Mixture Model Method Research Articles

Pengelompokan Provinsi di Indonesia Menggunakan Gaussian Mixture Model Berdasarkan Indikator Kemiskinan

Poverty is a multidimensional concept that can not only be seen from an economic perspective, but can also be seen from a social, cultural and political perspective. Poverty is generally expressed as a static concept with the amount of poverty as a description of welfare conditions at a certain time. In reality, poverty has a time period that is never cut off and has a continuous pattern over time. The Indonesian government is committed to efforts to eradicate poverty in order to reduce Indonesia's poverty rate. One way that can be done is by allocating aid to the community. Grouping is carried out by cluster analysis using the Gaussian Mixture Model method with the Expectation-Maximization (EM) algorithm. The optimal number of clusters in the Gaussian Mixture Model uses the Bayesian Information Criterion (BIC) method. Based on the research carried out, the results of the analysis of cluster grouping using the Gaussian Mixture Model method were obtained with the smallest BIC value, namely -397.6876 with a total of 2 clusters.

Unveiling the shared genetic architecture between testosterone and polycystic ovary syndrome

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation.

Association between comorbidity clusters and mortality in patients with cancer: A machine learning analysis of data from the US National Health and Nutrition Examination Survey 1999–2018.

11132 Background: Multimorbidity is common among patients with cancer; however, research on cancer prognosis has predominantly focused on cancers in isolation. This study identified comorbidity clusters among patients with cancer using machine learning and examine their associations with survival outcomes in a nationally representative sample of the US. Methods: This study used 10 survey cycles of the National Health and Nutrition Examination Survey from 1999 to 2018. Participants aged ≥20 years with a self-reported history of cancer were included. Comorbidities were ascertained through self-reports and quantitative measurements. Machine learning techniques, including Bernoulli mixture model and partition-based methods, were used to identify comorbidity clusters. Cox proportional hazards models were used to analyze the associations between comorbidity clusters and mortality outcomes, including all-cause mortality and cause-specific mortality (cancer, cardiovascular disease [CVD], and respiratory diseases), adjusting for relevant covariates. Results: The study included 4,390 participants. Four comorbidity clusters were identified: Low Comorbidity, Metabolic, CVD, and Respiratory. After adjusting for confounders, participants in the Respiratory Cluster had the highest risk of all-cause mortality (adjusted hazard ratio[aHR]=1.62, 95% confidence interval [CI]=1.26-2.08, p<0.001), followed by the CVD Cluster (aHR=1.50, 95%CI = 1.26-1.80, p<0.001) and the Metabolic Cluster (aHR=1.15, 95%CI=1.02-1.29, p=0.03) compared to the Low Comorbidity Cluster. The Metabolic, CVD, and Respiratory clusters were associated with higher risks of cardiovascular disease-related mortality (aHR=1.48-3.05, p<0.003), but no significant differences in cancer mortality were observed among the clusters. The effects of comorbidity clusters on all-cause mortality were modified by income-to-poverty ratio ( p for interaction=0.04), diet quality ( p for interaction=0.02), time since cancer diagnosis ( p for interaction=0.009), and cancer prognosis ( p for interaction=0.005). Conclusions: High comorbidity burden clusters showed increased all-cause and CVD-related mortality. Moreover, diet quality and socioeconomic status modified these associations. Machine learning approaches can provide valuable insights into complex multimorbidity profiles in patients with cancer. Further research is needed to deepen understanding of the relationships between multimorbidity, mortality, and cancer-specific outcomes.

A trajectory planning method for robotic arms based on improved dynamic motion primitives

PurposeTraditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).Design/methodology/approachThis study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized.FindingsExperiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning.Originality/valueIt provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases

BackgroundDespite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD.MethodsBased on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets.ResultsCompared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15–1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure.ConclusionsThis study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.

The GAPS Programme at TNG

Context. Stellar activity is the most relevant types of astrophysical noise that affect the discovery and characterization of extrasolar planets. On the other hand, the amplitude of stellar activity could hint at an interaction between the star and a close-in giant planet. Progress has been made in recent years in understanding how to deal with stellar activity and search for observational evidence of star-planet interactions. Aims. The aim of this work is to characterize the chromospheric activity of stars hosting short-period exoplanets by studying the correlations between the chromospheric emission (CE) in the Ca II H&K and the planetary parameters. Methods. We measured CE in the Ca II H&K lines using more than 1900 high-resolution spectra of a sample composed of 76 targets, observed with the HARPS-N spectrograph between 2012 and 2020. We transformed the fluxes into bolometric- and photospheric-corrected chromospheric emission ratios, R′HK. Furthermore, we completed the sample of hosts digging for data in previous works. Stellar parameters Teff, B–V, and V were retrieved homogeneously from the Gaia DR3. Then, M★, R★, and ages were determined from isochrone fitting. We retrieved planetary data from the literature and catalogs. The search for correlations between the log(R′HK) and planetary parameters have been performed through both Spearman’s rank and its statistics as well as the more sophisticated Gaussian mixture model method. Results. We found that the distribution of log(R′HK) for the transiting planet hosts is different from the distribution of field main-sequence and sub-giant stars. The log(R′HK) of planetary hosts is correlated with planetary parameters proportional to the planetary radius to the power of n (RPn, indicating a common origin for the correlations. The statistical analysis has also highlighted four clusters of host stars with different behavior in terms of their stellar activity with respect to the planetary surface gravity. Some of the host stars have a value of log(R′HK) that is lower than the basal level of activity for main sequence stars. The planets of these systems are very close to filling their Roche lobe, suggesting that they evaporate through hydrodynamic escape under the strong irradiation of the host star, creating shrouds that absorb the core of the chromospheric resonance lines.

An entire life-cycle rolling bearing remaining useful life prediction method using new degradation feature evaluation indicators

The rolling bearing remaining useful life (RUL) prediction is a hot topic issue in the field of rail transportation. The existing RUL prediction methods for rolling bearing have problems such as unreasonable division of rolling bearing degradation stages and incomplete extraction of degradation features by feature selection indicators. In order to solve these problems, an entire life-cycle rolling bearing RUL prediction method using new degradation feature evaluation indicators is proposed. Firstly, the degradation feature evaluation indicator is designed to evaluate the stability of the degradation feature. Then, the combination of stability evaluation indicator and correlation evaluation indicator is used as the basis for feature selection. Secondly, the Gaussian Mixture Model (GMM) method is fused with the Support Vector Machine (SVM) to divide the bearing entire life-cycle into three stages: normal stage, early degradation stage, and degradation stage. Finally, the Long Short-Term Memory (LSTM) network model is trained separately to predict the rolling bearing RUL for different rolling bearing degradation stages. The effectiveness of the proposed prediction method based on different degradation stages of rolling bearing in predicting the RUL of rolling bearing is verified through PRONOSTIA bearing dataset. The comparison with existing methods shows that this approach demonstrates superior accuracy and predictive performance. For example, the Mean Square Error (MSE) evaluation metric has decreased by 60%. The Root Mean Square Error (RMSE) evaluation metric has decreased by 36.5%. The Mean Absolute Error (MAE) evaluation metric has decreased by 48.6%. The Mean Absolute Percentage Error (MAPE) evaluation metric has decreased by 36.9%.

Association of blood glucose change with postoperative delirium after coronary artery bypass grafting in patients with diabetes mellitus: a study of the MIMIC-IV database.

Study results on blood glucose and the risk of delirium in patients receiving cardiac surgery are inconsistent, and there is also a gap in how to manage blood glucose after coronary artery bypass grafting (CABG). This study focused on patients with diabetes mellitus (DM) undergoing CABG and explored the associations of different blood glucose-related indexes and blood glucose change trajectory with postoperative delirium (POD), with the aim of providing some information for the management of blood glucose in this population. Data of patients with DM undergoing CABG were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database in this retrospective cohort study. The blood glucose-related indexes included baseline blood glucose, mean blood glucose (MBG), mean absolute glucose (MAG), mean amplitude of glycemic excursions (MAGE), glycemic lability index (GLI), and largest amplitude of glycemic excursions (LAGE). The MBG trajectory was classified using the latent growth mixture modeling (LGMM) method. Univariate and multivariate logistic regression analyses were utilized to screen covariates and explore the associations of blood glucose-related indexes and MBG trajectory with POD. These relationships were also assessed in subgroups of age, gender, race, estimated glomerular filtration rate (eGFR), international normalized ratio (INR), sepsis, mechanical ventilation use, and vasopressor use. In addition, the potential interaction effect between blood glucose and hepatorenal function on POD was investigated. The evaluation indexes were odds ratios (ORs), relative excess risk due to interaction (RERI), attributable proportion of interaction (AP), and 95% confidence intervals (CIs). Among the eligible 1,951 patients, 180 had POD. After adjusting for covariates, higher levels of MBG (OR = 3.703, 95% CI: 1.743-7.870), MAG >0.77 mmol/L/h (OR = 1.754, 95% CI: 1.235-2.490), and GLI >2.6 (mmol/L)2/h/per se (OR = 1.458, 95% CI: 1.033-2.058) were associated with higher odds of POD. The positive associations of MBG, MAG, and GLI with POD were observed in patients aged <65 years old, male patients, White patients, those with eGFR <60 and INR<1.5, patients with sepsis, and those who received mechanical ventilation and vasopressors (all p < 0.05). Patients with class 3 (OR = 3.465, 95% CI: 1.122-10.696) and class 4 (OR = 3.864, 95% CI: 2.083-7.170) MBG trajectory seemed to have higher odds of POD, compared to those with a class 1 MBG trajectory. Moreover, MAG (RERI = 0.71, 95% CI: 0.14-1.27, AP = 0.71, 95% CI: 0.12-1.19) and GLI (RERI = 0.78, 95% CI: 0.19-1.39, AP = 0.69, 95% CI: 0.16-1.12) both had a potential synergistic effect with INR on POD. Focusing on levels of MBG, MAG, GLI, and MBG trajectory may be more beneficial to assess the potential risk of POD than the blood glucose level upon ICU admission in patients with DM undergoing CABG.

Online Condition Monitoring of Industrial Loads Using AutoGMM and Decision Trees

Condition monitoring and fault management approaches can help with timely maintenance planning, assure industry-wide continuous production, and enhance both performance and safety in complex industrial operations. At the moment, data-driven approaches for condition monitoring and fault detection are the most attractive being conceived, developed, and applied with less of a need for sophisticated expertise and detailed knowledge of the addressed plant. Among them, Gaussian mixture model (GMM) methods can offer some advantages. However, conventional GMM solutions need the number of Gaussian components to be defined in advance and suffer from the inability to detect new types of faults and identify new operating modes. To address these issues, this paper presents a novel data-driven method, based on automated GMM (AutoGMM) and decision trees (DTree), for the online condition monitoring of electrical industrial loads. By leveraging the benefits of the AutoGMM and the DTree, after the training phase, the proposed approach allows the clustering and time allocation of nominal operating conditions, the identification of both already-classified and new anomalous conditions, and the acknowledgment of new operating modes of the monitored industrial asset. The proposed method, implemented on a commercial cloud-computing platform, is validated on a real industrial plant with electrical loads, characterized by a daily periodic working cycle, by using active power consumption data.

Spatiotemporal distribution of precipitation and its characteristics under tropical atmospheric systems of Brazil: Insights from a large sub‐hourly database

AbstractThe study of rainfall properties at various spatiotemporal scales is deeply important for understanding a large range of socio‐environmental processes and variables (e.g., water resources, agriculture, socio‐ecosystemic services, natural risk assessment). However, such studies on rainfall characteristics, especially at sub‐hourly resolutions, are scarce in large areas of South America due to the lack of a high‐resolution temporal database. The Brazilian National Centre for Monitoring and Early Warning of Natural Disasters (CEMADEN) has gradually implemented, starting in 2013, a sub‐hourly monitoring network composed of approximately 3500 automated rain gauges distributed across Brazil, enabling access to new hydrological studies in this vast tropical country featuring a range of biomes. This study analysed the characteristics of rainfall events for the whole of Brazil on sub‐daily and sub‐hourly timescales, using 7 years of data (from 2014 to 2021) provided by CEMADEN. Rainfall events were defined by the minimum inter‐event time (MIT) and the minimum depth (1 mm). Seven MITs (30, 60, 120, 180, 360, 720, and 1440 min) were considered to evaluate the behaviour of rainfall event characteristics and their relationships. The Gaussian mixture model method was applied to identify regions with similar rainfall patterns according to the MIT. Six groups with homogeneous characteristics were identified, evidencing the climatic diversity of Brazil. The results show that the MIT strongly influences precipitation properties (especially the dry time and the number of events). The highest number of events occurred in the North‐east Coast region, which exceeded 200 events per year (MIT &lt; 60 min), while the lowest number of events was observed in the Semiarid region, which reached only 38 events per year with an MIT of 1440 min. Moreover, the events with the highest rainfall intensities were found in the Central region. The results found in this study provide a better understanding of precipitation and its characteristics in Brazil, highlighting the climatological diversity of this country.

A Learning-Based Object Tracking Strategy Using Visual Sensors and Intelligent Robot Arm

This paper focuses on addressing the visual tracking problem using learning-based methods for object tracking tasks. This problem contains a major difficulty, i.e., how to acquire a satisfactory generalization ability of the developed system? In this paper, firstly, the object state tracking system, including a camera-in-hand, a 3D camera and a Rethink Baxter robot, is introduced. The problem formulation is also presented. Secondly, we propose a Kalman-based estimation strategy to acquire the object’s state. In addition, a learning-based tracking controller is developed using the Gaussian mixture models (GMM) method to steer the robot end-effector to track the mobile object. Thirdly, to guarantee system stability (i.e., the position and velocity errors between the object and end-effector will always converge to zeros), the controller parameter constraints are derived, which is a theoretical contribution of this paper. The controller parameter adjustment is avoided by the proposed training process. Thus, the proposed method becomes easy to implement, which is a practical contribution. Finally, the effectiveness of the proposed method is demonstrated by simulation and experimental examples, and the proposed method has satisfactory generalization ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper studies object tracking problems for different practical applications, such as industrial cutting, grasping and dynamic monitoring. Different trajectory tracking methods have been widely applied in the industrial area. However, users always complain that when the object or trajectory is changed, the tracking controller more or less needs to be re-adjusted. This re-adjust process always requires professional knowledge and programming experience, and thus a factory must employ some professional engineers. In addition, since the objects may be diverse in shape, color and size, to acquire the accurate object position and velocity, an appropriate solution is necessary. Motivated by the above introductions, this paper aims to develop a learning-based controller to track different complex trajectories without frequent and specific parameter adjustment processes. Firstly, a visual measurement system is developed to quickly find and estimate the position and velocity of an object. Secondly, with the object’s information, the learning from demonstration method (GMM method) is applied for the control policy design. Thirdly, the detailed system stability analysis is presented, and the corresponding controller parameter constraints are derived and considered in the proposed control policy. Subsequently, with the demonstration data, the packaged learning algorithm will automatically compute the controller parameters, and users can change the controller performance only by providing the desired demonstrations. In summary, this paper proposes a systematic object tracking solution, and it may bring a new idea to develop a practical object tracking system, using both the learning-based methods to improve the ability of generalization for tracking different objects.

Pengelompokkan kabupaten/kota di Pulau Sulawesi berdasarkan indikator indeks khusus penanganan stunting menggunakan Gaussian mixture model

Stunting or is a condition of growth failure in children under five years old (toddlers) who are malnourished. Children are called stunted if their height is below minus two standard deviations. The Special Index for Handling Stunting (IKPS) is one of the main indicators used as a measure of the achievement of handling the reduction in stunting rates. In implementing the 2024 development goals, namely setting a national stunting target that can decrease to 14%, it is necessary to identify based on the characteristics of a special index for handling stunting in each region. Method of Gaussian Mixture Model is a grouping with models that function to group a certain amount of data into a Gaussian distribution with mean and variance parameters. The main idea of model-based grouping is that object grouping is done using probabilities. This study was conducted with the aim of obtaining the results of grouping regencies/cities on Sulawesi Island based on the Special Index Indicator for Handling Stunting. The results of the study obtained different volume and shape models, coordinate axis orientation (VVI) with 4 optimum cluster. Cluster 1 with a low stunting handling index contains 24 districts/cities, cluster 2 with a medium stunting handling index contains 21 districts/cities, cluster 3 with a high stunting handling index contains 13 districts/cities, cluster 4 with a very high stunting handling index contains 23 districts/cities.Keywords: Four optimum cluster, low, medium, high and very highMSC2020: 62A09

Do controlling shareholders' equity pledges affect M&amp;A investment? Evidence from listed companies in China

As a new financing method, equity pledges are popular in China’s capital market. With the emergence of new financing models, will the relationship between financing and investment in corporate financial management change? The purpose of this study is to determine whether the controlling shareholders' equity pledges affect corporate merger and acquisition (M&amp;A) investment decisions. This paper uses data for listed companies from the China Stock Market &amp; Accounting Research (CSMAR) database, and Stata 17 is used as the data processing software. The M&amp;A variable is virtual and uses the logit model to study the relationship between controlling shareholders' equity pledges and M&amp;A investments. To test the robustness of the results, we replace the key variables, transform the model, and use the Gaussian Mixture Model (GMM) method to obtain the same results. The study found a positive correlation between controlling shareholders' equity pledges and M&amp;A investments. Further cross-sectional study shows that the positive correlation is stronger in state-owned companies, non-manufacturing companies, and companies with low pledge ratios. This study is conducive to standardizing the equity pledge behavior of listed companies and safeguarding the interests of investors.

Automated integration gate selection for Gaussian mixture model pulse shape discrimination

Pulse shapes differ between neutron and gamma particles when measured with detector devices employing pulse shape discriminating (PSD) scintillators. Digitized waveforms can be used in detection systems to perform pulse shape discrimination for this application. Prior Gaussian Mixture Model (GMM) methods require access to the pulse full-waveform. Reducing the waveform to a smaller set of combined samples reduces computational cost while affecting PSD performance. In this work, we develop a method for selecting the best performing combination of integration gates, or contiguous summed segments of the digitized pulse for PSD. The method uses a discrimination score based on the GMM PSD approach. The final selection is performed using Bayesian Optimization. PSD detection results are compared with varying numbers of selected gates on time-of-flight (TOF) data. This method can be used to fully automate the selection of gates in an unsupervised (without ground truth labels) setting.

The influence of the Pringle maneuver in laparoscopic hepatectomy: continuous monitor of hemodynamic change can predict the perioperatively physiological reservation.

This is the first study to investigate the correlation between intra-operative hemodynamic changes and postoperative physiological status. Patients receiving laparoscopic hepatectomy were routinely monitored using FloTract for goal-directed fluid management. The Pringle maneuver was routinely performed during parenchymal dissection and the hemodynamic changes were prospectively recorded. We retrospectively analyzed the continuous hemodynamic data from FloTrac to compare with postoperative physiological outcomes. The Pringle maneuver during laparoscopic hepatectomy. Stroke volume variation that did not recover from the relief of the Pringle maneuver during the last application of Pringle maneuver predicted elevated postoperative MELD-Na scores. The complexity of the hemodynamic data recorded by the FloTrac system during the Pringle Maneuver in laparoscopic hepatectomy can be effectively analyzed using the growth mixture modeling (GMM) method. The results can potentially predict the risk of short-term liver function deterioration.

Baseline level and change trajectory of the triglyceride-glucose index in relation to the development of NAFLD: a large population-based cohort study.

Insulin resistance (IR) and nonalcoholic fatty liver disease (NAFLD) are closely related. The triglyceride-glucose index (TyG index) has been proposed as a new indicator of IR. It remains unclear whether the triglyceride-glucose (TyG) index is prospectively associated with incident nonalcoholic fatty liver disease (NAFLD). This large-scale study comprised 1 prospective cohort totaling 22,758 subjects without NAFLD at baseline who underwent repeated health examinations and 1 subcohort totaling 7,722 subjects with more than three visits. The TyG index was ascertained mathematically by ln (fasting triglycerides [mg/dL] × fasting glucose [mg/dL]/2). NAFLD was diagnosed by ultrasound without other concomitant liver diseases. A combinatorial Cox proportional hazard model and latent class growth mixture modeling method were used to identify the association of the TyG index and its transition trajectories with NAFLD risk. During 53,481 person-years of follow-up, there were 5319 incident cases with NAFLD. Compared with those in the lowest quartile of the baseline TyG index, participants in the highest quartile had 2.52-fold (95% confidence interval, 2.21-2.86) higher odds of incident NAFLD. Similarly, restricted cubic spline analysis showed a dose-response relationship (p nonlinearity<0.001). Subgroup analyses showed a more significant association in the female and normal body size populations (p for interaction<0.001). Three distinct trajectories of changes in the TyG index were identified. Compared with the continued low group, the moderately increasing and highly increasing groups conferred 1.91-fold (1.65-2.21) and 2.19-fold (1.73-2.77) higher NAFLD risk, respectively. Participants with a higher baseline TyG index or a higher excessive TyG exposure were associated with an increased NAFLD risk. The findings imply that lifestyle interventions and modulation of IR might be considered to both reduce TyG index levels and prevent NAFLD development.

A Pilot Study of Nulling in 22 Pulsars Using Mixture Modeling

The phenomenon of pulsar nulling, observed as the temporary inactivity of a pulsar, remains poorly understood both observationally and theoretically. Most observational studies that quantify nulling employ a variant of Ritchings algorithm, which can suffer significant biases for pulsars where the emission is weak. Using a more robust mixture model method, we study pulsar nulling in a sample of 22 recently discovered pulsars, for which we publish the nulling fractions for the first time. These data clearly demonstrate biases of the former approach and show how an otherwise nonnulling pulsar can be classified as having significant nulls. We show that the population-wide studies that find a positive correlation of nulling with pulsar period/characteristic age can similarly be biased because of the bias in estimating the nulling fraction. We use our probabilistic approach to find the evidence for periodicity in the nulls in a subset of three pulsars in our sample. In addition, we also provide improved timing parameters for 17 of the 22 pulsars that had no prior follow-up.

Modeling red-light running behavior using high-resolution event-based data: a finite mixture modeling approach

To effectively reduce the number of red-light violations and crashes, it is crucial to explore RLR behavior at local intersections, understand the contributing factors, and identify the riskiest intersections by estimating RLR frequency. In this study, a finite mixture modeling method was utilized to understand the contributing factors to RLR behavior and estimate this violating behavior. To develop the RLR estimation models, performance metrics and signal phasing data were collected from the Automated Traffic Signal Performance Measures (ATSPMs) system in two jurisdictions in Arizona: Pima County and the Town of Marana. The results from calibrated models showed that an increase in traffic flow, intersection delay, number of approach lanes, and split failure is associated with an increase in the likelihood of observing red-light violations. In addition, it was found that an increase in cycle length is associated with a decrease in the likelihood of observing the red-light violation. The results of comparing the proposed RLR estimation method with several conventional methods, the Poisson Generalized Linear Model (PGLM), Zero-inflated Poisson Regression Model (ZIPM), and Zero-inflated Negative Binomial Regression Model (ZINB) showed the proposed method outperforms all the models in terms of both model fit and accuracy. The application of the proposed method could be used to analyze the intersections with the highest number of red-light violations. Furthermore, the presented transferability results can be advantageous to transportation agencies within Arizona and urban areas with similar characteristics by providing insight into which model specifications may provide the best RLR estimation accuracy.

Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM

Accurate forecasting of photovoltaic (PV) power is of great significance for the safe, stable, and economical operation of power grids. Therefore, a day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis method based on WT-CNN-BiLSTM-AM-GMM is proposed in this paper. Wavelet transform (WT) is used to decompose numerical weather prediction (NWP) data and photovoltaic power data into frequency data with time information, which eliminates the influence of randomness and volatility in the data information on the forecasting accuracy. A convolutional neural network (CNN) is used to deeply mine the seasonal characteristics of the input data and the correlation characteristics between the input data. The bidirectional long short-term memory network (BiLSTM) is used to deeply explore the temporal correlation of the input data series. To reflect the different influences of the input data sequence on the model forecasting accuracy, the weight of the calculated value of the BiLSTM model for each input data is adaptively adjusted using the attention mechanism (AM) algorithm according to the data sequence, which further improves the model forecasting accuracy. To accurately calculate the probability density distribution characteristics of photovoltaic forecasting errors, the Gaussian mixture model (GMM) method was used to calculate the probability density distribution of forecasting errors, and the confidence interval of the day-ahead PPF was calculated. Using a photovoltaic power station as the calculation object, the forecasting results of the WT-CNN-BiLSTM-AM, CNN-BiLSTM, WT-CNN-BiLSTM, long short-term memory network (LSTM), gate recurrent unit (GRU), and PSO-BP models were compared and analyzed. The calculation results show that the forecasting accuracy of the WT-CNN-BiLSTM-AM model is higher than that of the other models. The confidence interval coverage calculated from the GMM is greater than the given confidence level.

Density-adaptive registration of pointclouds based on Dirichlet Process Gaussian Mixture Models

We propose an algorithm for rigid registration of pre- and intra-operative patient anatomy, represented as pointclouds, during minimally invasive surgery. This capability is essential for development of augmented reality systems for guiding such interventions. Key challenges in this context are differences in the point density in the pre- and intra-operative pointclouds, and potentially low spatial overlap between the two. Solutions, correspondingly, must be robust to both of these phenomena. We formulated a pointclouds registration approach which considers the pointclouds after rigid transformation to be observations of a global non-parametric probabilistic model named Dirichlet Process Gaussian Mixture Model. The registration problem is solved by minimizing the Kullback-Leibler divergence in a variational Bayesian inference framework. By this means, all unknown parameters are recursively inferred, including, importantly, the optimal number of mixture model components, which ensures the model complexity efficiently matches that of the observed data. By presenting the pointclouds as KDTrees, both the data and model are expanded in a coarse-to-fine style. The scanning weight of each point is estimated by its neighborhood, imparting the algorithm with robustness to point density variations. Experiments on several datasets with different levels of noise, outliers and pointcloud overlap show that our method has a comparable accuracy, but higher efficiency than existing Gaussian Mixture Model methods, whose performance is sensitive to the number of model components.