Multivariate probit linear mixed models for multivariate longitudinal binary data.

When analyzing multivariate longitudinal binary data, we estimate the effects on the responses of the covariates while accounting for three types of complex correlations present in the data. These include the correlations within separate responses over time, cross-correlations between different responses at different times, and correlations between different responses at each time point. The number of parameters thus increases quadratically with the dimension of the correlation matrix, making parameter estimation difficult; the estimated correlation matrix must also meet the positive definiteness constraint. The correlation matrix may additionally be heteroscedastic; however, the matrix structure is commonly considered to be homoscedastic and constrained, such as exchangeable or autoregressive with order one. These assumptions are overly strong, resulting in skewed estimates of the covariate effects on the responses. Hence, we propose probit linear mixed models for multivariate longitudinal binary data, where the correlation matrix is estimated using hypersphere decomposition instead of the strong assumptions noted above. Simulations and real examples are used to demonstrate the proposed methods. An open source R package, BayesMGLM, is made available on GitHub at https://github.com/kuojunglee/BayesMGLM/ with full documentation to produce the results.

Doping Effects of Carbon Nanotubes and Graphene on the Flexural Properties and Tribological Performance of Needle-Punched Carbon/Carbon Composites Prepared by Liquid-Phase Impregnation.

The main goal of this study is to investigate the doping effects of carbon nanotubes (CNTs) and graphene on the needle-punched carbon/carbon (C/C) composites that are prepared by liquid-phase impregnation. In order to achieve, for the C/C composites, the purposes of high flexural strength, stable friction coefficient, low weight loss, and high thermal conductivity, our primary concern is to examine the flexural properties and the tribological performance, and then to explore a little further into the influence on thermal conductivity. In this study, carbon fiber preforms were first fabricated by needle-punched carbon-fiber cloth, and then liquid-phase phenolic resin, doped with different proportions of carbon nanotubes and graphene, was used as the impregnation solution to carry out multiple densification (impregnation-carbonization) cycles and fabricate various C/C composites. The main purpose was to probe into the doping effects of the CNTs and graphene, added to the impregnation solution, on the properties of C/C composites. The experimental results show that the addition of CNTs and graphene can improve the heat conductivity, flexural properties, and tribological performance of C/C composites, and the impact on these properties is more significant with the addition. Furthermore, the properties of graphene-doped C/C specimens are better than those of CNT-doped C/C specimens.

Use of spatial panel-data models to investigate factors related to incidence of end-stage renal disease: a nationwide longitudinal study in Taiwan.

BACKGROUND: The assumptions of conventional spatial models cannot estimate the responses across space and over time. Here we propose new spatial panel data models to investigate the association between the risk factors and incidence of end-stage renal disease (ESRD). METHODS: A longitudinal (panel data) study was conducted using data from the National Health Insurance Database in Taiwan. We developed an algorithm to identify the patient's residence and estimate the ESRD rate in each township. Corresponding covariates, including patient comorbidities, history of medication use, and socio-environmental factors, were collected. Local Indicators of Spatial Association were used to describe local spatial clustering around an individual location. Moreover, a spatial panel data model was proposed to investigate the association between ESRD incidence and risk factors. RESULTS: In total, 73,995 patients with ESRD were included in this study. The western region had a higher proportion of high incidence rates than the eastern region. The proportion of high incidence rates in the eastern areas increased over the years. We found that most "social environmental factors," except average income and air pollution (PM 2.5 and PM10), had a significant influence on the incidence rate of ESRD when considering spatial dependences of response and explanatory variables. Receiving non-steroidal anti-inflammatory drugs and aminoglycosides within 90 days prior to ESRD had a significant positive effect on the ESRD incidence rate. CONCLUSION: Future comprehensive studies on townships located in higher-risk clusters of ESRD will help in designing healthcare policies for suitable action.

The Impact of COVID-19 on the Stock Price of Socially Responsible Enterprises: An Empirical Study in Taiwan Stock Market.

This study examines the impact of the COVID-19 outbreak on the Taiwan stock market and investigates whether companies with a commitment to corporate social responsibility (CSR) were less affected. This study uses a selection of companies provided by CommonWealth magazine to classify the listed companies in Taiwan as CSR and non-CSR companies. The event study approach is applied to examine the change in the stock prices of CSR companies after the first COVID-19 outbreak in Taiwan. The empirical results indicate that the stock prices of all companies generated significantly negative abnormal returns and negative cumulative abnormal returns after the outbreak. Compared with all companies and with non-CSR companies, CSR companies were less affected by the outbreak; their stock prices were relatively resistant to the fall and they recovered faster. In addition, the cumulative impact of the COVID-19 on the stock prices of CSR companies is smaller than that of non-CSR companies on both short- and long-term bases. However, the stock price performance of non-CSR companies was not weaker than that of CSR companies during times when the impact of the pandemic was lower or during the price recovery phase.

Determination of correlations in multivariate longitudinal data with modified Cholesky and hypersphere decomposition using Bayesian variable selection approach.

In this article, we present a Bayesian framework for multivariate longitudinal data analysis with a focus on selection of important elements in the generalized autoregressive matrix. An efficient Gibbs sampling algorithm was developed for the proposed model and its implementation in a comprehensive R package called MLModelSelection is available on the comprehensive R archive network. The performance of the proposed approach was studied via a comprehensive simulation study. The effectiveness of the methodology was illustrated using a nonalcoholic fatty liver disease dataset to study correlations in multiple responses over time to explain the joint variability of lung functions and body mass index. Supplementary materials for this article, including a standardized description of the materials needed to reproduce the work, are available as an online supplement.

Preparation of efficient photothermal materials from waste coffee grounds for solar evaporation and water purification.

Effective water use is currently a critical global challenge needed to prevent water shortages and has attracted significant research attention. The realization of solar-driven water evaporation by using effective converters has attracted considerable attention in recent years owing to its potential for seawater desalination and wastewater treatment. Consequently, this paper proposes a simple two-step method to prepare low-cost and self-floating photothermal converters from waste coffee grounds. First, the coffee grounds were carbonized at 1,000 °C to develop broadband absorption, and the carbonized coffee grounds were modified using hydrophobic silane to enhance the water-floatation property of the grounds. The prepared hydrophobic carbonized coffee grounds exhibited good performance for desalination and water purification under sunlight irradiation. The self-floatation ability, low cost, well solar evaporation performance, and easy preparation contribute to the promising potential of using hydrophobic carbonized coffee grounds infuture steps toward eco-friendly desalination procedures.

Effect of Carbonaceous Components on Tribological Properties of Copper-Free NAO Friction Material.

Copper helps to accelerate heat transfer during the braking process, allowing the brake materials to produce a stable coefficient of friction (COF), which in turn reduces wear loss and braking noise. However, its properties are also quite harmful to aquatic organisms. Finding a suitable replacement that fits all functions of copper for brake materials is not an easy feat. In this paper, six different carbonaceous components (coke, carbon black, carbon fiber, artificial graphite, natural graphite and expanded graphite) were substituted for copper in non-asbestos organic (NAO) friction materials. The hardness, thermal conductivity and tribological behaviors of these copper-free NAO friction materials were examined. Experimental results indicate that carbonaceous components improve lubrication and assist the friction composites with generating friction layers on the worn surface. Specimens containing coke, carbon black or carbon fiber exhibit broken friction layers, whereas specimens containing artificial graphite, natural graphite or expanded graphite exhibit quite adherent and smooth friction layers. Among all the copper-free carbon containing specimens, the specimen containing expanded graphite appears to be the best choice. It has the highest thermal conductivity, a relatively low wear loss and a relatively high and stable COF.

Cerebral control of winking before and after learning: An event-related fMRI study.

INTRODUCTION: The main purpose of this study was to investigate the cerebral areas responsible for winking by observing the activation pattern and learning effects on cerebral cortices by comparing differences in activation pattern during winking before and after learning. METHODS: Sixty-three subjects were recruited, including 22 (11 males; 11 females) who could wink bilaterally and 41 (14 males; 27 females) who could wink unilaterally. Event-related functional magnetic resonance was performed. The subjects were asked to blink and wink according to projected instructions as the events for image analysis. The activation pattern was obtained by contrasting with the baseline images without eyelid movements. Those who could only wink unilaterally were asked to train themselves to wink the other eye. For those who succeeded (n = 24), another imaging study was performed and the results were compared with those before training. RESULTS AND CONCLUSION: Left winking resulted in activation in the left frontal lobe, while right winking resulted in activation in bilateral frontal lobes with predominance on the right side. For the subjects capable of only winking unilaterally, learning to wink on the other side activated similar cortical areas to those in the subjects capable of bilateral winking without training.

Facile Fabrication of Durable Superhydrophobic Films from Carbon Nanotube/Main-Chain Type Polybenzoxazine Composites.

Superhydrophobic materials have immense applications in the fields of industry and research. However, their durability is still a cause for concern. A facile method for preparing durable superhydrophobic films from carbon nanotubes (CNTs) and the main-chain type polybenzoxazine precursors is reported herein. We used probe ultrasonicator to prepare CNT/polybenzoxazine coatings. Compared with the general sonicating dispersion process, the dispersion time was greatly reduced from a few hours to 5 minutes and the prepared suspension exhibited film-forming characteristics well. The CNT/polybenzoxazine films, which do not contain any fluorinated compounds, exhibit remarkable durability against thermal treatment, organic solvents, corrosive liquids, and sandpaper abrasion, while retaining their superhydrophobicity. Furthermore, these CNT/polybenzoxazine films also showed durable superhydrophobicity after ultraviolet (UV) irradiation for 100 h. This CNT/polybenzoxazine film can be readily used for practical applications to make durable superhydrophobic coatings.

Biocompatible Meshes with Appropriate Wettabilities for Underwater Oil Transportation/Collection and Highly Effective Oil/Water Separation.

In this study, we prepared biocompatible superhydrophilic and underwater superoleophobic tannic acid (TA)/polyvinylpyrrolidone (PVP)-coated stainless-steel meshes that mediated extremely efficient separations of mixtures of oil and water. These TA/PVP-coated stainless-steel meshes displayed excellent antifouling properties and could be used to separate oil/water mixtures continuously for up to 24 h. Moreover, a funnel-like TA/PVP-coated stainless-steel mesh device could be used for underwater oil transportation and collection. In conjunction with our continuous oil removal system, this device allowed for the continuous collection and removal of oil pollutants from underwater environments. The high performance of these TA/PVP-coated stainless-steel meshes and their green, low-energy, cost-effective preparation suggests great potential for practical applications.

An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements.

People living near roadways are exposed to high concentrations of ultrafine particles (UFP, diameter < 100 nm). This can result in adverse health effects such as respiratory illness and cardiovascular diseases. However, accurately characterizing the UFP number concentration requires expensive sets of instruments. The development of an UFP surrogate with cheap and convenient measures is needed. In this study, we used a mobile measurement platform with a Fast Mobility Particle Sizer (FMPS) and sound level meter to investigate the spatiotemporal relations of noise and UFP and identify the hotspots of UFP. UFP concentration levels were significantly influenced by temporal and spatial variations (p < 0.001). We proposed a Generalized Additive Models to predict UFP number concentration in the study area. The model uses noise and meteorological covariates to predict the UFP number concentrations at an industrial site in Taichung, Taiwan. During the one year sampling campaign from fall 2013 to summer 2014, mobile measurements were performed at least one week for each season, both on weekdays and weekends. The proposed model can explain 80% of deviance and has coefficient of determination (R2) of 0.77. Moreover, the developed UFP model was able to adequately predict UFP concentrations, and can provide people with a convenient way to determine UFP levels. Finally, the results from this study could help facilitate the future development of noise mobile measurement.

Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data.

A common objective of fMRI (functional magnetic resonance imaging) studies is to determine subject-specific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate a Bayesian approach that incorporates spatial and temporal dependence and allows for the task-related change in the BOLD signal to change dynamically over the scanning session. In this way, our model accounts for potential learning effects in addition to other mechanisms of temporal drift in task-related signals. We study the properties of the model through its performance on simulated and real data sets.