Global Parameters Research Articles

Modeling of the Complexity Propagation of Crack in a Ductile Material Under Complex Solicitation in Crack Tip: Introduction of a Matrix of Stress Intensity Factors of Bifurcation

In fracture and damage mechanics, modeling of crack propagation has always been a source of difficulties. Numerous works have been carried out on this case at the crack tip, introducing new parameters: the Stress Intensity Factor (K); which is the local Irwin parameter, and also the Rice integral (J), the Griffith's energizing method, in which J and G are the global parameters around the crack tip. The problem of the crack remains very complex and difficult problem to be solved. Several methods are used to investigate the crack problem, namely the method of gradient, the numerical methods by finite elements, as well as the thermodynamic approach and the classical methods of Irwin, Griffith or Rice, according to the Intensity Stress Factor. This study adds to the work already carried out. Using the analytical analysis method of equations, we manage to show that the Stress Intensity Factor has a matrix of rank 3 at the crack tip, which is a better form since it includes complex combination cases of crack mode and bifurcation. Furthermore, when the material is subjected to complex stress, after analysis we emerge from a new singularity in (r) which is different from the classical mode. Finally, we are shown the new form of singularity, which is frequency dependent. This work can explain many situations, for example, the case of certain structural disasters showing the presence of cracks for complex or uncontrollable stress.

Influence of benzothiophene acceptor moieties on the non-linear optical properties of pyreno-based chromophores: first-principles DFT framework.

Herein, a series of heterocyclic organic compounds (PYFD1-PYFD7) are designed with different acceptor moieties at the terminal position of a reference compound (PYFR) for nonlinear optical (NLO) active materials. The optoelectronic characteristics of the designed chromophores were investigated using density functional theory (DFT) calculations with the M06/6-311G(d,p) functional. Frontier molecular orbital (FMO) analysis revealed a significant decrease in the energy of the band gaps (2.340-2.602 eV) for the derivatives as compared to the PYFR reference compound (3.12 eV). An efficient transfer of charge from the highest occupied molecular orbital (HOMO) to the lowest unoccupied molecular orbital (LUMO) was seen, which was further corroborated by the density of states (DOS) and transition density matrix (TDM) heat maps. The results of the global reactivity parameters (GRPs) indicated that all derivatives exhibited greater softness (σ = 0.384-0.427 eV) and lower hardness (η = 0.394-1.302 eV) as compared to PYFR, indicating a higher level of polarizability in the derivatives. Moreover, all of the derivatives showed significant findings in terms of nonlinear optical (NLO) results as compared to the reference chromophore. PYFD2 showed the most effective NLO response (α = 1.861 × 10-22 and βtot = 2.376 × 10-28 esu), including a lowered band gap of 2.340 eV, the maximum softness value of 0.4273 eV, and the lowest hardness value of 1.170 eV as compared to other chromophores. The incorporation of different acceptors and thiophene as a π-spacer in this structural alteration significantly contributed to achieving remarkable NLO responses. Therefore, our findings may motivate experimentalists to synthesize these designed NLO active materials for the current advanced technological applications.

An intelligent airflow perception model for metal mines based on CNN-LSTM architecture

In view of the harsh underground working environment and difficulties of airflow monitoring sensors placement, implementing an intelligent ventilation strategy is crucial for ensuring ventilation safety during mining process. The key issue lies in obtaining global real-time airflow parameters for ventilation safety and intelligent control. Thus, we propose an intelligent perception approach based on artificial intelligence (AI) method for acquiring airflow parameters, which operates by leveraging partial airflow data from specific monitoring points to predict airflow at undisclosed locations. Initially, this approach is facilitated by the training of an AI database through numerical simulations of ventilation networks. Subsequently, an intelligent airflow perception model is constructed, incorporating convolution neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM architectures. Through iterative updates and enhancements, these models demonstrate average deviations between predicted and actual airflow parameters of less than 5% in both simulated scenarios and empirical applications. Furthermore, in the case study, the CNN-LSTM architecture model exhibits superior performance for intelligent airflow perception. This architecture combing with airflow monitoring system, and utilizing partial real-time data inputs to obtain perception point outputs, can effectively enhance employee productivity, reduce energy consumption, and prevent resource wastage.

Exploring the spectral and nonlinear optical properties of phenyl isothiocyanate through density functional theory and molecular docking studies: A comprehensive analysis of a potent biologically active phytochemical

Quantum chemical calculations employing Density Functional Theory (DFT) with 6–31G (d,p) basis sets have yielded valuable insights into the molecular structure, Frontier Molecular Orbitals (FMOs), Mulliken Charges, and Molecular Electrostatic Potential Maps of the phenyl isothiocyanate (PITC) molecule. Spectral analysis of PITC has been conducted including Fourier transform infrared (FTIR), Fourier transform Raman (FT-RAMAN), Nuclear Magnetic Resonance (NMR), and ultraviolet–visible (UV–Vis) spectroscopy by theoretical method. Additionally, global descriptive parameters and thermal properties have also been determined. The Nonlinear Optical characteristic (NLO) has been assessed in the gas phase using the same level of theory. The investigation into toxicity revealed that the studied molecule exhibits drug-like characteristics and is non-toxic. Molecular docking studies have been employed to investigate hydrogen bond interactions, elucidating the minimal binding energy of the compound. The outcomes of the docking studies strongly suggest the significant biological activity of Phenyl Isothiocyanate, emphasizing its potential as a potent phytochemical.

Three-Stage Interpolation Method for Demosaicking Monochrome Polarization DoFP Images.

The emergence of polarization image sensors presents both opportunities and challenges for real-time full-polarization reconstruction in scene imaging. This paper presents an innovative three-stage interpolation method specifically tailored for monochrome polarization image demosaicking, emphasizing both precision and processing speed. The method introduces a novel linear interpolation model based on polarization channel difference priors in the initial two stages. To enhance results through bidirectional interpolation, a continuous adaptive edge detection method based on variance differences is employed for weighted averaging. In the third stage, a total intensity map, derived from the previous two stages, is integrated into a residual interpolation process, thereby further elevating estimation precision. The proposed method undergoes validation using publicly available advanced datasets, showcasing superior performance in both global parameter evaluations and local visual details when compared with existing state-of-the-art techniques.

Quantitative morphological analysis framework of infant cranial sutures and fontanelles based on CT images.

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2-4 month-old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin-plate-spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep-learning-based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2-4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture-related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model.

Detail-preserving noise suppression post-processing for low-light image enhancement

Low-light image enhancement is a challenging task for high-quality image display and visual applications. However, most of the existing methods are difficult to effectively improve brightness and suppress noise. To solve the problem of greatly amplifying noise in many low-light image enhancement methods, this paper presents a detail-preserving noise-suppressing post-processing method for low-light image enhancement. The main idea is that Gamma correction has good performance in noise suppression, by extending the single global parameter of Gamma correction to a Gamma correction map with the same size as the low-light image, we propose an improved domain transform recursive filter to optimize Gamma correction maps, aiming to smooth the Gamma correction map while preserving structural edges, thereby achieving the goal of suppressing noise levels in the enhanced image and preserving the details and contrast of the enhanced image. The proposed post-processing approach addresses the concern of enhancing noise when enhancing low-light images, and the results show that the proposed post-processing method has good detail preservation and noise suppression performance for different advanced low-light image enhancement methods.

3-Allyl-2-(allylthio)-5,5-diphenyl-3,5-dihydro-4H-imidazol-4-one: Synthesis, spectroscopic characterization, crystal structure, computational investigations, antibacterial activity and ADMET studies

In this investigation, we successfully synthesized a novel thiohydantoin derivative, 3-allyl-2-(allylthio)-5,5-diphenyl-3,5-dihydro-4H-imidazol-4-one (AM1). The compound underwent comprehensive characterization using various analytical techniques, including FT-IR, 1H NMR, 13C NMR, HRMS-ESI and X-ray crystal structure analysis. In the crystal, the 5-membered ring is planar with the allylthio group largely coplanar with it and the other allyl group rotated well out of the plane. Quantum computational methods were used to further information on the structure of the compound. The density functional theory was applied to optimize the structure. Using the optimized structure, vibration frequencies and global index parameters were computed, UV–Vis spectrum was simulated, and NBO analysis was carried out. Furthermore, we conducted an evaluation of its antibacterial activity against three different strains. The alkylation of thiohydantoin (AM0) resulted in a heightened antibacterial activity, leading to an increase in the inhibition zone from 13.25 ± 0.35 to 14.25±0.35 mm against Rhodococcus sp GK3. Similarly, an elevation from 12.75±0.35 to 14.15±0.35 mm was noted against Rhodococcus sp GK1. Additionally, ADMET studies were conducted to explore the potential of the title compound for use as an antibacterial drug. In silico ADMET study was also performed for predicting pharmacokinetic and toxicity profile of the title compound which expressed good drug-like behavior and a non-toxic nature.

Spiral wave dynamics in a neuronal network model

Spiral waves are spatial-temporal patterns that can emerge in different systems as heart tissues, chemical oscillators, ecological networks and the brain. These waves have been identified in the neocortex of turtles, rats, and humans, particularly during sleep-like states. Although their functions in cognitive activities remain until now poorly understood, these patterns are related to cortical activity modulation and contribute to cortical processing. In this work, ,we construct a neuronal network layer based on the spatial distribution of pyramidal neurons. Our main goal is to investigate how local connectivity and coupling strength are associated with the emergence of spiral waves. Therefore, we propose a trustworthy method capable of detecting different wave patterns, based on local and global phase order parameters. As a result, we find that the range of connection radius (R) plays a crucial role in the appearance of spiral waves. For R < 20 µm, only asynchronous activity is observed due to small number of connections. The coupling strength ( gsyn ) greatly influences the pattern transitions for higher R, where spikes and bursts firing patterns can be observed in spiral and non-spiral waves. Finally, we show that for some values of R and gsyn bistable states of wave patterns are obtained.

[formula omitted]: Self-sovereign identity based privacy-preserving federated learning

Traditional federated learning (FL) raises security and privacy concerns such as identity fraud, data poisoning attacks, membership inference attacks, and model inversion attacks. In the conventional FL, any entity can falsify its identity and initiate data poisoning attacks. Besides, adversaries (AD) holding the updated global model parameters can retrieve the plain text of the dataset by initiating membership inference attacks and model inversion attacks. To the best of our knowledge, this is the first work to propose a self-sovereign identity (SSI) and differential privacy (DP) based FL namely SSI−FL for addressing all the above issues. The first step in the SSI−FL framework involves establishing a secure connection based on blockchain-based SSI. This secure connection protects against unauthorized access attacks of any AD and ensures the transmitted data's authenticity, integrity, and availability. The second step applies DP to protect against model inversion attacks and membership inference attacks. The third step focuses on establishing FL with a novel hybrid deep learning to achieve better scores than conventional methods. The SSI−FL performance analysis is done based on security, formal, scalability, and score analysis. Moreover, the proposed method outperforms all the state-of-art techniques.

Determination of global geodetic parameters using satellite laser ranging to Galileo, GLONASS, and BeiDou satellites

The new Global Navigation Satellite System (GNSS) satellites, including GLONASS, Galileo, and BeiDou system, are equipped with Laser Retroreflector Arrays (LRA) to support Satellite Laser Ranging (SLR) tracking, which contributes to the estimation of global geodetic parameters. In this study, we estimate the global geodetic parameters using the SLR observations to GNSS satellites and also investigate the effects of different data processing strategies on the estimated Earth Rotation Parameters (ERP), geocenter motion, and terrestrial scale. The results indicate that setting range bias parameters for each satellite-station pair can effectively account for the satellite-specific biases induced by LRAs, leading to smaller Root Mean Square Errors (RMSE) of the post-fit SLR residuals. Furthermore, estimating the range biases for each satellite-station pair improves the accuracy of the estimated station coordinates and ERP. We also examine the impact of different arc lengths on the estimates of ERP, geocenter motion, and terrestrial scale. The results show that extending arc length can significantly reduce the formal error of ERP. The 7-day strategy produces the smallest RMSEs of 473 microarcseconds and 495 microarcseconds for the estimated X- and Y-component of pole coordinates, and 52 microseconds for length-of-day, respectively. However, the estimated geocenter motion is less affected by the arc length, even the shortest 1-day arc strategy can capture the seasonal variations of geocenter motion in Z component. For scale estimation, extending the arc length notably improves the accuracy of the estimated station coordinates and scale, but this advantage becomes less noticeable in longer arcs. The 7-day solution also obtains the closet scale results compared to ITRF2014, with the RMSE of 2.10 × 10–9.

Comparison of a quasi Newton method using Broyden’s update formula and an adjoint method for determining local magnetic material properties of electrical steel sheets

PurposePerforming accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data.Design/methodology/approachThe general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered.FindingsThe QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed.Originality/valueThe presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.

CFL：Data distribution edge clustering algorithm based on deep Federated learning

Federated learning (FL) allows end-devices to train local models on their respective local datasets and collaborate with the server to train a global predictive model, thus achieving the goal of machine learning while protecting the privacy and sensitive data of end-devices. However, simultaneous access to the server by a large number of end devices may result in increased transmission latency and some local models may have malicious behavior, converging in the opposite direction to the global model. As a result, additional communication costs can occur during the federation learning process. Existing research has mainly focused on reducing the number of communication rounds or cleaning dirty local data. In order to decrease the overall amount of local updates, we provide an edge-based model cleaning and device clustering strategy in this study. By computing the cosine similarity between local update parameters and global model parameters over a multi-dimensional space, the approach assesses if local updates are necessary while preventing pointless communications. In addition, end devices are grouped together based on where they are connected to the network and connect to the cloud via mobile edge nodes in clusters, therefore lowering the latency associated with high concurrent server access. Convolutional neural networks and Soft max regression are used, for instance, to perform MNIST handwritten digit recognition, and the effectiveness of the suggested approach to enhancing communication efficiency is confirmed. According to experimental findings, the edge-based model cleaning and device clustering technique decreases the quantity of local updates by 60% and speeds up the model's convergence by 10.3% when compared to the conventional FL approach.

Usability of myocardial work parameters in demonstrating myocardial involvement in INOCA patients.

Myocardial work (MW) is a novel echocardiographic modality, which has been shown to have diagnostic and prognostic values in patients with cardiovascular diseases, patients with obstructive coronary artery disease, in particular. However, only a handful of studies have examined the MW analysis in ischemia with nonobstructive coronary artery (INOCA) disease. This study, therefore, aimed to detect the early left ventricular involvement in INOCA patients diagnosed by an invasive coronary angiography performing the MW analysis. This study included a total of 119 patients with nonobstructive coronary artery disease diagnosed by invasive coronary angiography, who were checked for prior ischemia tests performing myocardial perfusion scintigraphy. Out of these 119 patients, 49 patients developed ischemia (i.e., ischemic group) diagnosed using cardiac single-photon emission computed tomography, whereas 70 patients did not (i.e., nonischemic group). The subjects were divided into three groups based on the global MW tertiles. The groups were compared in terms of the conventional, longitudinal strain, and MW findings by conducting echocardiographic examinations. The study subjects were divided into three groups based on the global constrictive work (GCW) value. The three groups were not statistically different in terms of the mean age of the patients (53.0 ± 12 vs. 52.4 ± 13.3 vs. 52.1 ± 12.3; p = 0.96). Furthermore, the three groups were not statistically different regarding the gender, height, weight, and laboratory parameters of the patients except albumin. There was no statistically difference among the tertiles of GCW groups in the measurements of cardiac chambers, LA diameter, interventricular septum, E wave, and A wave. Also, there was no statistical difference in tissue Doppler recordings. The parameters associated with MW were examined, three groups were not statistically different in terms of the global waste work (GWW) (116 ± 92, 122 ± 73, 135 ± 62, p = 0.52, respectively). In contrast, the three groups were different regarding the Global work index (GWI) (1716 ± 300, 1999 ± 130, 2253 ± 195, p < 0.001, respectively), GCW(1888 ± 206, 2298 ± 75, 2614 ± 155, p < 0.001, respectively), and Global work efficiency parameters (92.8 ± 3.6, 94.4 ± 3.2, 95.1 ± 1.8 p = 0.004, respectively). It was concluded that the MW parameters GCW and GWI may have been used for predicting INOCA in patients.

Interannual variability in dolphinfish Coryphaena hippurus Linnaeus, 1758 growth in the central-southeastern pacific ocean

The present research provides information on Coryphaena hippurus growth variability in the Central-Southeastern Pacific Ocean. For this purpose, fork length of 48 008 specimens obtained from artisanal fisheries (2006–2022) were used to analyze growth by the multi-model inference approach. According to the results, von Bertalanffy model best described species growth based on Akaike’s Information Criterion. Global growth parameters L∞ = 168.6 cm, k = 1.3, t0 = -0.04, indicated fast growth rates of dolphinfish during the study. This model presented growth and longevity values with greater biological coherence than the logistic and Gompertz models. Growth estimates between cohorts indicated that C. hippurus has interannual and decadal variations associated with the El Niño Southern Oscillation (ENSO) and decadal variation. The information in this study is essential for better understanding C. hippurus growth and serves as a basis for future actions on its management and conservation.

SC-XRD structural characterization, insights of key electronic, and nonlinear optical properties of dimethoxybenzenesulfonate crystals: A combine experimental and DFT approach

Currently, dimethoxybenzene sulfonyl-based crystals (MDBS, EDBS and DBSC) were synthesized by in situ nucleophilic substitution reactions and their structures were confirmed via SC-XRD analysis. The key-electronic and NLO properties of afore-said crystals were investigated through quantum chemical study. For this purpose, M06/6-311G(d,p) functional of DFT/TDDFT approaches was utilized. A comparative study between DFT and XRD values of gemeterical parameters shwed harmony.Frontier molecular orbital findings revealed that among synthesized crystals, DBSC showed lesser energy (4.18 eV) with more red shift (322.758 nm) than MDBS (5.306 eV) and EDBS (5.320 eV). Global reactivity parameters (GRPs) were also examined from the energies of HOMO/LUMO. Among all crystals EDBS had higher value of hardness (η = 2.660 eV) while DBSC showed higher global softness (σ=0.240 eV) that indicated greater polariziability and interamolecular charge transfer in DBSC. The decreasing order of dipole moments of synthesized crystals was found as: DBSC (7.934 D) > MDBS (7.420 D) > EDBS (7.380 D). Further, from GRP it is revealed that MDBS-DBSC crystals having greater hardness values were less reactive and more stable as suggested by NBO and SC-XRD investigations. Owing to the unique properties of DBSC, significant NLO properties: hyperpolarizability (βtot=1.30 × 10−30esu) and second-order hyperpolarizability (γtot=22.1 × 10−36esu) were investigated in this crystal. The efficient NLO properties observed in DBSC underscore its potential use for advanced NLO applications.

Global 1 km land surface parameters for kilometer-scale Earth system modeling

Abstract. Earth system models (ESMs) are progressively advancing towards the kilometer scale (“k-scale”). However, the surface parameters for land surface models (LSMs) within ESMs running at the k-scale are typically derived from coarse-resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1 km land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31 % to 54 % of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSM and ESM modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The 1 km land surface parameters are publicly available at https://doi.org/10.5281/zenodo.10815170 (Li et al., 2024).

Investigating Model Dependencies for Obscured Active Galactic Nuclei: A Case Study of NGC 3982

Abstract X-ray spectroscopy of heavily obscured active galactic nuclei (AGN) offers a unique opportunity to study the circumnuclear environment of accreting supermassive black holes. However, individual models describing the obscurer have unique parameter spaces that give distinct parameter posterior distributions when fit to the same data. To assess the impact of model-specific parameter dependencies, we present a case study of the nearby heavily obscured low-luminosity AGN NGC 3982, which has a variety of column density estimations reported in the literature. We fit the same broadband XMM-Newton+NuSTAR spectra of the source with five unique obscuration models and generate posterior parameter distributions for each. By using global parameter exploration, we traverse the full prior-defined parameter space to accurately reproduce complex posterior shapes and inter-parameter degeneracies. The unique model posteriors for the line-of-sight column density are broadly consistent, predicting Compton-thick N H &gt; 1.5 × 1024 cm−2 at the 3σ confidence level. The posterior median intrinsic X-ray luminosity in the 2–10 keV band was found to differ substantially, however, with values in the range log L 2–10 keV/ erg s−1 = 40.9–42.1 for the individual models. We additionally show that the posterior distributions for each model occupy unique regions of their respective multidimensional parameter spaces and how such differences can propagate into the inferred properties of the central engine. We conclude by showcasing the improvement in parameter inference attainable with the High Energy X-ray Probe, with its uniquely broad, simultaneous, and high-sensitivity bandpass of 0.2–80 keV.

Quantification of left ventricular myocardial strain: Comparison between MRI tagging, MRI feature tracking, and ultrasound speckle tracking.

Ultrasound speckle tracking is frequently used to quantify myocardial strain, and magnetic resonance imaging (MRI) feature tracking is rapidly gaining interest. Our aim is to validate cardiac MRI feature tracking by comparing it with the gold standard method (i.e., MRI tagging) in healthy subjects and patients. Furthermore, we aim to perform an indirect validation by comparing ultrasound speckle tracking with MRI feature tracking. Forty-two subjects (17 formerly preeclamptic women, three healthy women, and 22 left bundle branch block patients of both sexes) received 3-T cardiac MRI and echocardiography. Cine and tagged MRI, and B-mode ultrasound images, were acquired. Intrapatient global and segmental left ventricular circumferential (MRI tagging vs. MRI feature tracking) and longitudinal (MRI feature tracking vs. ultrasound speckle tracking) peak strain and time to peak strain were compared between the three techniques. Intraclass correlation coefficient (ICC) (< 0.50 = poor, 0.50-0.75 = moderate, > 0.75-0.90 = good, > 0.90 = excellent) and Bland-Altman analysis were used to assess correlation and bias; pless than 0.05 indicates a significant ICC or bias. Global peak strain parameters showed moderate-to-good correlations between methods (ICC = 0.71-0.83, p< 0.01) with no significant biases. Global time to peak strain parameters showed moderate-to-good correlations (ICC = 0.56-0.82, p< 0.01) with no significant biases. Segmental peak strains showed significant biases in all parameters and moderate-to-good correlation (ICC = 0.62-0.77, p< 0.01), except for lateral longitudinal peak strain (ICC = 0.23, p= 0.22). Segmental time to peak strain parameters showed moderate-to-good correlation (ICC = 0.58-0.74, p< 0.01) with no significant biases. MRI feature tracking is a valid method to examine myocardial strain, but there is bias in absolute segmental strain values between imaging techniques. MRI feature tracking shows adequate comparability with ultrasound speckle tracking.

Enhancing parameter calibration for micro-simulation models: Investigating improvement methods

Calibrating microscopic traffic simulation models is a prerequisite for simulation applications. This study proposes three novel methods to improve the accuracy and interpretability of the calibration model. The proposed approach involves selecting the calibration parameter, refining the model parameter system, and optimizing the calibration results. The first method expands the single-point mean into a multi-point distribution. The cumulative distribution curve of delay was selected as the calibration parameter. The second method divides the parameter system into global and local parameters. Global parameters were calibrated using NGSIM measured data, and local parameters were calibrated through intelligent algorithms. The third method proposes a methodology of parameter clustering recursion based on the genetic algorithm results, with information entropy selected as the analysis index. To evaluate the effectiveness of the proposed optimization methods, this study used NGSIM trajectory data as a case study. Eight simulation schemes based on the three optimization methods were designed, and simulation experiments were conducted using the VISSIM platform. The results show that the accuracy of the multi-point distribution calibration and parameter value optimization method is significantly higher than the default method. Additionally, the optimization method with calibration of both global and local parameters was more consistent with actual driving characteristics. This study provides a theoretical foundation for improving the practical application of traffic simulation technology, which has significant implications for transportation planning and management.