Posterior Distribution Research Articles

Hematocrits of red blood cell units increase during storage due to changes in mean corpuscular volume, impacting outcomes of red cell exchange procedures

Abstract Introduction Hematocrits of red blood cell (RBC) units are generally not measured by blood centers or transfusion services. Red blood cell exchange (RCE) is commonly used to treat sickle cell disease, and a key parameter needed in RCE is the average hematocrit of RBC units. However, there is no consensus on the average hematocrit to use or how it is affected by storage. The goal of our study is to determine the average hematocrit of RBC units by measuring hematologic parameters RBC units during storage and performing statistical modeling of hematocrit changes over time. Methods We sampled aliquots of leukoreduced RBC units in AS-1 (n=10) using the Terumo TSCD-II Sterile Tubing Welder to sterilely collect ~15mL of RBCs in transfer-pack containers. Aliquots were collected weekly from Day 7 to Day 42 of storage. Complete blood counts were performed using the Sysmex XN-3100 Hematology Analyzer. We statistically modeled changes in RBC units during storage by utilizing Bayesian multilevel linear regression models, allowing for capturing effects at the individual RBC-unit level. Posterior distributions of model parameters were estimated and processed using Stan and the rethinking and rstan packages in R. Posterior distributions are summarized with posterior means and 95%-credible intervals in parentheses. Model selection was done by calculating Akaike weights using the widely applicable information criterion (WAIC) to signify statistical support for each model. Results Multilevel linear regression models of hematologic parameters as a function of storage time were developed. Hematocrits increased with a posterior mean of 1.4% (0.98–1.75%) per week of storage, with posterior mean hematocrits of: 61.9% (60.2–63.6%) on Day 7, 63.9% (61.5–66.4%) on Day 21, and 66.0% (62.8–69.7%) on Day 42. These results indicate that a single average cannot be used for RBC units since hematocrits dramatically increase during storage. Mean corpuscular volume (MCV) increased with a posterior mean of 2.31 fL (1.4–3.71) per week whereas hemoglobin remained stable with a posterior mean increase of 0.008 g/dL (-0.016–0.031) per week. Using these results, we subsequently developed a multilevel regression model of hematocrit as a function of MCV. The model using MCV (WAIC=100.2, weight=100%) was favored over the model using time (WAIC=120.0, weight=0%), suggesting that changes in MCV are driving the increasing hematocrits. Conclusion Our study is the first to leverage Bayesian statistics to carefully quantify the average hematocrits of RBC units during storage time and to demonstrate that increasing hematocrits are caused by changes in MCV. These results indicate that transfusion protocols must consider the age of RBC units for RCE calculations, and provisions (e.g., requiring use of fresh units for RCE) may help minimize the variability in post-exchange testing. Moreover, our findings suggest that transfusion services should regularly measure hematocrits of RBC units in their inventory.

Naringenin modulates oxidative stress and lipid metabolism: Insights from network pharmacology, mendelian randomization, and molecular docking.

Previous studies have demonstrated that naringenin possesses lipid-lowering effects; however, the underlying mechanisms, particularly its specific molecular targets, remain uncertain. Using bioinformatics, three traditional Chinese medicine databases and one human disease database were integrated to establish two naringenin-target-hyperlipidemia modules: naringenin-oxidative stress (OS) and naringenin-lipid metabolism (LM). Data on 1,850 proteins from 1,871 genetic instruments were sourced from seven previous studies. Using Mendelian randomization based on data from the Integrative Epidemiology Unit genome-wide association study (case, n = 5,153; control, n = 344,069), we identified potential drug targets that were subsequently validated in the UK Biobank (396,565 individuals) and FinnGen (412,181 individuals) cohorts. Using molecular docking and molecular dynamics simulation to verify the binding ability of naringenin and causal protein. In plasma, every standard deviation increase in apolipoprotein B (APOB) was associated with an increased risk of hyperlipidemia (odds ratio [OR] = 9.37, 95% confidence interval [CI], 5.12-17.12; P = 3.58e-13; posterior probability of hypothesis 4 [PPH4] = 0.997), and the same was observed for proprotein convertase subtilisin/kexin type 9 (OR = 1.81, 95% CI, 1.51-2.16; P = 6.87e-11; PPH4 = 1) and neurocan (OR = 2.34, 95% CI, 1.82-3.01; P = 4.09e-11; PPH4 = 0.932). The intersection of two modules and Mendelian randomization result identified APOB as a key regulatory target of naringenin in the treatment of hyperlipidemia. The binding energy between naringenin and APOB was determined to be -7.7kcal/mol. Additionally, protein-protein interactions and protein-disease networks were analyzed to uncover potential connections between proteins and hyperlipidemia. This Mendelian randomization-based combined analysis offers a robust framework for elucidating the pharmacological effects of naringenin and identifying candidate proteins for further investigation in the context of hyperlipidemia treatment.

Joint Beamforming Design and User Clustering Algorithm in NOMA-Assisted ISAC Systems.

To enhance the performance of non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) systems in multi-user distributed scenarios, an improved Gaussian Mixture Model (GMM)-based user clustering algorithm is proposed. This algorithm is tailored for ISAC systems, significantly improving bandwidth reuse gains and reducing serial interference. First, using the Sum of Squared Errors (SSE), the algorithm reduces sensitivity to the initial cluster center locations, improving clustering accuracy. Then, direction weight factors are introduced based on the base station position and a penalty function involving users' Euclidean distances and sensing power. Modifications to the EM algorithm in calculating posterior probabilities and updating the covariance matrix help align user clusters with the characteristics of NOMAISAC systems. This improves users' interference resistance, lowers decoding difficulty, and optimizes the system's sensing capabilities. Finally, a fractional programming (FP) approach addresses the non-convex joint beamforming design problem, enhancing power and channel gains and achieving co-optimizing sensing and communication signals. The simulation results show that, under the improved GMM user clustering algorithm and FP optimization, the NOMA-ISAC system improves user spectral efficiency by 4.3% and base station beam intensity by 5.4% compared to traditional ISAC systems.

Mitochondrial DNA patterns describe the evolutionary history of the bonnethead shark Sphyrna tiburo (Linneus 1758) complex in the western Atlantic Ocean.

The apparent lack of physical barriers in the marine realm has created the conception that many groups have a constant gene flow. However, changes in ocean circulation patterns, glacial cycles, temperature, and salinity gradients are responsible for vicariant events in many fish species, including sharks. The bonnethead shark, Sphyrna tiburo, is an endangered small coastal shark species. Although considerable efforts have recently been undertaken, little remains known about the possible biogeographic scenario that can explain its actual distribution within the western Atlantic (WA). Here, we used 599 mitochondrial sequences to assess the phylogeographic structure and implement Bayesian phylogenetic analyses to obtain divergence times and reconstruct the ancestral geographic range. This allowed us to infer processes responsible for the diversification of S. tiburo into major divergent lineages. Our results indicated that S. tiburo in the WA represents three independent lineages, with Brazilian samples differentiated into a distinct genetic cluster. The posterior probability of ancestral range analysis indicated that the species likely originated in the northern region (Carolina Province and the southern Gulf of Mexico), where it colonized southward through the uplifting of the Central American Isthmus (CAI). The Northern and Caribbean genetic clusters appear to have arisen from the intensification of the Loop Current, which currently flows northward passing the Yucatan Peninsula, Gulf of Mexico, and east Florida. Following initial colonization, the Northeastern Brazil group differentiated from the Caribbean region due to the sediment and freshwater discharge of the Amazon-Orinoco Plume. Thus, the evolutionary history of the S. tiburo complex can be explained by a combination of dispersal and vicariance events that occurred over the last ~5 million years (MY). We established and confirmed the species and population limits, demonstrating that the Amazon-Orinoco Plume constitutes a significant dispersal barrier for coastal sharks. Finally, we discuss some recommendations for the conservation of the bonnethead shark.

Vine Copula-Based Classifiers with Applications

AbstractThe vine pair-copula construction can be used to fit flexible non-Gaussian multivariate distributions to a mix of continuous and discrete variables. With multiple classes, fitting univariate distributions and a vine to each class lead to posterior probabilities over classes that can be used for discriminant analysis. This is more flexible than methods with the Gaussian and/or independence assumptions, such as quadratic discriminant analysis and naive Bayes. Some variable selection methods are studied to accompany the vine copula-based classifier because unimportant variables can make discrimination worse. Simple numerical performance metrics cannot give a full picture of how well a classifier is doing. We introduce categorical prediction intervals and other summary measures to assess the difficulty of discriminating classes. Through extensive experiments on real data, we demonstrate the superior performance of our approaches compared to traditional discriminant analysis methods and random forests when features have different dependent structures for different classes.

Euclid preparation. LIII. LensMC, weak lensing cosmic shear measurement with forward modelling and Markov Chain Monte Carlo sampling

is a weak lensing shear measurement method developed for and Stage-IV surveys. It is based on forward modelling in order to deal with convolution by a point spread function (PSF) with comparable size to many galaxies, sampling the posterior distribution of galaxy parameters via Markov chain Monte Carlo, and marginalisation over nuisance parameters for each of the 1.5 billion galaxies observed by We quantified the scientific performance through high-fidelity images based on the Flagship simulations and emulation of the VIS images, realistic clustering with a mean surface number density of $ arcmin^ for galaxies, and $ arcmin^ for stars, and a diffraction-limited chromatic PSF with a full width at half maximum of $ $ and spatial variation across the field of view. measured objects with a density of $ arcmin^ in $4500 deg^2 $. The total shear bias was broken down into measurement (our main focus here) and selection effects (which will be addressed in future work). We found measurement multiplicative and additive biases of $m_1=(-3.6 $, $m_2=(-4.3 $, $c_1=(-1.78 $, and $c_2=(0.09 $; a large detection bias with a multiplicative component of $1.2 $ and an additive component of $-3 $; and a measurement PSF leakage of $ $ and $ $. When model bias is suppressed, the obtained measurement biases are close to requirement and largely dominated by undetected faint galaxies ($-5 $). Although significant, model bias will be straightforward to calibrate given its weak sensitivity on galaxy morphology parameters. is publicly available at

On the importance of assessing topological convergence in Bayesian phylogenetic inference.

Modern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution. These algorithms require careful evaluation of the quality of the generated samples. Within the field of phylogenetics, one frequently adopted diagnostic approach is to evaluate the effective sample size and to investigate trace graphs of the sampled parameters. A major limitation of these approaches is that they are developed for continuous parameters and therefore incompatible with a crucial parameter in these inferences: the tree topology. Several recent advancements have aimed at extending these diagnostics to topological space. In this reflection paper, we present two case studies-one on Ebola virus and one on HIV-illustrating how these topological diagnostics can contain information not found in standard diagnostics, and how decisions regarding which of these diagnostics to compute can impact inferences regarding MCMC convergence and mixing. Our results show the importance of running multiple replicate analyses and of carefully assessing topological convergence using the output of these replicate analyses. To this end, we illustrate different ways of assessing and visualizing the topological convergence of these replicates. Given the major importance of detecting convergence and mixing issues in Bayesian phylogenetic analyses, the lack of a unified approach to this problem warrants further action, especially now that additional tools are becoming available to researchers.

Predicting contrast sensitivity functions with digital twins

We developed and validated digital twins (DTs) for contrast sensitivity function (CSF) across 12 prediction tasks using a data-driven, generative model approach based on a hierarchical Bayesian model (HBM). For each prediction task, we utilized the HBM to compute the joint distribution of CSF hyperparameters and parameters at the population, subject, and test levels. This computation was based on a combination of historical data (N = 56), any new data from additional subjects (N = 56), and “missing data” from unmeasured conditions. The posterior distributions of the parameters in the unmeasured conditions were used as input for the CSF generative model to generate predicted CSFs. In addition to their accuracy and precision, these predictions were evaluated for their potential as informative priors that enable generation of synthetic quantitative contrast sensitivity function (qCSF) data or rescore existing qCSF data. The DTs demonstrated high accuracy in group level predictions across all tasks and maintained accuracy at the individual subject level when new data were available, with accuracy comparable to and precision lower than the observed data. DT predictions could reduce the data collection burden by more than 50% in qCSF testing when using 25 trials. Although further research is necessary, this study demonstrates the potential of DTs in vision assessment. Predictions from DTs could improve the accuracy, precision, and efficiency of vision assessment and enable personalized medicine, offering more efficient and effective patient care solutions.

Distributed adaptive moving horizon estimation for multi-sensor networks subject to quantization effects

This paper investigates the distributed state estimation problem for multi-sensor networks with quantized measurements. Within the Bayesian framework, a distributed adaptive moving horizon estimation algorithm is developed. Unlike the existing methods regarding quantized errors roughly as bounded uncertainties, the posterior distributions of the errors are demanded to be derived. To overcome the difficulty of evaluating the posterior distributions for series of the states and quantized errors jointly, the variational Bayesian methodology is adopted to approximate the true distributions. Based on the fixed-point iteration method, the update rules are analytically derived, with the convergence criterion provided. Furthermore, by incorporating the average consensus algorithm into the prediction process, all sensors can achieve consensus on their estimates in a distributed manner. Finally, a numerical example of target tracking under logarithmic and uniform quantization effects is given to illustrate the validity of the proposed algorithm.

A petrophysically driven seismic inversion method for the total organic carbon content of source rocks

Organic carbon content is the main index for evaluating organic matter abundance of source rocks, and it is still difficult to quantitatively predict total organic carbon (TOC) in source rocks based on seismic data. The study of seismic fluid identification driven by petrophysics can help to understand the fluid characteristics and distribution patterns of subsurface oil and gas reservoirs. First, this paper clarifies the sweet spot parameters (parameters characterizing hydrocarbon enrichment) and sensitive elastic parameters (a parameter characterizing the nature of an ideal elastic body) of source rocks through theoretical petrophysical modeling. Next, the paper establishes the relationship between sensitive elastic parameters and sweet spot parameters TOC and constructs a statistical petrophysical model that can characterize the relationship between the two on this basis. And then we construct the joint distribution of TOC and elastic impedance through the Bayesian theoretical framework to obtain the maximum posterior probability estimate as the final TOC inversion results of source rocks. Our method successfully predicts the spreading of high-quality source rocks in the Wen4 Section of Lufeng 13 Subsag, and the inversion results are within an uncertainty range of ±14 m for well data, which proves the reliability of the method. The prediction results show that the organic matter abundance of source rocks in the Wen4 Section is high, and the organic carbon content is generally higher than 2%, which provides a reliable basis for the further implementation of the resource scale of the depression and the clarification of the hydrocarbon-rich area, which provides technical support for the evaluation of the source rocks of the new depression in the new area.

2D Transdimensional Joint Inversion of Radio Magnetotelluric and Electrical Resistivity Tomography Data

Summary The joint inversion of radio magnetotelluric and electrical resistivity tomography data has the potential to reduce the uncertainties in the subsurface conductivity model. This is particularly beneficial when the datasets offer complementary information about the subsurface. However, the traditional gradient-based inversion methods pose challenges in quantifying uncertainty, as they yield a single model with limited appraisal of parameter uncertainty. The Bayesian inversion approach stands out for its capacity to provide quantitative assessments of uncertainty in the inverted model parameters. This is accomplished by generating an ensemble of models, leading to a posterior distribution that encapsulates both prior information concerning model parameters and the dataset information. We have implemented a transdimensional Markov chain Monte Carlo algorithm to perform the joint inversion of radio magnetotelluric and electrical resistivity tomography data. Through synthetic data studies, we illustrate how the inclusion of two complementary datasets can effectively reduce uncertainties in model parameters and how the model parameter uncertainties can be quantified. Subsequently, the developed algorithm is tested using exemplary field data from a waste site near Roorkee, India. Intensive prior geoelectric and transient electromagnetic as well as radio magnetotelluric studies investigated possible waste water seepage with a potential to contaminate the shallow aquifers. The derived subsurface structure from our transdimensional Bayesian results compare well with the deterministic results for the exemplary profile, but in addition provide comprehensive uncertainty estimates.

A Meta-Analysis of Mercury Biomagnification in Freshwater Predatory Invertebrates: Community Diversity and Dietary Exposure Drive Variability.

Accurate estimates of methylmercury (MeHg) exposure are valuable to actionably assess risk and protect wildlife and human health. MeHg trophic transfer is a critical driver of risk: MeHg is generally biomagnified by a factor of 8.3 ± 7.5 from one trophic level to the next, averaged across freshwater communities (mean ± standard deviation). This variability can produce disparate risks even where basal MeHg concentrations are similar. Taxonomy may be one driver of this variability: physiologically diverse groups, like vertebrates and invertebrates, may assimilate MeHg differently. To determine whether taxonomy affects trophic transfer efficiency, we conducted a meta-analysis characterizing predatory invertebrate MeHg biomagnification. Our analyses estimated that freshwater predatory invertebrates biomagnify MeHg by factors of 2.1 ± 0.2 to 4.3 ± 0.3, with a 98.9 ± 0.4% posterior probability that factors are below 5 (mean ± standard error). When vertebrates or primary producers were included, a site's trophic magnification factor was 18.6 ± 6.2 to 54.1 ± 7.7% higher than estimates for invertebrates alone. Biomagnification was inversely correlated to prey MeHg concentration and varied among systematic and functional groups. These data suggest that predatory invertebrates biomagnify MeHg less efficiently than vertebrates and that a community's diversity and structure determine its biomagnification efficiency. Incorporating organismal variation in trophic transfer estimates may improve the assessment, communication, and management of MeHg risk.

High PEEP with recruitment maneuvers versus Low PEEP During General Anesthesia for Surgery - a Bayesian individual patient data meta-analysis of three randomized clinical trials.

The influence of high positive end-expiratory pressure (PEEP) with recruitment maneuvers on the occurrence of postoperative pulmonary complications after surgery is still not definitively established. Bayesian analysis can help to gain further insights from the available data and provide a probabilistic framework that is easier to interpret. Our objective was to estimate the posterior probability that the use of high PEEP with recruitment maneuvers is associated with reduced postoperative pulmonary complications in patients with intermediate-to-high risk under neutral, pessimistic, and optimistic expectations regarding the treatment effect. Multilevel Bayesian logistic regression analysis on individual patient data from three randomized clinical trials carried out on surgical patients at Intermediate-to-High Risk for postoperative pulmonary complications. The main outcome was the occurrence of postoperative pulmonary complications in the early postoperative period. We studied the effect of high PEEP with recruitment maneuvers versus Low PEEP Ventilation. Priors were chosen to reflect neutral, pessimistic, and optimistic expectations of the treatment effect. Using a neutral, pessimistic, or optimistic prior, the posterior mean odds ratio (OR) for High PEEP with recruitment maneuvers compared to Low PEEP was 0.85 (95% Credible Interval [CrI] 0.71 to 1.02), 0.87 (0.72 to 1.04), and 0.86 (0.71 to 1.02), respectively. Regardless of prior beliefs, the posterior probability of experiencing a beneficial effect exceeded 90%. Subgroup analysis indicated a more pronounced effect in patients who underwent laparoscopy (OR: 0.67 [0.50 to 0.87]) and those at high risk for PPCs (OR: 0.80 [0.53 to 1.13]). Sensitivity analysis, considering severe postoperative pulmonary complications only or applying a different heterogeneity prior, yielded consistent results. High PEEP with recruitment maneuvers demonstrated a moderate reduction in the probability of PPC occurrence, with a high posterior probability of benefit observed consistently across various prior beliefs, particularly among patients who underwent laparoscopy.

Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia.

Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in typical speakers as well as in various clinical populations. However, determining which neural functions differ between typical speakers and clinical populations based on behavioral data alone is difficult because multiple mechanisms may lead to the same behavioral differences. For example, individuals with cerebellar ataxia (CA) produce atypically large compensatory responses to pitch perturbations in their auditory feedback, compared to typical speakers, but this pattern could have many explanations. Here, computational modeling techniques were used to address this challenge. Bayesian inference was used to fit a state feedback control (SFC) model of voice fundamental frequency (fo) control to the behavioral pitch perturbation responses of speakers with CA and typical speakers. This fitting process resulted in estimates of posterior likelihood distributions for five model parameters (sensory feedback delays, absolute and relative levels of auditory and somatosensory feedback noise, and controller gain), which were compared between the two groups. Results suggest that the speakers with CA may proportionally weight auditory and somatosensory feedback differently from typical speakers. Specifically, the CA group showed a greater relative sensitivity to auditory feedback than the control group. There were also large group differences in the controller gain parameter, suggesting increased motor output responses to target errors in the CA group. These modeling results generate hypotheses about how CA may affect the speech motor system, which could help guide future empirical investigations in CA. This study also demonstrates the overall proof-of-principle of using this Bayesian inference approach to understand behavioral speech data in terms of interpretable parameters of speech motor control models.

Utilizing Bayesian generalization network for reliable fault diagnosis of machinery with limited data

To address the issues of overfitting, domain generalization challenges, and lack of credibility brought by limited data samples in mechanical fault diagnosis in practical engineering, this paper proposes a reliable Bayesian generalization network (BGNet). A Bayesian convolutional layer is constructed based on variational inference, treating all parameters in the convolutional layer as random variables. This approach makes a single model function similar to an ensemble of an infinite number of models, and thus enhancing the model's capability of overfitting resistance and domain generalization. The parameters of the variational distribution are updated to approximate the posterior distribution by local reparametrization and Monte Carlo sampling to optimize the evidence lower bound (ELBO) loss. Confidence information is extracted from the model results and, uncertainty estimation and decomposition schemes are designed to provide interpretability. The proposed method is applied to analyze the experimental data of bearing and gearbox faults. The results show that in a multi-source domain scenario with limited samples, the proposed method demonstrates high diagnostic accuracy, effectively describes the relationship between domain variability and uncertainty, and significantly outperforms several benchmark and state-of-the-art models.

A Robust Trajectory Multi-Bernoulli Filter for Superpositional Sensors

This paper proposes a trajectory multi-Bernoulli filter applied to the superpositional sensor model for multi-target tracking in the presence of unknown measurement noise. This filter can provide a Multi-Bernoulli approximation of the posterior density on a set of alive trajectories at the current time step. We also provide a Gaussian mixture (GM) implementation of this filter, employing a mixture of Gaussian and inverse Wishart distributions to represent the combined state of measurement noise and target information. Subsequently, the variational Bayesian (VB) method is employed to approximate the posterior distribution, ensuring its form remains consistent with the prior distribution. This method is capable of directly generating trajectory estimates and can jointly estimate both multi-object tracking and measurement noise covariance. The performance of this algorithm is verified through simulation. Finally, a computationally more efficient L-scan approximation is provided. The simulation results indicate that the filter can achieve robust tracking performance, adapting to unknown measurement noise.

MCMC stopping rules in latent variable modelling.

Bayesian analysis relies heavily on the Markov chain Monte Carlo (MCMC) algorithm to obtain random samples from posterior distributions. In this study, we compare the performance of MCMC stopping rules and provide a guideline for determining the termination point of the MCMC algorithm in latent variable models. In simulation studies, we examine the performance of four different MCMC stopping rules: potential scale reduction factor (PSRF), fixed-width stopping rule, Geweke's diagnostic, and effective sample size. Specifically, we evaluate these stopping rules in the context of the DINA model and the bifactor item response theory model, two commonly used latent variable models in educational and psychological measurement. Our simulation study findings suggest that single-chain approaches outperform multiple-chain approaches in terms of item parameter accuracy. However, when it comes to person parameter estimates, the effect of stopping rules diminishes. We caution against relying solely on the univariate PSRF, which is the most popular method, as it may terminate the algorithm prematurely and produce biased item parameter estimates if the cut-off value is not chosen carefully. Our research offers guidance to practitioners on choosing suitable stopping rules to improve the precision of the MCMC algorithm in models involving latent variables.

Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model

Abstract Understanding the relationship between structural connectivity (SC) and functional connectivity (FC) of the human brain is an important goal of neuroscience. Highly detailed mathematical models of neural masses exist that can simulate the interactions between functional activity and structural wiring. These models are often complex and require intensive computation. Most importantly, they do not provide a direct or intuitive interpretation of this structure–function relationship. In this study, we employ the emerging concepts of spectral graph theory to obtain this mapping in terms of graph harmonics, which are eigenvectors of the structural graph’s Laplacian matrix. In order to imbue these harmonics with biophysical underpinnings, we leverage recent advances in parsimonious spectral graph modeling (SGM) of brain activity. Here, we show that such a model can indeed be cast in terms of graph harmonics, and can provide a closed-form prediction of FC in an arbitrary frequency band. The model requires only three global, spatially invariant parameters, yet is capable of generating rich FC patterns in different frequency bands. Only a few harmonics are sufficient to reproduce realistic FC patterns. We applied the method to predict FC obtained from pairwise magnitude coherence of source-reconstructed resting-state magnetoencephalography (MEG) recordings of 36 healthy subjects. To enable efficient model inference, we adopted a deep neural network-based Bayesian procedure called simulation-based inference. Using this tool, we were able to speedily infer not only the single most likely model parameters, but also their full posterior distributions. We also implemented several other benchmark methods relating SC to FC, including graph diffusion and coupled neural mass models. The present method was shown to give the best performance overall. Notably, we discovered that a single biophysical parameterization is capable of fitting FCs from all relevant frequency bands simultaneously, an aspect that did not receive adequate attention in prior computational studies.

Fault Impulse Inference and Cyclostationary Approximation: A feature-interpretable intelligent fault detection method for few-shot unsupervised domain adaptation

With the advancement of intelligent detection for rotating machinery, numerous domain adaptation methods have been devised to transfer detection knowledge from one source domain working condition to another target domain working condition, involving extensive transfer scenarios including labeled, few-shot labeled, and unlabeled target conditions. Yet, learning from sparsely labeled signals in the source domain working condition and transferring to unlabeled target conditions, termed few-shot unsupervised domain adaptation (FUDA), is closer to reality but almost unexplored. Diverging from the intuition of combining existing transfer and few-shot learning technologies, this paper pioneers a novel single learning principle focusing on the cyclostationary mechanism (CT) of fault signals. In its implementation, named cyclically enhanced cyclostationary variational autoencoder (CCTVAE), the CT principle motivates the encoder to infer domain-shared representations with fault impulses, and the decoder approximates the cyclostationary structure containing the clear fault and working condition information. Then, auxiliary samples for few-shot expansion are generated by adjusting cyclic parameters of the posterior distribution of representations. Experimentally, CCTVAE achieves commendable results on simulated and real fault datasets. Even for compound faults, domain-shared representations and generated auxiliary signals manifest interpretable fault-indicating spectral lines in the frequency domain, underscoring method reliability.

Influence of process shifts in case of Six Sigma-based Bayesian control charts

While traditional control charts play an important role in quality assurance activities, the work on such techniques evolved over the years particularly for early detection of process shifts. Many advanced level control charts have been proposed in the literature to even detect small shifts in the process as early as possible. In order to make use of the prior beliefs and likelihood of the observed data behavior, Bayesian control charts for parameter of the posterior distribution have been proposed by various authors. The Six Sigma-based control charts ensure smaller process variations by enforcing quality improvement activities and as a result, such processes would produce higher quality products with only 3.4 defects per million opportunities (DPMO) even after allowing a shift in the process up to ± 1.5 times of process standard deviation. In this paper, an attempt has been made to develop Six Sigma-based Bayesian control charts. The proposed control charts incorporate various shift (or no-shift) combinations to prior and observed data behaviors to study their influence on the posterior parameter. Illustrative examples are also given for understanding the proposed approach. The performance evaluation and comparisons are also presented. The paper ends with a discussion and concluding remarks.