Variable Model Research Articles

Gaussian-related undirected graphical models for circular variables

Graphical models are pivotal in studying the conditional independence structure of a set of random variables. Circular variables, arising in several contexts and fields, are characterized by periodicity. Models for studying the dependence/independence structure of circular variables are under-explored but of increasing interest. This paper delves into two multivariate circular distributions, the Wrapped Normal and the Inverse Stereographic Normal distribution as undirected graphical models. For each of these distributions, we study their key properties with respect to conditional independence and introduce specific classes of graphical models. The usefulness of the proposal is shown by modelling the conditional independence among dihedral angles that play a critical role in defining the three-dimensional structure and functionality of proteins. This can provide valuable insights, for instance, into the multiform protein folding understanding.

Skin tone discrimination and birth control avoidance among women in Harris County, Texas: a cross-sectional study

IntroductionStructural racism plays a major role in reproductive health inequities. Colorism, discrimination based on skin color, may profoundly impact reproductive health access and service delivery. However, quantitative research in this area is limited.MethodsWe administered an online survey of women (n = 1,299) aged 18–44 from Harris County, Texas to assess the relationship between skin color discrimination and reproductive health service avoidance. The survey included questions on demographics, self-reported skin tone, and dichotomous measures of previous discrimination experiences and avoidance of care because of perceived discrimination. Binary logistic regression was used to examine whether race/ethnicity, skin tone, and previous discrimination experiences were related to avoidance of contraceptive care because of perceived discrimination.ResultsApproximately one-third (31.5%) of the sample classified themselves as non-Hispanic Whites (31.5%), 22.4% as Black, 27.4% as Hispanic and born within the US, and 7.6% as Hispanic born outside of the US. Approximately one-third of women classified themselves in the lightest skin tones, whereas almost one in five women classified themselves in the darkest skin tone palates. Darker skin tones had increasingly greater odds of reporting that they avoided seeking birth control out of a concern for discrimination compared to the lightest skin tone. After adjusting for race/ethnicity and sociodemographic variables (model 3), darker skin tones remained significantly associated with avoiding birth control.DiscussionThis study demonstrates the role that skin color discrimination plays in negative reproductive health experiences. While this is not surprising given that those with racist ideologies developed the concept of these racial and ethnic categories, the apparent association with darker skin colors and avoidance of seeking birth control provides evidence that structural and individual racism continues to have far-reaching and insidious consequences.ConclusionContraception is recognized for reducing maternal mortality, improving child health, increasing female empowerment, and decreasing poverty. However, not all women equally enjoy the benefits of access to contraception. Addressing colorism within reproductive healthcare has become critically important as the nation becomes increasingly diverse. Focusing on skin tone-based discrimination and its roots in anti-blackness expands our understanding beyond a Black–White binary traditionally applied when addressing racism in healthcare delivery.

Improving evapotranspiration partitioning by integrating satellite vegetation parameters into a land surface model

Land evapotranspiration (ET) primarily involves vegetation transpiration, canopy interception loss, and soil evaporation. Previous studies have made significant progress in total ET estimation; however, substantial challenges remain in partitioning ET on a regional scale, largely due to the intricate water and energy balance that is disrupted by vegetation cover changes. The accuracy of land surface models in representing ET components may be constrained by their inadequate consideration of vegetation dynamics. In this study, we integrate satellite leaf area index (LAI) and fraction of vegetation coverage (FVC) into the Variable Infiltration Capacity model (VIC) to improve ET partitioning ability in the Loess Plateau of China, a region that has experienced substantial vegetation dynamics. The results showed that satellite dynamic vegetation parameters in modeling are effective in improving the estimation of ET components compared with the default/static vegetation parameters. Considering LAI dynamics in the model enhances the representation of the inter- and intra-annual variations in vegetation transpiration and canopy interception loss. Dynamic FVC reasonably allocates transpiration to soil evaporation, capturing evaporation in forest gaps effectively. This effect is particularly relevant in arid and semi-arid regions. Among the ET components, transpiration was the most sensitive to the two dynamic vegetation parameters, followed by canopy interception loss and soil evaporation. Through the VIC model with dynamic vegetation parameters, our study revealed that soil evaporation was twice that of transpiration in the Loess Plateau, which is consistent with its semi-arid region and relatively sparse vegetation coverage. Our study offers valuable insights regarding the use of vegetation coverage for partitioning ET and highlights the advantage of integrating satellite vegetation products into land surface models.

A variable turbulent Schmidt number model in Jet-in-Crossflow simulation and its applications

This study proposes a variable turbulent Schmidt number (Sct) model to improve the precision of Reynolds-Averaged Navier-Stokes (RANS) simulations for Jet-in-Crossflow (JICF) scalar mixing phenomena, with a particular focus on hydrogen/air mixing characterized by strong density variations. By incorporating a correction term associated with turbulent kinetic energy based on a selected Sct value, the model aims to amplify the diffusion rate of the jet, particularly in regions where the interaction between the mainstream flow and the jet is significant. This enhancement in simulation accuracy is validated against Large Eddy Simulation (LES) results, which serve as the benchmark for analysis. Statistical analysis of LES results reveals a notable increase in the turbulent diffusion coefficient in regions characterized by strong interactions between the jet and mainstream flow. Meanwhile, minimal variation in turbulent viscosity is observed, resulting in a reduced Sct in areas with high turbulent kinetic energy. The proposed model is validated within a open-space JICF case, a micro-tube JICF case and a free jet case. In micro-tube JICF scenarios, initial simulations with a fixed Sct value were conducted. Following comparison, a value of 0.7 was identified as the basis for correction. Subsequently, the variable Sct model was implemented, thereby enhancing the accuracy of RANS simulations in micro-tubes.

Associations between APOE genotypes, urine 8-isoprostane and blood trace elements in middle-aged mothers (CROME study)

BackgroundThere is almost no data on the combined associations between apolipoprotein E gene (APOE) genotypes, trace elements (TEs), and lipid peroxidation in vivo. The aim of our study was to evaluate the association between APOE genotypes and TE levels in blood (B-TEs) and erythrocytes (E-TEs), and 8-isoprostane in urine (U-8-isoprostane) in women with low exposure to potentially toxic TEs and with adequate supply of essential TEs. MethodsB-TEs, E-TEs and U-8-isoprostane were determined in 172 healthy women of childbearing age (30.1–51.4 years) using ICP-MS and ELISA competitive assay, respectively. All women were divided into three APOE genotype groups according to the presence of the ɛ4 allele, ɛ2 allele or ɛ3 homozygotic allele. The associations between B-TEs, E-TE, U-8-isoprostane, and the APOE genotype groups were estimated by multiple variable linear regression models with relevant explanatory variables (e.g., age, BMI, and seafood). ResultsAll TE and U-8-isoprostane levels were inside the reference ranges for the healthy population. In the multiple variable linear regression models, our results showed that urine 8-isoprostane levels increased by up to 43.3% in the APOE4 group compared to the APOE3 group and a negligible negative modifying effect for essential TEs. However, the APOE genotype groups were associated also with some TEs. A clear positive association was found between the APOE2 and APOE4 groups (vs. APOE3) with B-molybdenum. ConclusionsOur study suggests that the APOE4 genotype played an important role in 8-isoprostane variability in a population with an adequate supply of essential and with low exposure to potentially toxic TEs. Adequate copper, zinc and selenium status seemed to be protective against, while the levels of nonessential TEs were probably too low to play a decisive role in 8-isoprostane formation. The observed impact of the APOE2 and APOE4 groups on increased B-molybdenum opens a new research topic.

Coupling Soil Erosion and Sediment Transport Processes With the Variable Infiltration Capacity Model (VIC‐SED) for Applications Suitable With Coarse Spatial and Temporal Resolutions

Coupling Soil Erosion and Sediment Transport Processes With the Variable Infiltration Capacity Model (VIC‐SED) for Applications Suitable With Coarse Spatial and Temporal Resolutions

Modelling traveller mode shift behavior from personalized two-wheelers to public transit for work trips : A stated preference approach for Indian metropolitan city

This study focuses on assessing potential thrust areas that can attract personalized vehicle based daily work trips to mass transit. The proposed study specifically targets to Tier-II medium sized Indian metropolises for the estimation of a probability mode shift from personalized 2-wheelers’ to public transport on the introduction of hypothetically designed improved mass transit services. Data pertaining to study were collected through a stated preference approach that involved home-interview and online survey methods. The Latent variable enriched binary logit mode-shift behaviour model was calibrated using the Statistical Package for the Social Sciences platform and validated using hold-back data. The utility function developed using coefficients obtained through calibrated model can be further used as a user friendly tool for predicting pobability of mode shifts.The current research is an attempt to incorporate latent variables in context of Indian metropolitan area which has rarely been attempted by any researcher.

Early therapeutic drug monitoring of methotrexate and its association with acute kidney injury: A retrospective cohort study.

High-dose methotrexate (HDMTX) use can be limited by the development of acute kidney injury (AKI). Early AKI detection is paramount to prevent further renal injury and irreversible toxicities. This study sought to determine whether early elimination patterns of MTX would be useful as a biomarker of AKI in HDMTX treatment. This retrospective cohort study included two sites that collected ≥2 MTX levels within 16 h from completion of MTX infusion. Early levels were tagged and MTX elimination half-life (t½) were calculated from combinations of two of three different early time periods. Receiver operating characteristic (ROC) curves were synthesized for each elimination t½ (biomarker) with respect to AKI and delayed methotrexate elimination (DME); the biomarker with the highest area under the ROC curve (AUC) was tested in a multiple variable logistic regression model. Data from 169 patients who received a total of 556 courses of HDMTX were analyzed. ROC analysis revealed MTX elimination t½ calculated from the second and third time periods had the highest AUC for AKI at 0.62 (interquartile range [IQR] 0.56-0.69) and DME at 0.86 (IQR 0.73-1.00). After adjusting for age, sex, dose (mg/m2), infusion duration, HDMTX course, and baseline estimated glomerular filtration rate, it remained significant for AKI with an OR of 1.29 and 95% confidence interval of 1.03-1.65. Early MTX elimination t½ measured within 16 h of infusion completion was significantly associated with the development of AKI and serves as an early clearance biomarker that may identify patients who benefit from increased hydration, augmented leucovorin rescue, and glucarpidase administration.

Stock market index prediction based on market trend using LSTM

The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learning techniques are developing quickly, and because of the machine’s improved computational power, complex stock price prediction algorithms can be developed. This paper presents stock market index prediction based on market trend using long short-term memory (LSTM). Using built-in application programmable interface (API), Yahoo Finance offers a simple method to programmatically retrieve any historical stock prices of an organization using the ticker name. The standard and poor’s 500 index (S&P 500 index) include the firms that have been taken into consideration here. Utilizing the selected input variable, single-layer and multi-layer LSTM models are implemented, and the measurement parameters of mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are used to compare each performance. Nearly all of the real closing price’s curve and the prediction curve’s closing price for test data overlap. A potential stock investor may benefit significantly from such a prediction by using it to make well-informed choices that would increase his earnings.

Evaluating hydrological responses of satellite precipitation products over an Indian tropical catchment through a distributed physical model

AbstractSignificant advancements in satellite‐based precipitation retrieval algorithms have led to the development of continuous, quasi‐global precipitation products, offering unique opportunities for hydrometeorological and climate research. In this context, six satellite‐based precipitation products (SPPs), including CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were thoroughly investigated for their application in hydrological simulations over the Wardha River basin in India. The observed gridded precipitation product developed by India Meteorological Department (IMD) has been used as reference data to evaluate the performance of SPPs. Hydrological variables such as runoff, soil moisture (SM) and evapotranspiration (ET) were simulated using the Variable Infiltration Capacity model. The performances of SPPs are critically assessed using various statistical metrics, including Pearson's correlation coefficient (R), Klinga‐Gupta Efficiency (KGE), Percent Bias (PBIAS), root mean square error (RMSE), and the RMSE to standard deviation ratio (RSR). The model‐simulated discharge was compared with streamflow observations at a single gauging site, while a spatially distributed comparison was conducted between simulated SM and ET and satellite‐based SM and ET. The IMD dataset consistently shows superior performance for discharge simulation at both daily and monthly scales, with KGE values of 0.85 and 0.91, respectively. Among SPPs, MSWEP and CHIRPS excel in simulating daily and monthly discharge, respectively. For ET simulation, CHIRPS outperforms IMD and other SPPs, achieving an overall KGE value of 0.37. In contrast, PERSIANN is the most effective for simulating SM compared to other precipitation products.

GLAMLE: inference for multiview network data in the presence of latent variables, with an application to commodities trading

The statistical analysis of multiview network data provides researchers with insights on the mechanism governing many complex systems of interconnected elements. A graph is routinely applied to model this type of data: this poses several statistical challenges. Among them, there is the need for considering that some unobservable factors may determine the network connections and the graph topology. This requirement entails both complex models and computationally heavy inference procedures. To cope with these inference issues, a novel and flexible class of latent variable models labeled Graph Generalized Linear Latent Variable Model (GGLLVM) is introduced. Then, its parameters are estimated via the maximization of the Laplace-approximated likelihood. This leads to the creation of an M-estimator which is named Graph Laplace-Approximated Maximum Likelihood Estimator (GLAMLE). The statistical properties of this new estimator are characterized, and numerical experiments on simulated networks are performed to illustrate that the GLAMLE yields fast and accurate inference, in comparison to other widely used approaches. A real data application to commodities trading in European countries illustrates how the use of GGLLVM and GLAMLE unveils the import/export propensity that each node of the network has toward other nodes, along with additional information specific to each traded commodity.

A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation

In this paper, we introduce an efficient sparse Gaussian process (E-SGP) for the surrogate modelling of fluid mechanics. This novel Bayesian machine learning algorithm enables efficient model training using databases of different structures. It extends the approximated sparse GP algorithm by combining the concept of efficient GP (E-GP) and variational energy free sparse Gaussian processes (VEF-SGP). The developed E-SGP approach leverages the arbitrariness of inducing points and the monotonically increasing nature of the objective function with respect to the number of inducing points in VEF-SGP. By specifying the inducing points on the orthogonal grid/input subspace and using the Kronecker product, E-SGP significantly enhances computational efficiency without imposing constraints on the covariance matrix or increasing the number of parameters that need to be optimised during training.The E-SGP algorithm outperforms E-GP not only in terms of scalability but also in model quality, as evidenced by mean standardised logarithmic loss (MSLL). The computational complexity of E-GP grows cubically with an increasing structured training database, while E-SGP maintains computational efficiency as the model resolution (i.e., the number of inducing points) remains the same. The examples show that E-SGP produces more accurate predictions compared to E-GP when model resolutions are similar. In the application to a partially structured database, E-GP benefits from more training data but comes with higher computational demands, while E-SGP achieves a comparable level of accuracy but is more computationally efficient, making E-SGP a potentially preferable choice for fluid mechanics. Furthermore, E-SGP can produce more reasonable estimates of model uncertainty, whilst E-GP is more likely to produce over-confident predictions. In the application to partially structured databases, the performance of E-SGP is also compared to the structured Gaussian processes latent variable model (SGPLVM). In this case, E-SGP and SGPLVM both exhibit robust performance. However, E-SGP demonstrates a slight advantage over SGPLVM, particularly in terms of MSLL, indicating that E-SGP is better at producing trustworthy and accurate estimates of the uncertainty in the predictions. . While SGPLVM also improves computational efficiency by exploiting the Kronecker product and a small number of inducing points, E-SGP's orthogonal grid specification of inducing points further enhances its scalability and robustness, making it a superior choice for complex fluid mechanics problems with partially structured data.

The Impact of Live Streaming of Fresh Agricultural Products on Consumers' Purchase Intention-Consider the Regulatory Role of Policy Support

To understand the influence of fresh agricultural products online live on consumer purchase intention, based on SOR (Stimuli-Organism-Response) theory framework, with convenience, interactivity, anchor prestige, task fit as a stimulus variable, to customer participation as a psychological latent variable construction theory model, based on the questionnaire data, using the structural equation model of measurement model consistency, reliability, convergence validity and validity test, and explore the role of the latent variables, with path coefficient as an observation index analysis of the model results. The research results show that convenience, interactivity, anchor reputation and task fit have significant positive effects on customer participation, among which the biggest influence is convenience; in the process of convenience and interaction on customer participation, policy support plays a positive regulating role, in the process of anchor reputation and task fit on customer participation.

Psychometric Evaluation of the Brief-COPE Inventory and Exploration of Factors Associated with Perceived Stress among Peruvian Nurses.

This study aimed to analyze the psychometric properties of the Brief-COPE Inventory and to determine its concurrent validity by examining its association with perceived stress among Peruvian nurses. A psychometric study was conducted with 434 Peruvian nurses to evaluate the psychometric properties of the Brief-COPE Inventory through confirmatory factor analysis. Three stepwise variable selection regression models were implemented. The three-factor model of the Brief-COPE Inventory demonstrated adequate fit indices (root mean square error of approximation = 0.052, standardized root mean square residual = 0.068, and both the comparative fit index and the Tucker-Lewis index = 0.95). Additionally, the factors were significantly correlated (p < 0.001), and the reliability was adequate (ω = 0.90). Nurses reported a medium level of perceived stress, with associated factors including having received stress management training, fear of COVID-19, and problem-focused coping strategies (p < 0.05). This study confirms that the Brief-COPE Inventory is a valid tool for measuring coping strategies among Peruvian nurses due to its good model fit, excellent reliability, and concurrent validity with perceived stress. However, further research is needed to assess its validity in the specific areas of performance perceived by nursing professionals.

Stripe Noise Removal Algorithm for Infrared Remote Sensing Images Based on Adaptive Weighted Variable Order Model

In the infrared remote sensing imaging system, the output of the detector is uneven, resulting in noticeable stripe noise in the image, which significantly reduces the image quality. Therefore, in response to the problem of establishing a variational model for the image globally and fixing the fractional order of the regularization term in the existing stripe noise removal algorithms based on model optimization, a stripe noise removal algorithm for infrared remote sensing images based on an adaptive weighted variable order model is proposed. In this algorithm, the vertical and approximate components containing stripe noise in the image are first separated through multi-level and multi-scale wavelet transform, and only these two components are processed; secondly, the global sparsity and the gradient sparsity of stripe noise and the variable order gradient sparsity of the information component are constrained by L1 norm, so as to establish the stripe noise removal model. The order of the fractional derivative is adaptively assigned to each pixel of the information component through local variance; then, an adaptive weight operator is introduced, which can assign different weights to the pixels in the variable order derivative of the information component on the basis of the image gradient information. Finally, through the ADMM algorithm, we can obtain the optimal solution of this model. The processing results of simulated and actual data indicate that the proposed algorithm performs well in all indexes and has obvious advantages in removing stripe noise and preserving image details.

Reducing Radiation Dermatitis for PBS Proton Therapy Breast Cancer Patients Using SpotDelete

PurposeThe purpose of this work was to reduce the severity of radiation dermatitis for breast cancer patients receiving pencil beam scanning proton therapy. The hypothesis was that eliminating proton spots (SpotDelete) in the 0.5 cm skin rind would reduce the potentially higher relative biological effectiveness (RBE) known to occur at the Bragg Peak. Patients and MethodsOur center has been using an in-house developed Python script in RayStation since 2021 to remove spots from the skin rind of breast patients. In this work, we retrospectively reviewed the on-treatment visit data from a cohort of breast patients treated with hypofractionation (16 fractions) before this technique (MinDepth) and after (SpotDelete) to acquire the physician-reported radiation dermatitis scores. We evaluated the delivered treatment plans, calculating the linear energy transfer (LET) and applying 3 variable RBE models, Carabe-Fernandez, Wedenberg, and McNamara. An α/β of 10 was assumed for the skin. ResultsIn the MinDepth cohort (n = 28), grade 1, 2, and 3 dermatitis accounted for 57%, 36%, and 7% of the cases, respectively. For SpotDelete (n = 27), the incidence rate of grade 1 and 2 acute radiation dermatitis was 67% and 37%, respectively. There were 0 instances of grade 3 dermatitis observed in the SpotDelete cohort. The onset of radiation dermatitis in the SpotDelete cohort was delayed compared to MinDepth, occurring 1 week later in the course of treatment. There was no significant difference in LET or in any of the variable RBE models when analyzing the 0.5 cm skin rind between the cohorts. ConclusionDespite the lack of correlation in LET or RBE, SpotDelete has been shown to reduce the severity and onset of radiation dermatitis. Possibly, more research into the α/β for skin and RBE models based on skin cell lines could provide insight into the efficacy of the SpotDelete technique.

Modelling Petrol Prices in Kenya from 2014 to 2023 Using Sarimax Model: A Case Study of Nairobi County

The requirement for petrol price information is crucial for majority of enterprises. This is because fluctuations in petrol prices impact inflation hence affecting daily lives of citizens. In analyzing the prices of petrol, researchers have employed several models but encountered various limitations. These limitations include; the Error Correction Model can examine only one co-integrating association. The Vector Autoregression (VAR) model does not account for the structural changes in the data. Additionally, the AutoRegressive Integrated Moving Average (ARIMA) model does not take into consideration the seasonal component in the data. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model assumes that over time the volatility is constant. Moreover, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model does not integrate the external factors. Hence in this study Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model was employed since it captures seasonality in data and incorporates the exogenous variables. The research’s aim was to model prices of petrol in Kenya for the period between 2014 to 2023 with exchange rates as an external factor. Secondary data was obtained from Energy and Petroleum Regulatory Authority (EPRA), Kenya National Bureau of Statistics (KNBS) and Central Bank of Kenya (CBK) websites. R software was used to analyze the data. By the use of historical data of petrol prices and exchange rates, the study sought to fit the best Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, validate the model and predict the petrol prices. The petrol price data was found to be non-stationary using Augmented Dickey Fuller test (ADF). Regular differencing was conducted to make the data stationary. Seasonal differencing due to seasonality component available in the data was also performed. Best SARIMAX model was chosen from various SARIMAX models according to Box-Jenkins methodology which uses least Akaike Information Criterion (AIC) value. SARIMAX (0,1,1)(2,1,2)&amp;lt;sub&amp;gt;12&amp;lt;/sub&amp;gt; model was selected since it had least Akaike Information Criterion (AIC) value of 656.3733 and the model validated using the hold out technique. The forecasts errors from the training set were; Mean Squared Error (MSE)=10.4970, Root Mean Square Error (RMSE)=3.239911, Mean Absolute Percentage Error (MAPE)=2.309268% while those from the testing set were; Mean Squared Error (MSE)=3271.1012, Root Mean Square Error (RMSE)=57.193542, Mean Absolute Percentage Error (MAPE)=26.695390%. There was less error in the training set than in the testing set as it was expected hence the model suited the data well and could be used for future predictions. The model was then used for five year forecast into the future. This study’s findings will offer sound suggestions to policymakers, businesses and consumers. This study recommends a model to be fitted using other factors affecting petrol prices and fitting Fourier terms, Behavioral Assessment Tools (BATS) and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) models.

Modeling and design optimization of the performance of stone matrix asphalt mixtures containing low-density polyethylene and waste engine oil using the response surface methodology

In recent years, the use of waste plastic materials such as low-density polyethylene (LDPE) to modify asphalt binders and enhance mixture performance has garnered significant attention. One major concern with using such materials is the higher production temperature required, which necessitates the use of a bitumen extender agent, such as waste engine oil (WEO), to reduce the viscosity and mixing temperature. Therefore, using these stabilizing additives can reduce the consumption of virgin binder, especially for Stone Matrix Asphalt (SMA) mixtures, which require higher asphalt content. This study aimed to optimize the design of SMA modified by LDPE and WEO to minimize the optimum asphalt content (OAC) and mixing temperature while maximizing SMA performance. To achieve this, Response Surface Methodology (RSM) was utilized to develop the necessary models for optimizing design and predicting performance. The selected independent variables (factors) for the design include LDPE content, WEO content, OAC, and mixing temperature. Meanwhile, the responses (dependent variables) consist of Marshall stability, rut depth, tensile strength ratio, and resilient modulus, which were used to determine the best mix design. The findings demonstrated that the suggested models for the output variables can predict performance with a higher level of confidence. The Analysis of variance (ANOVA) demonstrated that predictive models were significant and well-fitted, with a coefficient of determination (R2) of higher than 0.80, an adequate precision value of greater than 4, and a low p-value (less than 0.05). The error percentage between the RSM-predicted and actual values was less than 5 %, indicating that RSM-established models can accurately and efficiently predict the SMA performance. The best mix design of the SMA mixture modified by LDPE and WEO was found to be 10 % LDPE, 3.91 % WEO, 5.92 % OAC, and mixing temperature of 152.24°C with a combined desirability of 82.8 %.

Public transport mode choice behavior of different-income passengers during large-scale public health incidents

Public transport ridership has been hit hard by the COVID-19 pandemic in many countries. Investigating passengers' public transport mode choice behavior during large-scale public health incidents can uncover the major influential factors and help propose policies and strategies to reduce the pandemic transmission and recover the public transport revenue. This study develops an integrated choice and latent variables (ICLV) model by income based on structural equation model to model passengers' public transport choice behavior during the normalized stage of the pandemic. The model considers passengers' socioeconomic attributes, travel attributes, and attitude-perception attributes, and can appropriately capture passengers' psychological latent attributes. Taking Beijing China as an example, we collect some revealed preference survey data online. The modeling results show that the risk perception as a mediator variable has a significant impact on mode preference. Moreover, the convenience of public transport has the largest influence on risk perception. These findings suggest that risk perception and the convenience of public transport play a major role in passengers' mode choice behavior. In addition, the impacts of the various influential factors on the public transport mode choices are significantly different across different income groups. Further, the ICLV model can achieve better performance and is superior to the traditional Multinomial Logit model. The modeling framework can help propose targeted and instructive strategies during the normalized stage of the pandemic by uncovering the major influential factors in passengers’ public transport mode choices, which is applicable to similar pandemics in the future.

D-nontrilocality of sparse probability tensors and the triangle network

Abstract Bell’s inequalities are linear and apply for cases of two entangled bodies. In this work, we consider the case of entanglement among three bodies as previously discussed in [Renou, et al Phys. Rev. Lett., 123, 140 401 (2019)] and based on triangle network. By discussing the question whether a sparse probability tensor (SPT) can be represented by a discrete trilocal hidden variable model (D-triLHVM), we show that every SPT having a D-triLHVM satisfies a set of concrete equalities and a nonlinear inequality, which can be used to detect whether a D-triLHVM can describe the network completely. As an application, we re-explore the D-nontrilocality of the correlations studied by Renou et al and that of the triangle network with shared entangled pure states. We also leave open questions about the closednees of the set of all D-trilocal probability tensors and the description with a continuous trilocal hidden variable model.