Explore the vast range of titles available.

Individual personality traits are powerful determinants of behavior, and they can profoundly influence consumer decisions as a comprehensive understanding of consumer personality traits. Their role in decision-making can improve the predictability of consumer-related behavior. In this study, data on consumers’ preferences and personality traits were collected through questionnaires using the Wood Furniture Product Characteristics Consumer Preference Scale and the Big Five Personality Inventory Simplified. Bivariate correlation analysis and stepwise multiple regression analysis were used to investigate the relationship between the Big Five personality traits (neuroticism, extraversion, openness, agreeableness, and conscientiousness) and wood furniture product characteristics consumer preferences. Correlation analysis indicated that neuroticism was correlated negatively with wood furniture product characteristic consumer preference scores. Extraversion, agreeableness, and conscientiousness were correlated positively with wood furniture product characteristic consumer preference scores. There was no correlation between openness and consumer preference. Regression analysis indicated that neuroticism, extraversion, agreeableness, and conscientiousness predicted wood furniture product trait consumer preferences. Overall, assessing personality traits can help provide insight into the psychological and behavioral characteristics of consumers when purchasing wood furniture products, allowing for a more comprehensive understanding of market demand and more effective marketing and product positioning strategies.

Compound flooding is a multidimensional consequence of the joint impact of multiple intercorrelated drivers, such as oceanographic, hydrologic, and meteorological. These individual drivers exhibit interdependence due to common forcing mechanisms. If they occur simultaneously or successively, the probability of their joint occurrence will be higher than expected if considered separately. The copula-based multivariate joint analysis can effectively measure hydrologic risk associated with compound events. Because of the involvement of multiple drivers, it is necessary to switch from bivariate (2D) to trivariate (3D) analyses. This study presents an original trivariate probabilistic framework by incorporating multivariate hierarchal models called asymmetric or fully nested Archimedean (or FNA) copula in the joint analysis of compound flood risk. The efficacy of the derived FNA copulas model, together with symmetric Archimedean and Elliptical class copulas, are tested by compounding the joint impact of rainfall, storm surge, and river discharge observations through a case study at the west coast of Canada. The obtained copula-based joint analysis is employed in multivariate analysis of flood risks in trivariate and bivariate primary joint and conditional joint return periods. The estimated joint return periods are further employed in estimating failure probability statistics for assessing the trivariate (and bivariate) hydrologic risk associated with compound events. The statistical tests found the fully nested Frank copula outperforms symmetric 3D copulas. Our work confirms that for practical compound flood risk analysis together with bivariate or univariate return periods, it is essential to account for the trivariate joint return periods to assess the expected compound flood risk and strength of influence of different variables if they occur simultaneously or successively. The bivariate (also univariate) events produce a lower failure probability than trivariate analysis for the OR-joint cases. Thus, ignoring the compounding impacts via trivariate joint analysis can significantly underestimate failure probability and joint return period.

Multivariate analysis of flood frequency was used extensively in water resources research. Often the only flood peak or volume is analyzed with statistical distributions, but for a perfect and exact result, the four main characteristics of a flood event, as well as peak, volume, duration, and time-to-peak, are needed. For this reason, multivariate statistical approaches like copula functions developed. This research aims to define and use the bivariate copula (2-copula) probability distribution functions (PDF) for flood characteristics multivariate analysis. When the joint distribution of characteristics such as volume and peak is known, it is possible to define the probability of simultaneous occurrence of design volume and peak flow values.

Sound ecosystems are a precondition for the sustainable survival and development of human society. However, ecological deterioration caused by socioeconomic activities can result in increasing pressure on ecosystems. Exploration of the spatial interaction between ecosystem and economic development under the background of high-quality and green development is, therefore, necessary. In this study, we analyzed the spatial interaction between the ecosystem services value (ESV) and economic development with the economic and ecological coupling index method based on high-resolution remote-sensing land-use data and socioeconomic statistical data in Hunan Province from 2000 to 2018. The results revealed that the ESV provided by the ecosystems in Hunan Province decreased by US$1256.166 million from 2000 to 2018. The areas with high ESV per unit area were distributed in the mountainous areas, while the areas with low ESV per unit area were distributed in the major cities and their surroundings. The bivariate spatial autocorrelation analysis showed that the ESV had significant spatial dependence on the economic development. In addition, the coupling analysis documented that the relationship between the ESV and economic density was mostly in the low conflict and potential crisis states. These results provide important guidance for the coordinated development of the regional economy and ecosystem conservation.

The rainfall characteristics of flash floods are highly sensitive to the joint impacts of different rainfall event features. In this study, we propose an objective framework for identifying rainfall characteristics for flash floods and assessing the risks while considering the combined impacts of multiple rainfall characteristics. The flash flood events are first classified into different types in terms of flood intensity and process using fuzzy C-means clustering with a subtractive clustering algorithm. Strong association rules between rainfall indices and flood types are subsequently identified based on an association rule mining method. The rainfall indices that strongly affect flash flood processes are obtained based on these association rules. Based on the results of the association analysis, the risks of different types of flash floods under different combinations of key rainfall indices are evaluated based on the Bayesian formula and copula function. The association rule analysis with single and multiple rainfall indices demonstrates that the maximum 12-h rainfall intensity and total antecedent cumulative rainfall are the major rainfall characteristics that affect flash flood processes in the study area. We also examine the risk of flash floods under different combinations of maximum 12-h rainfall intensity and total cumulative rainfall. This study provides an effective and quantitative approach to improve the risk analysis of flash floods and advances its application in the risk management of future flash floods.

The joint probability modelling of storm surges and rainfall events is the main task in assessing compound flood risk in low-lying coastal areas. These extreme or non-extreme events may not be dangerous if considered individually but can intensify flooding impact if they occur simultaneously or successively. Recently, the copula approach has been widely accepted in compound flooding but is often limited to parametric or semiparametric distribution settings in a limited number of cases. However, both parametric and semiparametric approaches assume the prior distribution type for univariate marginals and copula joint density. In that case, there is a high risk of misspecification if the underlying assumption is violated. In addition, both approaches suffer from a lack of flexibility. This study uses bivariate copula density in the nonparametric distribution setting. The joint copula structure is approximated nonparametrically by employing the Bernstein copula estimator and Beta kernel copula density, and their performances are also compared. The proposed model is tested with 46 years of rainfall and storm surge observations collected on Canada's west coast. The marginal distribution of the selected flood variables is modelled using nonparametric kernel density estimation (KDE). Based on the different model compatibility tests, the Bernstein copula with normal KDE margins defined the joint dependence structure well. The selected nonparametric copula model is further employed to estimate joint and conditional return periods. It is found that flood hazard characteristics occurrence simultaneously is less frequent in AND-joint cases than in OR-joint cases. Also, the derived model is further used to estimate failure probability (FP) statistics to assess the variation of bivariate hydrologic risk during the project lifetime. It is found that FP statistics could be underestimated when neglecting the compound effect of storm surge and rainfall in the coastal flood risk.

This study discusses the evaluation of the effect of using different weighting approaches in the process of landslide susceptibility assessment. Weighting process is needed, especially for landslide susceptibility assessment using bivariate statistical analysis, and can radically affect the resulting susceptibility map. The bivariate analysis belongs to a set of quantitative methods. The initial point for the bivariate analysis is selection and processing of input factors in the form of parametric maps, factors that play a dominant role in slope stability. The parametric maps were subsequently evaluated related to landslide inventory map. Another essential part of the bivariate analysis is the determination of the weight of the given input factors. Herein, four methods were applied to determine the weights of each class within reclassified input factors, as well as the total weight of the individual input factors. As a study area, the district of Kysucké Nové Mesto in Slovakia was chosen. Four prognostic maps were the result using entropy index, AHP method (AHP—analytic hierarchy process) (the input factors weighted as a whole), frequency ratio and landslide index (the weights were calculated for each class of input factor). Final landslide susceptibility maps were verified trough ROC curves (ROC—receiver operating characteristics). The accuracy of maps was ascertained by the size area (AUC—area under curve) under ROC curve. The highest accuracy was obtained for maps using weights calculated from the landslide index (88.5%) and the frequency ratio (88.4%).

The purpose of this study is to analyses the relationship between fat mass and inflammation marker, interleukin-6, clinical and metabolic data in 71 human immunodeficiency virus (HIV)-positive male patients using bivariate linear regression analyses and artificial neural network. The data used consisted of measurements collected from HIV male subjects aged 26 to 69 years, with body mass index (BMI) values between 15.47 and 36.98 kg m-2 and the fat mass values between 1.00 kg and 16.70 kg. The bivariate linear regression analyses showed that weight, waist-hip ratio, BMI, triglycerides, high-density lipoprotein and HIV viral load value were significant risk factors associated with the body fat mass in male HIV patients. Furthermore, an in-depth non-linear analysis has been performed using artificial neural network (ANN) to predict fat mass by using the significant predictors as input. ANN model with four hidden neurons obtained the highest mean predictive accuracy percentage of 85.26%. The finding of this study is able to help with the evaluation of the fat mass in the male HIV patients that consequently reflects the patients metabolic-related irregularity and immune response. It is also believed that the outcome from the analysis can help future HIV-related study on the prediction of body fat mass in male HIV patients especially in settings where dual energy X-ray absorptiometry assessments, the standard measurement method for fat mass are not available or affordable

We aim to use real-world data in evidence synthesis to optimize an evidence base for the effectiveness of biologic therapies in rheumatoid arthritis to allow for evidence on first-line therapies to inform second-line effectiveness estimates. We use data from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis to supplement randomized controlled trials evidence obtained from the literature, by emulating target trials of treatment sequences to estimate treatment effects in each line of therapy. Treatment effects estimates from the target trials inform a bivariate network meta-analysis (NMA) of first-line and second-line treatments. Summary data were obtained from 21 trials of biologic therapies including two for second-line treatment and results from six emulated target trials of both treatment lines. Bivariate NMA resulted in a decrease in uncertainty around the effectiveness estimates of the second-line therapies, when compared to the results of univariate NMA, and allowed for predictions of treatment effects not evaluated in second-line randomized controlled trials. Bivariate NMA provides effectiveness estimates for all treatments in first and second line, including predicted effects in second line where these estimates did not exist in the data. This novel methodology may have further applications; for example, for bridging networks of trials in children and adults.

This study investigates the spatial-temporal evolution and the interconnectedness of land use/cover change (LUCC) and ecosystem service value (ESV). Such analysis can offer theoretical guidance and support decision-making for sustainable land resource development and ecological preservation in ecologically vulnerable cities within the Loess Plateau-Maowusu Desert transition zone. Utilizing Landsat data spanning 2000–2020, the paper examines the synergistic relationship between ESV and land use intensity in Shenmu City through bivariate spatial autocorrelation and the coupled coordination degree (CCD) model. Our findings indicate that the area of construction land in Shenmu City experienced the most significant change between 2000 and 2020, with a dynamism rate of 76.8%. This shift resulted in a decrease in the total ESV, from RMB 10.059 billion in 2000 to RMB 9.906 billion in 2020. The bivariate spatial autocorrelation analysis reveals a significant positive spatial correlation between ESV and land use intensity, while the CCD levels for both demonstrate a fluctuating yet overall upward trend over the 20-year period. The paper uncovers the spatial-temporal evolution of LUCC and ESV in Shenmu City along with their interconnected dynamics. The research outcomes can contribute valuable insights for reinforcing land resource utilization and promoting sustainable regional development within cities in the Loess Plateau-Maowusu Desert transition zone.