Simulated Curves Research Articles

Improving Power Spectrum Estimation Using Multitapering: Efficient Asteroseismic Analyses for Understanding Stars, the Milky Way, and Beyond

Asteroseismic time series data have imprints of stellar oscillation modes, whose detection and characterization through time series analysis allows us to probe stellar interior physics. Such analyses usually occur in the Fourier domain by computing the Lomb–Scargle (LS) periodogram, an estimator of the power spectrum underlying unevenly sampled time series data. However, the LS periodogram suffers from the statistical problems of (1) inconsistency (or noise) and (2) bias due to high spectral leakage. Here, we develop a multitaper power spectrum estimator using the nonuniform fast Fourier transform (mtNUFFT) to tackle the inconsistency and bias problems of the LS periodogram. Using a simulated light curve, we show that the mtNUFFT power spectrum estimate of solar-like oscillations has lower variance and bias than the LS estimate. We also apply our method to the Kepler-91 red giant, and combine it with PBjam peakbagging to obtain mode parameters and a derived age estimate of 3.97 ± 0.52 Gyr. PBjam allows the improvement of age precision relative to the 4.27 ± 0.75 Gyr APOKASC-2 (uncorrected) estimate, whereas partnering mtNUFFT with PBjam speeds up peakbagging thrice as much as LS. This increase in efficiency has promising implications for Galactic archaeology, in addition to stellar structure and evolution studies. Our new method generally applies to time-domain astronomy and is implemented in the public Python package tapify, available at https://github.com/aaryapatil/tapify.

Acausal Fuel Cell Simulation Model for System Integration Analysis in Early Design Phases

Hydrogen technologies have the potential to reduce aviation’s CO2 emissions but come with many challenges. This paper introduces a scalable hydrogen fuel cell model tailored for system integration analysis in early aircraft design phases. The model focuses on Proton Exchange Membrane Fuel Cells (PEMFCs) and is based on thermodynamic equations and empirical data to simulate performance under different ambient and operating conditions; it also includes a simplified model of the Balance of Plant (BOP) systems and is implemented in OpenModelica. The model performance is validated through a comparison of the simulated polarization curves with real datasheet data. A case study highlights the peculiarities of this model by studying the sizing of the fuel cell stacks for a modified ATR 72 aircraft. The developed model effectively supports the early design exploration of the aircraft with a greater level of detail for system integration studies, essential to better explore the potential of aircraft featuring hydrogen-based power systems.

Nudging consumers’ choices for niche milk: A real purchase experiment

Changing behaviours causing poor health is an ongoing global health challenge that requires developing adequate food policies and strengthening their effectiveness. Policies based on nudging have become increasingly popular internationally, but the literature provides mixed evidence of their effectiveness, with an intense academic debate. This study contributes to the debate by investigating the effect of different nudging techniques (availability enhancement, visibility enhancement and healthy eating calls) via a framed field experiment involving purchases of milk types. Participants were split into different nudging treatment groups and were endowed with a cash amount to purchase any desired quantity of the different products. We modelled observed choices via the multiple discrete–continuous nested extreme value model, which has yet to receive attention for the analysis of nudging effects on food choices. Results show that only the healthy eating call nudge effectively drove participants towards healthier milk choices, while the effect of other nudges is statistically insignificant. We provide simulated demand curves conditional on nudging treatments, which measure the effect of the latter on consumers’ consumption levels of the different milk types available.

Citric acid functionalized chitosan hydrogel beads for enhanced Cr(VI) removal: Experimental assessment and COMSOL modelling of adsorption in a fixed-bed system

Chitosan hydrogel beads (CHB) and cross-linked CHB functionalized with tricarboxylic citric acid (CA-GLA-CHB) were synthesized and used for the removal of Cr(VI) from aqueous solutions in batch and dynamic adsorption systems. Applicability of COMSOL Multiphysics software as a tool for predictive modelling of column dynamics using a minimum set of experimental parameters was tested and compared with experimental results. Batch experiments demonstrated that CA-GLA-CHB showed almost 20% higher adsorption capacity and wider operational pH range compared to non-functionalized CHB. In a dynamic system, CA-GLA-CHB was used as column packing material and the column dynamics was investigated at different conditions. The breakthrough and exhaustion time of the column increased with an increase in bed height and a decrease in feed flow rate and initial Cr(VI) concentration. Computational modeling by COMSOL software with implemented Advection-Dispersion-Reaction (ADR) equation was used to simulate breakthrough curves for the applied dynamic system in order to validate the possibility of its application for process design in the scaled-up fixed-bed column systems for wastewater treatment. The simulated breakthrough curves of Cr(VI) adsorption matched well with the experimental, thus proving that COMSOL Multiphysics software can be used for scaling-up fixed-bed column systems packed with low-cost and effective CA-GLA-CHB bioadsorbent.

Design of virtual interactive educational games based on human perception

With the development of human body perception technology and virtual interactive experience, this technology has also been applied more in the field of driver education. However, the technology is not able to achieve 100 % real simulation in the process of simulating driving movement. In order to solve this problem, the study proposes to improve it by using the body sensing control wash-out filtering algorithm, and at the same time, further optimize it by genetic algorithm, and then carry out a study on the virtual interactive driving education game based on the improved technology. The proposed improved algorithm’s performance comparison results validated that it achieved the highest accuracy rate of 96 %, a final accuracy rate of 92 %, a final loss value of 1.99 %, a running time of 6.5 s, and a mean squared error of 3.8 %, all of which surpass the compared algorithm. After conducting experiments on the actual effectiveness of the designed driving education game, the maximum deviation between the simulated X-direction displacement curve of the game and the actual value was found to be 0.06 m, which outperformed the comparative experimental group. Additionally, both the angular displacement curve and perceived acceleration were the most accurate. In summary, the proposed virtual interactive driving education game based on human perception can more accurately reproduce the real scene during the driving process, providing users with better learning conditions.

Exploring uncatalogued genetic variation in antimicrobial resistance gene families in Escherichia coli: an observational analysis

Antimicrobial resistance (AMR) in Escherichia coli is a global problem associated with substantial morbidity and mortality. AMR-associated genes are typically annotated based on similarity to variants in a curated reference database, with the implicit assumption that uncatalogued genetic variation within these is phenotypically unimportant. In this study, we evaluated the performance of the AMRFinder tool and, subsequently, the potential for discovering new AMR-associated gene families and characterising variation within existing ones to improve genotype-to-susceptibility phenotype predictions in Ecoli. In this cross-sectional study of international genome sequence data, we assembled a global dataset of 9001 Ecoli sequences from five publicly available data collections predominantly deriving from human bloodstream infections from: Norway, Oxfordshire (UK), Thailand, the UK, and Sweden. 8555of these sequences had linked antibiotic susceptibility data. Raw reads were assembled using Shovill and AMR genes (relevant to amoxicillin-clavulanic acid, ampicillin, ceftriaxone, ciprofloxacin, gentamicin, piperacillin-tazobactam, and trimethoprim) extracted using the National Center for Biotechnology Information AMRFinder tool (using both default and strict [100%] coverage and identity filters). We assessed the predictive value of the presence of these genes for predicting resistance or susceptibility against US Food and Drug Administration thresholds for major and very major errors. Mash was used to calculate the similarity between extracted genes using Jaccard distances. We empirically reclustered extracted gene sequences into AMR-associated gene families (≥70% match) and antibiotic-resistance genes (ARGs; 100% match) and categorised these according to their frequency in the dataset. Accumulation curves were simulated and correlations between gene frequency in the Oxfordshire and other datasets calculated using the Spearman coefficient. Firth regression was used to model the association between the presence of blaTEM-1 variants and amoxicillin-clavulanic acid or piperacillin-tazobactam resistance, adjusted for the presence of other relevant ARGs. The performance of the AMRFinder database for genotype-to-phenotype predictions using strict 100% identity and coverage thresholds did not meet US Food and Drug Administration thresholds for any of the seven antibiotics evaluated. Relaxing filters to default settings improved sensitivity with a specificity cost. For all antibiotics, most explainable resistance was associated with the presence of a small number of genes. There was a proportion of resistance that could not be explained by known ARGs; this ranged from 75·1% for amoxicillin-clavulanic acid to 3·4% for ciprofloxacin. Only 18 199 (51·5%) of the 35 343ARGs detected had a 100% identity and coverage match in the AMRFinder database. After empirically reclassifying genes at 100% nucleotide sequence identity, we identified 1042unique ARGs, of which 126 (12·1%) were present ten times or more, 313 (30·0%) were present between two and nine times, and 603 (57·9%) were present only once. Simulated accumulation curves revealed that discovery of new (100% match) ARGs present more than once in the dataset plateaued relatively quickly, whereas new singleton ARGs were discovered even after many thousands of isolates had been included. We identified a strong correlation (Spearman coefficient 0·76 [95% CI 0·73-0·80], p<0·0001) between the number of times an ARG was observed in Oxfordshire and the number of times it was seen internationally, with ARGs that were observed six times in Oxfordshire always being found elsewhere. Finally, using the example of blaTEM-1, we showed that uncatalogued variation, including synonymous variation, is associated with potentially important phenotypic differences; for example, two common, uncatalogued blaTEM-1 alleles with only synonymous mutations compared with the known reference were associated with reduced resistance to amoxicillin-clavulanic acid (adjusted odds ratio 0·58 [95% CI 0·35-0·95], p=0·031) and piperacillin-tazobactam (0·50 [95% CI 0·29-0·82], p=0·005). We highlight substantial uncatalogued genetic variation with respect to known ARGs, although a relatively small proportion of these alleles are repeatedly observed in a large international dataset suggesting strong selection pressures. The current approach of using fuzzy matching for ARG detection, ignoring the unknown effects of uncatalogued variation, is unlikely to be acceptable for future clinical deployment. The association of synonymous mutations with potentially important phenotypic differences suggests that relying solely on amino acid-based gene detection to predict resistance is unlikely to be sufficient. Finally, the inability to explain all resistance using existing knowledge highlights the importance of new target gene discovery. National Institute for Health and Care Research, Wellcome, and UK Medical Research Council.

Explanation for the Absence of Secondary Peaks in Black Hole Light Curve Autocorrelations.

The observed radiation from hot gas accreting onto a black hole depends on both the details of the flow and the spacetime geometry. The lensing behavior of a black hole produces a distinctive pattern of autocorrelations within its photon ring that encodes its mass, spin, and inclination. In particular, the time autocorrelation of the light curve is expected to display a series of peaks produced by light echoes of the source, with each peak delayed by the characteristic time lapse τ between light echoes. However, such peaks are absent from the light curves of observed black holes. Here, we develop an analytical model for such light curves that demonstrates how, even though light echoes always exist in the signal, they do not produce autocorrelation peaks if the characteristic correlation timescale λ_{0} of the source is greater than τ. We validate our model against simulated light curves of a stochastic accretion model ray traced with a general-relativistic code, and then fit the model to an observed light curve for Sgr A^{*}. We infer that λ_{0}>τ, providing an explanation for the absence of light echoes in the time autocorrelations of Sgr A^{*} light curves. Our results highlight the importance for black hole parameter inference of spatially resolving the photon ring via future space-based interferometry.

Detecting Exoplanet Transits with the Next Generation of X-Ray Telescopes

Detecting exoplanet transits at X-ray wavelengths would provide a window into the effects of high-energy irradiation on the upper atmospheres of planets. However, stars are relatively dim in the X-ray, making exoplanet transit detections difficult with current X-ray telescopes. To date, only one exoplanet (HD 189733 b) has an X-ray transit detection. In this study, we investigate the capability of future X-ray observatories to detect more exoplanet transits, focusing on both the NewAthena Wide Field Imager instrument and the proposed Advanced X-ray Imaging Satellite (AXIS), which provide more light-collecting power than current instruments. We examined all the transiting exoplanet systems in the NASA Exoplanet Archive and gathered X-ray flux measurements or estimates for each host star. We then predicted the stellar count rates for both AXIS and NewAthena and simulated light curves, using null-hypothesis testing to identify the top 15 transiting planets ranked by potential detection significance. We also evaluate transit detection probabilities when the apparent X-ray radius is enlarged due to atmospheric escape, finding that ≥five of these planetary systems may be detectable on the >4σ level in this scenario. Finally, we note that the assumed host star coronal temperature, which affects the shape of an X-ray transit, can also significantly affect our ability to detect the planet.

TOPAS-Tissue: A Framework for the Simulation of the Biological Response to Ionizing Radiation at the Multi-Cellular Level.

This work aims to develop and validate a framework for the multiscale simulation of the biological response to ionizing radiation in a population of cells forming a tissue. We present TOPAS-Tissue, a framework to allow coupling two Monte Carlo (MC) codes: TOPAS with the TOPAS-nBio extension, capable of handling the track-structure simulation and subsequent chemistry, and CompuCell3D, an agent-based model simulator for biological and environmental behavior of a population of cells. We verified the implementation by simulating the experimental conditions for a clonogenic survival assay of a 2-D PC-3 cell culture model (10 cells in 10,000 µm2) irradiated by MV X-rays at several absorbed dose values from 0-8 Gy. The simulation considered cell growth and division, irradiation, DSB induction, DNA repair, and cellular response. The survival was obtained by counting the number of colonies, defined as a surviving primary (or seeded) cell with progeny, at 2.7 simulated days after irradiation. DNA repair was simulated with an MC implementation of the two-lesion kinetic model and the cell response with a p53 protein-pulse model. The simulated survival curve followed the theoretical linear-quadratic response with dose. The fitted coefficients α = 0.280 ± 0.025/Gy and β = 0.042 ± 0.006/Gy2 agreed with published experimental data within two standard deviations. TOPAS-Tissue extends previous works by simulating in an end-to-end way the effects of radiation in a cell population, from irradiation and DNA damage leading to the cell fate. In conclusion, TOPAS-Tissue offers an extensible all-in-one simulation framework that successfully couples Compucell3D and TOPAS for multiscale simulation of the biological response to radiation.

Validation evidence with experimental and clinical data to establish credibility of TAVI patient-specific simulations

PurposeThe objective of this study is to validate a novel workflow for implementing patient-specific finite element (FE) simulations to virtually replicate the Transcatheter Aortic Valve Implantation (TAVI) procedure. MethodsSeven patients undergoing TAVI were enrolled. Patient-specific anatomical models were reconstructed from pre-operative computed tomography (CT) scans and subsequentially discretized, considering the native aortic leaflets and calcifications. Moreover, high-fidelity models of CoreValve Evolut R and Acurate Neo2 valves were built. To determine the most suitable material properties for the two stents, an accurate calibration process was undertaken. This involved conducting crimping simulations and fine-tuning Nitinol parameters to fit experimental force-diameter curves. Subsequently, FE simulations of TAVI procedures were conducted. To validate the reliability of the implemented implantation simulations, qualitative and quantitative comparisons with post-operative clinical data, such as angiographies and CT scans, were performed. ResultsFor both devices, the simulation curves closely matched the experimental data, indicating successful validation of the valves mechanical behaviour. An accurate qualitative superimposition with both angiographies and CTs was evident, proving the reliability of the simulated implantation. Furthermore, a mean percentage difference of 1,79 ± 0,93 % and 3,67 ± 2,73 % between the simulated and segmented final configurations of the stents was calculated in terms of orifice area and eccentricity, respectively. ConclusionThis study shows the successful validation of TAVI simulations in patient-specific anatomies, offering a valuable tool to optimize patients care through personalized pre-operative planning. A systematic approach for the validation is presented, laying the groundwork for enhanced predictive modeling in clinical practice.

Light curve attitude estimation using particle swarm optimizers

Knowledge of the attitude of a space object is useful in space situational awareness for independently evaluating a satellite’s health and characterizing unknown objects. In cases where only non-resolved optical observations are available, the object’s attitude may be estimated using a time sequence of brightness observations, also known as the light curve. This attitude estimation problem is plagued with multiple difficulties: even in the absence of noise and when all other relevant factors are perfectly known, the non-uniqueness of the problem means that multiple attitude time histories may fit the light curve equally well. In addition, there is often insufficient information about the object to generate an initial state guess for an estimator. This paper presents a method that estimates an observed object’s attitude and angular velocity while accounting for ambiguities and without needing any initial state guess. The only inputs are the light curve, the object’s albedo shape, and the object’s position relative to the Sun and the observer. The ability of the estimator to resolve attitude time histories is demonstrated using simulated light curves by comparing state estimates against known true states.

Hybridization effect of Kevlar and glass fiber on carbon/epoxy composites to enhance the damage strength under low velocity impact. Part II: Numerical simulations

The research outlined in this paper extends the authors previous work to explore the impact resistance enhancement of CFRP composites through numerical simulations, focusing on the incorporation of a hybrid combination of Kevlar and glass fibers. Experimental drop weight low velocity impact and advanced NDT tests on CFRP and hybrid laminates were carried out in previous work. The current work focuses on the analysis of the dynamic behavior of hybrid composite subjected to LVI by numerical simulation methods. The numerical simulations have been validated with experiments and the damage area of numerical and experimental tests were compared. CFRP and hybrid laminates were modeled using Abaqus/Explicit FEA software to investigate the damage modes and mechanisms. The history curves of simulation results such as load/displacement-time etc. compared with experimental. Results show that incorporating 25% Kevlar fibers on the outer layer of the CFRP, denoted as [01K/06C/01K] resulted in a reduction of laminate deflection by 63.64%. Additionally, the stress distribution expanded over a larger area. A good concurrence between the simulation and experimental findings has been established, indicating that the modeling approach is suitable for conducting further parametric investigations.

Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling.

Small-angle X-ray scattering (SAXS) is a characterization technique that allows for the study of colloidal interactions by fitting the structure factor of the SAXS profile with a selected model and closure relation. However, the applicability of this approach is constrained by the limited number of existing models that can be fitted analytically, as well as the narrow operating range for which the models are valid. In this work, we demonstrate a proof of concept for using an artificial neural network (ANN) trained on SAXS curves obtained from Monte Carlo (MC) simulations to predict values of the effective macroion valency (Z eff) and the Debye length (κ-1) for a given SAXS profile. This ANN, which was trained on 200,000 simulated SAXS curves, was able to predict values of Z eff and κ-1 for a test set containing 25,000 simulated SAXS curves, where most predicted values had errors smaller than 20%. Subsequently, an ANN was used as a surrogate model in a Markov chain Monte Carlo sampling algorithm to obtain maximum a posteriori estimates of Z eff and κ-1, as well as the associated confidence intervals and correlations between Z eff and κ-1 for an experimentally obtained SAXS profile.

Detection of transiting exoplanets and phase-folding their host star’s light curves from K2 data with 1D-CNN

Abstract In this research, we present two 1D Convolutional Neural Network (CNN) models that were trained, validated and tested using simulated light curves designed to mimic those expected from the Kepler Space Telescope during its extended mission (K2). We also tested them on real K2 data. Our light curve simulator considers different stellar variability phenomena, such as rotations, pulsations and flares, which along with the stellar noise expected for K2 data, hinders the transit signal detection, as in real data. The first model effectively identifies transit-like signals in light curves, classifying them based on the presence or absence of such signals. Furthermore, the second model not only phase-folds the light curves but also eliminates stellar noise, a crucial step when fitting transits to the Mandel and Agol theoretical transit shape. The obtained results include an accuracy of $\sim 99\%$ when classifying the light curves based on the presence or absence of transit-like signals, and $MAPE\sim 6\%$ regarding to the transits’ depth and duration when phase folding the light curves, showing the great capabilities of 1D-CNN for automatizing the transit search in light curves, both on simulated and real data.

Evaluation of a Volume-Averaged Species Transport Model with Micro-Macro Coupling for Breakthrough Curve Prediction.

In porous water filters, the transport and entrapment of contaminants can be modeled as a classic mass transport problem, which employs the conventional convection-dispersion equation to predict the transport of species existing in trace amounts. Using the volume-averaging method (VAM), the upscaling has revealed two possible macroscopic equations for predicting contaminant concentrations in the filters. The first equation is the classical convection-dispersion equation, which incorporates a total dispersion tensor. The second equation involves an additional transport coefficient, identified as the adsorption-induced vector. In this study, the aforementioned equations were solved in 1D for column tests using 3D unit cells. The simulated breakthrough curves (BTCs), using the proposed micro-macro-coupling-based VAM model, are compared with the direct numerical simulation (DNS) results based on BCC-type unit cells arranged one-after-another in a daisy chain manner, as well as with three previously reported experimental works, in which the functionalized zeolite and zero-valent iron fillings were used as an adsorbent to remove phosphorous and arsenic from water, respectively. The disagreement of VAM BTC predictions with DNS and experimental results reveals the need for an alternative closure formulation in VAM. Detailed investigations reveal time constraint violations in all the three cases, suggesting this as the main cause of VAM's failure.

Pyrolysis of End-Of-Life Tires: Moving from a Pilot Prototype to a Semi-Industrial Plant Using Auger Technology.

This work, carried out within the framework of the BlackCycle project, demonstrates the robustness of an auger reactor for the pyrolysis of end-of-life tires (ELTs) to be considered within the seventh level of technology readiness (TRL-7). For this purpose, the resulting pyrolysis products are compared with those obtained from a pilot scale facility ranging within the fifth technology readiness level (TRL-5). Using the same type of ELTs, tire trucks (TTs), operating conditions used at the TRL-5 plant are attempted to mimic those expected at a semi-industrial plant: tailored temperature profile (450, 550, and 775 °C) and residence time for vapors (30 s) and solids (15 min). The feed mass rate is 4 and 400 kg/h for the pilot and semi-industrial plants, respectively. The yields of tire pyrolysis oil (TPO), tire pyrolysis gas (TPG), and raw recovered carbon black (RRCB) from both plants, as well as their key properties and characteristics, are in good agreement with each other. The TPO produced by both plants contains comparable concentrations of value-added chemicals such as benzene, toluene, xylene, ethylbenzene, and limonene. There is also a very similar pattern between the simulated distillation curves. The TPG obtained from both plants is also very rich in H2 and CH4 and has a lower calorific value of 52-54 MJ/Nm3 (N2 free basis). Although the RRCBs produced by the two plants are more demanding and require more labor, they do have a number of comparable characteristics. All this information demonstrates not only the reliability of the experimental campaigns to scale up the pyrolysis process but also the robustness of the semi-industrial scale plant based on the auger technology to be classified at TRL-7.

Mechanism of thermal gravimetric difference between simulated and experimental of coal group components

Understanding the molecular mechanism underlying coal's thermal gravimetric behavior is crucial for advancing efficient and clean coal utilization technology, a task that remains challenging to address experimentally. ReaxFF molecular dynamic simulation has been widely applied across various systems and materials, particularly excelling in simulating combustion and pyrolysis processes with complex coal models. It is necessary to establish more connections between simulation and experiment, and to analyze and discuss the differences between them. This will lay the foundation for the reliability of simulation results in revealing experimental phenomena. In this study, coal was divided into four components using a full component classification method: the dense medium component, loose medium component, heavy component, and light component. The connections of the simulated and experimental thermal gravimetric behaviors of the coal group component skeletons were conducted, and founding the inconsistencies in the later stages of the curves. To understand these discrepancies, the test environments were analyzed, identifying the behavior of tar free radicals generated during pyrolysis as a key factor influencing the thermo-gravimetric curve. To mitigate the influence of tar free radicals and reduce the observed differences, a correction coefficient was introduced into the calculation formula for the yield of non-volatile products, achieving an improved alignment between the simulated and experimental thermo-gravimetric curves. This study significantly advances the application of ReaxFF molecular dynamics simulation in elucidating the mechanisms of coal pyrolysis.

Study of Effect of Radiation Hazards in Human Life

In this paper, we have investigated the effect of radiations on human life by expressing the exponential decay law curve, dose curve, shielding effectiveness curve and radiation exposure simulation curve. Also this research investigates the impact of radiation hazards on human health, focusing on both quantitative data and qualitative case studies. Utilizing a comprehensive analysis of radiation exposure levels and their associated health outcomes, we plotted various graphs to illustrate the correlation between radiation dose and adverse health effects. Our research combines empirical data with in-depth case studies to assess how different levels and types of radiation influence human life. The findings reveal a significant relationship between radiation exposure and increased risks of several health conditions, including cancer and radiation sickness. By analyzing the plotted data and case studies, this paper provides a detailed understanding of the short-term and long-term effects of radiation hazards. The results underscore the importance of effective radiation safety measures and informed public health policies to mitigate these risks.

INVESTIGATION INTO THE THERMAL DEFORMATION BEHAVIOR AND THE ESTABLISHMENT OF A CONSTITUTIVE RELATIONSHIP FOR Mn-Cr-Ni-Mo STEEL

Isothermal compression tests were conducted on Mn-Cr-Ni-Mo steel at temperatures ranging from 1173 K to 1473 K and strain rates from 0.01 s–1 to 10 s–1 using a Gleeble-3800 thermal simulation tester. Four constitutive models for Mn-Cr-Ni-Mo steel, namely the Arrhenius model, Fields-Backofen model (F-B), original Johnson-Cook model (J-C), and the improved Johnson-Cook model (mJ-C) were established. A correlation coefficient (R) and the average absolute relative error (AARE) were employed to evaluate the predictive capability of these four models. Among them, the Arrhenius model exhibited superior accuracy in predicting the behavior of Mn-Cr-Ni-Mo steel. It and the isothermal thermal compression finite-element model were imported into Deform-3D software for a numerical simulation, aiming to analyze the distribution law of the equivalent stress field. A comparison was made between the time-stress data obtained from numerical simulation under different conditions and that from the isothermal compression tests. The results demonstrate good agreement between the time-stress curves of the numerical simulation and the experimental measurements, indicating that the established Arrhenius model can effectively simulate the thermal deformation of Mn-Cr-Ni-Mo steel. These research findings provide valuable fundamental data for simulating the plastic-deformation process of Mn-Cr-Ni-Mo steel.

Characterization of cardiac resynchronization therapy response through machine learning and personalized models

Introduction: The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a novel hybrid approach, integrating machine-learning and personalized models, to identify explainable phenogroups of HF patients and predict their CRT response. Methods:The paper proposes the creation of a complete personalized model population based on preoperative CRT patient strain curves. Based on the parameters and features extracted from these personalized models, phenotypes of patients are identified thanks to a clustering algorithm and a random forest classification is provided. Results:A close match was observed between the 162 experimental and simulated myocardial strain curves, with a mean RMSE of 4.48% (±1.08) for the 162 patients. Five phenogroups of personalized models were identified from the clustering, with response rates ranging from 52% to 94%. The classification results show a mean area under the curves (AUC) of 0.86 ± 0.06 and provided a feature importance analysis with 22 features selected. Results show both regional myocardial contractility (from 22.5% to 33.0%), tissue viability and electrical activation delays importance on CRT response for each HF patient (from 55.8 ms to 88.4 ms). Discussion:The patient-specific model parameters’ analysis provides an explainable interpretation of HF patient phenogroups in relation to physiological mechanisms that seem predictive of the CRT response. These novel combined approaches appear as promising tools to improve understanding of LV mechanical dyssynchrony for HF patient characterization and CRT selection.