Diagnostic Quantities Research Articles

Enhancing Surface Wind Speed and Temperature Prediction Using Surface-Layer Emulator and Transfer Learning

Abstract Accurate prediction of surface wind speed and temperature is crucial for many sectors. Physical schemes in numerical weather prediction (NWP) and data-driven correction approaches have limitations due to uncertainties in parameterization and lack of robustness, respectively. This study introduces a physics-informed data model, PhyCorNet, which combines a deep learning–based physics emulator (PhyNet) and a subsequent correction network (CorNet). PhyNet imitates the revised surface-layer parameterization scheme [default option of the Weather Research and Forecasting (WRF) Model]. CorNet refines predictions by mitigating the difference between PhyNet and observations. PhyCorNet enables gridded forecasts despite the training data being point based. Therefore, PhyCorNet can be regarded as a rediagnostic scheme for surface wind speed at 10 m (WS10) and temperature at 2 m (T2). Compared to WRF, it reduced diagnostic root-mean-square errors in the 24-h forecasts for WS10 and T2 by 40% and 36%, respectively, across China, with unbiased forecasts at almost all sites. PhyCorNet addresses the oversmoothing prediction issue of other deep learning models by providing the ability to represent fine-scale features and perform well in statistically extreme samples. In grid cells without observations for training, PhyCorNet performed much better than WRF, demonstrating the zero-shot learning capability. This study implies that the emulator plus bias correction provided by PhyCorNet could be used as a simple but effective approach to improve the performance of other diagnostic quantities in NWP. Significance Statement We developed a new model called PhyCorNet to improve the surface wind speed and temperature forecasts. Existing weather forecast models have limitations in accurately predicting these variables. PhyCorNet first uses a neural network (PhyNet) to mimic an existing physics-based wind and temperature calculation method. Another neural network [correction network (CorNet)] improves the PhyNet forecasts by comparing them with observations. Across China, PhyCorNet reduced 24-h forecast errors for wind speed by 40% and temperature by 36% compared to a conventional model. It performed well under diverse conditions and overcame the oversmoothing issues faced by other machine learning models. Importantly, PhyCorNet outperformed traditional models in areas without historical observations. We suggest that this new framework can also be used to enhance the forecasts of other atmospheric variables.

Active inference and deep generative modeling for cognitive ultrasound.

Ultrasound has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultra-portable and cost-effective, akin to the stethoscope. Nevertheless, and despite many advances, ultrasound image quality and diagnostic efficacy are still highly operator- and patient-dependent. In difficult-to-image patients, image quality is often insufficient for reliable diagnosis. In this paper, we put forth the idea that ultrasound imaging systems can be recast as information-seeking agents that engage in reciprocal interactions with their anatomical environment. Such agents autonomously adapt their transmit-receive sequences to fully personalize imaging and actively maximize information gain in-situ. To that end, we will show that the sequence of pulse-echo experiments that an ultrasound system performs can be interpreted as a perception-action loop: the action is the data acquisition, probing tissue with acoustic waves and recording reflections at the detection array, and perception is the inference of the anatomical and or functional state, potentially including associated diagnostic quantities. We then equip systems with a mechanism to actively reduce uncertainty and maximize diagnostic value across a sequence of experiments, treating action and perception jointly using Bayesian inference given generative models of the environment and action-conditional pulse-echo observations. Since the representation capacity of the generative models dictates both the quality of inferred anatomical states and the effectiveness of inferred sequences of future imaging actions, we will be greatly leveraging the enormous advances in deep generative modelling (generative AI), that are currently disrupting many fields and society at large. Finally, we show some examples of cognitive, closed-loop, ultrasound systems that perform active beamsteering and adaptive scanline selection, based on deep generative models that track anatomical belief states.

Fundamental study on diagnostic reference level quantities for endoscopic retrograde cholangiopancreatography using a C-arm fluoroscopy system

The diagnostic reference level (DRL) is an effective tool for optimising protection in medical exposures to patients. However regarding air kerma at the patient entrance reference point (K a,r), one of the DRL quantities for endoscopic retrograde cholangiopancreatography (ERCP), manufacturers use a variety of the International Electrotechnical Commission and their own specific definitions of the reference point. The research question for this study was whether K a,r is appropriate as a DRL quantity for ERCP. The purpose of this study was to evaluate the difference between K a,r and air kerma incident on the patient’s skin surface (K a,e) at the different height of the patient couch for a C-arm system. Fluoroscopy and radiography were performed using a C-arm system (Ultimax-i, Canon Medical Systems, Japan) and a over-couch tube system (CUREVISTA Open, Fujifilm Healthcare, Japan). K a,e was measured by an ion chamber placed on the entrance surface of the phantom. Kerma-area product (P KA) and K a,r were measured by a built-in P KA meter and displayed on the fluoroscopy system. K a,e decreased while K a,r increased as the patient couch moved away from the focal spot. The uncertainty of the K a,e/K a,r ratio due to the different height of the patient couch was estimated to be 75%–94%. K a,r may not accurately represent K a,e. P KA was a robust DRL quantity that was independent of the patient couch height. We cautioned against optimising patient doses in ERCP with DRLs set in terms of K a,r without considering the patient couch height of the C-arm system. Therefore, we recommend that K a,r is an inappropriate DRL quantity in ERCP using the C-arm system.

Biological Properties and Antibiotic Resistance of <i>Klebsiella pneumoniae</i> and Its Role in the Etiological Structure of Community-Acquired Pneumonia Pathogens

Background. In a novel coronavirus pandemic, the most common complications of viral pneumonia are secondary infections of bacterial and fungal etiology. At the same time, the spread of multidrug-resistant bacteria remains a global threat to public health. One of such microorganisms of the Enterobacteriaceae family is Klebsiella pneumoniae, which belongs to dangerous resistant pathogens of the ESKAPE group.The aim of the study was the comparative analysis of the biological properties of classical and hypermucoid strains of K.pneumoniae isolated from patients with community-acquired pneumonia (CAP): characterization of their sensitivity to antibacterial drugs, Klebsiella bacteriophage, and a disinfectant (polyhexamethylene guanidine hydrochloride), as well as assessment of the strains’ virulence in the model of experimental infection in white mice.Material and methods. 56 strains of Klebsiella isolated in diagnostic quantities from patients’ sputum samples were studied. Species identification of cultures was carried out using bacteriological and mass spectrometric methods. The sensitivity of bacteria to antibiotics, bacteriophage, and the disinfectant was determined in accordance with regulatory documents.Results. In the course of the study, 243 gram-negative cultures were isolated, of which 30% were bacteria of the genus Klebsiella spp. An analysis of their species composition showed that K.pneumoniae occupied a dominant place in the structure. Based on colony morphology, Burri-Gins smear staining, and a positive string test, 14 strains with a hypermucoid phenotype were identified. These strains differed from the classic K.pneumoniae strain by the presence of a thicker capsule in smears, virulence in white mice (DCL≤103 mc), and increased resistance to commercial Klebsiella bacteriophage. At the same time, they were characterized by a wider spectrum of sensitivity to antibiotics. There were no significant differences in sensitivity to the disinfectant in strains of both morphotypes.Conclusion. The results obtained demonstrated the important role of K.pneumoniae in the etiological structure of CAP pathogens.

Fighting against COVID‐19: Innovations and applications

Fighting against <scp>COVID</scp>‐19: Innovations and applications

Diagnostic reference level quantities for adult chest and abdomen-pelvis CT examinations: correlation with organ doses

ObjectivesTo evaluate correlations between DRL quantities (DRLq) stratified into patient size groups for non-contrast chest and abdomen-pelvis CT examinations in adult patients and the corresponding organ doses.MethodsThis study presents correlations between DRLq (CTDIvol, DLP and SSDE) stratified into patient size ranges and corresponding organ doses shared in four groups: inside, peripheral, distributed and outside. The demographic, technical and dosimetric parameters were used to identify the influence of these quantities in organ doses. A robust statistical method was implemented in order to establish these correlations and its statistical significance.ResultsMedian values of the grouped organ doses are presented according to the effective diameter ranges. Organ doses in the regions inside the imaged area are higher than the organ doses in peripheral, distributed and outside regions, excepted to the peripheral doses associated with chest examinations. Different levels of statistical significance between organ doses and the DRLq were presented.ConclusionsCorrelations between DRLq and target-organ doses associated with clinical practice can support guidance’s to the establishment of optimization criteria. SSDE demonstrated to be significant in the evaluation of organ doses is also highlighted. The proposed model allows the design of optimization actions with specific risk-reduction results.

A Lagrangian construction of rotating star models

ABSTRACTWe present a new formulation for numerically obtaining axisymmetric equilibrium structures of rotating stars in two spatial dimensions. With a view to apply it to the secular evolution of rotating stars, we base it on the Lagrangian description, i.e. we solve the force-balance equations to find the spatial positions of fluid elements endowed individually with a mass, specific entropy and angular momentum. The system of non-linear equations obtained by finite-differencing the basic equations is solved with the W4 method, which is a new multidimensional root-finding scheme of our own devising. We augment it with a remapping scheme to avoid distortions of the Lagrangian coordinates. In this first one of a series of papers, we will give a detailed description of these methods initially. We then present the results of some test calculations, which include the construction of both rapidly rotating barotropic and baroclinic equilibrium states. We gauge their accuracies quantitatively with some diagnostic quantities as well as via comparisons with the counterparts obtained with an Eulerian code. For a demonstrative purpose, we apply the code to a toy-model cooling calculation of a rotating white dwarf.

Suitability of an Artificial Viscosity Model for Compressible Under-Resolved Turbulence Using a Flux Reconstruction Method

In the simulation of compressible turbulent flows via a high-order flux reconstruction framework, the artificial viscosity model plays an important role to ensure robustness in the strongly compressible region. However, the impact of the artificial viscosity model in under-resolved regions on dissipation features or resolving ability remains unclear. In this work, the performance of a dilation-based (DB) artificial viscosity model to simulate under-resolved turbulent flows in a high-order flux reconstruction (FR) framework is investigated. Comparison is conducted with results via several typical explicit subgrid scale (SGS) models as well as implicit large eddy simulation (iLES) and their impact on important diagnostic quantities including turbulent kinetic energy, total dissipation rate of kinetic energy, and energy spectra are discussed. The dissipation rate of kinetic energy is decomposed into several components including those resulting from explicit SGS models or Laplacian artificial viscosity model; thus, an explicit evaluation of the dissipation rate led by those modeling terms is presented. The test cases consist of the Taylor-Green vortex (TGV) problem at Re=1600, the freely decaying homogeneous isotropic turbulence (HIT) at Mat0=0.5 (the initial turbulent Mach number ), the compressible TGV at Mach number 1.25 and the compressible channel flow at Reb= 15,334 (the bulk Reynolds number based on bulk density, bulk velocity and half-height of the channel), Mach number 1.5. The first two cases show that the DB model behaves similarly to the SGS models in terms of dissipation and has the potential to improve the insufficient dissipation of iLES with the fourth-order-accurate FR method. The last two cases further demonstrate the ability of the DB method on compresssible under-resolved turbulence and/or wall-bounded turbulence. The results of this work suggest the general suitability of the DB model to simulate under-resolved compressible turbulence in the high order flux reconstruction framework and also suggest some future work on controlling the potential excessive dissipation caused by the dilation term.

Does ERA5 Mark a New Era for Resolving the Tropical Cyclone Environment?

Abstract The synoptic environment around tropical cyclones plays a significant role in vortex evolution. To capture the environment, the operational and research communities calculate diagnostic quantities. To aid with applications and research, the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) combines disparate data sources. A key part of TC PRIMED is the environmental context. Often, environmental diagnostics come from multiple sources. However, TC PRIMED uses the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) product to provide a more complete representation of the storm environment from a single source. Reanalysis products usually poorly resolve tropical cyclones and their surrounding environment. To understand the uncertainty of large-scale diagnostics, ERA5 is compared to the Statistical Hurricane Intensity Prediction Scheme developmental dataset and the National Oceanic and Atmospheric Administration Gulfstream IV-SP dropwindsondes. This analysis highlights biases in the ERA5 environmental diagnostic quantities. Thermodynamic fields show the largest biases. The boundary layer exhibits a cold temperature bias that limits the amount of convective instability; also, the upper troposphere contains temperature biases and shows a high relative humidity bias. However, the upper-troposphere large-scale kinematic fields and derived metrics are low biased. In the lower troposphere, the temperature gradient and advection calculated from the thermal wind suggest that the low-level wind field is not representative of the observed distribution. These diagnostics comparisons provide uncertainty so that users of TC PRIMED can assess the implications for specific research and operational applications.

Quantum phase with coexisting localized, extended, and critical zones

Conventionally a mobility edge (ME) marks a critical energy that separates two different transport zones where all states are extended and localized, respectively. Here we propose a novel quasiperiodic spin-orbit coupled lattice model with experimental feasibility to realize a fundamentally new quantum phase with three coexisting energy-dependent zones, i.e., the extended, critical and localized zones, and uncover the underlying generic mechanism for the occurrence of the new quantum phase. Accordingly, this phase exhibits new types of MEs which separate the extended states from critical ones, and the localized states from critical ones, respectively. We introduce the diagnostic quantities to characterize and distinguish the different zones, and show that the predicted phase can be detected by measuring the fractal dimension or conductivities. The experimental realization is also proposed and studied. This work extends the concept of ME and enriches the quantum phases in disordered systems, which sheds light on searching for new localization and critical phenomena with novel transport and thermoelectric effects.

No-go guide for late-time solutions to the Hubble tension: Matter perturbations

The Hubble tension seems to be a crisis with $\sim5\sigma$ discrepancy between the most recent local distance ladder measurement from type Ia supernovae calibrated by Cepheids and the global fitting constraint from the cosmic microwave background data. To narrow down the possible late-time solutions to the Hubble tension, we have used in a recent study [Phys. Rev. D 105, L021301 (2022)] an improved inverse distance ladder method calibrated by the absolute measurements of the Hubble expansion rate at high redshifts from the cosmic chronometer data, and found no appealing evidence for new physics at the late time beyond the $\Lambda$CDM model characterized by a parametrization based on the cosmic age. In this paper, we further investigate the perspective of this improved inverse distance ladder method by including the late-time matter perturbation growth data. Independent of the dataset choices, model parametrizations, and diagnostic quantities ($S_8$ and $S_{12}$), the new physics at the late time beyond the $\Lambda$CDM model is strongly disfavored so that the previous late-time no-go guide for the Hubble tension is further strengthened.

A model grid for the reflected light from transition disks

Context. The dust in protoplanetary disks is an important ingredient in planet formation and can be investigated with model simulations and quantitative imaging polarimetry of the scattered stellar light. Aims. This study explores circumstellar disks with calculations for the intensity and polarization of the reflected light. We aim to describe the observable radiation dependencies on parameters in order to constrain the dust scattering properties and the disk geometry. Methods. The photon scattering and absorption by the disk are calculated with a Monte Carlo method for a grid of simple, rotationally symmetric models approximated at each point by a plane–parallel dusty atmosphere. The adopted geometry is described by a strongly illuminated inner wall of a transition disk with inclination i, a constant wall slope χ, and an angular wall height a. Dust scattering parameters are the single scattering albedo ω, the Henyey–Greenstein scattering phase function with the asymmetry parameter ɡ, and the maximal fractional polarization pmax induced by the scattering. First, the results for the reflectivity, the polarized reflectivity, and the fractional polarization of a plane–parallel surface element are calculated as functions of the incidence angle and the escape direction of the photons and as functions of the scattering parameters. Integration over all escape directions yields the surface albedo and the fraction of radiation absorbed by the dust. Second, disk images of the reflected intensity and polarization are calculated, and the appearance of the disk is described for various parameter combinations. The images provide many quantitative radiation parameters for a large range of model calculations, which can be compared to observations. These include the disk integrated intensity I¯/I★, azimuthal polarization Q¯φ/I★, the polarization aligned with the apparent disk axes Q¯/I★, the quadrant polarization parameters Qxxx and Uxxx, the disk-averaged fractional polarization 〈pφ〉 or 〈pQ〉, but also the front-to-back intensity ratio I180/I000 or the maximum fractional scattering polarization тах(pφ). Results. The results of our simple disk models reproduce well the measurements for I/I⋆,Qφ/I⋆, and 〈pφ〉 reported for well-observed transition disks. They describe the dependencies of the scattered radiation on the disk geometry and the dust scattering parameters in detail. Particularly strong constraints on disk properties can be obtained from certain diagnostic quantities: for example the fractional polarization 〈pφ〉 or тах(pφ) depend predominantly on the dust-scattering parameters ω and pmax; for disks with well-defined inclination, ratios of the quadrant polarization parameter depend mainly on the scattering asymmetry ɡ and the wall slope χ; wavelength dependencies of I/I✶ and Qφ/I✶ can mostly be attributed to the wavelength dependence of the dust scattering parameters ω(λ), ɡ(λ), and pmmах(λ); and the ratio between the scattered and thermal light of the disk roughly constrains the disk reflectivity R and the single scattering albedo of the dust ω. Conclusions. This computational investigation of the scattered radiation from transition disks shows well-defined dependencies on model parameters and the results can therefore be used as a diagnostic tool for the analysis of quantitative measurements, specifically in constraining or even determining the scattering properties of the dust particles in disks. Collecting and comparing such information for many systems is required to understand the nature of the scattering dust in planet-forming disks.

A Linear Theory of Wind Farm Efficiency and Interaction

Abstract We investigate the role of gravity waves (GW), farm shape, and wind direction on the efficiency and interaction of wind farms using a two-layer linearized dynamical model with Rayleigh friction. Five integrated diagnostic quantities are used: total wind deficit, the first moment of vorticity, turbine work, disturbance kinetic energy, and vertical energy flux. The work done on the atmosphere by turbine drag is balanced by dissipation of disturbance kinetic energy. A new definition of wind farm efficiency is proposed based on “turbine work.” While GWs do not change the total wind deficit or the vorticity pattern, they alter the spatial pattern of wind deficit and typically make a wind farm less efficient. GWs slow the winds upwind and reduce the wake influence on nearby downstream wind farms. GWs also propagate part of the disturbance energy upward into the upper atmosphere. We applied these ideas to the proposed 45 km × 15 km wind energy areas off the coast of New England. The proximity of these farms allows GWs to play a significant role in farm interaction, especially in winter with northwesterly winds. The governing equations are solved directly and by using fast Fourier transforms (FFT). The computational speed of the linear FFT model suggests its future use in optimizing the design and day-by-day operation of these and other wind farms. Significance Statement When a wind farm is generating electricity, the drag of the wind turbines slows the regional winds. As wind farms grow larger and more closely spaced, this wind reduction will limit the efficiency of wind farms and their economic return. In this paper we analyze an idealized mathematical model of the atmospheric response to wind farm drag including nonlocal gravity wave effects. We propose a new definition of farm efficiency based on the atmospheric disturbance that a farm creates. We also propose a fast Fourier transform (FFT) method for carrying out these estimates.

Sensing Method Using Multiple Quantities for Diagnostic of Insulators in Different Ambient Conditions

Insulators are one of the many components responsible for the reliability of electricity supply as part of transmission and distribution lines. Failure of the insulator can cause considerable economic problems that are much greater than the insulator cost. When the failure occurs on the transmission line, a large area can be without electricity supply or other transmission lines will be overloaded. Because of the consequences of the insulator’s failure, diagnostics of the insulator plays a significant role in the reliability of the power supply. Basic diagnostic methods require experienced personnel, and inspection requires moving in the field. New diagnostic methods require online measurement if it is possible. Diagnostic by measuring the leakage current flowing on the surface of the insulator is well known. However, many other quantities can be used as a good tool for diagnostics of insulators. We present in this article results obtained on the investigated porcelain insulators that are one of the most used insulation materials for housing the insulator’s core. Leakage current, dielectric loss factor, capacity, and electric charge are used as diagnostic quantities to investigate porcelain insulators in different pollution conditions and different ambient relative humidity. Pollution and humidity are the main factors that decrease the insulator´s electric strength and reliability.

Validation of a NTM model using databases of disruptive plasmas at JET

Pressure-driven instabilities such as the neoclassical tearing modes (NTMs) limit the maximum achievable plasma normalized β N needed for a reliable and efficient tokamak device. For a better understanding of the involved phenomena an almost fully analytic, dynamic, interpretative, multi-mode model describing the evolution of the NTMs is presented. An explicitly derived final formula of the NTM perturbed flux function is provided having a clear time dependent expression. To obtain the latter, the equations the perturbations satisfy are basically solved in the whole space: the ideal plasma perturbed momentum equations, the magnetic island perturbed resistive equations, the perturbed circuit equations in the plasma surrounding resistive wall and coils and finally the perturbed equations in the vacuum between the plasma and the external structures. The external coils play the role of feedback coils and/or error field generating coils. The system of the perturbed equations is completed by all the existing boundary equations across boundaries and the jump equation across the magnetic island that takes into account a heuristic bootstrap term. The assumption of the small perturbations from the equilibrium state is used in order to track the problem analytically, therefore the system of the perturbed equations is a linear one. Obviously the real diagnostic data quantities are not static and cannot be treated as equilibrium quantities from a strict mathematical point of view. This would drive the perturbed system of equations profoundly nonlinear and basically impossible to be analytically treated. Based on the assumption that usually the perturbed quantities have a significantly higher growth rate compared to the diagnostic quantities growth rate, it has been used the approximation that the plasma quantities collected from the diagnostic data behave like equilibrium quantities on the perturbations time scale. This approximation allows us to keep our proposed model linear and to provide a clear, quasi-analytic NTM solution. This solution and therefore the entire model validity have been tested against various discharges at JET.

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

A Reliability Assessment of the NCEP/FNL Reanalysis Data in Depicting Key Meteorological Factors on Clean Days and Polluted Days in Beijing

In this study, based on the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data, the reliability and performances of their application on clean days and polluted days (based on the PM2.5 mass concentrations) in Beijing were assessed. Conventional meteorological factors and diagnostic physical quantities from the NCEP/FNL data were compared with the L-band radar observations in Beijing in the autumns and winters of 2017–2019. The results indicate that the prediction reliability of the temperature was the best compared with those of the relative humidity and wind speed. It is worth noting that the relative humidity was lower and the near-surface wind speed was higher on polluted days from the NCEP/FNL data than from the observations. As far as diagnostic physical quantity is concerned, it was revealed that the temperature inversion intensity depicted by the NCEP/FNL data was significantly lower than that from the observations, especially on polluted days. For example, the difference in the temperature inversion intensity between the NCEP/FNL data and the observation ranged from −0.56 to −0.77 °C on polluted days. In addition, the difference in the wind shears between the NCEP/FNL reanalysis data and the observations increased to 0.40 m/s in the lower boundary layer on polluted days compared with that on clean days. Therefore, it is suggested that the underestimation of the relative humidity and temperature inversion intensity, and the overestimation of the near-surface wind speed should be seriously considered in simulating the air quality in the model, particularly on polluted days, which should be focused on more in future model developments.

A Study on the Life Estimation and Cavity Surface Degradation Due to Partial Discharges in Spherical Cavities within Solid Polymeric Dielectrics Using a Simulation Based Approach.

Partial Discharges (PD) in cavities are responsible for the greatest ageing rate in polymeric solid dielectrics due to chemical and physical deterioration mechanisms activated by the charge carriers, Ultra Violet (UV) radiation and local temperature rising during PDs activity. From the above, it is necessary to develop prognosis tools based on PDs measurements as diagnostic quantities in order to infer the time-to-breakdown, life, of solid dielectrics for improving the reliability of electrical assets, especially in current applications where they are subject to great electrical stresses in voltage frequency and magnitude. In this paper, the degradation in polymeric materials induced by PDs in cavities is briefly discussed from a phenomenological point of view, and then it is quantitatively evaluated using a simulation-based approach and a new proposed damage function. The time-to-breakdown calculated from simulations exhibits good agreement when compared with experimental measurements. Additionally, an analysis on the effect of the magnitude and frequency of the applied voltage on the degradation rate is also presented and the effectiveness of a degradation indicator, proposed by other authors, is evaluated under different stress conditions.

Colon cancer detection by using Poincaré sphere and 2D polarimetric mapping of ex vivo colon samples.

This work is dedicated to the diagnosis and grading of colon cancer by a combined use of Poincaré sphere and 2D Stokes vector polarimetry mapping approaches. The major challenge consists in exploring the applicability of polarized light for noninvasive screening of the histological abnormalities within the samples of biological tissues. Experimental studies were conducted in ex vivo colon sample, excised after surgical procedure for colon tumor removal of G2-adenocarcinoma lesion. Polarimetric measurements in linear and circular regime were carried via personally developed polarimetric, optical set-up, using supercontinuous fiber laser with irradiation fixed at 635 nm. We apply the Poincaré sphere and two-dimensional Stokes vector scanning approach for screening the corresponding tissue samples. A comparison between linear and circular polarization states is made both for quantitative and qualitative evaluations. It is shown that circular polarization has better diagnostic capabilities than linear polarization, with higher dynamic ranges of the polarimetric parameters and better values of the diagnostic quantities. In addition to the standard polarimetry parameters, utilized as essential diagnostic markers, we apply statistical analysis to obtain more detailed information in frame of the applied diagnostic approach.

Diagnostics for eddy viscosity models of turbulence including data-driven/neural network based parameterizations

Classical eddy viscosity models add a viscosity term with turbulent viscosity coefficient whose specification varies from model to model. Turbulent viscosity coefficient approximations of unknown accuracy are typically constructed by solving associated systems of nonlinear evolution equations or by data driven approaches such as deep neural networks. Often eddy viscosity models over-diffuse, so additional fixes are added. This process increases model complexity and decreases model comprehensibility, leading to the following two questions: Is an eddy viscosity model needed? Does the eddy viscosity model fail? This report derives diagnostic quantities of interest that answer these two questions. A notable quality of the derived quantities of interest for the eddy viscosity model is that they are a posteriori computable and require no a priori knowledge of the parameterization. For neural network based parameterizations these diagnostic quantities provide an indication of when the eddy viscosity model fails due to over diffusion of the flow.