Simulated Data Research Articles

Measuring Black Hole Light Echoes with Very Long Baseline Interferometry

Light passing near a black hole can follow multiple paths from an emission source to an observer due to strong gravitational lensing. Photons following different paths take different amounts of time to reach the observer, which produces an echo signature in the image. The characteristic echo delay is determined primarily by the mass of the black hole, but it is also influenced by the black hole spin and inclination to the observer. In the Kerr geometry, echo images are demagnified, rotated, and sheared copies of the direct image and lie within a restricted region of the image. Echo images have exponentially suppressed flux, and temporal correlations within the flow make it challenging to directly detect light echoes from the total light curve. In this Letter, we propose a novel method to search for light echoes by correlating the total light curve with the interferometric signal at high spatial frequencies, which is a proxy for indirect emission. We explore the viability of our method using numerical general relativistic magnetohydrodynamic simulations of a near-face-on accretion system scaled to M87-like parameters. We demonstrate that our method can be used to directly infer the echo delay period in simulated data. An echo detection would be clear evidence that we have captured photons that have circled the black hole, and a high-fidelity echo measurement would provide an independent measure of fundamental black hole parameters. Our results suggest that detecting echoes may be achievable through interferometric observations with a modest space-based very long baseline interferometry mission.

SsVERDICT: Self-supervised VERDICT-MRI for enhanced prostate tumor characterization.

Demonstrating and assessing self-supervised machine-learning fitting of the VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumors) model for prostate cancer. We derive a self-supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares and supervised deep learning. We do this quantitatively on simulated data by comparing the Pearson's correlation coefficient, mean-squared error, bias, and variance with respect to the simulated ground truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods' performance in discriminating benign from cancerous tissue via Wilcoxon's signed-rank test. In simulations, ssVERDICT outperforms the baseline methods (nonlinear least squares and supervised deep learning) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and mean-squared error. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods. ssVERDICT significantly outperforms state-of-the-art methods for VERDICT model fitting and shows, for the first time, fitting of a detailed multicompartment biophysical diffusion MRI model with machine learning without the requirement of explicit training labels.

What can we infer about mutation calling by using time‐series mutation accumulation data and a Bayesian Mutation Finder?

AbstractAccurate estimates of mutation rates derived from genome‐wide mutation accumulation (MA) data are fundamental to understanding basic evolutionary processes. The rapidly improving high‐throughput sequencing technologies provide unprecedented opportunities to identify single nucleotide mutations across genomes. However, such MA derived data are often difficult to analyze and the performance of the available methods of analysis is not well understood. In this study, we used the existing Bayesian Genotype Caller adapted for MA data that we refer to as Bayesian Mutation Finder (BMF) for identifying single nucleotide mutations while considering the characteristics of the data. We compared the performance of BMF with the widely used Genome Analysis Toolkit (GATK) by applying these two methods to time‐series MA data as well as simulated data. The time‐series data were obtained by propagating Daphnia pulex over an average of 188 generations and performing whole‐genome sequencing of 14 MA lines across three time points. The results indicate that BMF enables more accurate identification of single nucleotide mutations than GATK especially when applied to the empirical data. Furthermore, BMF involves the use of fewer parameters and is more computationally efficient than GATK. Both BMF and GATK found surprisingly many candidate mutations that were not confirmed at later time points. We systematically infer causes of the unconfirmed candidate mutations, introduce a framework for estimating mutation rates based on genome‐wide candidate mutations confirmed by subsequent sequencing, and provide an improved mutation rate estimate for D. pulex.

Investigating the Impact of the Regularization Parameter on EEG Resting-State Source Reconstruction and Functional Connectivity Using Real and Simulated Data

Accurate EEG source localization is crucial for mapping resting-state network dynamics and it plays a key role in estimating source-level functional connectivity. However, EEG source estimation techniques encounter numerous methodological challenges, with a key one being the selection of the regularization parameter in minimum norm estimation. This choice is particularly intricate because the optimal amount of regularization for EEG source estimation may not align with the requirements of EEG connectivityanalysis, highlighting a nuanced trade-off. In this study, we employed a methodological approach to determine the optimal regularization coefficient that yields the most effective reconstruction outcomes across all simulations involving varying signal-to-noise ratios for synthetic EEG signals. To this aim, we considered three resting state networks: the Motor Network, the Visual Network, and the Dorsal Attention Network. The performance was assessed using three metrics, at different regularization parameters: the Region Localization Error, source extension, and source fragmentation. The results were validated using real functional connectivity data. We show that the best estimate of functional connectivity is obtained using 10−2, while 10−1 has to be preferred when source localization only is at target.

A Review of the Challenges of Adaptive Filtering Technology in High-fidelity Audio Signal Processing

Abstract. High-fidelity audio signal processing plays an important role in modern audio technology. With the increasing demand for this technology in various audio application scenarios, the optimization of adaptive filters has emerged as a significant challenge in this field. This paper focuses on improving the performance of adaptive filters in high-fidelity audio signal processing which aims to improve the adaptability and efficiency of filters in complex audio environments by improving algorithms. In this study, the adaptive filtering algorithm based on wavelet transform and particle swarm optimization is used to verify the audio data processing by simulation and real data processing. The research data was collected using the standard audio signal database and the actual collected high-fidelity audio samples. The results show that the improved adaptive filter can significantly improve the performance of complex audio environments, improve the clarity and fidelity of audio signals and reduce the computational complexity. It is also suitable for real-time processing scenarios with limited resources. The conclusion shows that this method provides an efficient solution for high-fidelity audio signal processing and has a wide application prospect.

GPU based discrete element modeling for convex polyhedral shape particles: Development and validation

Particle dynamics simulations face a significant challenge in understanding the intricate behaviors of convex polyhedral particles due to their complex geometries and interactions. DEM emerges as a key method, illuminating the concealed intricacies of these geometric entities. Traditional algorithms often require cumbersome processes to check each type of contact individually. However, Gilbert-Johnson-Keerthi’s (GJK) and the expanding polytope algorithm (EPA) provide efficient numerical solutions for polyhedral contact detection and contact resolution. These Minkowski difference-based methods streamline contact detection and overlap computation, paving the way for deeper exploration of three-dimensional contact theory within DEM simulations. By leveraging GPU computational power, this paper outlines key algorithmic steps and verifies the code’s accuracy through comparison with simulated and experimental data, with an average deviation of less than 5%. This study explores the impact of particle shape on the dynamics and mechanical behavior of densely packed systems, particularly in hoppers and tumblers. Spherical particles discharge faster but mix more slowly than polyhedral shapes, with icosahedrons achieving quicker full mixing. These results align with experimental findings, further validating the simulation approach.

Precipitation Extremes and Their Modulation by Convective Organization in RCEMIP

AbstractWe examine the influence of convective organization on extreme tropical precipitation events using model simulation data from the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP). At a given SST, simulations with convective organization have more intense precipitation extremes than those without it at all scales, including instantaneous precipitation at the grid resolution (3 km). Across large‐domain simulations with convective organization, models with explicit convection exhibit better agreement in the response of extreme precipitation rates to warming than those with parameterized convection. Among models with explicit convection, deviations from the Clausius‐Clapeyron scaling of precipitation extremes with warming are correlated with changes in organization, especially on large spatiotemporal scales. Though the RCEMIP ensemble is nearly evenly split between CRMs which become more and less organized with warming, most of the models which show increased organization with warming also allow super‐CC scaling of precipitation extremes. We also apply an established precipitation extremes scaling to understand changes in the extreme condensation events leading to extreme precipitation. Increased organization leads to greater increases in precipitation extremes by enhancing both the dynamic and implied efficiency contributions. We link these contributions to environmental variables modified by the presence of organization and suggest that increases in moisture in the aggregated region may be responsible for enhancing both convective updraft area fraction and precipitation efficiency. By leveraging a controlled intercomparison of models with both explicit and parameterized convection, this work provides strong evidence for the amplification of tropical precipitation extremes and their response to warming by convective organization.

Osaka Feedback Model. III. Cosmological Simulation CROCODILE

We introduce our new cosmological simulation data set CROCODILE, executed using the GADGET4-Osaka smoothed particle hydrodynamics code. This simulation incorporates an updated supernova (SN) feedback model of Y. Oku et al. and an active galactic nuclei (AGN) feedback model. A key innovation in our SN feedback model is the integration of a metallicity- and redshift-dependent, top-heavy initial mass function. Our SN model introduces a new consideration that results in an order of magnitude difference in the energy injection rate per unit stellar mass formed at high redshift. The CROCODILE data set is comprehensive, encompassing a variety of runs with diverse feedback parameters. This allows for an in-depth exploration of the relative impacts of different feedback processes in galactic evolution. Our initial comparisons with observational data, spanning the galaxy stellar mass function, the star formation main sequence, and the mass–metallicity relation, show promising agreement, especially for the Fiducial run. These results establish a solid foundation for our future work. We find that SN feedback is a key driver in the chemical enrichment of the intergalactic medium (IGM). Additionally, the AGN feedback creates metal-rich, bipolar outflows that extend and enrich the circumgalactic medium and IGM over a few Mpc scales.

Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors

Reconstructing images from the Event Horizon Telescope (EHT) observations of M87*, the supermassive black hole at the center of the galaxy M87, depends on a prior to impose desired image statistics. However, given the impossibility of directly observing black holes, there is no clear choice for a prior. We present a framework for flexibly designing a range of priors, each bringing different biases to the image reconstruction. These priors can be weak (e.g., impose only basic natural-image statistics) or strong (e.g., impose assumptions of black hole structure). Our framework uses Bayesian inference with score-based priors, which are data-driven priors arising from a deep generative model that can learn complicated image distributions. Using our Bayesian imaging approach with sophisticated data-driven priors, we can assess how visual features and uncertainty of reconstructed images change depending on the prior. In addition to simulated data, we image the real EHT M87* data and discuss how recovered features are influenced by the choice of prior.

Fault Diagnosis of Power Components with Reliability Assessment in Extraterrestrial Microgrids

This research investigates the possible failures caused by aging and other environmental and external factors that could significantly impact the performance of extraterrestrial power systems. Additionally, it presents a reliability assessment model for the space microgrid based on fault tree analysis (FTA). The reliability assessment model developed in this paper represents a tool that can be used by engineers to harden the system design for operational and economic benefits. To improve the reliability of the system, this work provides a broad review of the different fault detection and diagnosis (FDD) algorithms used for power microgrids and space applications. Using data sets from the Habitat Simulator developed through the NASA-funded Resilient Extraterrestrial Habitat Institute, this paper compares the applicability and accuracy of the different FDD methods. The primary FDD approach proposed and assessed in this work is based on the Markov reliability model. It predicts and detects future faults in the space microgrids by using past data samples and categorizing them into different classes. Data-driven-based models such as artificial neural networks are also investigated, tested, and evaluated using simulation data sets. According to the simulation results and the broad FDD algorithm comparison, this study provides the crew or maintenance engineers with a clear methodology to detect and localize power system failures.

Ontology-assisted GPT-based building performance simulation and assessment: Implementation of multizone airflow simulation

Building performance simulation (BPS) is crucial for building performance assessments across its lifecycle. However, the complexity of buildings and the iterative nature of simulation poses challenges, leading to high costs and low values. Previous studies focused on simplification, but did not fully utilize advanced simulation engines. Despite recent advancements, there is a lack of research on leveraging artificial intelligence (AI), specifically generative pre-trained transformer (GPT), for BPS. Therefore, this study proposes a GPT-based BPS system, enhancing simulation efficiency and value by integrating simulation engines and advanced data analytics in the GPT environment. The ontology for GPT-based BPS is also developed to enable comprehensive, reliable, informative BPS environments. Based on this framework, case studies were conducted for GPT-based multizone airflow network simulation in a high-rise residential building using CONTAM software. They demonstrate GPT’s capabilities in retrieving simulation data, visualizing results with data mining, answering questions based on building knowledge, checking compliance with design guidelines, and proposing design alternatives. Finally, this study emphasizes expert interventions with ontological engineering informatics to utilize strictly structured BPS engines.

CME Velocity Field Calculation Model Based on an Unsupervised Transformer Optical Flow Network

The optical flow algorithm (OF) is one the main methods for calculating image velocity field and has many applications in space weather. Most OF calculations are applied to the motion of labeled rigid objects and are not suitable for velocity detection of high-energy particles, such as in a coronal mass ejection (CME). Fluctuations in exposure time and the influence of space weather will lead to inconsistent brightness of the same feature point at different times. To address this problem, we propose an unsupervised multiscale optical flow network based on Vision Transformer, named UTFlowNet. The network comprises a multiscale feature extraction module and a coarse-to-fine global optical flow calculation module. The movement of high-energy particles emitted during a CME eruption follows certain physical rules. Therefore, we apply fluid motion–based loss functions to analyze the motion of high-energy particles more effectively, addressing the problem of CME motion field extraction. Our method can be applied to the real-time automatic extraction of a CME’s velocity field and performs well with inconsistent brightness, large-scale motion, and strong CME noise. Additionally, we can estimate subpixel level fine-grained velocity. Our model may be affected by overfitting during cross–data set inference, so we encourage performing a small amount of transfer learning on new data sets to mitigate this issue. In order to verify the accuracy of our method, we conducted experiments and verification on the Solar and Heliospheric Observatory LASCO C2 data and the High Altitude Observatory MLSO data. We constructed a large-scale displacement simulation data set based on LASCO C2 data and tested on it, achieving the best results.

Comparative analysis of machine learning models for particle flow reconstruction

Abstract. Using the simulation data of different calorimeters, we investigate the use of machine learning and python algorithms for the simulation and reconstruction of particle flow energy in high-energy physics. We train models based on simulated pixel value image instead of common numerical data. We define two models that use different Regressor algorithms. Multi-layer Perceptron (MLP) Regressor offer stable and accurate prediction under less affection situation. Convolutional Neutral Networks (CNN) Regressor provided better and stronger modeling and reconstruction ability; however, it could cause overfitting results in certain situations. Furthermore, we optimize our models with the use of PyTorch. These models can serve as fast method for particle flow reconstruction in future studies and experiments.

Evaluation method of carbon-fiber-reinforced polymer material anti-radiation performance based on synergistic effect model

Aiming at the high computational costs of the current multi-source X-ray radiation numerical simulations, a multi-source X-ray evaluation method based on a synergistic effect model is studied. First, based on the advantage of the low computational cost of single-source X-ray radiation numerical simulations, a hierarchical Gaussian process model is used to construct a superimposed single-source numerical simulation data fusion model. Second, by constructing composite correlation functions, the data fusion ability of the Gaussian process model is improved. The unknown parameters in the data fusion evaluation model are solved with the help of the Markov chain Monte Carlo method and a nonlinear optimization algorithm. Based on the obtained sampling values of the unknown parameters, an approximate solution method for the estimated value of the superimposed single-source X-ray radiation numerical simulations with unknown input conditions is given. Then a synergistic effect model is constructed based on the established linear regression surrogate model. The synergistic evaluation of multi-source X-ray radiation tests based on single-source X-ray radiation numerical simulation superimposed test data is studied. Finally, carbon-fiber-reinforced polymer experiments show the proposed method in better than existing Kriging methods, for prediction of multi-source X-ray radiation data.

Investigating genotype by environment interaction for beef cattle fertility traits in commercial herds in northern Australia with multi-trait analysis

BackgroundGenotype by environment interactions (GxE) affect a range of production traits in beef cattle. Quantifying the effect of GxE in commercial and multi-breed herds is challenging due to unknown genetic linkage between animals across environment levels. The primary aim of this study was to use multi-trait models to investigate GxE for three heifer fertility traits, corpus luteum (CL) presence, first pregnancy and second pregnancy, in a large tropical beef multibreed dataset (n = 21,037). Environmental levels were defined by two different descriptors, burden of heat load (temperature humidity index, THI) and nutritional availability (based on mean average daily gain for the herd, ADWG). To separate the effects of genetic linkage and real GxE across the environments, 1000 replicates of a simulated phenotype were generated by simulating QTL effects with no GxE onto real marker genotypes from the population, to determine the genetic correlations that could be expected across environments due to the existing genetic linkage only. Correlations from the real phenotypes were then compared to the empirical distribution under the null hypothesis from the simulated data. By adopting this approach, this study attempted to establish if low genetic correlations between environmental levels were due to GxE or insufficient genetic linkage between animals in each environmental level.ResultsThe correlations (being less than <0.8) for the real phenotypes were indicative of GxE for CL presence between ADWG environmental levels and in pregnancy traits. However, none of the correlations for CL presence or first pregnancy between ADWG levels were below the 5th percentile value for the empirical distribution under the null hypothesis from the simulated data. Only one statistically significant (P < 0.05) indication of GxE for first pregnancy was found between THI environmental levels, where rg = 0.28 and 5th percentile value = 0.29, and this result was marginal.ConclusionsOnly one case of statistically significant GxE for fertility traits was detected for first pregnancy between THI environmental levels 2 and 3. Other initial indications of GxE that were observed from the real phenotypes did not prove significant when compared to an empirical null distribution from simulated phenotypes. The lack of compelling evidence of GxE indicates that direct selection for fertility traits can be made accurately, using a single evaluation, regardless of environment.

HepNet: Deep Neural Network for Classification of Early-Stage Hepatic Steatosis Using Microwave Signals.

Hepatic steatosis, a key factor in chronic liver diseases, is difficult to diagnose early. This study introduces a classifier for hepatic steatosis using microwave technology, validated through clinical trials. Our method uses microwave signals and deep learning to improve detection to reliable results. It includes a pipeline with simulation data, a new deep-learning model called HepNet, and transfer learning. The simulation data, created with 3D electromagnetic tools, is used for training and evaluating the model. HepNet uses skip connections in convolutional layers and two fully connected layers for better feature extraction and generalization. Calibration and uncertainty assessments ensure the model's robustness. Our simulation achieved an F1-score of 0.91 and a confidence level of 0.97 for classifications with entropy ≤0.1, outperforming traditional models like LeNet (0.81) and ResNet (0.87). We also use transfer learning to adapt HepNet to clinical data with limited patient samples. Using 1H-MRS as the standard for two microwave liver scanners, HepNet achieved high F1-scores of 0.95 and 0.88 for 94 and 158 patient samples, respectively, showing its clinical potential.

Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery

Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala’s heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community.

Monte Carlo source verification of Elekta Synergy for pMLC collimated electron beams

IntroductionIn previous works, an electron beam source was developed that models its energy spectrum with a Lévy distribution. This source is used in BEAMnrc Monte Carlo simulation. It was benchmarked against water tank measurements for electrons delivered through an applicator collimation system, and it was found to agree with 2%/2 mm. This study uses this electron source further by investigating its ability to simulate electrons collimated by the photon multi-leaf collimators for MERT applications. Simulated and Gafchromic EBT3 depth dose and profile data were compared. MethodMeasured data were obtained from film exposures in a mini phantom with water-equivalent slabs at SSDs of 60 and 70 cm, respectively. Output factors and R90 values were calculated from film strip data taken in the depth direction. Corresponding dose data were simulated in BEAMnrc to obtain phase-space files for each beam and used in DOSXYZnrc to obtain dose data in a 3D water tank with 2 mm side length subvoxels. ResultsSimulation data corresponded with film data within 3 %/2 mm in most cases. Uncertainties in film measurements prevent a more accurate agreement. The output factors showed good agreement and could be fitted satisfactorily with an analytic function. A clinical example of a scalp treatment showed good agreement between simulated and film data. ConclusionIt is concluded that the electron source with the Lévy-based energy distribution can model electron dose for photon multi-leaf collimated electron beams within 3 %/2 mm.

A Study on the Impact of Overtaking Lane-Changing Behaviour in Expressway Interchange Weaving Areas

This study investigates the overtaking lane-changing (OLC) behaviour in expressway interchange weaving areas, aiming to analyse these behaviours’ causes and potential impacts. Field data are utilised to analyse the statistical characteristics of lane-changing points and spatio-temporal utilisation in weaving areas. A modified NS model, which considers the distribution pattern of vehicle speeds, and a rigid lane-changing rule based on Gaussian distribution are proposed. Additionally, a cellular automaton simulation model is constructed to quantify the influence of OLC behaviour on traffic efficiency and spatio-temporal utilisation based on simulated data. The findings indicate that the imbalanced distribution of lane-changing points and spatio-temporal utilisation in weaving segments, caused by rigid lane-changing behaviour, is an objective factor that triggers OLC behaviour. When the traffic volume in weaving areas ranges from 500 to 1,100 pcu/5 min and the proportion of OLC behaviour is between 0.35 and 0.7, the behaviour will significantly enhance the average vehicle speeds of the outermost lane of the main road and normal rigid lane-changing (NRLC) vehicles, with increases of up to 48% and 51%, respectively. Moreover, OLC behaviour also improves the balance of spatio-temporal utilisation in weaving areas and reduces the average spatio-temporal utilisation. This study clarifies the positive impact of OLC behaviour on expressway interchange weaving areas and provides new research ideas for enhancing the efficiency of these areas.

Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows

Abstract Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, Taiji, and Tianqin. &amp;#xD;The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals.&amp;#xD;However, such analyses usually entail significant computational costs.&amp;#xD;To address these challenges, inspired by the latest progress in generative models, we explore the application of continuous normalizing flows (CNFs) on the parameter estimation of MBHBs. &amp;#xD;Specifically, we employ linear interpolation and trig interpolation methods to construct transport paths for training CNFs. &amp;#xD;Additionally, we creatively introduce a parameter transformation method based on the symmetry in the detector's response function. This transformation is integrated within CNFs, allowing us to train the model using a simplified dataset, and then perform parameter estimation on more general data, hence also acting as a crucial factor in improving the training speed.&amp;#xD;In conclusion, for the first time, within a comprehensive and reasonable parameter range, we have achieved a complete and unbiased 11-dimensional rapid inference for MBHBs in the presence of astrophysical confusion noise using CNFs. In the experiments based on simulated data, our model produces posterior distributions comparable to those obtained by nested sampling.