Mixture Model Research Articles

Contaminated Gibbs-Type Priors

Gibbs-type priors are combinatorial processes widely used as key components in several Bayesian nonparametric models. By virtue of their flexibility and mathematical tractability, they turn out to be predominant priors in species sampling problems and mixture modeling. We introduce a new family of processes which extends the Gibbs-type one, by including a contaminant component in the model to account for an excess of observations with frequency one. We first investigate the induced random partition, the associated predictive distribution, the asymptotic behavior of the total number of blocks and the number of blocks with a given frequency: all the results we obtain are in closed form and easily interpretable. A remarkable aspect of contaminated Gibbs-type priors relies on their predictive structure, compared to the one of the standard Gibbs-type family: it depends on the additional sampling information on the number of observations with frequency one out of the observed sample. As a noteworthy example we focus on the contaminated version of the Pitman-Yor process, which turns out to be analytically tractable and computationally feasible. Finally we pinpoint the advantage of our construction in different applications: we show how it helps to improve predictive inference in a species-related dataset exhibiting a high number of species with frequency one; we also discuss the use of the proposed construction in mixture models to perform density estimation and outlier detection.

A cohort study of the multipollutant effects of PM2.5, NO2, and O3 on C-reactive protein levels during pregnancy.

PM2.5, NO2, and O3 contribute to the development of adverse pregnancy complications. While studies have investigated the independent effects of these exposures, literature on their combined effects is limited. Our objective was to study the multipollutant effects of PM2.5, NO2, and O3 on maternal systemic C-reactive protein (CRP) levels. We used data from 1170 pregnant women enrolled in the Maternal-Infant Research on Environmental Chemicals Study (MIREC) study in Canada. Air pollution exposures were assigned to each participant based on residential location. CRP was measured in third-trimester blood samples. We fit multipollutant linear regression models and evaluated the effects of air pollutant mixtures (14-day averages) using repeated-holdout Weighted Quantile Sum (WQS) regression and by calculating the Air Quality Health Index (AQHI). In multipollutant models adjusting for NO2, O3, and green space, each interquartile range (IQR) increase in 14-day average PM2.5 (IQR: 6.9 µg/m3) was associated with 27.1% (95% confidence interval [CI] = 6.2, 50.7) higher CRP. In air pollution mixture models adjusting for green space, each IQR increase in AQHI was associated with 37.7% (95% CI = 13.9, 66.5) higher CRP; and an IQR increase in the WQS index was associated with 78.6% (95% CI = 29.7, 146.0) higher CRP. PM2.5 has the strongest relationship of the individual pollutants examined with maternal blood CRP concentrations. Mixtures incorporating all three pollutants, assessed using the AQHI and WQS index, showed stronger relationships with CRP compared with individual pollutants and illustrate the importance of conducting multipollutant analyses.

Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system

A dual-frequency measurement is employed in conjunction with an innovative Ifree filtering technique for mitigating the primary sources of Ifree influence on ground-based augmentation systems (GBAS) to safeguard the reliability of GBAS. The protective level achieved through the conventional Gaussian overbounding approach that are considered as much conventional technique. This adherence to tradition results in decreased reliability and a higher likelihood of false alarms. In contrast, the utilization of the Ifree algorithm contributes to reducing errors associated with dual-frequency measurements. This paper proposes the overbounding process according to Bayesian Gaussian mixture model (GMM) for maintaining Ifree-based GBAS range error. The Bayesian GMM is utilized for single-frequency model errors to examine the ambiguity estimations. The Monte Carlo (MC) simulation is established for defining estimated GMM assurance level accuracy which is attained through the general estimation method. Then, the last Bayesian GMM which is utilized for overbounding Ifree error distribution is investigated. According to the property of convolution invariance, the vertical protection in position field is determined without presenting difficult numerical calculations.

The Importance of Including Variable Exposure Concentrations When Assessing Toxicity of Sediment-Associated Pharmaceuticals to an Amphipod.

Pharmaceuticals have been classified as an environmental concern due to their increasing consumption globally and potential environmental impact. We examined the toxicity of sediment-associated diclofenac and citalopram administered as both single compounds and in a mixture to the sediment-living amphipod Corophium volutator. This laboratory-based study addressed the following research questions: (1) What is the toxicity of sediment-associated diclofenac and citalopram to C. volutator? (2) Can the mixture effect be described with either of the two mixture models: concentration addition (CA) or independent action (IA)? (3) What is the importance of the choice of (i) exposure measure (start concentration, time-weighted average [TWA], full exposure profile) and (ii) effect model (concentration-response vs. the toxicokinetic-toxicodynamic model general unified threshold model for survival in its reduced form [GUTS-RED]) for the derived effect concentration values? Diclofenac was more toxic than citalopram to C. volutator as a single compound (10-day exposure). Diclofenac exposure to C. volutator provided median lethal concentrations (LC50s) within the same range (11 µg g-1 dry wt sediment) using concentration-response based on TWA and both GUTS-RED models. However, concentration-response based on measured start concentrations provided an approximately 90% higher LC50 (21.6 ± 2.0 µg g-1 dry wt sediment). For citalopram, concentration-response parameters were similar regardless of model or concentration used (LC50 85-97 µg g-1 dry wt sediment), however, GUTS-RED with the assumption of individual tolerance resulted in a lower LC50 (64.9 [55.3-74.8] µg g-1 dry wt sediment). The mixture of diclofenac and citalopram followed the CA quite closely, whereas the result was synergistic when using the IA prediction. In summary, concentration-response based on TWA and GUTS-RED provided similar and reasonably good fits compared to the data set. The implications are that GUTS-RED will provide a more flexible model, which, in principle, can extend beyond the experimental period and make predictions based on variable exposure profiles (toxicity at different time frames and at different variable exposure scenarios) compared to concentration-response, which provides contaminant toxicity at one point in time. Environ Toxicol Chem 2024;00:1-11. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Trajectories and predictors of emotional exhaustion in clinical nurses in the context of healthcare crisis: a case study in Xi’an, China

AbstractThis study investigated the growth trajectory of emotional exhaustion and its predictors in clinical nurses in the context of healthcare crisis in the post pandemic era. A total of 422 Chinese clinical nurses were followed up three times over two months from December 2022 to January 2023, and data from the final 358 clinical nurses were analyzed. The growth mixed model was used to identify the trajectory categories, and the predictive factors of the trajectory types were analyzed by logistic regression. The relationship of the trajectory types of emotional exhaustion with psychological capital were analyzed by variance analysis. The best-fit growth mixture modeling revealed three class models: Class 1 characterized by high and increasing levels of emotional exhaustion, Class 2 characterized by moderate and decreasing levels of emotional exhaustion, and Class 3 characterized by low and decreasing levels of emotional exhaustion. These classes accounted for 9.78%, 83.52%, and 6.70% of clinical nurses, respectively. The results of the univariate analysis indicated that age, years of working experience, gender, past participation in emergency public health event rescue, and sleep quality were associated with the trajectory of emotional exhaustion. Logistic regression analysis revealed that gender, past participation in emergency public health event rescue, and sleep quality were independent predictors of the emotional exhaustion trajectory. The variance analysis of the four dimensions of psychological capital showed that there were statistically significant differences in scores of self-efficacy and hope dimensions among the three groups of emotionally exhausted individuals. Our study demonstrated the heterogeneity of emotional exhaustion among clinical nursing personnel in coping with healthcare system overload. The psychological capital of nursing staff with different categories of emotional exhaustion varies. Managers should actively pay attention to the predictive role of gender, past participation in emergency public health event rescue, and sleep quality on the development of emotional exhaustion in clinical nurses.

Color-based discrimination of color hues in rock paintings through Gaussian mixture models: a case study from Chomache site (Chile)

The article explores advanced image processing techniques for pigment discrimination in rock art paintings, emphasizing color separation using RGB (red, green, blue) and LHCUv (Luminance, Hue, Chroma) imagery. It highlights the use of dimensionality reduction methods such as Principal Components Analisys PCA and Independent Component Analysis (ICA), with a focus on Gaussian Mixture Models (GMM) for probabilistic classification of image elements. This approach, applied to the Chomache archaeological site on the northernmost coast of the Atacama Desert in Chile, reveals previously undetected motifs and details, offering a nuanced perspective in rock art documentation and analysis. This proposal reinforces the value of rock art panel not only as a finished product but as a process.

Robust Bayesian nonparametric variable selection for linear regression

AbstractSpike‐and‐slab and horseshoe regressions are arguably the most popular Bayesian variable selection approaches for linear regression models. However, their performance can deteriorate if outliers and heteroskedasticity are present in the data, which are common features in many real‐world statistics and machine learning applications. This work proposes a Bayesian nonparametric approach to linear regression that performs variable selection while accounting for outliers and heteroskedasticity. Our proposed model is an instance of a Dirichlet process scale mixture model with the advantage that we can derive the full conditional distributions of all parameters in closed‐form, hence producing an efficient Gibbs sampler for posterior inference. Moreover, we present how to extend the model to account for heavy‐tailed response variables. The model's performance is tested against competing algorithms on synthetic and real‐world datasets.

Novel gene-specific Bayesian Gaussian mixture model to predict the missense variants pathogenicity of Sanfilippo syndrome.

MPS III is an autosomal recessive lysosomal storage disease caused mainly by missense variants in theNAGLU, GNS, HGSNAT, and SGSH genes. Thepathogenicity interpretation of missense variants is still challenging. We aimed to develop unsupervised clustering-based pathogenicity predictor scores using extracted features from eight in silico predictors to predict the impact of novel missense variants of Sanfilippo syndrome. The model was trained on a dataset consisting of 415 uncertain significant (VUS) missense NAGLU variants. Performance The SanfilippoPred tool was evaluated by validation and test datasets consisting of 197-labelled NAGLU missense variants, and its performance was compared versus individual pathogenicity predictors using receiver operating characteristic (ROC) analysis. Moreover, we tested theSanfilippoPred tool using extra-labelled 427 missense variants to assess its specificity and sensitivity threshold. Application of the trained machine learning (ML) model on the test dataset of labelled NAGLU missense variants showed that SanfilippoPred has an accuracy of 0.93 (0.86-0.97 at CI 95%), sensitivity of 0.93, and specificity of 0.92. The comparative performance of theSanfilippoPred showed better performance (AUC = 0.908) than the individual predictors SIFT (AUC = 0.756), Polyphen-2 (AUC = 0.788), CADD (AUC = 0.568), REVEL (AUC = 0.548), MetaLR (AUC = 0.751), and AlphMissense (AUC = 0.885). Using high-confidence labelled NAGLU variants, showed that SanfilippoPred has an85.7% sensitivity threshold. The poor correlation between the Sanfilippo syndrome phenotype and genotype represents a demand for a new tool to classify its missense variants. This study provides asignificant tool forpreventing themisinterpretation of missense variants of the Sanfilippo syndrome-relevant genes. Finally, it seems that ML-based pathogenicity predictors and Sanfilippo syndrome-specific prediction tools could be feasible andefficient pathogenicity predictors in the future.

Predicting the stereoselectivity of chemical reactions by composite machine learning method.

Stereoselective reactions have played a vital role in the emergence of life, evolution, human biology, and medicine. However, for a long time, most industrial and academic efforts followed a trial-and-error approach for asymmetric synthesis in stereoselective reactions. In addition, most previous studies have been qualitatively focused on the influence of steric and electronic effects on stereoselective reactions. Therefore, quantitatively understanding the stereoselectivity of a given chemical reaction is extremely difficult. As proof of principle, this paper develops a novel composite machine learning method for quantitatively predicting the enantioselectivity representing the degree to which one enantiomer is preferentially produced from the reactions. Specifically, machine learning methods that are widely used in data analytics, including Random Forest, Support Vector Regression, and LASSO, are utilized. In addition, the Bayesian optimization and permutation importance tests are provided for an in-depth understanding of reactions and accurate prediction. Finally, the proposed composite method approximates the key features of the available reactions by using Gaussian mixture models, which provide suitable machine learning methods for new reactions. The case studies using the real stereoselective reactions show that the proposed method is effective and provides a solid foundation for further application to other chemical reactions.

Spectral similarities in galaxies through an unsupervised classification of spaxels

We present the first unsupervised classification of spaxels in hyperspectral images of individual galaxies. Classes identify regions by spectral similarity and thus take all the information into account that is contained in the data cubes (spatial and spectral). We used Gaussian mixture models in a latent discriminant subspace to find clusters of spaxels. The spectra were corrected for small-scale motions within the galaxy based on emission lines with an automatic algorithm. Our data consist of two MUSE/VLT data cubes of JKB 18 and NGC 1068 and one NIRSpec/JWST data cube of NGC 4151. Our classes identify many regions that are most often easily interpreted. Most of the 11 classes that we find for JKB 18 are identified as photoionised by stars. Some of them are known regions, but we mapped them as extended, with gradients of ionisation intensities. One compact structure has not been reported before, and according to diagnostic diagrams, it might be a planetary nebula or a denser region. For NGC 1068, our 16 classes are of active galactic nucleus-type (AGN) or star-forming regions. Their spatial distribution corresponds perfectly to well-known structures such as spiral arms and a ring with giant molecular clouds. A subclassification in the nuclear region reveals several structures and gradients in the AGN spectra. Our unsupervised classification of the MUSE data of NGC 1068 helps visualise the complex interaction of the AGN and the jet with the interstellar medium in a single map. The centre of NGC 4151 is very complex, but our classes can easily be related to ionisation cones, the jet, or H$_2$ emission. We find a new elongated structure that is ionised by the AGN along the N-S axis perpendicular to the jet direction. It is rotated counterclockwise with respect to the axis of the H$_2$ emission. Our work shows that the unsupervised classification of spaxels takes full advantage of the richness of the information in the data cubes by presenting the spectral and spatial information in a combined and synthetic way.

Association of Life's Essential 8 with all-cause mortality and risk of cancer: a prospective cohort study.

No study has concentrated on the association of LE8 with cancer risk and death. We aim to examine the association of LE8 with death and cancer. A total of 94733 adults aged 51.42 ± 12.46 years and 77551 participants aged 54.09±12.06 years were enrolled in longitudinal and trajectory analysis respectively. Baseline LE8 was divided into three groups based on the American Heart Association criteria and three trajectory patterns by latent mixture models. We reviewed medical records and clinical examinations to confirm incident cancer during the period from 2006 to 2020. Death information was collected from provincial vital statistics offices. Cox models were used. 12807 all-cause deaths and 5060 cancers were documented during a 14-year follow-up. Relative to participants with high LE8 at baseline, participants with lower levels of LE8 have a significantly increased risk of mortality and incident cancer. All these risks have an increasing trend with LE8 level decreasing. Meanwhile, the trajectory analysis recorded 7483 all-cause deaths and 3037 incident cancers after approximately 10 years. The associations of LE8 with death and cancer were identical to the longitudinal study. In the subtype cancer analysis, LE8 has a strong effect on colorectal cancer risk. Moreover, the cut point is 56.67 in the association between LE8 and death, while the cut point altered to 64.79 in the association between LE8 and incident cancers. These associations were enhanced among younger adults. There was a significant association of LE8 with death and cancer risk, especially for the young population.

Speech Emotion Recognition

Speech emotion recognition is a vital area of research with applications starting from human-computer interaction to mental health monitoring. This paper will provide a comprehensive survey of the techniques, methods, applications, and challenges in speech emotion recognition. It begins by checking the significance of recognizing emotions from speech and its diverse applications across various fields. The paper then shows the method employed for emotion speech recognition, encompassing traditional machine learning techniques, such as support vector machines and Gaussian mixture models, as well as contemporary approaches, including deep learning and multimodal fusion. Moreover, it examines benchmark datasets commonly used for training and evaluation purposes in emotion speech recognition research. Speech Emotion Recognition (SER) has a wide range of applications and there has been a lot of research going on in this fascinating area in recent years. However, the entertainment sector suffers from a lack of study in this research. Many of use Neural Network (NN) and Long Short-Term Memory (LSTM) architectures to categorize the emotions in audio recordings captured by actors expressing various emotions. Moreover, our survey explores real-world applications of emotion speech recognition like virtual assistants, health sector, market sector, education, Mental health diagnosis. The paper discusses the challenge associated with emotion speech recognition, including the variability of emotional expressions, cultural influences, humor and privacy concerns. In future it will help others to deal with many Noisy dataset and various cultural effects of emotion.

Trajectories of cognitive function among people aged 45 years and older living with diabetes in China: Results from a nationally representative longitudinal study (2011~2018).

Diabetes is associated with decline of cognitive function. Exploring different trajectories of cognitive function occurring in people with diabetes is important to improved prognosis. This study aimed to investigate differential patterns of trajectories of cognitive function and baseline determinants of trajectory group membership utilizing data from middle-aged and older Chinese adults with diabetes. Participants of the Chinese Health And Retirement Longitudinal Study (CHARLS) aged 45 years and above received biennial assessments between 2011 and 2018. The primary outcome was overall cognitive function score operationalized as sum of mental intactness and episodic memory scores derived from the Telephone Interview of Cognitive Status (TICS). A weighted growth mixture model was used to estimate cognitive function trajectories of CHARLS participants with diabetes, and baseline factors associated with trajectory group membership were investigated with weighted multinomial logistic regression. Data from 1,463 participants with diabetes aged 45 years and above were analyzed, a three-group trajectory model showed the best fit for overall cognitive scores: low baseline, linear declining (22.1%); moderate baseline, linear declining (37.5%) and high-stable (40.3%). Older participants, females, participants with low education, with nighttime sleep <6 h, without daytime napping habits, and with depressive symptoms were at a higher risk of unfavorable cognitive function trajectories. We identified heterogeneous trajectories of cognitive function among middle-aged and older people living with diabetes in China. Socially vulnerable groups including females, rural residents, and those with low education were at a higher risk for unfavorable trajectories. In health programs aimed at preventing and mitigating cognitive decline in individuals with diabetes more attention should be given to vulnerable groups. Reduced nighttime sleep, lack of daytime napping, and depressive symptoms appear to be modifiable risk factors.

Archetypes of influential users in social question-answering sites

PurposeThis paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?Design/methodology/approachBased on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.FindingsFindings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.Originality/valueThis study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.

Predictors of quality of life improvement in patients undergoing catheter ablation for atrial fibrillation: a cluster-based analysis

Abstract Introduction Traditional classification of Atrial Fibrillation (AF) based on temporal patterns doesn't fully encompass the condition's complexity. Unsupervised cluster analyses have been used for classifying patients into groups with similar comorbid profiles. However, inconsistent comorbidity patterns have been found across different cohorts. Bayesian profile regression, a semi-supervised machine learning technique, may improve the precision of clustering by integrating outcome data, diminishing the variability observed with previous methods. Since quality of life (QoL) primarily drives the indication for AF ablation, we utilized the AF Effect on QualiTy-of-Life (AFEQT) questionnaire to cluster patients. Purpose Our aim is to use patient-reported outcome measures to identify clinically relevant phenotypes of AF that benefit the most from catheter ablation. Methods We implemented a single center follow-up (FUP) program for patients with AF referred for ablation since august 2020, including scheduled visits and remote monitoring through a digital health platform. AFEQT score reported by patients was analyzed using a non-linear mixed model and change in AFEQT by month 12 was used as outcome. Comorbidities, echocardiographic data, and biomarkers were collected. Profile regression mixture modelling guided by QoL improvement was performed to create clinically relevant patient groupings. Penalized multinomial logistic regression model was used for validation. Results From the 466 ablations performed, 354 patients were enrolled until July 2023. 211 patients had FUP &amp;gt; 12 months (age 59±10 years, 31% female, 80% paroxysmal). The overall AFEQT questionnaire completeness rate was 72%. Patients were classified into 3 clusters: 1) patients with long duration AF, left heart cavities enlargement and diastolic dysfunction (n=57); 2) patients with self-reported anxiety/depression (n=50); and 3) patients with low rates of comorbidities (n=104). For clinical application, 8 variables (dyslipidemia, AF type and duration since diagnosis, mitral E/A ratio, systolic pulmonary arterial pressure, left ventricle ejection fraction, and PROMIS T-score for anxiety and depression) were sufficient for classifying patients (85% accuracy in internal cross-validation). Patients with higher depressive and anxiety burden (cluster 2) had worse baseline AFEQT score (median: 46, IQR: 39-60) and derived less benefit (median improvement: 21, IQR: 12-32), while patients with low comorbid burden (cluster 3) displayed higher baseline (median: 58, IQR: 48-62) and final QoL (median improvement: 28, IQR: 21-36, p&amp;lt;0.01). Conclusion Our analysis identified 3 reproducible clinically relevant phenotypes of AF using widely available clinical data. These clusters have distinct associations with QoL, underscoring the heterogeneity of AF and importance of comorbidities and substrates. This approach may allow for the prediction of which patients are most likely to benefit from treatment.

A preliminary study of latent trajectories of change in dietary restraint during CBT-E for bulimia-spectrum eating disorders and their associations with treatment response

ABSTRACT Objective Dietary restraint is a primary target of CBT-E. However, little research has examined how specific types of dietary restraint change during CBT-E for bulimia-spectrum eating disorders (BN-EDs) or the association between changes in dietary restraint and treatment response. This study examined latent trajectories of change in eating enough, eating a range of macronutrients, and following dietary rules during CBT-E for BN-EDs and the relationships between these trajectories and pre- to post-treatment change in BN symptoms and remission. Method Participants were 56 adults with BN-EDs who received 16 sessions of CBT-E and completed the Eating Disorder Examination and ecological momentary assessments (EMA) of eating behaviors and BN symptoms. Latent growth mixture modeling identified trajectories of change in dietary restraint, which were compared on pre- to post-treatment BN symptom change and remission. Results Three trajectories of change were identified for eating enough, eating a range of macronutrients, and food rules. Trajectories of change in eating enough were differentially associated with pre- to post-treatment change in BN symptoms, and trajectories of change in eating a range of macronutrients and food rules were differentially associated with remission. Conclusions CBT-E yields heterogeneous trajectories of change in dietary restraint, which are associated with treatment response.

TSH Trajectories During Levothyroxine Treatment in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Cohort.

Thyroid-stimulating hormone (TSH) trajectory classification represents a novel approach to defining the adequacy of levothyroxine (LT4) treatment for hypothyroidism over time. This is a proof of principle study that uses longitudinal clinical data, including thyroid hormone levels from a large prospective study to define classes of TSH trajectories and examine changes in cardiovascular (CV) health markers over the study period. Growth mixture modeling (GMM), including latent class growth analysis (LCGA), was used to classify LT4-treated individuals participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) based on serial TSH levels. Repeated measure analyses were then utilized to assess within-class changes in blood pressure, lipid levels, hemoglobin A1c, and CV-related medication utilization. From the 621 LT4-treated study participants, the best-fit GMM approach identified 4 TSH trajectory classes, as defined by their relationship to the normal TSH range: (1) high-high normal TSH, (2) normal TSH, (3) normal to low TSH, and (4) low to normal TSH. Notably, the average baseline LT4 dose was lowest in the high-high normal TSH group (77.7 µg, P < .001). There were no significant differences in CV health markers between the classes at baseline. At least 1 significant difference in CV markers occurred in all classes, highlighted by the low to normal class, in which total and high-density lipoprotein cholesterol, triglycerides, and A1c all increased significantly (P = .049, P < .001, P < .001, and P = .001, respectively). Utilization of antihypertensive, antihyperlipidemic, and antidiabetes medications increased in all classes. GMM/LCGA represents a viable approach to define and examine LT4 treatment by TSH trajectory. More comprehensive datasets should allow for more complex trajectory modeling and analysis of clinical outcome differences between trajectory classes.

Uncovering the progressive failure process of primary coal-rock mass specimens: Insights from energy evolution, acoustic emission crack patterns, and visual characterization

The failure and instability of deep coal-rock mass structures, which are influenced by mining disturbances, are major contributors to disasters like rock bursts. This study involved conducting indoor experiments using uniaxial loading test and acoustic emission (AE) experiments on indigenous primary coal-rock mass (PCRM). We utilize chaotic bifurcation based on strain energy to characterize the progressive damage stages of PCRM, demonstrating the chaotic characteristics of energy evolution during the gradual destruction process. The degree of damage resulting from PCRM failure was evaluated by jointly applying P-wave velocity tomography and AE event localization. Additionally, the classification of crack patterns in PCRM was conducted using the RA-AF correlation analysis method. The segmentation boundaries of crack classification patterns were then redefined using the Gaussian mixture model (GMM) algorithm. The research findings indicate that the chaotic bifurcation model of strain energy classifies stress into four stages: the stable region (Ⅰ), the metastable region (Ⅱ), the bifurcation region (Ⅲ), and the chaotic region (Ⅳ). The presence of a low-velocity zone in P-waves may indicate crack accumulation and damage within the system. Additionally, areas with abnormal P-wave velocities exhibit numerous AE events. The crack classification patterns of the four stages undergo an evolution from predominantly tensile cracks to a mixture of tensile and shear cracks, and the GMM algorithm successfully identifies the optimal separation path for crack classification boundaries. We propose an adaptive kernel density estimation (AKDE) algorithm to quantify the spatial distribution of AE events, thus offering a visual representation of the damage patterns at various stages.

An inquiry into the drivers of an entrepreneurial economy: A Bayesian clustering approach

AbstractUnderstanding the worldwide drivers of qualified entrepreneurship is a key issue in economic policy design. To help policy decisions exert their intended impact, we aim to cluster a wide range of countries on the basis of their levels and trends in self-employment productivity using a finite mixture model applied to a new large dataset of 121 countries covering the period of 1991–2019. Our results point to three groups of high-, medium-, and low-productive means and tendencies, the geographical distribution of which suggests that they can be reinterpreted using the three stages of economic development, namely, innovation-, efficiency-, and factor-driven economies. Notably, we find that widespread digitalization and low unemployment enhance the probability of transitioning into a highly productive cluster. However, we failed to find that industry weight or employment protection legislation strictness serve as determinants in the transition between groups. Suggestive rationales for these results and implications for the entrepreneurship policy agenda are also provided.

A Dynamic Detection Method for Railway Slope Falling Rocks Based on the Gaussian Mixture Model Segmentation Algorithm

Rockfall intrusion detection is crucial for the safety management of railway operations, and video detection methods help reduce deployment costs and improve detection efficiency. Mainstream neural network-based video detection methods have rapidly evolved in recent years but struggle to adapt to complex scenarios such as existing railway slope constructions due to weak generalization ability, low accuracy, and limited information acquisition. Therefore, this paper introduces a dynamic neural network detection model and establishes a dataset for rockfall intrusions in existing railway slope scenarios. The model initially relies on the YOLOv5 neural network, adopting an activation function suitable for the target scenario and addressing overfitting to achieve precise target recognition. Based on the neural network, the model dynamically detects rolling rockfalls by integrating a background subtraction algorithm based on the Gaussian Mixture Model and captures target dimensions using monocular vision technology, thus broadening the dimensions of detection information. Trials conducted on a railway in Shandong, China, demonstrate that the model accurately identifies moving rockfalls along the railway slopes and acquires the dimensions of moving rockfalls, successfully filtering out low-risk targets in the scene.