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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.

With population growth, the number of high-rise residential buildings has continuously increased. Consequently, fire safety and evacuation issues in high-rise buildings have gradually garnered public attention. This study conducted an empirical survey to evaluate the fire safety knowledge and awareness among residents of high-rise buildings in China. The questionnaire sample comprised Chinese residents living in high-rise buildings, with a total of 606 valid responses collected and analyzed. The results revealed that while most residents possessed basic knowledge of high-rise building fire safety, their preparedness for fire incidents remained insufficient. It is recommended that stricter enforcement of safety regulations and the implementation of measures to mitigate fire risks in buildings. Based on descriptive statistical analysis, a hierarchical multiple regression analysis was employed to identify key factors influencing residents’ fire safety awareness and knowledge. The findings indicated that gender, age, educational level, and participation in fire drills were critical factors affecting residents’ fire safety knowledge and awareness. These results provide supportive references for stakeholders to reduce fire risks and associated losses.

This study aims to identify the relationship among the maximum wall deflection, system stiffness, and factor of safety ( FS ) against push-in failure for deep excavations in loose to medium-dense sand. It is concluded that when the FS against push-in failure is greater than or approximately equal to 1.2, the excavation remains stable, and the abovementioned relationship can be used to determine the maximum wall deflection. Empirical approaches for determining the maximum wall deflection are classified by the FS against push-in into two categories: 1.2 ≤  FS  < 1.5 and 1.5 ≤  FS  ≤ 2. Furthermore, the impacts from the strutting systems, such as the strut sizes and horizontal strut spacing, are further scrutinized by using non-linear multiple regression analysis to improve the reliable prediction of the wall deflection for deep excavation in loose to medium-dense sand. The outcome is also validated by excavation cases that have similar ground and retaining systems in this study.

Background To reflect the health literacy (HL) skills needed for managing type 2 diabetes (T2DM) in everyday life, HL in people with T2DM should be measured from a broader perspective than basic skills, such as proficiency in reading and writing. The HLS-Q12, based on the European Health Literacy Survey Questionnaire (HLS-EU-Q47), assesses four cognitive domains across three health domains. International studies on people with T2DM show inconsistent results regarding the association between HL and general health and the association between HL and glycaemic control. Moreover, knowledge is needed related to the link between HL and empowerment for those with T2DM. The aims of this study were to examine the association between i) HL and general health and diabetes outcomes, ii) HL and health behaviours and iii) HL and empowerment in people with T2DM. Methods During March and April 2015, 388 adults with T2DM responded to a paper-and-pencil self-administered questionnaire. A sequential multiple regression analysis was applied to explore the association between HL, as measured by the HLS-Q12, and health conditions, HbA1c, health behaviours and empowerment. Results For people with T2DM, higher levels of HL were associated with higher levels of education, better overall health conditions and higher self-perceived empowerment. No empirical evidence strengthening either the link between HL and glycaemic control or the link between HL and health behaviours was found. Conclusions The independent variables education level, overall health condition and empowerment explained about one-third of the total observed variance in HL.

We have used multiple regression analyses to develop a series of metabolisable energy prediction equations from chemical analyses of pig diets that can be extended to murine diets. We compiled four datasets from an extensive range of published metabolism studies with grower/finisher and adult pigs. The analytes in the datasets were increasingly complex, comprising (1) the proximate or Weende analysis, (2) the previous analysis but with neutral detergent fibre replacing crude fibre, (3) the neutral detergent fibre package plus starch and (4) the neutral detergent fibre package plus starch and sugars. Diet manufacturers routinely provide most of the analytes for batches of murine diet, or they are easily obtainable. The study uniquely compares the four analytical packages side by side. The number of records in the datasets varies from 367 to 827. With increasing analytical complexity, adjusted R 2 values for metabolisable energy prediction improved from 0·751 to 0·869 and the mean absolute error from 0·422 to 0·289 kJ/g. Overall, the models’ prediction interval improved from 1 to 0·7 kJ/g, which is ± 7 to 5 % for a typical dietary metabolisable energy density of 14·8 kJ/g. Although prediction accuracy increases as one extends the range and complexity of the analytes measured, the improvement is slight and may not justify the substantial increase in analytical cost. The equations were validated for use on future datasets by k-fold analysis. Although the equations are developed from pig data, they are suitable for rat and mouse diets, based on comparable digestibility measurements, and substantially improve existing methods.

A multi-spectral diffuse reflectance imaging method based on a single snap shot of Red-Green-Blue images acquired with the exposure time of 65 ms (15 fps) was investigated for estimating melanin concentration, blood concentration, and oxygen saturation in human skin tissue. The technique utilizes the Wiener estimation method to deduce spectral reflectance images instantaneously from an RGB image. Using the resultant absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are numerically deduced in advance by the Monte Carlo simulations for light transport in skin. Oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments on fingers during upper limb occlusion demonstrated the ability of the method to evaluate physiological reactions of human skin.

In this study, we use Artificial Neural Networks (ANN) and Multiple Regression Analysis to evaluate the prediction of two crucial self-compacting concrete properties: compressive strength and split tensile strength. It was possible to create four different datasets, each of which had different concrete mix proportions along with their respective ages in days, compressive strengths (MPa), and split tensile strengths (MPa). Separate ANN models and Regression models were trained and tested using these datasets. As a gauge of prediction accuracy, Mean Squared Error (MSE) was used to assess the performance of the models. This study offers insightful information on the application of multiple regression analysis and artificial neural networks to forecast the characteristics of self-compacting concrete using GGBS and Alccofine. Here Alccofine functions as an additive and GGBS acts as a partial substitute for cement at 0 to 60% with a fluctuation of 10%. The outcomes highlight the potential of neural networks as a tool for concrete mix design optimization and quality control since they can capture complex correlations between input variables and concrete strength.

This study analyzes the association between age integration and residential satisfaction in urban regeneration areas. A questionnaire survey was conducted with 569 residents who visited ten Urban Regeneration Community Facilities (URCFs) in Daegu Metropolitan City, South Korea. Age integration was set as the main independent variable, and blockwise (sequential-entry) multiple regression analysis was performed while controlling for life satisfaction, community wellbeing, and socio-demographic characteristics. The results indicate that higher levels of age integration are significantly associated with higher residential satisfaction, demonstrating that intergenerational interactions and inclusive relationships play an important role in enhancing satisfaction with the neighborhood. This positive association was also consistent across age cohorts, with no statistically significant differences in correlation strength between age groups. Several control variables, including life satisfaction, selected components of community wellbeing, and income level, also show significant positive associations with residential satisfaction, confirming that personal, social, and environmental factors jointly influence residential satisfaction in urban regeneration areas. These findings highlight the importance of fostering age-integrated environments in urban regeneration policies to enhance the social sustainability of urban neighborhoods. By showing that age integration is associated with higher residential satisfaction even after controlling for life satisfaction, community wellbeing, and socio-demographic characteristics, this study provides empirical evidence on how age-integrated environments can contribute to the social sustainability and community wellbeing of urban regeneration neighborhoods from a social sustainability perspective.

Background: Parenting quality experienced in childhood affects depressive symptoms in adulthood, and neuroticism and resilience are attracting attention as personality traits that mediate the effects of parental rearing quality experienced in childhood on adulthood depressive symptoms. However, the interaction between neuroticism and resilience remains unclear. In this study, we hypothesized that resilience and neuroticism are mediators between parental rearing quality experienced in childhood and depressive symptoms in adulthood, and furthermore, that resilience and neuroticism interact with each other in their effects on depressive symptoms. To test these hypotheses, we conducted structural equation modeling and hierarchical multiple regression analysis including interactions in adult volunteers. Methods: A self-administered questionnaire survey was conducted on 528 adult volunteers recruited at Tokyo Medical University for 1 year from April 2017 to April 2018. The Parental Bonding Instrument (PBI), Eysenck Personality Questionnaire-revised short version, Connor-Davidson Resilience Scale, and Patient Health Questionnaire-9 were used as questionnaires, and their scores were analyzed using structure equation modeling. The interaction between resilience and neuroticism was analyzed using hierarchical multiple regression analysis. Results: Structural equation modeling showed that parenting quality (care and overprotection) experienced in childhood had a significant indirect effect on the severity of depressive symptoms in adulthood, mediated by both neuroticism and resilience. Among the subscores of the PBI, \"care\" showed opposite effects to \"overprotection\". Structural equation modeling of \"care\" and \"overprotection\" explained 36.9% and 36.6% of the variability in depressive symptoms in adulthood, respectively. Hierarchical multiple regression analysis showed that the negative interaction between neuroticism and resilience had a significant effect on depressive symptom severity in adulthood. Conclusion: The results of this study show that resilience and neuroticism are mediators of the effects from parental child-rearing to depressive symptoms in adulthood. Furthermore, resilience antagonizes the effect of neuroticism on adulthood depressive symptoms. Keywords: parenting, resilience, neuroticism, depressive symptoms, structure equation modeling, hierarchical multiple regression analysis

Embedment properties are vital to timber structural designs, and many types of wood-based structural panels have been developed for diverse uses. Comprehensive and systematic studies regarding the embedment properties of wood-based structural panels are limited. In this study, a jig that allows the observation of fracture processes is developed, and a monotonic tensile embedment test is conducted on plywood, oriented strandboard (both strong and weak axes), particleboard, medium density fiberboard, and hardboard. The parameters used in the test are the dowel diameter, pilot hole size, and end and edge distances. The effects of these parameters on the embedment properties (i.e., the failure mode, ductility, maximum stress, and yield stress) are discussed comprehensively. The failure mode is determined by the edge distance. At a sufficient edge distance, ductile failure occurs, and the load is maintained until the remaining end distance reaches a certain value. The maximum stress and yield stress are analyzed quantitatively via standardized multiple regression analysis. The results suggest the following: (i) The ratio of the in-plane strength to the internal bond strength is related to the failure behavior; (ii) the dowel diameter, fiber direction, and load levels affect the stress spread pattern of the embedment pressure.