Synthetic Measurements Research Articles

Internal Solitary Waves in the White Sea: Hot-Spots, Structure, and Kinematics from Multi-Sensor Observations

A detailed picture of internal solitary waves (ISWs) in the White Sea is presented based on an analysis of historical spaceborne synthetic aperture radar (SAR) data and field measurements. The major hot-spot of ISW generation locates in the southwestern (SW) Gorlo Strait (GS), characterized by the presence of strong tides, complex topography, and two distinct fronts. Here, pronounced high-frequency isopycnal depressions of 5–8 m were regularly observed during flood and flood/ebb slackening. Other regions of pronounced ISW activity are found near Solovetsky Islands and in the northwestern Onega Bay. The spatial and kinematic properties of the observed ISWs are linked to water depth, with larger wave trains and higher propagation speeds being observed over the deep regions. Direct estimates of ISW propagation speeds from sequential and single SAR images agree well, while theoretical ones obtained using a two-layer model overestimate the observed values by 2–3 times. This is explained by the effective modulation of ISW propagation speed during the tidal cycle by background currents that are not accounted for in the model. Enhanced values of diapycnic diffusion coefficient in the pycnocline layer were registered near the frontal zones, where intense 14–17 m high ISWs were regularly observed.

FusIon Data Tracking System (FITS)

The field of generating movement profiles of individuals is valuable in many real-world applications (e.g., controlling disease spread or evaluating marketing engagement). Existing solutions often rely on global positioning systems (GPS) or similar systems, primarily targeted at outdoor use cases. However, the indoor tracking capabilities of current solutions either lack precision or are available in closed buildings only. The literature proposes sensor fusion approaches, but many of those are based on specific sensors. These approaches do not reveal implementation details or data to allow for their independent evaluation. Therefore, this article presents FusIon Data Tracking System ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FITS</monospace> ) as an approach and proof-of-concept to facilitate the correlation of data from different indoor sensors to movement profiles of different individuals. Functionally, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FITS</monospace> does this by generating synthetic sensor measurement data based on real-world movement data and correlating objects tracked from distinct sensors by effectively solving clustering and position prediction tasks. This correlation is evaluated based on different metrics [multiple object tracker accuracy/precision (MOTA/MOTP)] in four different scenarios, for example, sparse data, high density of sensors, low density of sensors, and a base case. Finally, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FITS</monospace> ’s performance was evaluated by increasing the load test (dataset up to 100000 measurements and 1000 visitors) to assess whether near real-time processing is feasible under a high workload.

Real-time thermoacoustic data assimilation

Low-order thermoacoustic models are qualitatively correct, but typically, they are quantitatively inaccurate. We propose a time-domain bias-aware method to make qualitatively low-order models quantitatively (more) accurate. First, we develop a Bayesian ensemble data assimilation method for a low-order model to self-adapt and self-correct any time that reference data become available. Second, we apply the methodology to infer the thermoacoustic states and heat-release parameters on the fly without storing data (real time). We perform twin experiments using synthetic acoustic pressure measurements to analyse the performance of data assimilation in all nonlinear thermoacoustic regimes, from limit cycles to chaos, and interpret the results physically. Third, we propose practical rules for thermoacoustic data assimilation. An increase, reject, inflate strategy is proposed to deal with the rich nonlinear behaviour; and physical time scales for assimilation are proposed in non-chaotic regimes (with the Nyquist–Shannon criterion) and in chaotic regimes (with the Lyapunov time). Fourth, we perform data assimilation using data from a higher-fidelity model. We introduce an echo state network to estimate in real time the forecast bias, which is the model error of the low-fidelity model. We show that: (i) the correct acoustic pressure, parameters, and model bias can be inferred accurately; (ii) the learning is robust as it can tackle large uncertainties in the observations (up to 50 % of the mean values); (iii) the uncertainty of the prediction and parameters is naturally part of the output; and (iv) both the time-accurate solution and statistics can be inferred successfully. Data assimilation opens up new possibility for real-time prediction of thermoacoustics by combining physical knowledge and experimental data synergistically.

Possibilities of Full Waveform Inversion as an imaging method in heterogeneous solids

Ultrasonic imaging of single objects in heterogeneous solids such as concrete is mainly performed using linearized methods that are limited to one wave type and neglect geometric reflections and mode conversions. This limits the reconstruction accuracy and can induce artifacts. By considering more information of the complete resulting wavefield, full waveform inversion (FWI) promises a more accurate imaging. The method compares the measured signals with the results of the forward calculation of the scattered field of the source signals. The distribution of all three isotropic material parameters is iteratively adjusted to minimize the difference between the calculated and measured signals. By using a viable forward model, no limiting assumptions about wave propagation are necessary. Since the problem is ill-posed, regularization techniques are used for stabilization. The paper explains the operation and possibilities of the model-based approach considering both synthetic and experimental measurement data. First results on idealized scatterer geometries in homogeneous and heterogeneous solids are presented. Beginning with synthetic measurement results for the identification of anomalies in the solid body, the method is then applied to laboratory experimental data in the ultrasonic frequency regime. In both cases the same concrete structure is adressed where a known inclusion made of extruded polystyrene foam (XPS) is representing the anomaly to be detected.

Data-Driven Identification of Nonlinear Power System Dynamics Using Output-Only Measurements

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements.

SWASTi-SW: Space Weather Adaptive Simulation Framework for Solar Wind and Its Relevance to the Aditya-L1 Mission

Solar wind streams, acting as a background, govern the propagation of space weather drivers in the heliosphere, which induce geomagnetic storm activities. Therefore, predictions of the solar wind parameters are the core of space weather forecasts. This work presents an indigenous three-dimensional (3D) solar wind model (SWASTi-SW). This numerical framework for forecasting the ambient solar wind is based on a well-established scheme that uses a semiempirical coronal model and a physics-based inner heliospheric model. This study demonstrates a more generalized version of the Wang–Sheeley–Arge relation, which provides a speed profile input to the heliospheric domain. Line-of-sight observations of GONG and Helioseismic and Magnetic Imager magnetograms are used as inputs for the coronal model, which in turn provides the solar wind plasma properties at 0.1 au. These results are then used as an initial boundary condition for the magnetohydrodynamics model of the inner heliosphere to compute the solar wind properties up to 2.1 au. Along with the validation run for multiple Carrington rotations, the effect of variation of specific heat ratio and study of the stream interaction region (SIR) are also presented. This work showcases the multidirectional features of SIRs and provides synthetic measurements for potential observations from the Solar Wind Ion Spectrometer subsystem of the Aditya Solar wind Particle Experiment payload on board ISRO’s upcoming solar mission Aditya-L1.

Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates

Event-related potentials (ERP) are among the most widely measured indices for studying human cognition. While their timing and magnitude provide valuable insights, their usefulness is limited by our understanding of their neural generators at the circuit level. Inverse source localization offers insights into such generators, but their solutions are not unique. To address this problem, scientists have assumed the source space generating such signals comprises a set of discrete equivalent current dipoles, representing the activity of small cortical regions. Based on this notion, theoretical studies have employed forward modeling of scalp potentials to understand how changes in circuit-level dynamics translate into macroscopic ERPs. However, experimental validation is lacking because it requires in vivo measurements of intracranial brain sources. Laminar local field potentials (LFP) offer a mechanism for estimating intracranial current sources. Yet, a theoretical link between LFPs and intracranial brain sources is missing. Here, we present a forward modeling approach for estimating mesoscopic intracranial brain sources from LFPs and predict their contribution to macroscopic ERPs. We evaluate the accuracy of this LFP-based representation of brain sources utilizing synthetic laminar neurophysiological measurements and then demonstrate the power of the approach in vivo to clarify the source of a representative cognitive ERP component. To that end, LFP was measured across the cortical layers of visual area V4 in macaque monkeys performing an attention demanding task. We show that area V4 generates dipoles through layer-specific transsynaptic currents that biophysically recapitulate the ERP component through the detailed forward modeling. The constraints imposed on EEG production by this method also revealed an important dissociation between computational and biophysical contributors. As such, this approach represents an important bridge between laminar microcircuitry, through the mesoscopic activity of cortical columns to the patterns of EEG we measure at the scalp.

Research on a hydrate saturation prediction method based on an analysis of factors that influence the prediction accuracy of sea area hydrate saturation

It has been predicted that China has about 80 billion tons of oil equivalent of natural gas hydrate resources in its sea. China has conducted at least six drilling and two trial exploitations in the South China Sea, achieving good results. However, these achievements do not necessarily mean that the research area can support commercial exploitation. Only when the natural gas hydrate resources in the research area reach a certain scale will it be valuable for commercial exploitation. A reliable quantitative prediction method is necessary to clarify the scale of gas hydrate in the research area. However, the classical Wood method’s application to the prediction of suspended hydrate saturation in the Shenhu maritime area of China results in a large prediction error; the analysis shows that an unreliable measurement of reservoir parameters is the main reason for the large prediction error. In order to clarify the influence of reservoir parameters, this paper—by analyzing the measurement sources of the parameters of reservoir parameters, firstly indicates that the inaccurate measurement of three reservoir parameters—matrix composition, porosity, and density—is the main cause of prediction error. Then, using the design of different reservoir parameter measurement schemes to conduct comparative analysis, this paper points out that unreliable porosity and density measurement may lead to large prediction errors, while unreliable measurements of matrix composition have a relatively small impact on prediction accuracy. Further analysis shows that the absolute value of the prediction error caused by uncertainty in synthetic reservoir parameter measurement is sometimes larger than the sum of the absolute prediction error caused by the single parameter measurement uncertainty. In addressing the problem of large prediction errors caused by the inaccurate measurement of reservoir parameters, this paper proposes a hydrate saturation prediction method based on non-hydrate correction—called the “Wood-Bao method”. Simulation and actual data studies show that the prediction effect of this method is superior to that of the Wood method.

Generating Synthesized Ultrasound Biomicroscopy Images from Anterior Segment Optical Coherent Tomography Images by Generative Adversarial Networks for Iridociliary Assessment.

IntroductionThe aim of this study was to investigate the feasibility of generating synthesized ultrasound biomicroscopy (UBM) images from swept-source anterior segment optical coherent tomography (SS-ASOCT) images using a cycle-consistent generative adversarial network framework (CycleGAN) for iridociliary assessment on a cohort presenting for primary angle-closure screening.MethodsThe CycleGAN architecture was adopted to synthesize high-resolution UBM images trained on the SS-ASOCT dataset from the department of ophthalmology, Xinhua Hospital. The performance of the CycleGAN model was further tested in two separate datasets using synthetic UBM images from two different ASOCT modalities (in-distribution and out-of-distribution). We compared the ability of glaucoma specialists to assess the image quality of real and synthetic images. UBM measurements, including anterior chamber, iridociliary parameters, were compared between real and synthetic UBM images. Intra-class correlation coefficients, coefficients of variation, and Bland–Altman plots were used to assess the level of agreement. The Fréchet Inception Distance (FID) was measured to evaluate the quality of the synthetic images.ResultsThe whole trained dataset included anterior chamber angle images, of which 4037 were obtained by SS-ASOCT and 2206 were obtained by UBM. The image quality of real versus synthetic SS-ASOCT images was similar as assessed by two glaucoma specialists. The Bland–Altman analysis also suggested high consistency between measurements of real and synthetic UBM images. In addition, there was fair to excellent agreement between real and synthetic UBM measurements for the in-distribution dataset (ICC range 0.48–0.97) and the out-of-distribution dataset (ICC range 0.52–0.86). The FID was 21.3 and 24.1 for the synthetic UBM images from the in-distribution and out-of-distribution datasets, respectively.ConclusionWe developed a CycleGAN model to translate UBM images from non-contact SS-ASOCT images. The CycleGAN synthetic UBM images showed fair to excellent reproducibility when compared with real UBM images. Our results suggest that the CycleGAN technique is a promising tool to evaluate the iridociliary and anterior chamber in an alternative non-contact method.

Simultaneous Retrieval of Trace Gases, Aerosols, and Cirrus Using RemoTAP—The Global Orbit Ensemble Study for the CO2M Mission

In support of the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission, this study evaluates the performance of the Remote sensing of Trace gas and Aerosol Product (RemoTAP) algorithm based on synthetic orbit measurements of realistic atmospheric and geophysical scenes over land. To make use of the added value of the multi-angle polarimeter (MAP) aboard the CO2M mission, the RemoTAP algorithm is developed to perform simultaneous retrieval of trace gas and aerosol properties from both MAP and CO2 imager (CO2I) measurements. At the same time, it has the capability to perform the retrieval of trace gas from only CO2I measurements. To set up the baseline tests, we apply a simple filter based on non-scattering retrievals in different CO2I bands which is able to filter out 80% of the cirrus-contaminated pixels, and after posterior filtering based on goodness of fit, 95% of the cirrus-contaminated cases are screened out. The MAP-CO2I retrieval method is able to reduce the aerosol-induced retrieval error in column-averaged dry-air mole fraction of CO2 (XCO2) in terms of RMSE and bias by more than a factor of 2, compared to CO2I-only retrievals on the filtered pixels. A strong correlation between XCO2 error and surface albedo in CO2I-only retrievals is significantly reduced for MAP-CO2I retrievals. Moreover, XCO2 biases in CO2I-only retrievals exhibit a significant spatiotemporal variability caused by a strong dependence on aerosol load. The biases can be up to 2 ppm over some regions, which are much larger than for the global case. It shows that only by the inclusion of MAP measurements, the large aerosol-induced biases can be mitigated, resulting in the retrievals that meet the mission requirement (precision &lt;0.7 ppm and bias &lt;0.5 ppm). The error estimates for XCO2 retrievals cover the uncertainties related to the instrument, aerosol, and cirrus, although other error sources, for example, in temperature and pressure profiles, may increase the overall error somewhat. The impact of cirrus on the retrieval, which can be significant, is also investigated. When not accounted for in the retrieval, the presence of a thin layer of cirrus with an optical thickness at 550 nm smaller than 0.3 can increase XCO2 errors by a factor of about 3 for MAP-CO2I retrievals, leading to an RMSE of 2.3 ppm for cirrus-contaminated scenes. When fitting cirrus properties, this can be reduced to 1.27 ppm for cirrus-contaminated cases. For CO2 retrievals using the proxy method, in a highly idealized situation where it is assumed that a perfect CH4 prior is available, an RMSE of 0.93 ppm and a bias of 0.3 ppm are achieved. These retrievals are hardly influenced by cirrus but depend linearly on the accuracy of the CH4 prior.

A new Kalman filter approach for structural parameter tracking: Application to the monitoring of damaging structures tested on shaking-tables

In this paper, a new data assimilation framework for correcting finite element models from datasets acquired on-the-fly in low-frequency dynamics is presented. An Unscented Kalman filter algorithm is coupled with a modified Constitutive Relation Error (mCRE) observer, leading to a Modified Dual Kalman Filter algorithm (MDKF). Built as a Hermitian data-to-model distance written in the frequency domain enriched with a CRE residual accounting for model bias, the mCRE functional has shown interesting assets for model updating purposes, in particular enhanced convexity and robustness to measurement noise. The proposed data assimilation strategy integrates the latter through a metric change in the measurement update equation. It thus differs from classical nonlinear Kalman filtering for parameter estimation as the comparison between measurements and model predictions is achieved through the mCRE functional itself. Besides, the calibration of MDKF internal parameters is facilitated by a set of general guidelines that ensure the performance of the algorithm. The methodology is applied to two earthquake engineering examples. The performances of MDKF are first assessed using synthetic measurements from a plane frame subjected to random ground acceleration. Actual measurements from the SMART2013 benchmark are then assimilated in a real-time context to monitor the eigenfrequency drop of a reinforced-concrete structure submitted to a sequence of gradually damaging shaking-table tests. The nice correlation with (i) data-driven identification results, and (ii) sequential mCRE-based model updating results, illustrates the relevance of this new approach and suggests promising use of MDKF for on-the-fly adaptive control prospects and applications involving data-to-model interaction.

Synthetic Dimensions of the Potential of the Western Ukrainian People’s Republic

The article analyzes the specifics of synthetic measurements of state potential on the example of the Western Ukrainian People's Republic (WUPR). The author of the study suggests that modeling the potential of the Western Ukrainian People’s Republic will enable modern historiography of the Ukrainian Revolution of 1914–1923 to elucidate the morphology of state power by elucidating the nature of political power, the functioning of the state body, its strategic goals, etc. in its western Ukrainian segment. The main research models of Western scientists of the second half of the 20 – early 21th centuries are clarified on identifying the strengths and weaknesses of the state’s potential, in particular in the field of international relations: Clifford Herman, Wilhelm Fucks, Ray Klein and Ahmet Davutoğlu. The potential of the WUPR is also considered on the basis of studying the models of gravity, stratification and escalation / de-escalation, which made it possible to clarify the WUPR’s place in the system of international relations and collective security of the early 20th century. It is established that the model of gravity in the case of Western Ukraine diplomacy, in particular during its emigration period, can only partially indicate a certain cultural and / or ideological influence on the balance of international relations, and the “gravitational field” of Western Ukraine was in postwar Europe. An important feature of the stratification model is the prestige of the WUPR in the international arena, which has become the only and common interest of the united Ukrainian People’s Republic. The model of escalation / de-escalation in the realities of Western Ukraine shows that this Ukrainian state and foreign policy activity in the coordinate system of contemporary international relations was located between two points – between absolute peace and absolute war – in the context of the Polish-Ukrainian war of 1918–1919 for Eastern Galicia. According to the author, the most logical in relation to the imagined model of the potential of the WUPR was the escalation from the point of view of the stronger side (Poles), in particular in view of the escalation of armaments. It acted in conditions of asymmetric imbalance: the weaker side (Ukrainians) shifted the “difficulty” of escalation to ideological and moral-ideological problems.

Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach

AbstractShear wave splitting (SWS) analysis is widely used to provide critical constraints on crustal and mantle structure and dynamic models. In order to obtain reliable splitting measurements, an essential step is to visually verify all the measurements to reject problematic measurements, a task that is increasingly time consuming due to the exponential increase in the amount of data. In this study, we utilized a convolutional neural network (CNN) based method to automatically select reliable SWS measurements. The CNN was trained by human‐verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data recorded in south central Alaska reveals that CNN classifies 97.0% of human selected measurements as acceptable, and revealed ∼30% additional measurements. To our knowledge, this is the first study to systematically explore the potential of a machine‐learning based technique to assist with SWS analysis.

Matched illumination: using light modulation as a proxy for a color filter that makes a camera more colorimetric.

In previous work, it was shown that a camera can theoretically be made more colorimetric-its RGBs become more linearly related to XYZ tristimuli-by placing a specially designed color filter in the optical path. While the prior art demonstrated the principle, the optimal color-correction filters were not actually manufactured. In this paper, we provide a novel way of creating the color filtering effect without making a physical filter: we modulate the spectrum of the light source by using a spectrally tunable lighting system to recast the prefiltering effect from a lighting perspective. According to our method, if we wish to measure color under a D65 light, we relight the scene with a modulated D65 spectrum where the light modulation mimics the effect of color prefiltering in the prior art. We call our optimally modulated light, the matched illumination. In the experiments, using synthetic and real measurements, we show that color measurement errors can be reduced by about 50% or more on simulated data and 25% or more on real images when the matched illumination is used.

Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach

In wireless communication networks, the network provider serves certain licensed primary users who pay for a dedicated use of the frequency channels. However, not all the channels are occupied by the primary users at all times. For efficient spectrum utilization, in centralized cognitive radio networks (CRNs), a cognitive base station (CBS) dynamically identifies the spectrum holes and allocates the frequency channels to the on-demand unlicensed secondary users known as cognitive radios (CRs). Although existing literature has developed various dynamic spectrum access mechanisms for CBS, there is still a dearth of studies due to the wide range of assumptions made in the solutions. Most of the existing works study the CBS scheduling problem scheme by adopting optimization-based methods and rely on the prior knowledge of the network parameters such as primary users’ activity. Moreover, the impact of channel switching costs on the network throughput has not been well studied. In this paper, we aim to maximize the CRNs total throughput, and we formulate the CBS scheduling problem as a non-stochastic (i.e., adversarial) combinatorial multi-armed bandit problem with semi-bandit feedback and arm switching costs. We propose two novel online learning algorithms for CBS scheduling with and without channel switching costs, where their regret performances are proved sublinear order-optimal in time as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{1/2}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2/3}$ </tex-math></inline-formula> , respectively, offering throughput-optimal scheduling for CRNs. Experiments on the synthetic and real-world spectrum measurement data complement and validate our theoretical findings.

Interval inversion of multiwell logging data for estimating laterally varying petrophysical parameters and formation boundaries

The present paper introduces a Legendre polynomials based interval inversion method for processing multi-borehole logging data. The method allows the determination of lateral changes of the layer-thicknesses together with the vertical and lateral variations of petrophysical parameters along a 2D cross-section of several boreholes. The method is assessed using noisy synthetic measurements of a petrophysical model made of two-layers structure related to hydrocarbon bearing formations. The numerical experiments aided to investigate the stability and convergence of the 2D interval inversion procedure. To ensure the accuracy and reliability of the inversion results, the misfit of data and model distance are tested, beside the calculation of estimation errors and correlation coefficients. A large amount of input data relative to the number of unknowns results in a high overdetermined ratio, consequently more precise estimates are obtained in stable and convergent procedure than in conventional local (1D) inversion schemes. The feasibility of the 2D interval inversion method is shown by analysing in-situ well logging data acquired in four wells situated in an Egyptian hydrocarbon field.

Machine learning for continuous quantum error correction on superconducting qubits

Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

DETERMINATION OF 3D WATER TURBIDITY PARAMETER FIELDS FROM LIDAR BATHYMETRY DATA BY VOLUMETRIC DATA ANALYSIS

Abstract. Accurate information on turbidity in water bodies is relevant to numerous limnological and oceanological issues. However, the collection of turbidity parameters using conventional in-situ measurement methods is time-consuming and cost-intensive and therefore usually limited to very small study areas. The use of airborne LiDAR bathymetry data is a promising alternative. However, existing methods for deriving turbidity parameters from airborne LiDAR bathymetry data are limited to the determination of one single turbidity parameter per water column element. The paper presents a novel approach that overcomes the existing limitations enables the determination of 3D water turbidity fields. By volumetric data analysis, the vertical turbidity stratification in the water body can be determined. For validation purposes, the approach was applied to synthetic measurement data generated in a simulation as well as a real measurement data set of a shallow coastal water.

Physics‐Informed Machine Learning Method for Large‐Scale Data Assimilation Problems

AbstractWe develop a physics‐informed machine learning approach for large‐scale data assimilation and parameter estimation and apply it for estimating transmissivity and hydraulic head in the two‐dimensional steady‐state subsurface flow model of the Hanford Site given synthetic measurements of said variables. In our approach, we extend the physics‐informed conditional Karhunen‐Loéve expansion (PICKLE) method to modeling subsurface flow with unknown flux (Neumann) and varying head (time‐dependent Dirichlet) boundary conditions. We demonstrate that the PICKLE method is comparable in accuracy with the standard maximum a posteriori (MAP) method, but is significantly faster than MAP for large‐scale problems. Both methods use a mesh to discretize the computational domain. In MAP, the parameters and states are discretized on the mesh; therefore, the size of the MAP parameter estimation problem directly depends on the mesh size. In PICKLE, the mesh is used to evaluate the residuals of the governing equation, while the parameters and states are approximated by the truncated conditional Karhunen‐Loéve expansions with the number of parameters controlled by the smoothness of the parameter and state fields, and not by the mesh size. For a considered example, we demonstrate that the computational cost of PICKLE increases near linearly (as N1.15) with the number of grid nodes N, while that of MAP increases much faster (as N3.28). We also show that once trained for one set of Dirichlet boundary conditions (i.e., one river stage), the PICKLE method provides accurate estimates of the hydraulic head for any value of the Dirichlet boundary conditions (i.e., for any river stage).

29: INCIDENCE, DIAGNOSIS AND MANAGEMENT OF MALABSORPTION FOLLOWING OESOPHAGECTOMY: A SYSTEMATIC REVIEW

Abstract Background and aim Survival following oesophagectomy for oesophageal cancer is increasing. This has resulted in increased focus on quality-of-life and improved survivorship. Weight loss and malnutrition occurs in 25–46% of patients after three years, with associated adverse gastrointestinal symptoms. Malabsorption syndrome is multifactorial and includes exocrine pancreatic insufficiency (EPI), small intestinal bacterial overgrowth (SIBO) and bile acid malabsorption (BAM), however there is little literature available in patients following oesophagectomy. The aim of this study was to evaluate the reported incidence and management of malabsorption syndromes post-oesophagectomy. Methods A systematic search of PubMed, EMBASE, MEDLINE, Scopus and the Cochrane Library evaluating incidence, diagnosis and management of malabsorption was performed for studies published until March 2021. Results Of 461 identified studies, seven studies (6/7 non-randomised observational studies) were included, with a combined population of 344 (range 7–87). Incidence of malabsorption syndromes including EPI, SIBO and BAM were 10.2–100%, 37.8–100% and 3.33–100% respectively. There was no consensus definition for EPI, SIBO or BAM; and there was variation in diagnostic methods. Diagnostic criteria varied from clinical (gastrointestinal symptoms or weight loss), or biochemical (faecal elastase, hydrogen breath test and Selenium-75-labelled synthetic bile acid measurements). Treatment modalities using pancreatic enzyme replacement, rifaximin and colesevelam showed improvement in symptoms and weight in all studies. Conclusion Malabsorption syndromes following oesophagectomy are likely to be severely underestimated. The resultant gastrointestinal symptoms have a negative effect on post-operative quality of life. Current literature suggests benefit with outlined therapies, however greater understanding of these conditions, their diagnosis, and management is required to further understand which patients will benefit from treatment.