Post-processing Tools Research Articles

Cardiovascular magnetic resonance imaging-derived intraventricular pressure gradients in ST-segment elevation myocardial infarction: a long-term follow-up study.

Recently, novel post-processing tools have become available that measure intraventricular pressure gradients (IVPGs) on routinely obtained long-axis cine cardiac magnetic resonance (CMR) images. IVPGs provide a comprehensive overview of both systolic and diastolic left ventricular (LV) functions. Whether IVPGs are associated with clinical outcome after ST-elevation myocardial infarction (STEMI) is currently unknown. Here, we investigated the association between CMR-derived LV-IVPGs and major adverse cardiovascular events (MACE) in a large reperfused STEMI cohort with long-term outcome. In this prospectively enrolled multi-centre cohort study, 307 patients underwent CMR within 14 days after the first STEMI. LV-IVPGs (from apex-to-base) were estimated on the long-axis cine images. During a median follow-up of 9.7 (5.9-12.5) years, MACE (i.e. composite of cardiovascular death and de novo heart failure hospitalisation) occurred in 49 patients (16.0%). These patients had larger infarcts, more often microvascular injury, and impaired LV-IVPGs. In univariable Cox regression, overall LV-IVPG was significantly associated with MACE and remained significantly associated after adjustment for common clinical risk factors (hazard ratio (HR) 0.873, 95% confidence interval (CI) 0.794-0.961, P = 0.005) and myocardial injury parameters (HR 0.906, 95% CI 0.825-0.995, P = 0.038). However, adjusted for LV ejection fraction and LV global longitudinal strain (GLS), overall LV-IVPG does not provide additional prognostic information (HR 0.959, 95% CI 0.866-1.063, P = 0.426). Early after STEMI, CMR-derived LV-IVPGs are univariably associated with MACE and this association remains significant after adjustment for common clinical risk factors and measures of infarct severity. However, LV-IVPGs do not add prognostic value to LV ejection fraction and LV GLS.

Does matrix transducer technology improve quality and repeatability of four-dimensional tricuspid annular plane systolic excursion and mitral annular plane systolic excursion measurements?

Novel ultrasound technology and software processing allow offline evaluation of tricuspid annular plane systolic excursion and mitral annular plane systolic excursion measurements. We wished to compare both novel 4D matrix (eM6C) and conventional (RAB6-D) transducers with variable settings (electronic spatiotemporal image correlation, spatiotemporal image correlation and four-dimensional real time) to determine if there was a significant difference in absolute value, quality and repeatability of the resultant reconstructed image and M-mode trace. A blinded prospective cross-sectional study of normal fetuses from 23 to 38 weeks' gestation were recruited. After routine sonography, four-dimensional volumes were stored and analysed using GE 4DView™ software. Statistical analysis explored variability, correlations and repeatability of the measurements with chi-square analysis, intraclass correlations and the Bland-Altman comparison plots. A scoring system was devised for image quality. Eighteen participants generated 282 data volumes. Absolute values demonstrated some inconsistencies for both tricuspid annular plane systolic excursion and mitral annular plane systolic excursion measurements with variations between transducers: the highest for the RAB6-D/spatiotemporal image correlation setting and the lowest for the four-dimensional real-time settings. The RAB6-D/spatiotemporal image correlation setting was the most repeatable combination (intraclass correlation coefficient = 0.85). Poorest image quality (M-mode trace, four-chamber view, annuli) came from the RAB6-D/four-dimensional real-time combination with the eM6C/electronic spatiotemporal image correlation and RAB6-D/spatiotemporal image correlation settings being nearly identical. We show that transducer and setting combinations influence absolute tricuspid annular plane systolic excursion/mitral annular plane systolic excursion measurements, so need to be articulated in future research. The transducer setting (electronic spatiotemporal image correlation/spatiotemporal image correlation/four-dimensional real time) was a more significant factor than the type of transducer (conventional vs matrix). Subjective image evaluation does not correlate well with repeatability of image acquisition. Further studies are needed to compare measurements using four-dimensional post-processing tools against conventional real-time measurements.

Inter-study reproducibility of cardiovascular magnetic resonance-derived hemodynamic force assessments

Cardiovascular magnetic resonance (CMR)-derived hemodynamic force (HDF) analyses have been introduced recently enabling more in-depth cardiac function evaluation. Inter-study reproducibility is important for a widespread clinical use but has not been quantified for this novel CMR post-processing tool yet. Serial CMR imaging was performed in 11 healthy participants in a median interval of 63 days (range 49–87). HDF assessment included left ventricular (LV) longitudinal, systolic peak and impulse, systolic/diastolic transition, diastolic deceleration as well as atrial thrust acceleration forces. Inter-study reproducibility and study sample sizes required to demonstrate 10%, 15% or 20% relative changes of HDF measurements were calculated. In addition, intra- and inter-observer analyses were performed. Intra- and inter-observer reproducibility was excellent for all HDF parameters according to intraclass correlation coefficient (ICC) values (> 0.80 for all). Inter-study reproducibility of all HDF parameters was excellent (ICC ≥ 0.80 for all) with systolic parameters showing lower coeffients of variation (CoV) than diastolic measurements (CoV 15.2% for systolic impulse vs. CoV 30.9% for atrial thrust). Calculated sample sizes to detect relative changes ranged from n = 12 for the detection of a 20% relative change in systolic impulse to n = 200 for the detection of 10% relative change in atrial thrust. Overall inter-study reproducibility of CMR-derived HDF assessments was sufficient with systolic HDF measurements showing lower inter-study variation than diastolic HDF analyses.

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.

RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate. We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs. CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.

Modelling Substrate Analysis for Remanufacturing: Investigating the Plasma Beam Remelting Process (PBRP) in Surface Modification of Additively Manufactured SS316L

Additively manufactured components often exhibit inferior surface quality and mechanical anisotropy, necessitating additional post-production processing, which in turn raises both manufacturing costs and lead times. This study investigates the impact of modifying plasma power and scanning speed in the Plasma Beam Remelting Process (PBRP) on SS316L. This technique induces a rise in contact surface temperature, which subsequently alters the mechanical characteristics and surface roughness of the additively fabricated SS316L. Grain size refinement down to the sub-micron level was observed after PBRP. Finite Element Analysis (FEA) will be employed in this study to analyse temperature distribution and fluctuations in thermal zones using commercially available software. The interrelationship of various parameters will be explored to determine the optimal conditions necessary for effective surface modification. The temperature distribution in the x-direction was measured, and zonal area identification was conducted at varied scanning speeds. The effective area for reducing thermal stresses was projected to be the length of the Heat Treatment Effect Zone (HTEZ) as an extended zone. Plasma powers ranging from 300 to 340 W, coupled with scanning speeds between 63 and 90 mm/min, yielded the best results for processing. These preliminary findings indicate that the PBRP technique holds promising potential as a post-processing tool for modifying the surfaces of additively manufactured parts.

A resource for development and comparison of multimodal brain 3 T MRI harmonisation approaches

Abstract Despite the huge potential of magnetic resonance imaging (MRI) in mapping and exploring the brain, MRI measures can often be limited in their consistency, reproducibility, and accuracy which subsequently restricts their quantifiability. Nuisance nonbiological factors, such as hardware, software, calibration differences between scanners, and post-processing options, can contribute to, or drive trends in, neuroimaging features to an extent that interferes with biological variability. Such lack of consistency, known as lack of harmonisation, across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI. Here, we build a new resource for comprehensively mapping the extent of the problem and objectively evaluating neuroimaging harmonisation approaches. We use a travelling-heads paradigm consisting of multimodal MRI data of 10 travelling subjects, each scanned at five different sites on six different 3 T scanners from all the three major vendors and using five neuroimaging modalities, providing more comprehensive coverage than before. We also acquire multiple within-scanner repeats for a subset of subjects, setting baselines for multimodal scan-rescan variability. Having extracted hundreds of imaging-derived phenotypes, we compare three forms of variability: (i) between-scanner, (ii) within-scanner (within-subject), and (iii) biological (between-subject). We characterise the reliability of features across scanners and use our resource as a testbed to enable new investigations that until now have been relatively unexplored. Specifically, we identify optimal pipeline processing steps that minimise between-scanner variability in extracted features (implicit harmonisation). We also test the performance of post-processing harmonisation tools (explicit harmonisation) and specifically check their efficiency in reducing between-scanner variability against baseline standards provided by our data. Our explorations allow us to come up with good practice suggestions on processing steps and sets of features where results are more consistent, while our publicly released dataset (which we refer to as ON-Harmony) establishes references for future studies in this field.

Hybrid quantum algorithms for flow problems.

For quantum computing (QC) to emerge as a practically indispensable computational tool, there is a need for quantum protocols with end-to-end practical applications-in this instance, fluid dynamics. We debut here a high-performance quantum simulator which we term QFlowS (Quantum Flow Simulator), designed for fluid flow simulations using QC. Solving nonlinear flows by QC generally proceeds by solving an equivalent infinite dimensional linear system as a result of linear embedding. Thus, we first choose to simulate two well-known flows using QFlowS and demonstrate a previously unseen, full gate-level implementation of a hybrid and high precision Quantum Linear Systems Algorithms (QLSA) for simulating such flows at low Reynolds numbers. The utility of this simulator is demonstrated by extracting error estimates and power law scaling that relates [Formula: see text] (a parameter crucial to Hamiltonian simulations) to the condition number [Formula: see text] of the simulation matrix and allows the prediction of an optimal scaling parameter for accurate eigenvalue estimation. Further, we include two speedup preserving algorithms for a) the functional form or sparse quantum state preparation and b) in situ quantum postprocessing tool for computing nonlinear functions of the velocity field. We choose the viscous dissipation rate as an example, for which the end-to-end complexity is shown to be [Formula: see text], where [Formula: see text] is the size of the linear system of equations, [Formula: see text] is the solution error, and [Formula: see text] is the error in postprocessing. This work suggests a path toward quantum simulation of fluid flows and highlights the special considerations needed at the gate-level implementation of QC.

Analysis of the short-term noise situation around Bangkok International Airport prior to the second phase of airport expansion begins

Short-term noise measurements carried out around the Bangkok International Airport during 2014-2015 provided baseline datasets of noise levels to illustrate the overview of actual aircraft noise exposure to people living underneath the flight path. The datasets will be useful for social surveys to draw the dose-response relationship curve, validation of noise prediction studies, and noise management plans around the airport before the second phase expansion. This study proposes a post-processing tool for extracting aircraft noise events due to aircraft flyovers from the measurements. The actual noise exposure levels of aircraft fly-over noise events were reported to reflect the existing noise situation around the airport and contribute to the analysis of other studies conducted during the same period.

Development and testing of the hydrogen behavior tool for Falcon – HYPE

The presence of hydrogen absorbed by zirconium-based cladding materials during reactor operation can trigger degradation mechanisms and endanger the rod integrity. Ensuring the durability of the rods in extended timeframes like dry storage requires anticipating hydrogen behavior using numerical modeling. In this context, the present paper describes a hydrogen post-processing tool for Falcon – HYPE, a PSI's in-house tool able to calculate hydrogen uptake, transport, thermochemistry, reorientation of hydrides and hydrogen-related failure criteria. The tool extracts all necessary data from a Falcon output file; therefore, it can be considered loosely coupled to Falcon. HYPE has been successfully validated against experimental data and applied to reactor operation and interim storage scenarios to present its capabilities.

Statistical Study of the Process Parameters for Achieving Continuous Consolidation of a Thermoplastic Composite.

Manufacturing components using thermoplastic composite materials necessitates a judicious balance among fabrication parameters, cost considerations and the ultimate quality of the elements produced. Continuous manufacturing technologies, exemplified by methods such as continuous compressing molding and glide forming, seek to revolutionize production through their continuous processing approach. This study aimed to investigate the effects different process parameters have on the final quality of the manufactured parts when a continuous manufacturing technology, such as glide forming, is applied to thermoplastic composite materials. An experimental rig was designed, and 19 samples were prepared using a unidirectional-carbon-fiber-reinforced LM-PAEK (low-melting polyaryletherketone) composite. The process parameters studied were temperature, pressure and forming speed. The quality of the final parts was evaluated based on their thickness and consolidation levels. The findings underscore the feasibility of leveraging continuous manufacturing technologies for producing components using thermoplastic composite materials, but the process parameters must be carefully controlled to ensure the quality of the final part. The models obtained could be used as a post-processing tool to predict thickness and consolidation levels when simulating the manufacture of a component on macroscale levels. Further research is needed to optimize the process parameters and study their effects on other thermoplastic composite materials.

Epiq: An open-source software for the calculation of electron-phonon interaction related properties

epiq (Electron-Phonon wannier Interpolation over k and q-points) is an open-source software for the calculation of electron-phonon interaction related properties from first principles. Acting as a post-processing tool for a density-functional perturbation theory code (Quantum ESPRESSO) and wannier90, epiq exploits the localization of the deformation potential in the Wannier function basis and the stationary properties of a force-constant functional with respect to the first-order perturbation of the electronic charge density to calculate many electron-phonon related properties with high accuracy and free from convergence issues related to Brillouin zone sampling. epiq features include: the adiabatic and non-adiabatic phonon dispersion, superconducting properties (including the superconducting band gap in the Migdal-Eliashberg formulation), double-resonant Raman spectra and lifetime of excited carriers. The possibility to customize most of its input makes epiq a versatile and interoperable tool. Particularly relevant is the interaction with the Stochastic Self-Consistent Harmonic Approximation (SSCHA) allowing anharmonic effects to be included in the calculation of electron-properties. The scalability offered by the Wannier representation combined with a straightforward workflow and easy-to-read input and output files make epiq accessible to the wide condensed matter and material science communities. Program summaryProgram Title:epiqCPC Library link to program files:https://doi.org/10.17632/f2syws66d7.1Developer's repository link:https://gitlab.com/the-epiq-team/epiqLicensing provisions: GPLv3Programming language: FORTRAN95External routines: BLAS (http://www/netlib.org/blas), LAPACK (http://www.netlib.org/lapack), Quantum ESPRESSO (https://www.quantum-espresso.org/), wannier90 (https://wannier.org/)Nature of problem: Direct first principles calculation of quantities obtained via linear response methods in solid-state systems, such as the deformation potential, can be computationally demanding, hindering proper convergence.Solution method: An interpolation scheme exploiting the localization of the deformation potential in the Wannier function basis and the stationary properties of a force-constant functional with respect to the first-order perturbation of the electronic charge density is implemented in epiq. Within this approach it is possible to calculate many electron-phonon related properties with high accuracy and a low computational effort.

LibFastMesh: An optimized finite-volume framework for computational aeroacoustics

This paper presents an optimized framework for simulating aeroacoustic flow on unstructured meshes. The libFastMesh (LFM) framework is based on a low-dissipation finite–volume discretization, together with high-order explicit time integration, for direct computation of aeroacoustic fields. These methods were previously implemented in caafoam, an OpenFOAM-based solver developed by D'Alessandro et al. [1] to resolve aeroacoustic flows. The new framework is specifically developed from scratch to alleviate two of the main bottlenecks currently limiting the performance of OpenFOAM-based solvers at large scale: memory bandwidth and inter-node communication. Compact data structures and compute kernel fusion are employed to enhance cache utilization and re-use, respectively. Separate treatment of inter-process boundary cells, together with non-blocking MPI communication enable effective overlap between communication and flux computations. The accuracy and robustness of libFastMesh are established via simulations of well-established aeroacoustic flow benchmarks. Comparison to previous simulation results obtained with caafoam, and to DNS data serve to validate the code. Finally, the effectiveness of the aforementioned optimizations is demonstrated through rigorous analysis of the proposed solver performance in single-node and multi-node operation modes. The obtained results show that libFastMesh offers a speed-up of up to 20x with respect to the previously developed OpenFOAM-based solver, caafoam, in large-scale computations. Moreover, LFM is shown to scale extremely for cell-per-core ratios of less than 500. These results are particularly appealing given the highly resource-demanding nature of direct computational aeroacoustics (CAA). Consequently, LFM could be an extremely attractive tool for scientists who wish to conduct large-scale CAA simulations on modern, exascale architectures. Program summaryProgram Title: libFastMeshCPC Library link to program files:https://doi.org/10.17632/7w9fy2xtcf.1Developer's repository link:https://github.com/TRC-HPC/LFM_PublicLicensing provisions: GPLv3Programming language: C++Nature of problem: This software solves compressible Navier–Stokes equations. The adopted numerics and flow models allow to capture with high accuracy aerodinamically generated sound. The solver is also designed to take advantage of the massively parallel computing systems which are strictly required for facing real–life aeroacoustic problems.Solution method: The libFastMesh (LFM) framework is based on a low-dissipation finite–volume discretization, together with high-order explicit time integration, for direct computation of aeroacoustic fields on unstructured meshes. Convective terms can be approximated using either the Kurganov–Noelle–Petrova (KNP) scheme or Pirozzoli's energy conserving approach. Time integration is fully–explicit and relies on a low-storage, 4th order Runge–Kutta scheme. The solver is specifically developed to run efficiently on modern many-core CPUs, and at large-scale where it exhibits excellent strong scaling features. This is made possible thanks to several optimizations that alleviate memory bandwidth and inter-node communication bottlenecks.Additional comments: OpenFOAM-v2112+ is an installation prerequisite. Standard OpenFOAM pre-processing and post-processing tools, as well as mesh decomposition and reordering methods may be used with the solver.

Pavement Monitoring Using Unmanned Aerial Vehicles: An Overview

Pavement monitoring involves periodic damage detection and condition assessment of pavements for efficient pavement management. Unmanned aerial vehicle (UAV)-based pavement monitoring requires multidisciplinary knowledge of pavement distress, drone type, payload, flight parameters, drone deployment, and image processing. Owing to the availability of various UAVs, data sensing devices, operating ecosystems, and post-processing tools, selecting an appropriate combination of these systems is crucial. Therefore, the primary objective of this study is to provide essential knowledge on the prevalent challenges of existing monitoring techniques and discuss the potential advantages of UAVs over conventional pavement monitoring practice. A state-of-the-art review emphasizing UAV technicalities in the context of image-based pavement monitoring is presented. A detailed workflow and checklist for drone deployment is drafted for novice users to ensure safe and high-quality data acquisition. Finally, the present challenges and future scope of UAV-based pavement monitoring is discussed. Overall, this study aims to provide inclusive and comprehensive information on UAV-based pavement monitoring to beginner researchers.

Semiautomatic Training Load Determination in Endurance Athletes

Background: Despite endurance athletes recording their training data electronically, researchers in sports cardiology rely on questionnaires to quantify training load. This is due to the complexity of quantifying large numbers of training files. We aimed to develop a semiautomatic postprocessing tool to quantify training load in clinical studies. Methods: Training data were collected from two prospective athlete’s heart studies (Master Athlete’s Heart study and Prospective Athlete Heart study). Using in-house developed software, maximal heart rate (MaxHR) and training load were calculated from heart rate monitored during cumulative training sessions. The MaxHR in the lab was compared with the MaxHR in the field. Lucia training impulse score, based on individually based exercise intensity zones, and Edwards training impulse, based on MaxHR in the field, were compared. A questionnaire was used to determine the number of training sessions and training hours per week. Results: Forty-three athletes recorded their training sessions using a chest-worn heart rate monitor and were selected for this analysis. MaxHR in the lab was significantly lower compared with MaxHR in the field (183 ± 12 bpm vs. 188 ± 13 bpm, p < .01), but correlated strongly (r = .81, p < .01) with acceptable limits of agreement (±15.4 bpm). An excellent correlation was found between Lucia training impulse score and Edwards training impulse (r = .92, p < .0001). The quantified number of training sessions and training hours did not correlate with the number of training sessions (r = .20) and training hours (r = −.12) reported by questionnaires. Conclusion: Semiautomatic measurement of training load is feasible in a wide age group. Standard exercise questionnaires are insufficiently accurate in comparison to objective training load quantification.

A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke

To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Large datasets are therefore imperative, as well as fully automated image post-processing tools to analyze them. The development of such tools, particularly with artificial intelligence, is highly dependent on the availability of large datasets to model training and testing. We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. The dataset provides high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps.

Martensite Variant Identification Method for shape memory alloys by using graph neural network

Detailed microstructure evolution in shape memory alloys (SMAs) can be studied by molecular dynamics (MD) simulations. However, the conventional post-processing methods for atomistic calculations, such as Common Neighbor Analysis (CNA), fail to identify distinct crystal variants and to reveal twin alignments in SMAs. In the current work, a powerful and efficient post-processing tool based on GraphSAGE neural network is developed, which can identify multiple phases in martensitic transformation, including the orthorhombic, monoclinic and R phases. The model was trained by the results of sets of temperature- and stress-induced martensitic transformation MD calculations. The accuracy and generality were also verified by the application to the cases which did not appear in the training dataset, such as the unrecoverable nanoindentation process. The proposed method is rapid, accurate, and ready to be integrated with any visualization tool for MD simulations. The outcome of the current work is expected to accelerate the pace of atomistic studies on SMAs and related materials.

Recognition of multivariate geochemical anomalies using a geologically-constrained variational autoencoder network with spectrum separable module – A case study in Shangluo District, China

This study has developed a novel variational autoencoder architecture by incorporating the spectrum separable module, termed SSM-VAE, so as to recognize the multi-mineral-species geochemical patterns over the Shangluo district, central China, and then facilitate a better understanding of the regional metallogenesis. The primary advantage of SSM-VAE is its ability to explore the interlayer correlations and then integrate them into the reconstructed features, which greatly improved the geological interpretability of the model products. In the training process, a hybrid loss function with a geologically-constrained term derived from the fractal analysis was designed to guide the model to focus on anomalies related to mineralization. Afterward, the factor analysis together with the EM-MML thresholding algorithm were integrated as a post-processing tool. Finally, the mineral-spot identification rate (δ) versus the size of the anomalous area (S) are used to validate the ore-bearing potential of different anomaly divisions. Comparing to other state-of-the-art models without regard to the interlayer correlations, SSM-VAE can produce well-zoned, mineral species-specific, and more metalliferous anomalous patches. To be specific, we separated out 10 zoned anomaly divisions, and on average, when S = 1% there are 8.5013 mineral spots falling within.

Easy to go and innovative validation process using the spot weld inspection system PHAsis and related software

In the industry 4.0 the need for a cost-saving and efficient production process is of high significance. This includes every area of the production processes and leads to appropriate testing procedures by means of the quality management. A combination of ultrasonic inspection devices and software tools for result post processing reduces the need of several cost-intensive validation circles with destructive testing to a very low level. This can be shown for example with the imaging phased array spot weld inspection system PHAsis in combination with a software app for post processing. This is including the software part for the data and result management as well as a device software for the ultrasonic inspection of the spot welds. In combination with the usage of an automation interface for robotic usage, even more cost reduction is possible. Several validation processes on typical resistance spot welding applications with plate pairings of two or three plates (aluminium as well as steel combinations) by the use of PHAsis and the connected PostProcessor will be exemplified. The general procedure of the validation process will be accentuated step by step. Therefore, the original data of phased array testing and a comparative method in the shape of a destructive testing (hammer & chisel test or horizontal respectively vertical cross section) will be presented. Furthermore, the steps of the post processing procedure to define the ultrasound system parameters for a best fit will be described. During these steps, the influence of the different sensitivity and system parameters on the spot weld size will be explained. Also, the measurement uncertainties given by the destructive testing that have an influence to the comparation with ultrasonic testing in general will be addressed. The efficiency of the post processing software tool due to reaching a high match between PHAsis and the comparative destructive method will be illustrated.

Prediction Quality Meta Regression and Error Meta Classification for Segmented Lidar Point Clouds

We present a post-processing tool for semantic segmentation of Lidar point clouds, called LidarMetaSeg, which estimates the prediction quality segmentwise and classifies prediction errors. For this purpose, we compute dispersion measures based on network probability outputs as well as feature measures based on point cloud input features and aggregate them on segment level. These aggregated measures are used to train a meta classification model to predict whether a predicted segment is an error, i.e., it is a false positive or not. We also train a meta regression model to predict the segmentwise prediction quality in terms of intersection over union. Both models can then be applied to semantic segmentation inferences without knowing the ground truth. In our experiments we use different Lidar segmentation models and datasets and analyze the power of our method. We show that our results outperform other standard approaches based on single uncertainty measures like entropy. Furthermore, we present an in-depth evaluation of our method on predicted classes as well as on predicted categories.

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Abstract. Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model and need many forcing variables at high temporal resolution. However, when trying to model snow depth (HS) on climatological timescales, which is often needed for winter tourism-related applications, these preconditions can cause barriers. Often, for these types of applications, empirical snow models are used. These can estimate snow accumulation and snowmelt based on daily precipitation and temperature data only. To convert the resultant snow water equivalent (SWE) time series into snow depth, we developed the empirical model SWE2HS. SWE2HS is a multilayer densification model which uses daily snow water equivalent as sole input. A constant new snow density is assumed and densification is calculated via exponential settling functions. The maximum snow density of a single layer changes over time due to overburden and SWE losses. SWE2HS has been calibrated on a data set derived from a network of manual snow stations in Switzerland. It has been validated against independent data derived from automatic weather stations (AWSs) in the European Alps (Austria, France, Germany, Switzerland) and against withheld data from the Swiss manual observer station data set which was not used for calibration. The model fits the calibration data with root mean squared error (RMSE) of 8.4 cm, coefficient of determination (R2) of 0.97, and bias of −0.3 cm; it is able to achieve RMSE of 20.5 cm, R2 of 0.92, and bias of 2.5 cm on the validation data set from automatic weather stations and RMSE of 7.9 cm, R2 of 0.97, and bias of −0.3 cm on the validation data set from manual stations. The temporal evolution of the bulk density can be reproduced reasonably well on all three data sets. Due to its simplicity, the model can be used as post-processing tool for output of any other snow model that provides daily snow water equivalent output. Owing to its empirical nature, SWE2HS should only be used in regions with a similar snow climatology as the European Alps or has to be recalibrated for other snow climatological conditions. The SWE2HS model is available as a Python package which can be easily installed and used.