Mixture Model Research Articles

Differential healthcare direct costs of asthma and chronic rhinosinusitis with nasal polyps in Catalonia (Spain).

This study compares the direct healthcare costs associated with asthma and chronic rhinosinusitis with nasal polyps (CRSwNP) from 2013 to 2017 in Catalonia (Spain) with a population control group without these conditions. A population administrative database containing healthcare information was used. The database contained information on primary care, hospitalisations, and emergency care from 2013 to 2017 in Catalonia. The unit cost of each healthcare procedure was imputed using a complete list of public prices for primary care services, hospital, and speciality services. Differential costs were estimated using a finite mixture model. Individuals diagnosed with asthma or CRSwNP showed a higher incidence of comorbidities than the control group. Mean annual direct costs per patient were €1,102 for asthma, €1,612 for CRSwNP and €2,197 for those with both conditions. According to our estimations, differential costs were €162 - €274 for patients with asthma and €481 - €1,257 for patients with CRSwNP compared to the reference population. These costs were significantly higher when asthma and CRSwNP coexist and especially in their severe condition. This population-based study revealed that asthma and CRSwN are associated with great economic burdens for healthcare systems. These costs were significantly higher when comorbidity was present (asthma and CRSwNP) and especially in their severe condition (€4,441).

Prediction of post-hemorrhagic ventricular dilatation trajectory using a growth mixture model in preterm infants.

Early intervention for post-hemorrhagic ventricular dilatation (PHVD), guided by ventricular size measurements from cranial ultrasound (cUS), is associated with improved neurodevelopmental outcomes in preterm infants but benefits must be balanced against intervention risks. Anterior horn width (AHW) and ventricular index (VI) were measured from cUS for preterm infants (<29 weeks) with intraventricular hemorrhage admitted from 2010-2018. PHVD was defined as AHW > 6 mm or VI >97th percentile for postmenstrual age. Individual ventricular size trajectories were plotted, and a growth mixture model (GMM) used to identify latent trajectory classes and compare these to predetermined outcome of neurosurgical intervention. Measurements were obtained from 1543 cUS in 249 infants, of whom 39 had PHVD without and 17 PHVD with neurosurgical intervention based on signs of raised intracranial pressure. The GMM predicted trajectory identified: 93.3% of infants without PHVD, 88.2% and 30.8% of infants with PHVD with and without intervention using AHW; 100% of infants without PHVD, 52.9% and 59.0% of infants with PHVD with and without intervention using VI. The AHW GMM identified a significant proportion of infants with severe PHVD. Model refinement offers a promising approach for identifying differences in PHVD trajectory at an early stage to guide management. It is difficult to distinguish the trajectory of PHVD in the early stage of development, in particular PHVD that spontaneously arrests from slowly progressive PHVD which eventually requires intervention. We report the first modeling-based evaluation of PHVD trajectory for the prediction of short-term outcome of PHVD progression and neurosurgical intervention. With additional clinical validation and optimization to increase accuracy, predictive modeling has the potential to identify important differences in PHVD trajectory at an early stage in the clinical course, allowing for more individualized data-driven risk-benefit assessments to guide decisions on early intervention.

Disposable e-cigarette use: Factors, frequency and cigarette smoking among United States high school students.

Disposable e-cigarette use has increased among United States (US)high school students in recent years. However, there is limited research on the profile of these users, how often they use these products, and whether they displace cigarette smoking. This study aimed to measure how disposable e-cigarette use among US youth varies according to demographic characteristics and whether there is any association between e-cigarette use and reduced use of traditional cigarettes. We used cross-sectional data from the 2022 National Youth Tobacco Survey and conducted a multinomial logistic regression to examine factors associated with the types of e-cigarette devices used in the prior 30 days, adjusting for sex, sexual identity, grade level and race/ethnicity. We also used a finite mixture model to account for unobserved differences among users and identify e-cigarette use patterns in different subgroups of users. United States. High school students in grades 9-12 (n = 14 389). Survey participants self-reported the type of e-cigarette device used, the frequency of e-cigarettes used and cigarettes smoked over the past 30 days. Disposable e-cigarettes were the most popular e-cigarette type. Sex, sexual orientation, grade level and race/ethnicity were associated with disposable e-cigarette use. The odds of disposable e-cigarette use were lower in male students than in female students (odds ratio [OR] = 0.78, 95% confidence interval [CI] = [0.64-0.96]), and higher in students who identified as gay or lesbian (OR = 1.70, 95% CI = [1.11-2.61]) or bisexual (OR = 1.52, 95% CI = [1.16-1.99]) than in heterosexual students. The odds of disposable use were higher among students in higher grades (10th, 11th and 12th) than in 9th graders (OR = 1.71, 2.24 and 2.52, respectively). Disposable e-cigarette users had a lower frequency of traditional cigarette use than other e-cigarette users, both in the low-frequency class (incidence rate ratio [IRR] = 0.33, 95% CI = [0.12-0.92]) and the high-frequency class (IRR = 0.27, 95% CI = [0.08-0.92]). Disposable e-cigarette use appears to be higher among United States high school students who are female, older and/or identify as gay, lesbian or bisexual. Disposable e-cigarettes appear to be associated with reduced traditional cigarette use.

Compositions of the Hercules-Aquila Cloud and Virgo Over-density

Abstract Based on a sample of K giant from Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST) Data Release 8 and a sample of RR Lyrae (RRL) from Gaia Data Release 3, we investigate the compositions of the Hercules-Aquila Cloud (HAC) and Virgo Over-density (VOD) and their collective contribution to the tilt and triaxiality of the stellar halo (r &amp;lt; 40 kpc) as well as two breaks at ≈15 kpc and 30 kpc. We apply the Gaussian mixture model (GMM) to divide the stellar halo into the isotropic component and the radially biased anisotropic component, namely Gaia-Sausage-Enceladus (GSE), and find that both HAC and VOD are dominated by the GSE debris stars with weights of $0.67^{+0.09}_{-0.07}$ and $0.57^{+0.07}_{-0.06}$, respectively. In addition, using the K giants with orbital parameters, we identify the member stars of known substructures, including GSE, Sagittarius (Sgr), Helmi Streams, Sequoia, Thamnos, Pontus, Wukong, and Metal-weak Thick Disk (MWTD), to probe the compositions of low-eccentricity stars in the HAC and VOD regions. In density fittings of the RRL sample, we note that the absence of HAC and VOD has a weak effect on the shape of halo. Finally, we find that the radially biased anisotropic halo contributes majorly to the stellar halo that can be modelled with a tilted triaxial ellipsoid and a doubly broken power law with breaking radii at $18.08^{+2.04}_{-3.22}\, {\rm kpc}$ and $33.03^{+1.30}_{-1.21}\, {\rm kpc}$. This has important significance for understanding the status of large diffuse over-densities in the Milky Way.

Active Bidirectional Self-Training Network for Cross-Domain Segmentation in Remote-Sensing Images

Semantic segmentation with cross-domain adaptation in remote-sensing images (RSIs) is crucial and mitigates the expense of manually labeling target data. However, the performance of existing unsupervised domain adaptation (UDA) methods is still significantly impacted by domain bias, leading to a considerable gap compared to supervised trained models. To address this, our work focuses on semi-supervised domain adaptation, selecting a small subset of target annotations through active learning (AL) that maximize information to improve domain adaptation. Overall, we propose a novel active bidirectional self-training network (ABSNet) for cross-domain semantic segmentation in RSIs. ABSNet consists of two sub-stages: a multi-prototype active region selection (MARS) stage and a source-weighted class-balanced self-training (SCBS) stage. The MARS approach captures the diversity in labeled source data by introducing multi-prototype density estimation based on Gaussian mixture models. We then measure inter-domain similarity to select complementary and representative target samples. Through fine-tuning with the selected active samples, we propose an enhanced self-training strategy SCBS, designed for weighted training on source data, aiming to avoid the negative effects of interfering samples. We conduct extensive experiments on the LoveDA and ISPRS datasets to validate the superiority of our method over existing state-of-the-art domain-adaptive semantic segmentation methods.

Exposure to metal mixtures and telomere length in Bangladeshi children.

Telomere length is associated with chronic diseases and in younger populations, may represent a biomarker of disease susceptibility. As growing evidence suggests that environmental factors, including metals, may impact telomere length, we investigated the association between 17 metals measured in toenail samples and leukocyte relative telomere length (RTL), among 472 five- to seven-year-old children enrolled in the Bangladesh Environmental Research in Children's Health (BiRCH) cohort. In single exposure linear regression models, a doubling of arsenic (As) and mercury (Hg) (μg/g) were associated with a -0.21 (95%CI: -0.032, -0.010; p=0.0005) and -0.017 (95%CI: -0.029, -0.004; p=0.006) difference in RTL, respectively. In Bayesian Kernel Machine Regression (BKMR) mixture models, the overall metal mixture was inversely associated with RTL (P-for-trend <0.001). Negative associations with RTL were observed with both log2-As and log2-Hg, while an inverted U-shaped association was observed for log2-zinc (Zn) with RTL. We found little evidence of interaction among metals. Sex-stratification identified stronger associations of the overall mixture and log2-As with RTL among females, compared to males. Our study suggests that As and Hg may independently influence RTL in mid-childhood. Further studies are needed to investigate potential long-term impacts of metal-associated telomere shortening in childhood on health outcomes in adult life.

Mixture and Non-mixture Cure Models for Health Technology Assessment: What You Need to Know.

There is increasing interest in the use of cure modelling to inform health technology assessment (HTA) due to the development of new treatments that appear to offer the potential for cure in some patients. However, cure models are often not included in evidence dossiers submitted to HTA agencies, and they are relatively rarely relied upon to inform decision-making. This is likely due to a lack of understanding of how cure models work, what they assume, and how reliable they are. In this tutorial we explain why and when cure models may be useful for HTA, describe the key characteristics of mixture and non-mixture cure models, and demonstrate their use in a range of scenarios, providing Stata code. We highlight key issues that must be taken into account by analysts when fitting these models and by reviewers and decision-makers when interpreting their predictions. In particular, we note that flexible parametric non-mixture cure models have not been used in HTA, but they offer advantages that make them well suited to an HTA context when a cure assumption is valid but follow-up is limited.

Mapping the world health organization disability assessment scale 2.0 to the EQ-5D-5L in patients with mental disorders

ABSTRACT Objective The current study aims to develop an algorithm that mapping the WHODAS 2.0 to the EQ-5D-5 L for patients with mental disorders. Methods This cross-sectional study was conducted at the Institute of Mental Health and the Community Wellness Clinics in Singapore between June 2019 and November 2022. We included four regression methods including the Ordinary Least Square (OLS) regression, Tobit regression model (Tobit), the robust regression with MM estimator (MM) and the adjusted limited dependent variable mixture models (ALDVMM) to map EQ-5D-5 L utility scores from the WHODAS 2.0. Results A total of 797 participants were included. The mean EQ-5D-5 L utility and WHODAS 2.0 total scores were 0.61 (SD = 0.34) and 11.96(SD = 8.97), respectively. We found that the EQ-5D-5 L utility score was best predicted by the robust regression model with the MM estimator. Our findings suggest that the WHODAS 2.0 total scores were significantly and inversely associated with the EQ-5D-5 L utility scores. Conclusion This study provides a mapping algorithm for converting the WHODAS 2.0 scores into EQ-5D-5 L utility scores which can be implemented using a simple online calculator in the following web application: https://eastats.shinyapps.io/whodas_eq5d/.

ASOD: an adaptive stream outlier detection method using online strategy

In the current era of information technology, blockchain is widely used in various fields, and the monitoring of the security and status of the blockchain system is of great concern. Online anomaly detection for the real-time stream data plays vital role in monitoring strategy to find abnormal events and status of blockchain system. However, as the high requirements of real-time and online scenario, online anomaly detection faces many problems such as limited training data, distribution drift, and limited update frequency. In this paper, we propose an adaptive stream outlier detection method (ASOD) to overcome the limitations. It first designs a K-nearest neighbor Gaussian mixture model (KNN-GMM) and utilizes online learning strategy. So, it is suitable for online scenarios and does not rely on large training data. The K-nearest neighbor optimization limits the influence of new data locally rather than globally, thus improving the stability. Then, ASOD applies the mechanism of dynamic maintenance of Gaussian components and the strategy of dynamic context control to achieve self-adaptation to the distribution drift. And finally, ASOD adopts a dimensionless distance metric based on Mahalanobis distance and proposes an automatic threshold method to accomplish anomaly detection. In addition, the KNN-GMM provides the life cycle and the anomaly index for continuous tracking and analysis, which facilities the cause analysis and further interpretation and traceability. From the experimental results, it can be seen that ASOD achieves near-optimal F1 and recall on the NAB dataset with an improvement of 6% and 20.3% over the average, compared to baselines with sufficient training data. ASOD has the lowest F1 variance among the five best methods, indicating that it is effective and stable for online anomaly detection on stream data.

Two-Part Mixed Effects Mixture Model for Zero-Inflated Longitudinal Compositional Data

AbstractCompositional data (CD) is mostly analyzed using ratios of components and log-ratio transformations to apply known multivariable statistical methods. Therefore, CD where some components equal zero represents a problem. Furthermore, when the data is measured longitudinally, and appear to come from different sub-populations, the analysis becomes highly complex. Our objective is to build a statistical model addressing structural zeros in longitudinal CD and apply it to the analysis of radiation-induced lung damage (RILD) over time. We propose a two-part mixed-effects model extended to the case where the non-zero components of the vector might come from a two-component mixture population. Maximum likelihood estimates for fixed effects and variance components were calculated by an approximate Fisher scoring procedure base on sixth-order Laplace approximation. The expectation-maximization (EM) algorithm estimates the mixture model’s probability. This model was used to analyze the radiation therapy effect on tissue change in one patient with non-small cell lung cancer (NSCLC), utilizing five CT scans over 24 months. Instead of using voxel-level data, voxels were grouped into larger subvolumes called patches. Each patch’s data is a CD vector showing proportions of dense, hazy, or normal tissue. Proposed method performed reasonably for estimation of the fixed effects, and their variability. However, the model produced biased estimates of the nuisance parameters in the model.

O-257 Comparing the accuracy of the novel clustering model versus phenotype classification in predicting live birth in polycystic ovary syndrome women undergoing In-Vitro fertilization

Abstract Study question Is the clustering model more accurate than phenotype classification in predicting live birth in polycystic ovary syndrome (PCOS) women undergoing in vitro fertilization (IVF)? Summary answer Novel PCOS clusters, identified by patients’ endocrine and metabolic profiles, more accurately predict live birth rates in women undergoing IVF than the traditional phenotype classification What is known already PCOS is a heterogeneous condition that affects fertility and IVF success. It is classified based on three factors: hyperandrogenism (HA), ovulatory dysfunction (OD), and polycystic ovaries (PCOM). The ESHRE/ASRM classification system identifies four phenotypes: A (including all three factors), B (HA and OD), C (HA and PCOM), and D (OD and PCOM). Although this classification is widely used, its focus solely on clinical signs and symptoms, while neglecting underlying endocrine and metabolic factors, may limit its effectiveness in predicting fertility outcomes in PCOS patients. Therefore, classifying PCOS patients may require a more comprehensive and inclusive assessment approach. Study design, size, duration This prospective cohort study was conducted from June 2020 to August 2022 at a tertiary IVF center in Ho Chi Minh City, Vietnam. Clustering models were built based on the endocrine and metabolic profiles of 731 PCOS patients. Patients were grouped into novel clusters based on their endocrine and metabolic profiles and compared with traditional phenotypes classification. The primary outcome measured was the live birth rate. Participants/materials, setting, methods Patients aged 18–45, diagnosed with PCOS according to the Rotterdam 2003 criteria, and undergoing IVF treatment were included in the study. Patients with hypothyroidism, Cushing’s syndrome, congenital adrenal hyperplasia, or contraindications to hormonal treatment were excluded. We employed Gaussian Mixture Models for clustering, using endocrine and metabolic data as the key features. We then compared the accuracy of the clustering model with phenotype classification in predicting IVF outcomes using a logistic regression model. Main results and the role of chance We identified three distinct clusters among polycystic ovary syndrome (PCOS) patients. Cluster 0 (n = 298) was characterized by significant insulin resistance and dyslipidemia. Cluster 1 (n = 219) displayed the lean PCOS phenotype, and Cluster 2 (n = 214) had moderate insulin resistance with severe hyperandrogenism. Our findings indicated that Clusters 1 and 2 had a higher cumulative live birth rate (CLBR) (39.7% and 37.9% versus 28.5% in Cluster 0, p = 0.015) and lower gestational diabetes rates (8.68% and 6.07%, respectively) compared to Cluster 0 (12.4%, p = 0.048). Additionally, the prevalence of hypertensive disorders during pregnancy showed differences across clusters (3.69% in Cluster 1, 0.46% in Cluster 2, and 2.8% in Cluster 0, p = 0.059). In contrast, the traditional phenotype classification did not demonstrate significant differences in CLBR, gestational diabetes, and hypertensive disorders. The cluster-based method provided a more accurate prediction of CLBR using logistic regression, with an AUC of 0.67 (95% CI: 0.57-0.76) compared to 0.49 (95% CI: 0.41-0.59) for traditional classifications (p = 0.001) Limitations, reasons for caution This is a single-center study conducted on Vietnamese patients. Therefore, the generalizability may be limited. The novel clustering’s predictive value for IVF outcomes in PCOS patients requires further validation in prospective studies. Wider implications of the findings This study suggests a potential shift towards personalized IVF treatment strategies for PCOS, using machine learning-based PCOS clustering identified by patients’ endocrine and metabolic profiles. Trial registration number NCT04364087

Improved control of Trialeurodes vaporariorum using mixture combinations of entomopathogenic fungi and the chemical insecticide spiromesifen

Greenhouse whitefly (Trialeurodes vaporariorum) is a major global pest, causing direct damage to plants and transmitting viral plant diseases. Management of T. vaporariorum is problematic because of widespread pesticide resistance, and many greenhouse growers rely on biological control agents to regulate T. vaporariorum populations. However, these are often slow and vary in efficacy, leading to subsequent application of chemical insecticides when pest populations exceed threshold levels. Combining chemical and biological pesticides has great potential but can result in different outcomes, from positive to negative interactions. In this study, we evaluated co-applications of the entomopathogenic fungi (EPF) Beauveria bassiana and Cordyceps farinosa and the chemical insecticide spiromesifen in laboratory bioassays. Complex interactions between the EPFs and insecticide were described using an ecotoxicological mixtures model, the MixTox analysis. Depending on the EPF and chemical concentrations applied, mixtures resulted in additivity, synergism, or antagonism in terms of total whitefly mortality. Combinations of B. bassiana and spiromesifen, compared to single treatments, increased the rate of kill by 5 days. Results indicate the potential for combined applications of EPF and spiromesifen as an effective integrated pest management strategy and demonstrate the applicability of the MixTox model to describe complex mixture interactions.

Identifying longitudinal cognitive resilience from cross-sectional amyloid, tau, and neurodegeneration

BackgroundLeveraging Alzheimer’s disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR.MethodsWe identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women).ResultsThe favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline.ConclusionThese findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States

Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO2 Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr−1 and pH decrease of 0.0014 ± 0.0004 yr−1. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.

Using Learning from Demonstration (LfD) to perform the complete apple harvesting task

Learning from Demonstration (LfD) can be used to make it easier for robots to learn to perform tasks such as apple harvesting. The required apple harvesting motions can be split into four steps: 1. Approaching, 2. Grasping, 3. Detaching, 4. Placing. So far, only parts of the apple harvesting task have been learned using LfD. In our work, we learned an abstracted version of the complete apple harvesting task using a cube. We used two methods and tested the sensitivity to model parameters and the number of demonstrations. We selected (1) the Gaussian Mixture Model (GMM) with Gaussian Mixture Regression (GMR) and (2) the Hidden Markov Model (HMM) with Linear Quadratic Regression (LQR). Both methods make use of Task Parameterization (TP), which allows models to combine multiple phases from the apple harvesting task. Combining multiple harvesting phases is important for effective LfD. We analysed the performance of these algorithms on four performance metrics, namely the grasp pose, the detachment motion, the place pose, and the success rate when using the real robot. A high grasp pose accuracy was achieved, specifically a position error of 0.004 and an orientation error of 0.004 for the GMM with GMR, trained with 100 demonstrations and 9 Gaussian components. The detachment motion was performed but with a reduced range. The model that performed the best was the GMM with GMR, trained with 60 demonstrations and 5 Gaussian components. A high place pose accuracy was achieved, specifically a position error of 0.003 and an orientation error of 0.003 for the GMM with GMR, trained with 100 demonstrations and 15 Gaussian components. The task was executed by a real robot successfully, resulting in a success rate of 67% for HMM with LQR, trained with 40 demonstrations, 5 states, and a scaling factor of 1.0. For the number of components, no clear relation was found. The same was true for the number of states in the HMM with LQR. For the scaling factor, a clear relation could be found. For this task, a value in the range of -1.0 to 1.0 should be used. For the number of demonstrations, there was an optimum of 40 demonstrations. Further improvements are needed to deal with challenges such as apple shift and detecting detachment of fruits.

Thermomechanical bending of functionally graded carbon nanotubes reinforced composite plate by meshless method

AbstractFunctionally graded (FG) carbon nanotubes (CNTs) reinforced composite possessing continuously varied material properties have received extensive concern in the area of aviation, automotive, military, and spaceflight. The nonlinear bending of FG CNT composite is performed by developing a novel meshless model on the base of Smoothed Particle Hydrodynamics (SPH) method. The equivalent temperature relative material properties of composite is established in the light of the extended rule of mixture model. Different gradient dispersions of CNTs are taken into account and modeled. The governing equations are explored using the Mindlin theory and discretized though a symmetric SPH method with high prediction ability. The developed model is verified by analyzed several examples and compared to the solution obtained in literature. Influences of CNT dispersion, content, plate aspect ratio, boundary conditions and lamination angle on the bending response are elaborated. The present study provides an alternative potential method to solve the mechanical performances of FG CNT composites based on the meshless SPH formulation.Highlights Thermo‐mechanics of FG‐CNTRC is firstly solved by SPH method. The model is verified by solving the benchmarks in literature. Nonlinear bending behaviors of FG‐CNTRC are demonstrated. The minimum deflection take place in FG‐X type composite. For laminate plate, the smallest deformation occurs in 0° CNTRC.

Accelerating analysis of Boltzmann equations using Gaussian mixture models: Application to quantum Bose-Fermi mixtures

The Boltzmann equation is a powerful theoretical tool for modeling the collective dynamics of quantum many-body systems subject to external perturbations. Analysis of the equation gives access to linear response properties including collective modes and transport coefficients, but often proves intractable due to computational costs associated with multidimensional integrals describing collision processes. Here, we present a method to resolve this bottleneck, enabling the study of a broad class of many-body systems that appear in fundamental science contexts and technological applications. Specifically, we demonstrate that a Gaussian mixture model can accurately represent equilibrium distribution functions, thereby allowing efficient evaluation of collision integrals. Inspired by cold atom experiments, we apply this method to investigate the collective behavior of a quantum Bose-Fermi mixture of cold atoms in a cigar-shaped trap, a system that is particularly challenging to analyze. We focus on monopole and quadrupole collective modes above the Bose-Einstein transition temperature, and find a rich phenomenology that spans interference effects between bosonic and fermionic collective modes, dampening of these modes, and the emergence of hydrodynamics in various parameter regimes. These effects are readily verifiable experimentally. Published by the American Physical Society 2024

Optimizing Patient Stratification in Healthcare: A Comparative Analysis of Clustering Algorithms for EHR Data

Advanced data analytics are increasingly being employed in healthcare research to improve patient classification and personalize medicinal therapies. In this paper, we focus on the critical problem of clustering electronic health record (EHR) data to enable appropriate patient categorization. In the era of personalized medicine, optimizing patient classification is critical to healthcare analytics. This research presents a comparative assessment of different clustering algorithms for Electronic Health Record (EHR) data, with the goal of improving the efficacy and productivity of patient clustering methods. Our study focuses on Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) as a Multi-Criteria Decision-Making (MCDM) strategy, includes an in-depth assessment of eight clustering algorithms: K-Means, DBSCAN, Hierarchical Clustering, Mean Shift, Affinity Propagation, Spectral Clustering, Gaussian Mixture Models (GMM), as well as Self-Organizing Maps. The evaluation factors used for evaluation in this research are Cluster Quality Metrics, Scalability, Robustness to Noise, Cluster Shape and Density, Interpretability, Cluster Number, Dimensionality, and Consistency and Stability. These criteria and alternatives were chosen after conducting a thorough assessment of the literature and consulting with domain experts. All participated specialists actively engaged in the decision-making process, bringing unique insights into the best clustering algorithms for healthcare data. The results of this study illustrate each algorithm’s strengths and weaknesses in the setting of patient stratification, providing insight into their performance across multiple dimensions. The fuzzy TOPSIS MCDM strategy is a reliable instrument for synthesizing expert opinions and methodically evaluating the found clustering alternatives. This study advances healthcare analytics by giving practitioners and researchers with informative perspectives on the selection of clustering algorithms designed to address the unique problems of patient stratification utilizing EHR data.

Optimising miniaturised hydrocyclones for enhanced separation of microplastics

Microplastics, particularly those in the 5–20μm range, pose substantial risks in aquatic environments, yet effective removal techniques are limited. This study assesses two critical geometric parameters of a 10 mm mini-hydrocyclone: inlet radius and insertion angle. Along with the inlet velocity, they are optimised collectively to enhance the MPs’ removal. Computational Fluid Dynamics (CFD) simulations using the Mixture model and Reynolds stress model were employed to track the particle-water-air interactions, revealing the flow dynamics and absence of an air core. The Response Surface Methodology (RSM) using a Box-Behnken design of experiments was adopted to assess the impact of the geometric and operational parameters and determine the optimal design. Recovery efficiency, concentration efficiency, and split rate were recorded during experiments and compared with CFD results, showing a good agreement between experiments and CFD simulations. The resulting novel design achieved an 88.53% MPs recovery and a concentration efficiency of 1.83, which represents an obvious improvement over the commercial design’s MPs recovery efficiency of 79.97% and concentration efficiency of 1.68. Simulations reveal that the novel design shortens the path for MPs through the forced turbulence region and modifies the velocity and turbulence fields, enhancing the concentration gradient of MPs within the mini-hydrocyclone’s chamber. Moreover, the novel design exhibits a more robust response to inlet velocity variations, a crucial advantage for its application in arrays. Experiments further confirm that a matrix composed of 10 novel mini-hydrocyclones achieves 10 percentage points higher recovery efficiency than the matrix with 10 commercial mini-hydrocyclones. These findings mark a significant step towards effective MP removal, addressing a critical environmental issue.

Probabilistic Principal Curves on Riemannian Manifolds.

This paper studies a new curve-fitting approach to data on Riemannian manifolds. We define a principal curve based on a mixture model for observations and unobserved latent variables and propose a new algorithm to estimate the principal curve for given data points on Riemannian manifolds.