Beyond Difference Scores: Unlocking Insights with Polynomial Regression in Studies on the Effects of Implicit-Explicit Congruency

The aim of our study was to investigate whether theories of congruence are better tested using polynomial regression analysis, rather than expressing discrepancy between implicit and explicit measures as continuous or categorical difference scores. This paper also aims to make knowledge more accessible by providing a step-by-step explanation of both methods, illustrating differences between them, and making materials openly available for other researchers. In this paper, implicit and explicit measures of self-esteem are used as predictors for depressive symptoms, anxiety, and aggression in a general population sample (N = 135). Explicit self-esteem was measured using the Rosenberg Self-Esteem Scale, implicit self-esteem was measured using the Implicit Association Test, and the Symptom Questionnaire was used to measure depressive symptoms, anxiety, and aggression. The results show those difference score models all imply that the discrepancy between implicit and explicit self-esteem explains depression and anxiety, but not aggression. However, polynomial regression analysis shows that depression and anxiety are not accounted for by the explicit-implicit discrepancy as such, but are foremost explained by explicit self-esteem. Polynomial regression has the potential to evaluate more complex and more detailed hypotheses than what would be possible using statistical approaches based on discrepancy scores. It is therefore recommended for future research aimed at disentangling the roles of explicit and implicit self-esteem in psychological outcomes.

The effect of perceived stress on social isolation in young and middle-aged maintenance haemodialysis patients: A polynomial regression and response surface analysis

Patients on maintenance haemodialysis (MHD) face social isolation due to the far-reaching effects of their disease and treatment. Based on the Transactional Model of Stress and Coping, we analysed the relationship between perceived stress and social isolation in patients, and the mediating role of internal health-related locus of control (IHLC) from the perspective of co-existence of perceived helplessness (HEL) and perceived self-efficacy (SEL). We used structured questionnaires to investigate 332 cases of young and middle-aged MHD patients in four tertiary hospitals in Guangdong, China. Polynomial regression and response surface analysis were conducted, along with tests of mediating effects on the data. The research was based on STROBE guidelines. The results showed that perceived stress is significantly and positively associated with social isolation. When the HEL and SEL of perceived stress were congruent, the joint effect of the two was in a positive curvilinear relationship to social isolation, when the two were incongruent, the risk of social isolation was lower for "low HEL-high SEL" patients compared to the "high HEL-low SEL" combination. The more congruent in HEL -SEL, the lower the risk of social isolation for the patients, with the IHLC playing a mediating role in this association. This study reveals that the coexistence of perceived stress's HEL and SEL affects social isolation through congruent and incongruent matching mechanism, providing additional explanations for the mechanism of occurrence of patients' social isolation.

Team Efficacy Profiles: Congruence Predicts Objective Performance of Athlete Pairs

Efficacy beliefs targeting personal (self-efficacy), teammates' (other-efficacy), and conjoint (collective efficacy) abilities are each associated with performance of athlete pairs. The purpose of this study was to examine (a) congruence/incongruence of efficacy beliefs between athletes in a pair as a predictor of quality of individual and team performance and (b) quality of performance relative to efficacy congruence at high, moderate, and low levels of efficacy. Eighty-two cheerleading pairs, composed of one base and one flyer, completed questionnaires assessing self-, other, and collective efficacy prior to a national collegiate competition. Individual and team performances were assessed using objective criteria. Polynomial regression analyses indicated that team performance was predicted by congruence of (a) both athletes' collective efficacy beliefs and (b) base self-efficacy and flyer other efficacy. Findings supported that congruence at moderate to high levels of efficacy was associated with better performance relative to incongruent efficacy beliefs across the two athletes.

Visualization with Prediction Scheme for Early DDoS Detection in Ethereum

Blockchain technologies have gained widespread use in security-sensitive applications due to their robust data protection. However, as blockchains are increasingly integrated into critical data management systems, they have become attractive targets for attackers. Among the various attacks on blockchain systems, distributed denial of service (DDoS) attacks are one of the most significant and potentially devastating. These attacks render the systems incapable of processing transactions, causing the blockchain to come to a halt. To address the challenge of detecting DDoS attacks on blockchains, existing visualization schemes have been developed. However, these schemes often fail to provide early DDoS detection since they typically display only past and current system status. In this paper, we present a novel visualization scheme that not only portrays past and current values but also forecasts future expected system statuses. We achieve these future predictions by utilizing polynomial regression with blockchain data. Additionally, we offer an alternative DDoS detection method employing statistical analysis, specifically the coefficient of determination, to enhance accuracy. Through our experiments, we demonstrate that our proposed scheme excels at predicting future blockchain statuses and anticipating DDoS attacks with minimal error. Our work empowers system managers of blockchain-based applications to identify and mitigate DDoS attacks at an earlier stage.

Predictive Modeling of Tensile Strength in Aluminum Alloys via Machine Learning

Aluminum alloys are widely used due to their exceptional properties, but the systematic relationship between their grain size and their tensile strength has not been thoroughly explored in the literature. This study aims to fill this gap by compiling a comprehensive dataset and utilizing machine learning models that consider both the alloy composition and the grain size. A pivotal enhancement to this study was the integration of hardness as a feature variable, providing a more robust predictor of the tensile strength. The refined models demonstrated a marked improvement in predictive performance, with XGBoost exhibiting an R2 value of 0.914. Polynomial regression was also applied to derive a mathematical relationship between the tensile strength, alloy composition, and grain size, contributing to a more profound comprehension of these interdependencies. The improved methodology and analytical techniques, validated by the models' enhanced accuracy, are not only relevant to aluminum alloys, but also hold promise for application to other material systems, potentially revolutionizing the prediction of material properties.

Machine-Learning-Based Calibration of Temperature Sensors

Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.

Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space

Previous research has demonstrated the potential to reconstruct human facial skin spectra based on the responses of RGB cameras to achieve high-fidelity color reproduction of human facial skin in various industrial applications. Nonetheless, the level of precision is still expected to improve. Inspired by the asymmetricity of human facial skin color in the CIELab* color space, we propose a practical framework, HPCAPR, for skin facial reflectance reconstruction based on calibrated datasets which reconstruct the facial spectra in subsets derived from clustering techniques in several spectrometric and colorimetric spaces, i.e., the spectral reflectance space, Principal Component (PC) space, CIELab*, and its three 2D subordinate color spaces, La*, Lb*, and ab*. The spectra reconstruction algorithm is optimized by combining state-of-art algorithms and thoroughly scanning the parameters. The results show that the hybrid of PCA and RGB polynomial regression algorithm with 3PCs plus 1st-order polynomial extension gives the best results. The performance can be improved substantially by operating the spectral reconstruction framework within the subset classified in the La* color subspace. Comparing with not conducting the clustering technique, it attains values of 25.2% and 57.1% for the median and maximum errors for the best cluster, respectively; for the worst, the maximum error was reduced by 42.2%.

The Effect of Resilience and Family Support Match on Psychological Distress among Women in the Menopausal Transition Based on Polynomial Regression and Response Surface Analysis

Menopausal transition (MT) is a natural process in women from reproductive decline to aging. During this period, women present with an increased prevalence of psychological distress. The aim of this study was to investigate how resilience and family support interact to influence psychological distress among women during MT. A convenience sampling method was used to recruit 858 women during MT from Shandong Province. All participants completed the 10-item Connor-Davidson Resilience Scale, the family care degree questionnaire, and the Kessler Psychological Distress Scale. Data were analyzed by using SPSS 24.0. Response surface analysis was used for polynomial regression and response surface analysis. The results of response surface analysis show that in the case of agreement between resilience and family support, the joint effect manifested as a negative curve (inverted U shape) related to the psychological distress of women during MT. In the case of disagreement, the joint effect manifested as a negative curve (inverted U shaped) related to psychological distress. Specifically, with increased variance in the degree of resilience and family support, women experienced less psychological distress. Both resilience and family support play an important role in protecting women from psychological distress, either alone or in combination. Future intervention studies targeting women during MT should consider the coordinated actions of resilience and family support.

[Autonomous and controlled motivation fit on college students' physical fitness:based on the polynomial regression and response surface analysis]

OBJECTIVE

Based on the self-determination theory, to explore the relationship between motivation quality and college students' physical fitness and the mediating role of physical activities from the perspective of the coexistence of autonomous motivation(AM) and controlled motivation(CM).

METHODS

From October to November 2019, a total of 682 freshmen and sophomores(252 males and 430 females) were recruited with cluster-sampling method from 4 colleges and universities in Wuhu City, filled with questionnaires of Perceived Locus of Causality scale and Godin's leisure-time physical activity questionnaire, and tested physical fitness according to China National Fitness Test Program after 6 weeks. The data were analyzed by polynomial regression combined with response surface analysis and mediation effect test.

RESULTS

Physical fitness presented a "convex" curve increase with the consistency of AM and CM(a1=1.547, a2=1.254, P<0.01). The physical fitness of high AM-high CM combination was higher than that of low AM-low CM combination(Z_(hat)=3.111, 95% CI 0.446-5.896). Under the condition of AM and CM differentiation, physical fitness was higher when the discrepancy was such that AM was higher than CM(a3=5.280, P<0.01; a4=0.232, P>0.05). AM positively predicted physical activity in a nonlinear form(β_( AM)=1.605, β_(AM)~2=1.602, P<0.01). Physical activity showed a "convex" change with the consistency of AM-CM(a1=0.811, P > 0.05; a2=1.618, P <0.01), but there was no significant difference in the level of physical activity between high and low AM-CM combination(Z_(hat)=1.407, 95% CI-0.084-3.391). Physical activity was higher in high AM-low CM combination than that in high CM-low AM combination(Z_(hat)=5.008, 95% CI 2.348-7.113). Matching of AM and CM influenced college students' physical fitness directly(β=0.453, P<0.01) and indirectly through physical activity(β=0.184, 95% CI 0.145-0.240).

CONCLUSION

The coexistence of AM and CM effects physical fitness through their consistency and inconsistency matching.

Response Surface Analysis with Missing Data

Response Surface Analysis (RSA) is gaining popularity in psychological research as a tool for investigating congruence hypotheses (e.g., consequences of self-other agreement, person-job fit, dyadic similarity). RSA involves the estimation of a nonlinear polynomial regression model and the interpretation of the resulting response surface. However, little is known about how best to conduct RSA when the underlying data are incomplete. In this article, we compare different methods for handling missing data in RSA. This includes different strategies for multiple imputation (MI) and maximum-likelihood (ML) estimation. Specifically, we consider the "just another variable" (JAV) approach to MI and ML, an approach that is in regular use in applications of RSA, and the more novel "substantive-model-compatible" (SMC) approach. In a simulation study, we evaluate the impact of these methods on focal outcomes of RSA, including the accuracy of parameter estimates, the shape of the response surface, and the testing of congruence hypotheses. Our findings suggest that the JAV approach can sometimes distort parameter estimates and conclusions about the shape of the response surface, whereas the SMC approach performs well overall. We illustrate applications of the methods in a worked example with real data and provide recommendations for their application in practice.

High-Accuracy 3D Gaze Estimation with Efficient Recalibration for Head-Mounted Gaze Tracking Systems

The problem of 3D gaze estimation can be viewed as inferring the visual axes from eye images. It remains a challenge especially for the head-mounted gaze tracker (HMGT) with a simple camera setup due to the complexity of the human visual system. Although the mainstream regression-based methods could establish the mapping relationship between eye image features and the gaze point to calculate the visual axes, it may lead to inadequate fitting performance and appreciable extrapolation errors. Moreover, regression-based methods suffer from a degraded user experience because of the increased burden in recalibration procedures when slippage occurs between HMGT and head. To address these issues, a high-accuracy 3D gaze estimation method along with an efficient recalibration approach is proposed with head pose tracking in this paper. The two key parameters, eyeball center and camera optical center, are estimated in head frame with geometry-based method, so that a mapping relationship between two direction features is proposed to calculate the direction of the visual axis. As the direction features are formulated with the accurately estimated parameters, the complexity of mapping relationship could be reduced and a better fitting performance can be achieved. To prevent the noticeable extrapolation errors, direction features with uniform angular intervals for fitting the mapping are retrieved over human’s field of view. Additionally, an efficient single-point recalibration method is proposed with an updated eyeball coordinate system, which reduces the burden of calibration procedures significantly. Our experiment results show that the calibration and recalibration methods could improve the gaze estimation accuracy by 35 percent (from a mean error of 2.00 degrees to 1.31 degrees) and 30 percent (from a mean error of 2.00 degrees to 1.41 degrees), respectively, compared with the state-of-the-art methods.

Comparison between polynomial regression and weighted least squares regression analysis for verification of analytical measurement range

OBJECTIVES

Recently, the linearity evaluation protocol by the Clinical & Laboratory Standards Institute (CLSI) has been revised from EP6-A to EP6-ED2, with the statistical method of interpreting linearity evaluation data being changed from polynomial regression to weighted least squares linear regression (WLS). We analyzed and compared the analytical measurement range (AMR) verification results according to the present and prior linearity evaluation guidelines.

METHODS

The verification of AMR of clinical chemistry tests was performed using five samples with two replicates in three different laboratories. After analyzing the same evaluation data in each laboratory by the polynomial regression analysis and WLS methods, results were compared to determine whether linearity was verified across the five sample concentrations. In addition, whether the 90% confidence interval of deviation from linearity by WLS was included in the allowable deviation from linearity (ADL) was compared.

RESULTS

A linearity of 42.3-56.8% of the chemistry items was verified by polynomial regression analysis in three laboratories. For analysis of the same data by WLS, a linearity of 63.5-78.3% of the test items was verified where the deviation from linearity of all five samples was within the ADL criteria, and the cases where the 90% confidence interval of all deviation from linearity overlapped the ADL was 78.8-91.3%.

CONCLUSIONS

Interpreting AMR verification data by the WLS method according to the newly revised CLSI document EP6-ED2 could reduce laboratory workload, enabling efficient laboratory practice.

Modeling and forecasting the total number of cases and deaths due to pandemic

The COVID‐19 pandemic has appeared as the predominant disease of the 21st century at the end of 2019 and was a drastic start with thousands of casualties and the COVID‐19 victims in 2020. Due to the drastic effect, COVID‐19 scientists are trying to work on pandemic diseases and Governments are interested in the development of methodologies that will minimize the losses and speed up the process of cure by providing vaccines and treatment for such pandemics. The development of a new vaccine for any pandemic requires long in vitro and in vivo trials to use. Thus the strategies require understanding how the pandemic is spreading in terms of affected cases and casualties occurring from this disease, here we developed a forecasting model that can predict the no of cases and deaths due to pandemic and that can help the researcher, government, and other stakeholders to devise their strategies so that the damages can be minimized. This model can also be used for the judicial distribution of resources as it provides the estimates of the number of casualties and number of deaths with high accuracy, Government and policymakers on the basis of forecasted value can plan in a better way. The model efficiency is discussed on the basis of the available dataset of John Hopkins University repository in the period when the disease was first reported in the six countries till the mid of May 2020, the model was developed on the basis of this data, and then it is tested by forecasting the no of deaths and cases for next 7 days, where the proposed strategy provided excellent forecasting. The forecast models are developed for six countries including Pakistan, India, Afghanistan, Iran, Italy, and China using polynomial regression of degrees 3–5. But the models are analyzed up to the 6th‐degree and the suitable models are selected based on higher adjusted R‐square (R²) and lower root‐mean‐square error and the mean absolute percentage error (MAPE). The values of R² are greater than 99% for all countries other than China whereas for China this R² was 97%. The high values of R² and Low value of MAPE statistics increase the validity of proposed models to forecast the total no cases and total no of deaths in all countries. Iran, Italy, and Afghanistan also show a mild decreasing trend but the number of cases is far higher than the decrease percentage. Although India is expected to have a consistent result, more or less it depicts some other biasing factors which should be figured out in separate research.

Salesperson Self-Regulated Learning and Online Customers' Patronage: An Ambidexterity Perspective

Although the roles of exploratory and exploitative learning as alternative sales skills have been documented, there is not yet a clear consensus, and empirical evidence in the online sales context is lacking. In addition, existing studies have tended to examine the two activities in parallel, without looking into the dyadic situation of balanced or imbalanced exploratory-exploitative learning. Grounded in the WeChat business context, this study explores how online sales agents' balanced and imbalanced ambidextrous learning influence customers' e-loyalty and, in turn, their patronage intention and behavior. Polynomial regression and response surface analysis are performed on 226 dyads, and the results support the hypothesized balance effect. Further, asymmetrical imbalance effects are identified, with customers exhibiting higher e-loyalty and better patronage outcomes when online sales agents adopt more exploitative learning than exploratory learning. This study helps improve understanding of the efficiency of personal selling in a virtual context.

Understanding Loneliness in Adolescence: A Test of Competing Hypotheses on the Interplay of Extraversion and Neuroticism

Given that adolescents often experience fundamental changes in social relationships, they are considered to be especially prone to loneliness. Meanwhile, theory and research highlight that both extraversion and neuroticism are closely intertwined with individual differences in loneliness. Extant research has explored the linear main effects of these personality traits, yet potential non-linear associations (e.g., exponential effects) and the potential interplay of extraversion and neuroticism (e.g., mutual reinforcement effects) remain unknown. We addressed these open questions using cross-sectional and one-year longitudinal data from two adolescent samples (overall N = 583, Mage = 17.57, 60.55% girls) and an information-theoretic approach combined with polynomial regression. Analyses showed little evidence for interaction effects but revealed non-linear effects in addition to the main effects of extraversion and neuroticism on loneliness. For example, the positive cross-sectional association between neuroticism and loneliness was stronger at higher neuroticism levels (i.e., exponential effect). Results differed across loneliness facets in that both traits predicted emotional loneliness, but only extraversion predicted social loneliness. Longitudinal analyses showed that loneliness changes were mainly related to neuroticism. We discuss results in the light of sample differences, elaborate on the importance to differentiate between emotional versus social aspects of loneliness, and outline implications for adolescent development.

Analysis of Laser Sensors and Camera Vision in the Shoe Position Inspection System

Inspection systems are currently an evolving field in the industry. The main goal is to provide a picture of the quality of intermediates and products in the production process. The most widespread sensory system is camera equipment. This article describes the implementation of camera devices for checking the location of the upper on the shoe last. The next part of the article deals with the analysis of the application of laser sensors in this task. The results point to the clear advantages of laser sensors in the inspection task of placing the uppers on the shoe's last. The proposed method defined the resolution of laser scanners according to the type of scanned surface, where the resolution of point cloud ranged from 0.16 to 0.5 mm per point based on equations representing specific points approximated to polynomial regression in specific places, which are defined in this article. Next, two inspection systems were described, where one included further development in the field of automation and Industry 4.0 and with a high perspective of development into the future. The main aim of this work is to conduct analyses of sensory systems for inspection systems and their possibilities for further work mainly based on the resolution and quality of obtained data. For instance, dependency on scanning complex surfaces and the achieved resolution of scanned surfaces.

Regression Based Iterative Illumination Compensation Method for Multi-Focal Whole Slide Imaging System

Image quality, resolution and scanning time are critical in digital pathology. In order to create a high-resolution digital image, the scanner systems execute stitching algorithms to the digitized images. Due to the heterogeneity of the tissue sample, complex optical path, non-acceptable sample quality or rapid stage movement, the intensities on pictures can be uneven. The evincible and visible intensity distortions can have negative effect on diagnosis and quantitative analysis. Utilizing the common areas of the neighboring field-of-views, we can estimate compensations to eliminate the inhomogeneities. We implemented and validated five different approaches for compensating output images created with an area scanner system. The proposed methods are based on traditional methods such as adaptive histogram matching, regression-based corrections and state-of-the art methods like the background and shading correction (BaSiC) method. The proposed compensation methods are suitable for both brightfield and fluorescent images, and robust enough against dust, bubbles, and optical aberrations. The proposed methods are able to correct not only the fixed-pattern artefacts but the stochastic uneven illumination along the neighboring or above field-of-views utilizing iterative approaches and multi-focal compensations.

Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning

At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common. In this paper, we describe the development of a tire inspection system for the tire industry. We provide methods for processing tire sidewall data obtained from a camera and a laser sensor. The captured data comprise visual and geometric data characterizing the tire surface, providing a real representation of the captured tire sidewall. We use an unfolding process, that is, a polar transform, to further process the camera-obtained data. The principles and automation of the designed polar transform, based on polynomial regression (i.e., supervised learning), are presented. Based on the data from the laser sensor, the detection of abnormalities is performed using an unsupervised clustering method, followed by the classification of defects using the VGG-16 neural network. The inspection system aims to detect trained and untrained abnormalities, namely defects, as opposed to using only supervised learning methods.

Industrial Masonry Chimney Geometry Analysis: A Total Station Based Evaluation of the Unmanned Aerial System Photogrammetry Approach

High industrial chimney inclination monitoring and analysis is crucial from a stability point of view because, if not maintained, it can pose a great potential hazard for its surroundings. Various modern approaches of chimneys’ geometrical parameters determination have been proposed and are actively in use. However, little research regarding the applicability of the unmanned aerial system (UAS)-based photogrammetric approach of chimney structural monitoring has been conducted and a comprehensive analysis with validated methods is lacking. Therefore, this research is focused on the determination of geometrical structural parameters of a masonry chimney whereby two independent methods have been applied. Reference values of the chimney geometrical parameters have been determined by precise total station (TS) measurements and, in relation to them, the applicability of the UAS-based photogrammetric approach is evaluated. Methodologically, the reference and validation values of the chimney geometrical parameters have been determined based on double modeling of the chimney structure. Firstly, cross-sectional elliptical regression has been applied to determine the geometrical values of the chimney at predefined above-ground levels (AGLs). Secondly, the spatial chimney axis has been derived by polynomial regression to determine the inclination of the full chimney structure. Lastly, the structural stability of the chimney is validated based on its axis inclination whereby permitted deviations are determined according to the European Standard EN 1996-1-1:2005. Experimental results of our research show that consistently better results are gained by TS-based surveys and, although the determination of the chimney’s geometrical values by the UAS-based approach is certainly possible, great attention must be given to the accuracy of the UAS-generated point cloud (PC) if high accuracy results are needed.

The Effect of Perceived Supervisor-Subordinate Congruence in Honesty on Emotional Exhaustion: A Polynomial Regression Analysis

Do subordinates unequivocally prefer honest superv =isors? This study investigates whether congruence in supervisor–subordinate honesty has a positive effect on lowering the emotional exhaustion experienced by subordinates. For the research data, a two-part survey with a one-month time interval was conducted among office workers, and 409 responses were collected. These were empirically analyzed using polynomial regression analysis and response surface analysis, instead of the common methodology based on difference values used in past studies on the fit between a person and their environment. The analysis results confirmed that supervisor–subordinate congruence in honesty has a negative relationship with subordinates’ emotional exhaustion and supervisor–subordinate congruence at higher levels of honesty will have stronger negative relationships with emotional exhaustion. This study expands the intrapersonal context of the existing research on supervisors’ honesty to the interpersonal context and empirically demonstrates the effect of honesty congruence. It also discusses its theoretical and practical implications as well as limitations, and it provides suggestions for future studies.

Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP

In this study, multiple regression analysis (MRA) and polynomial regression analysis (PRA), which are traditional statistical methods, were applied to analyze factors affecting the tensile strength of basalt and glass fiber-reinforced polymers (FRPs) exposed to alkaline environments and predict the tensile strength degradation. The MRA and PRA are methods of estimating functions using statistical techniques, but there are disadvantages in the scalability of the model because they are limited by experimental results. Therefore, recently, highly scalable artificial neural networks (ANN) have been studied to analyze complex relationships. In this study, the prediction performance was evaluated in comparison to the MRA, PRA, and ANN. Tensile strength tests were conducted after exposure for 50, 100, and 200 days in alkaline environments at 20, 40, and 60 °C. The tensile strength was set as the dependent variable, with the temperature (TP), the exposure day (ED), and the diameter (D) as independent variables. The MRA and PRA results showed that the TP was the most influential factor in the tensile strength degradation of FRPs, followed by the exposure time (ED) and diameter (D). The ANN method provided the best correlation between predictions and experimental values, with the lowest error and error rate. The PRA method applied to the response surface method outperformed the MRA method, which is most commonly used. These results demonstrate that ANN can be the most efficient model for predicting the durability of FRPs.

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

Leader-Follower Congruence in Work Engagement and Leader-Member Exchange: The Moderating Role of Conscientiousness of Followers

Extant research has investigated the relationship between work engagement and various outcomes, such as job performance and organizational commitment, neglecting the effect of work engagement on social relationships at work. Drawing upon person-environment fit theory and LMX theory, the present study aims to examine the effect of (in)congruence between leader and follower work engagement on leader–member exchange (LMX) and the moderating effect of conscientiousness. About 273 employees and 72 leaders participated in this study and completed the measurements of work engagement, conscientiousness, and LMX at two time points. Using cross-level polynomial regressions, we found that, compared with incongruent work engagement, employees perceived high levels of LMX quality when their work engagement was aligned with that of their leaders. Regarding the congruence, the employees reported higher levels of LMX when congruence in work engagement was at higher rather than lower levels. Regarding the incongruence, when the employees engaged less in their work tasks than their leaders, they were more likely to experience lower LMX. Moreover, the negative relationship between incongruence in leader and follower work engagement and LMX was mitigated when followers were more conscientious. All our hypotheses were supported. Both theoretical and practical implications for work engagement as well as future directions are discussed.

Predictive Modeling and Validation on Growth, Production of Asexual Spores and Ochratoxin A of Aspergillus Ochraceus Group under Abiotic Climatic Variables

This study aimed to generate predictive models for growth, sporulation, and ochratoxin A (OTA) production under abiotic climatic variables, including temperatures (15-35 °C) and water activity levels (0.99-0.90 aw) by Aspergillus ochraceus group. The data were divided into three sets: one for training, one for testing, and the third one for model validation. Optimum growth occurred at 0.95 aw and 25 °C and 0.95 aw and 30 °C for A. westerdijkiae and A. steynii, respectively. Significantly improved A. westerdijkiae and A. steynii spore production occurred at 0.95 aw and 20 °C and 0.90 aw and 35 °C, respectively. A. steynii and A. westerdijkiae produced the majority of OTA at 35 °C and 0.95 aw and 25-30 °C at 0.95-0.99 aw, respectively. The accuracy of the third-order polynomial regression model reached 96% in growth cases, 94.7% in sporulation cases, and 90.9% in OTA production cases; the regression coefficients (R2) ranged from 0.8819 to 0.9978 for the Aspergillus ochraceus group. A reliable agreement was reached between the predicted and observed growth, sporulation, and OTA production. The effects of abiotic climatic variables on growth, sporulation, and OTA production of A. ochraceus group have been effectively defined, and the models generated were responsible for adequately predicted and validated models against data from other strains within A. ochraceus group that had been published in the literature under the current treatments. These models could be successfully implemented to predict fungal growth and OTA contamination on food matrices for these strains under these conditions.

Are we seeing "eye to eye" on qualitative job insecurity in healthcare? Employee-teammates perceptual (in)congruence and individual well-being

Background: In healthcare, employees are exposed to continuous change when new methods are implemented to optimize care. Such changes may result in qualitative job insecurity (QJI), i.e., a fear concerning the potential loss of important job features. QJI is an individual experience; however, it may be shared within a team to a varying extent. This study examines how QJI perceptual (in)congruence between individuals and their teammates relates to individuals' well-being. Method: Healthcare employees (N = 305) from 30 healthcare units completed questionnaires measuring QJI, work engagement, and recovery. Results: Multi-level polynomial regression analyses showed that QJI congruence had a curvilinear relationship with well-being: employees reported higher work engagement when QJI perceptions were in agreement, both when QJI was low and high. We observed a negative relationship between QJI congruence and recovery. Recovery was lower when perceptions of QJI were in agreement and were high (vs. low). Finally, we found support for the effects of perceptual incongruence: when employees reported higher QJI than their teammates, they experienced lower recovery and engagement. Conclusions: To understand how employees' QJI relates to their well-being, it is essential to consider their teammates' perceptions. The social context can augment or reduce individuals' stress reactions to job insecurity.

Evaluating cell viability heterogeneity based on information fusion of multiple adhesion strengths

Cell viability evaluation is significantly meaningful for cellular assays. Some cells with weak viability are easily killed in the detection of anticancer drugs, while others with strong viability survive and proliferate, ultimately leading to the treatment failure or the inaccuracy of biological assays. Accurately evaluating cell viability heterogeneity still remains difficult. This article proposed a multiphysical property information fusion method for evaluating cell viability heterogeneity based on polynomial regression in a single-channel integrated microfluidic chip. In this method, adhesion strengths τ_N , that are defined as the magnitude of shear stress needed to detach (100 - N) % of cell population, were extracted as the independent variables of polynomial regression model by calculating the nonlinear fitting of the impedance-response curves for shear stress (cell detachment assay). Besides, by calculating the nonlinear fitting of the drug dose-response curves for cancer cells (IC₅₀ assay), the half-maximal inhibitory concentration (IC₅₀ ) was extracted as the dependent variables of polynomial regression model. The results show that the mean relative error of our fusion method averagely reduces by 6.04% and 62.79% compared with the multiple linear regression method and the cell counting method. Moreover, a simplified theoretical model used to describe the quantitative relationship between cell viability and its adhesion strengths was built to provide a theoretical basis for our fusion method.

Leader-Employee Congruence in Humor and Innovative Behavior: The Moderating Role of Dynamic Tenure

Drawing upon the literature on complementary fit theory, the purpose of this study is to examine how the dynamic tenure moderates the relationship between leader–employee congruence/incongruence in humor and employee innovative behavior. Data were collected from 108 leader–employee dyads from information technology companies in China. Polynomial regression combined with the response surface methodology was used to test the hypotheses. Four conclusions were drawn. First, employee innovative behavior was maximized when leaders and employees were incongruent in humor. Second, in the case of incongruence, employees had higher innovative behavior when employees were more humorous than their leaders. Third, in the case of congruence, employees had higher innovative behavior when a leader’s and an employee’s humor matched at high levels. Fourth, dynamic tenure moderated the leader–employee congruence/incongruence effect of humor on employee innovative behavior. This study enhanced theoretical developments by considering the importance of leaders’ congruence with employees in humor for the first time. Additionally, the research results provided better practical guidance for effectively promoting employee innovative behavior.

Leader-Follower Congruence in MD Propensity and UPB: A Polynomial Regression Analysis

Drawing on previous work on moral disengagement and balance theory, the (in)congruence effect of leader and follower moral disengagement (MD) propensity on followers' unethical pro-organizational behavior (UPB) was examined in this study. Dyad data were gathered from 185 CEOs and 555 followers from 185 firms located in China. The polynomial regression analysis results suggest that followers in the presence of leader-follower congruence in MD propensity conduct more UPBs. Moreover, followers conduct more UPBs with MD propensity in the presence of congruence. We also found asymmetrical incongruence effects. Followers engage more in UPBs when their MD propensity exceeds their leaders' MD propensity than when their leaders' MD propensity exceeds their own. Overall, the findings emphasize the value of considering followers' and leaders' traits for shedding light on the occurrence of UPBs.

An Indoor Visible Light Positioning System Using Tilted LEDs with High Accuracy

The accuracy of the received signal strength-based visible light positioning (VLP) system in indoor applications is constrained by the tilt angles of transmitters (Txs) and receivers as well as multipath reflections. In this paper, for the first time, we show that tilting the Tx can be beneficial in VLP systems considering both line of sight (LoS) and non-line of sight transmission paths. With the Txs oriented towards the center of the receiving plane (i.e., the pointing center F), the received power level is maximized due to the LoS components on F. We also show that the proposed scheme offers a significant accuracy improvement of up to ~66% compared with a typical non-tilted Tx VLP at a dedicated location within a room using a low complex linear least square algorithm with polynomial regression. The effect of tilting the Tx on the lighting uniformity is also investigated and results proved that the uniformity achieved complies with the European Standard EN 12464-1. Furthermore, we show that the accuracy of VLP can be further enhanced with a minimum positioning error of 8 mm by changing the height of F.

The Relationship Between Couples' Gender-Role Attitudes Congruence and Wives' Family Interference with Work

Background

Previous research on female employees’ family interference with work (FIW) has demonstrated that such conflict is affected by their or their spouses’ gender-role attitudes. However, few studies have considered the perspective of husbands-wives congruence, which is further meaningful of the research on FIW. The purpose of this paper is to examine the relationship between husband-wife congruence of gender-role attitudes and wife’s FIW.

Methods

Data were collected from 148 husband-wife dyads from eight companies in China. The average age of the husbands was 31.86 years (SD = 8.75) and that of the wives was 28.39 (SD = 6.38). The hypotheses were tested by the combination of polynomial regression and response surface methodology.

Results

Four results were drawn. First, wives’ role overload is lower when husbands and wives are aligned in terms of gender-role attitudes than when they are not aligned (a₄ = 0.53, p < 0.01). Second, on the condition of husband-wife congruence, role overload is positively correlated with their gender-role attitudes (a₁ = 0.59, p < 0.001). Third, on the condition of incongruence, wives’ role overload is stronger when husbands’ traditional gender-role attitudes are higher than wives’, compared to when wives’ traditional gender-role attitudes are higher than husbands’ (a₃ = 0.23, p < 0.05). Fourth, role overload mediates the relationship between husband-wife congruence of gender-role attitudes and wives’ FIW (indirect effect = 0.15; 95% CI [0.05, 0.27]).

Conclusion

Guided by the role theory, the current study suggests that the husband-wife incongruence of gender-role attitudes augments wives’ role overload and further leads to FIW.

Modeling the Joint Effect of Leader and Follower Authenticity on Work and Non-Work Outcomes

Recent theoretical and empirical work has developed the concept of authenticity, both as it relates to leader authenticity and as it relates to authenticity in followers. The present study examines perceptions of employee authenticity and perceived leader authenticity and highlights the utility of jointly examining the congruence between the two to predict individual-level outcomes. The present study utilized Qualtrics Panels to recruit three waves of survey participants. A total of 420 participants responded to all three time points. Polynomial regression, response surface modeling, and a block variable approach with indirect effects were used to test our hypotheses. Results supported the effect of authenticity congruence on individual-level work-related outcomes. Leader-member exchange was found to be a mediating variable between leader-follower authenticity congruence and outcomes in the work and non-work domains.

Understanding the spatio-temporal pattern of COVID-19 outbreak in India using GIS and India's response in managing the pandemic

Due to the outbreak of Coronavirus, humans all over the world are facing several health problems. The present study has explored the spatio-temporal pattern of Coronavirus spread in India including spatial clustering, identification of hotspot, spatial heterogeneity, and homogeneity, spatial trend, and direction of COVID-19 cases using spatial statistical analysis during the period of 30 January to 20 June 2020. Besides, the polynomial regression model has been used for predictions of COVID-19 affected population and related deaths. The study found positive spatial heterogeneity in COVID-19 cases in India. The study has also identified 17 epicentres across the country with high incidence rates. The directional distribution of ellipse polygon shows that the spread of COVID-19 now trending towards the east but the concentration of cases is mainly in the western part of the country. The country's trend of COVID-19 follows a fourth-order polynomial growth and is characterized by an increasing trend. The prediction results show that as on 14 October India will reach 14,660,400 COVID-19 cases and the death toll will cross 152,945. Therefore, a "space-specific" policy strategy would be a more suitable strategy for reducing the spatial spread of the virus in India. Moreover, the study has broadly found out seven sectors, where the Government of India lacks in terms of confronting the ongoing pandemic. The study has also recommended some appropriate policies which would be immensely useful for the administration to initiate strategic planning.

The 'Necessity-Concerns Framework' as a means of understanding non-adherence by applying polynomial regression in three chronic conditions

OBJECTIVE

The 'Necessity-Concerns Framework' is an important framework which can support healthcare professionals in targeting patients' medication beliefs and decisions on adherence. Our aim was to determine how the interdependence of 'necessity' and 'concerns' beliefs for medication adherence compares across three chronic conditions.

METHODS

Patients diagnosed with asthma, cardiovascular conditions or diabetes attending out-patient clinics completed a self-administered questionnaire. The questionnaire gathered information regarding demographics, medication adherence using the 'Tool for Adherence Behaviour Screening', presence of unused medication in households and medication beliefs using the 'Beliefs about Medicines Questionnaire-Specific'. Polynomial regression was applied to determine the multidimensional interdependence of 'necessity' and 'concerns' beliefs for adherence. P-values ≤ 0.05 were taken to be significant.

RESULTS

Confirmatory polynomial regression rejected the differential score model in all three groups. For each condition, exploratory polynomial regression found that linear terms indicated the best fitting model for predicting adherence. In all groups, adherence increased as necessity beliefs increased and concerns decreased. Patients suffering from cardiovascular conditions and diabetes with low necessity and low concerns beliefs reported higher medication adherence compared to those with high necessity and high concerns beliefs.

DISCUSSION

Alleviating patients' concerns can enhance medication adherence and potentially curb the issue of medication wastage.

The Influence of Leader-Follower Cognitive Style Similarity on Followers' Organizational Citizenship Behaviors

While cognitive style congruence has been highlighted as a potentially important variable influencing performance outcomes in work-related contexts, studies of its influence are scarce. This paper examines the influence of leader-follower cognitive style similarity on followers' organizational citizenship behaviors (OCBs). Data from 430 leader-follower dyads were analyzed using polynomial regression and response surface analysis. Results demonstrate that congruence of leader/follower cognitive style is a predictor of follower OCBs. Organizations may therefore benefit from considering issues of similarity of cognitive styles in their attempts to develop effective leader-follower partnerships leading to increased OCBs and concomitant improvements in both individual and organizational level success.

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and ΔEab* color differences. Values of less than 3 CIELAB units were achieved for ΔEab*. The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and ΔEab*. We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.

Predicting Work-Family Balance: A New Perspective on Person-Environment Fit

Work–family balance continues to be a burgeoning topic of organizational research, yet, while the various antecedents of work–family balance have been identified, researchers have, to date, neglected the effect of congruence versus incongruence with regard to work–family integration preferences and the corresponding supplies at work. The current research investigates whether work–family integration preferences and organizational supplies jointly affect work–family balance, and the distal family–related outcomes including marital satisfaction and family functioning, from a person–environment fit perspective. Polynomial regression analysis and response surface methodology are used to test the study’s hypotheses. Results of the polynomial regressions on 393 employees are found to support the congruence effect hypotheses. In particular, the results show that employee work–family balance is higher when work–family integration preferences and organizational supplies are congruent, as opposed to incongruent. An individual’s balance is higher when preferences and supplies are aligned at higher levels rather than at lower levels. In addition, the asymmetrical shape of the surface along the incongruence line indicated that an employee’s work–family balance tends to be damaged once organizational supplies exceed personal preferences. Moreover, through creating a block variable based on the five polynomial terms, we found that congruence/incongruence in respect of work–family integration preferences and supplies yields distal effects on both family functioning and marital satisfaction. Our findings support our hypotheses and are also in line with both person–environment fit theory and balance theory. Theoretical and practical implications for keeping work-family balance are also discussed.

Investigating Leader Role Congruity and Counterproductive Work Behavior

The present study sought to examine the role of fulfilled vs. unfulfilled expectations in work-related and non-work domains. Specifically, we examined how congruence and incongruence between implicit leadership theories across multiple categories of leaders (typical, ideal, and effective leaders) and characteristics recognized on one's supervisor affect leader-member exchange (LMX), work-family conflict, and subsequent counter-productive work behavior. We tested our hypotheses using polynomial regression and response surface modeling. The results of this study showed that congruence between implicit leadership theories (ILT's) of typical, ideal, and effective leaders and supervisor recognition, as well as incongruity between ILT's of ideal leaders and characteristics recognized in one's supervisor, significantly impacts perceived LMX quality with supervisors. Incongruity between ILT's of typical and ideal leaders and supervisor recognition was also associated with higher ratings of work-family conflict (WFC). Finally, the results showed support for a significant indirect effect of congruence between ILTs of typical and ideal leaders and characteristics recognized in one's supervisor on CWB through WFC. These results add meaningful depth to literature on congruent implicit theories in two ways: (a) we add to existing knowledge of outcomes associated with congruence on ILT's, and, (b) we examine these associations using ILT's across multiple categories of leaders. Implications are also discussed.

The Relationship Between Psychological Detachment and Employee Well-Being: The Mediating Effect of Self-Discrepant Time Allocation at Work

Although research has demonstrated the benefit of psychological detachment for employee well-being, the explanatory mechanisms related to work behaviors underlying this effect remain underdeveloped. Addressing this research gap, we consider self-discrepant time allocation (preferred–actual allocation) as a mediating mechanism through which psychological detachment affects employee well-being. We hypothesize that psychological detachment is associated with self-discrepant time allocation at work. Specifically, we suggest that employees with low detachment tend to allocate more time than preferred to work activities that demand fewer self-regulatory resources and allocate less time than preferred to activities demanding greater self-regulatory resources. These self-discrepant time allocations at work are associated with employee well-being. Polynomial regression analysis and response surface methodology were used to test the hypotheses. The results, based on a sample of 390 faculty members from 19 universities, showed that, when psychological detachment during weekends is low and self-regulatory resources are insufficient, employees will allocate less time than preferred to work activities that require more self-regulatory resources (i.e., researching activities) during the subsequent work period. Instead, employees tend to allocate more time to activities that require less resources (i.e., teaching activities). These discrepancies between actual and preferred time allocation for work activities, in turn, negatively affect employee well-being and mediate the relationship between psychological detachment and employee well-being.

Use of Polynomial Regression to Investigate Biased Self-Perceptions and ADHD Symptoms in Young Adolescents

OBJECTIVE

This study investigated biased self-perceptions of academic and social competence among young adolescents with a range of ADHD symptoms. The goal was to better understand how to measure agreement and disagreement between competence ratings from multiple informants.

METHOD

The commonly used discrepancy methodology was used along with polynomial regression/response surface analyses to explore the relationship between biased self-perceptions and ADHD symptoms. Participants were 164 middle school students and their homeroom teachers. Students and teachers completed measures about academic and social competence, and teachers rated ADHD symptoms.

RESULTS

Discrepancy score and polynomial regression/response surface analyses both supported the relationship between student overestimation of competence and ADHD symptoms. Response surface analyses also suggest that some students with ADHD symptoms accurately perceive their impairments, particularly in the academic domain.

CONCLUSION

Findings demonstrate the importance of using more advanced methods to understand the relationship between both accurate and discrepant perceptions of competence and ADHD symptoms.

Using sensitivity analysis to identify key factors for the propagation of a plant epidemic

Identifying the key factors underlying the spread of a disease is an essential but challenging prerequisite to design management strategies. To tackle this issue, we propose an approach based on sensitivity analyses of a spatiotemporal stochastic model simulating the spread of a plant epidemic. This work is motivated by the spread of sharka, caused by plum pox virus, in a real landscape. We first carried out a broad-range sensitivity analysis, ignoring any prior information on six epidemiological parameters, to assess their intrinsic influence on model behaviour. A second analysis benefited from the available knowledge on sharka epidemiology and was thus restricted to more realistic values. The broad-range analysis revealed that the mean duration of the latent period is the most influential parameter of the model, whereas the sharka-specific analysis uncovered the strong impact of the connectivity of the first infected orchard. In addition to demonstrating the interest of sensitivity analyses for a stochastic model, this study highlights the impact of variation ranges of target parameters on the outcome of a sensitivity analysis. With regard to sharka management, our results suggest that sharka surveillance may benefit from paying closer attention to highly connected patches whose infection could trigger serious epidemics.

Interdependent Effects of Autonomous and Controlled Regulation on Exercise Behavior

Self-determination theory (SDT) is used to predict individual differences in goal-directed behavior. A fundamental tenet of SDT is that autonomously regulated behavior is more likely to be engaged in and sustained than externally controlled behavior. Unidimensional treatment of regulation is suboptimal. The current study utilizes a multidimensional approach-polynomial regression-to evaluate the interdependent effects of autonomous and controlled regulation on physical activity. Results from three samples of healthy, younger adults demonstrate a mostly positive influence of autonomous regulation but a curvilinear effect of controlled regulation on behavior such that greater activity was associated with moderate levels of controlled regulation-an effect that cannot be identified with "unidimensional" methods. Results from Sample 3 showed that autonomous regulation was associated with greater exercise levels only when controlled regulation was moderate or high. Results suggest that controlled regulation is not wholly detrimental to behavioral promotion.

Positive and negative affectivity, stress, and well-being in African-Americans: Initial demonstration of a polynomial regression and response surface methodology approach

OBJECTIVE

The extent to which positive (PA) and negative (NA) affect conjointly impact well-being is not yet well understood. Additionally, research investigating the role of affectivity in maintaining well-being among ethnic and racial minorities is scant. The current research demonstrates how polynomial regression and response surface methodology (PR and RSM) may be used to better understand how PA and NA jointly influence stress and well-being.

DESIGN

In Study 1, 291 African-American undergraduates (M_age = 22.91, SD = 6.91; 67% female) completed measures of affectivity and psychological well-being. In Study 2, a community sample of 117 African-Americans (M_age = 31.87, SD = 13.83; 69% female) completed affectivity measures and a laboratory-based social stressor task to assess links between affectivity and salivary cortisol reactivity.

MAIN OUTCOME MEASURES

Study 1 included life satisfaction, perceived stress and self-reported depressive symptoms. Study 2 included salivary cortisol reactivity.

RESULTS

Across both studies, PA ascendency (i.e. high PA combined with low NA) was associated with better well-being, while NA ascendancy (i.e. high NA combined with low PA) was associated with poorer outcomes.

CONCLUSION

PR and RSM may provide new insight into the conjoint influence of PA and NA on health and well-being. We discuss potential implications for affectivity research, including race-related explorations.

The Effect of Implicit-Explicit Followership Congruence on Benevolent Leadership: Evidence from Chinese Family Firms

Benevolent leadership, a traditional Chinese leadership style generated under the influence of Confucianism, has been under growing discussion since its proposal. However, existing research has focused mainly on the consequences of benevolent leadership, and research probing into its antecedents is scarce. To fill such research gap, the current study aims to explore the effect of the congruence between implicit positive followership prototype (PFP) and explicit positive followership trait (PFT) on benevolent leadership. Polynomial regression combined with the response surface methodology was used to test the hypotheses herein. The results, based on a sample of 241 leader–follower dyads from four Chinese family firms, indicated the following: (1) benevolent leadership is higher when leader PFP is congruent with follower PFT than when they are incongruent; (2) in cases of congruence, benevolent leadership is higher when leader PFP and follower PFT are both high rather than low; (3) in the case of incongruence, there is no significant difference for the level of benevolent leadership in two scenarios: “low leader PFP – high follower PFT” and “high leader PFP – low follower PFT”.

Using polynomials to simplify fixed pattern noise and photometric correction of logarithmic CMOS image sensors

An important class of complementary metal-oxide-semiconductor (CMOS) image sensors are those where pixel responses are monotonic nonlinear functions of light stimuli. This class includes various logarithmic architectures, which are easily capable of wide dynamic range imaging, at video rates, but which are vulnerable to image quality issues. To minimize fixed pattern noise (FPN) and maximize photometric accuracy, pixel responses must be calibrated and corrected due to mismatch and process variation during fabrication. Unlike literature approaches, which employ circuit-based models of varying complexity, this paper introduces a novel approach based on low-degree polynomials. Although each pixel may have a highly nonlinear response, an approximately-linear FPN calibration is possible by exploiting the monotonic nature of imaging. Moreover, FPN correction requires only arithmetic, and an optimal fixed-point implementation is readily derived, subject to a user-specified number of bits per pixel. Using a monotonic spline, involving cubic polynomials, photometric calibration is also possible without a circuit-based model, and fixed-point photometric correction requires only a look-up table. The approach is experimentally validated with a logarithmic CMOS image sensor and is compared to a leading approach from the literature. The novel approach proves effective and efficient.

Factors influencing the diameter of human anterior and posterior intercostal arteries

At present, there have not been any detailed studies examining the size relationships of the intercostal arteries. This study was carried out to investigate the relationship between the vessel lumenal diameter of ipsilateral, paired anterior and posterior IC arteries, as well as with the length of the IC space supplied by each artery. Samples were collected from the second-sixth anterior and posterior IC arteries near their site of origin, and the lengths of the corresponding IC spaces were measured in 42 cadavers. Lumenal diameters of both the anterior and posterior IC arteries at consecutive IC space closely followed second degree polynomial regression models (R(2) = 0.9655, and R(2) = 0.9741, respectively), and reached maximum size at the fifth IC space, which was found to be the longest of the IC spaces. No direct relationship was observed between diameters of the paired anterior and posterior IC arteries, although there was a trend for the larger anterior IC arteries to be paired with the larger posterior IC arteries. The calculated rate of blood flow at each IC artery was approximately two-fold greater in males than in females. These results suggest that the length of the IC space, and hence the extent of the thoracic wall supplied, is a major factor in determining the diameter of both anterior and posterior IC arteries. Since COPD is such a prevalent disease, this study also examined its influence on the IC arteries, and found that the posterior IC arteries are significantly larger among afflicted subjects.

Postdeployment threat-related attention bias interacts with combat exposure to account for PTSD and anxiety symptoms in soldiers

BACKGROUND

Recent studies suggest that assessment of threat-related attention bias may be useful in identifying soldiers at risk for clinical symptoms. The present study assessed the degree to which soldiers experienced combat events and showed attentional threat avoidance affected their reported levels of post-traumatic stress disorder (PTSD) and anxiety symptoms.

METHODS

Four months after a combat deployment to Iraq, 63 US soldiers completed a survey assessing combat exposures and clinical symptoms as well as a dot-probe task assessing threat-related attention bias.

RESULTS

Significant three-way interactions regressing threat reaction times (RTs), neutral RTs, and combat exposure on PTSD and anxiety symptoms were observed. Specifically, soldiers with high levels of combat exposure, who were more likely to demonstrate attentional bias away from threat, were also more symptomatic.

CONCLUSION

These results demonstrate the potential of threat-related attention bias as a behavioral marker of PTSD and anxiety symptoms in a high-risk military occupational context.

Further Insight and Additional Inference Methods for Polynomial Regression Applied to the Analysis of Congruence

In their seminal paper, Edwards and Parry (1993) presented the polynomial regression as a better alternative to applying difference score in the study of congruence. While this method is increasingly applied in congruence research, its complexity relative to other methods for assessing congruence (e.g., difference score methods) was one of the main claims against its use. The objective of this work is to gain additional insight into the use of polynomial regression in the area of social and behavioral sciences. First, we elaborate on the meaning and the inference based on the coefficients obtained by applying polynomial regression and explain the mathematical terms that are used to describe response surfaces. Then, we introduce additional inference methods and illustrate their application on a real life example from the area of supportive exchanges, using data collected by the Smithers Institute at Cornell University (supported by the National Institute on Alcohol Abuse and Alcoholism). Our work is aimed to provide a guide to researchers who apply polynomial regression in order to assess the effect of congruence between two constructs and enable better understanding and interpretation of the results obtained.