suburban site in Nanjing city from 1st July to 30th September 2014 and 1st August to 31st August 2015, obvious decrease in atmospheric CO2 was observed between YOG and the rest period. By coupling the a priori emission inventory with atmospheric transport model, we applied the scale factor Bayesian inversion approach to derive the posteriori CO2 emissions in YOG period and regular period. Results indicate CO2 emissions from power industry decreased by 45%, and other categories also decreased by 16% for manufacturing combusting, and 37% for non-metallic mineral production. Monthly total anthropogenic CO2 emissions were 9.8 (±3.6) × 109 kg/month CO2 for regular period and decreased to 6.2 (±1.9) × 109 kg/month during the YOG period in Nanjing city, with a 36.7% reduction. When scaling up to whole Jiangsu Province, anthropogenic CO2 emissions were 7.1 (±2.4) × 1010 kg/month CO2 for regular period and decreased to 4.4 (±1.2) × 1010 kg/month CO2 during the YOG period, yielding a 38.0% reduction.

In order to quantitatively predict nano- as well as other particle-size distributions, one needs to have both a mathematical model and estimates of the parameters that appear in these models. Here, we show how one can use Bayesian inversion to obtain statistical estimates for the parameters that appear in recently derived mechanism-enabled population balance models (ME-PBM) of nanoparticle growth. The Bayesian approach addresses the question of \"how well do we know our parameters, along with their uncertainties?.\" The results reveal that Bayesian inversion statistical analysis on an example, prototype Ir 0 n nanoparticle formation system allows one to estimate not just the most likely rate constants and other parameter values, but also their SDs, confidence intervals, and other statistical information. Moreover, knowing the reliability of the mechanistic model\'s parameters in turn helps inform one about the reliability of the proposed mechanism, as well as the reliability of its predictions. The paper can also be seen as a tutorial with the additional goal of achieving a \"Gold Standard\" Bayesian inversion ME-PBM benchmark that others can use as a control to check their own use of this methodology for other systems of interest throughout nature. Overall, the results provide strong support for the hypothesis that there is substantial value in using a Bayesian inversion methodology for parameter estimation in particle formation systems.

To develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer.
While the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. A joint likelihood function incorporating diagnostic radiomic features is formulated which can compute likelihood of cancer and its pathological stage. The proposed research study also investigates and validates diagnostic features to discriminate accurately between early stage (I, II) and advanced stage (III, IV) cancer.
The proposed stochastic framework achieved 86% accuracy on the benchmark database which is better than the other prominent cancer detection methods.
The presented classification framework can aid radiologists in accurate interpretation of lung CT images at an early stage and can lead to timely medical treatment of cancer patients.

We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. The focus is on SIR-like models, that are being commonly used when attempting to predict the trend of the COVID-19 pandemic. In particular, we raise a warning flag about identifiability of the parameters of SIR-like models; often, it might be hard to infer the correct values of the parameters from data, even for very simple models, making it non-trivial to use these models for meaningful predictions. Most of the points that we touch upon are actually generally valid for inverse problems in more general setups.