Trust (in)congruence, open innovation, and circular economy performance: Polynomial regression and response surface analyses

Engaging partner firms in open innovation is critical to overcoming internal resource/capability constraints to achieve firm and supply chain circular economy (CE) performance, yet scholars have not examined this link empirically. Further, while researchers have repeatedly emphasized trust as a key driver of open innovation performance, little is known from a dyadic trust perspective (e.g., congruence vs. incongruence, high-high congruence vs. low-low congruence). To fill these gaps, we propose a theoretical model based on the social exchange theory (SET) and test it using a dyadic dataset of listed Chinese manufacturing firms. The results suggest that 1) rather than unilateral trust from the focal firm or its partners, trust congruence is more predictive of successful open process and product innovations, 2) regarding congruence types (low-low vs. high-high), congruence at higher levels of trust facilitates open product innovation more than low-low trust congruence; interestingly, such an effect is not significant for open process innovation, 3) open process innovation has a positive influence on the focal firm's CE performance, but its impact on supply chain CE performance is not statistically significant, and 4) open product innovation has a significantly positive impact on the focal firm and supply chain CE performance. Our findings still hold after analyzing time-lagged models and alternative measurements as robustness checks. Our study provides meaningful theoretical contributions to the literature and useful, practical insights for managing inter-organizational relationships, open innovation, and CE performance.

Impact of coronavirus pandemic on stock index: A polynomial regression with time delay

Motivation

Under contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the possible nonlinear relations between the two indicators. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated.

Objectives

To contribute to filling the gaps identified in existing research, this study models relationship between the stock market index and coronavirus pandemic by leveraging volatility in the stock market and covid data through time delay and best degree in a polynomial environment. The resultant optimal time delay and best degree model is used to derive a high-accuracy prediction of stock market index.

Novelty

In line with the possible relations, the novelty of this study is that it proposes, validates and implements polynomial regression with time delay to model nonlinear relationship between the stock index and covid.

Methods

This study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment.

Findings

The empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation of the nonlinear relationship such predictors have with stock index for China. Relative to results from the polynomial regression without delay, the empirical evidence from the model with delay show that an optimal time delay of 17 weeks makes it possible to predict the stock index at high accuracy and record improvements of 16-fold or higher. The representative delay model is used to project for up to 17 weeks for future trends in the stock index.

Implication

The implication of the findings herein is that the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases to achieve better results.

Theory of Mind and Social Informant Discrepancy in Autism

When autistic youth are asked to assess their own social skills, they frequently rate themselves more favorably than their parents rate them. The magnitude of this informant discrepancy has been shown to relate to key clinical outcomes such as treatment response. It has been proposed that this discrepancy arises from difficulties with Theory of Mind. Participants were 167 youth 11 to 17 years old; 72% male, and their parents. Youth completed self-report measures of social skills and social cognitive tasks, while their parents completed questionnaires regarding social skills. A repeated-measures ANOVA indicated both non-autistic and autistic youth rated themselves more favorably than their parents rated them across all measures. Zero-order correlations revealed that raw differences between parent- and participant-report were negatively correlated with scores on parent-reported Theory of Mind measures. However, polynomial analysis did not indicate interaction effects between parent- and participant-report on any of the measures used. Polynomial regression revealed that increases in parent-reported social skill predicted larger increases in parent-report Theory of Mind at low levels of parent-reported social skill compared to high levels of parent-reported social skill. Participant-report social skills predicted performance on a behavioral Theory of Mind test in a curvilinear fashion, such that the relationship was positive at low levels of participant-reported social skills, but negative at high levels. This study replicates the finding that raw difference score analyses may result in illusory effects that are not supported when using more contemporary analysis methods, and that more complex and subtle relationships between social insight and perspective-taking exist within autistic youth.

Parent-adolescent Congruence and Discrepancy in Perceived Parental Emotion Socialization to Anger and Sadness: Using Response Surface Analysis to Examine the Links with Adolescent Depressive Symptoms

Parents and adolescents often hold discrepant perceptions of parental emotion socialization, which reflect misunderstandings in parent-adolescent communication on emotions and have potential detrimental effects on mental health of adolescents. The present study investigated the associations between parent-adolescent congruence and discrepancy in parental emotion socialization perception to two specific negative emotions (anger and sadness) and depressive symptoms in Chinese adolescents. A total of 372 adolescents (48.4% female, Mage = 13.43, SDage = 0.49) and their parents (79.6% mother, Mage = 41.15, SDage = 5.46) participated in this study. Both parents and adolescents reported perceived parental emotion socialization to anger and sadness, and adolescents reported depressive symptoms. Data were analyzed using polynomial regression and response surface analyses. Both congruence and discrepancy in parent and adolescent's reports were associated with adolescent depressive symptoms. A higher level of adolescent depressive symptoms was associated with higher parent-adolescent congruence in supportive responses to anger, sadness, and nonsupportive responses to anger. A higher level of depressive symptoms was associated with inconsistent reporting of supportive responses to sadness and nonsupportive responses to anger (only when parents had a more positive view than adolescents). This study highlights the significance of evaluating parent-child communication process by assessing perceived emotion socialization from both parents and adolescents and analyzing the reporting congruence and discrepancy. It also suggests that enhancing effective communication regarding parental emotion socialization could be a promising target for adolescent mental health promotion programs.

Effective electronic waste valorization via microwave-assisted pyrolysis: investigation of graphite susceptor and feedstock quantity on pyrolysis using experimental and polynomial regression techniques

Waste printed circuit board (WPCB) was subjected to microwave-assisted pyrolysis (MAP) to investigate the energy and pyrolysis products. In MAP, pyrolysis experiments were conducted, and the effects of WPCB to graphite mass ratio on three-phase product yields and their compositions were analyzed. In addition, the role of the initial WPCB mass (10, 55, and 100 g) and susceptor loading (2, 22, and 38 g) on the quality of product yield was also evaluated. By using design of experiments, the effects of graphite susceptor addition and WPCB feedstock quantity was investigated. A significant liquid yield of 38.2 wt.% was achieved at 38 g of graphite and 100 g of WPCB. Several other operating parameters, including average heating rate, pyrolysis time, microwave energy consumption, specific microwave power used, and product yields, were optimized for the MAP of WPCB. Pyrolysis index (PI) was calculated at the blending of fixed quantity WPCB (100 g) and various graphite quantities in the following order: 2 g (21) > 20 g (20.4) > 38 g (19.5). The PI improved by increasing the WPCB quantity (10, 55, and 100 g) with a fixed quantity of graphite. This work proposes the product formation and new reaction pathways of the condensable compounds. GC-MS of the liquid fraction from the MAP of WPCBs without susceptor resulted in the generation of phenolic with 46.1% relative composition. The addition of graphite susceptor aided in the formation of phenolic and the relative composition of phenolics was found to be 83.6%. The area percent of phenol increased from 42.8% (without susceptor) to 78.6% (with susceptor). Without a susceptor, cyclopentadiene derivative was observed in a very high composition (~ 31 area %).

How green intrinsic and extrinsic motivations interact, balance and imbalance with each other to trigger green purchase intention and behavior: A polynomial regression with response surface analysis

This research aims to examine how green extrinsic and intrinsic motivations individually and jointly affect green purchase intention and actual behavior, drawing on the integration of self-determination theory and the theory of planned behavior. Based upon a sample of 4062 consumers in Vietnam, we methodologically adopted a polynomial regression with response surface analysis to shed the light on how a higher degree of eco-friendly consumption intention and behavior is synthesized from the balance between high green extrinsic and intrinsic motivations. Conversely, a large imbalance between green extrinsic and intrinsic motivations will lower the level of environmentally friendly consumption. Additionally, this study indicates that green purchase intention is the most important predictor of green purchase behavior, and that green purchase intention significantly mediates the insolated and joint effects of green extrinsic and intrinsic motivations on eco-friendly consumption behavior.

Exploring the Multidimensional Relationship Between Medication Beliefs and Adherence to Medications Among Older Adults Living With Multimorbidity Using Polynomial Regression: An Observational Cohort Study

BACKGROUND

People living with multimorbidity may hold complex beliefs about medicines, potentially influencing adherence. Polynomial regression offers a novel approach to examining the multidimensional relationship between medication beliefs and adherence, overcoming limitations associated with difference scores.

PURPOSE

To explore the multidimensional relationship between medication beliefs and adherence among people living with multimorbidity.

METHODS

Secondary analysis was conducted using observational data from a cohort of older adults living with ≥2 chronic conditions, recruited from 15 family practices in Ireland in 2010 (n = 812) and followed up in 2012 (n = 515). Medication beliefs were measured with the Beliefs about Medicines Questionnaire-Specific. Adherence was assessed with the medication possession ratio using prescription data from the national primary care reimbursement service. Polynomial regression was used to explore the best-fitting multidimensional models for the relationship between (i) beliefs and adherence at baseline, and (ii) beliefs at baseline and adherence at follow-up.

RESULTS

Confirmatory polynomial regression rejected the difference-score model, and exploratory polynomial regression indicated quadratic models for both analyses. Reciprocal effects were present in both analyses (slope [Analysis 1]: β = 0.08, p = .007; slope [Analysis 2]: β = 0.07, p = .044), indicating that adherence was higher when necessity beliefs were high and concern beliefs were low. Nonreciprocal effects were also present in both analyses (slope [Analysis 1]: β = 0.05, p = .006; slope [Analysis 2]: β = 0.04, p = .043), indicating that adherence was higher when both necessity and concern beliefs were high.

CONCLUSIONS

Among people living with multimorbidity, there is evidence that the relationship between medication beliefs and adherence is multidimensional. Attempts to support adherence should consider the combined role of necessity and concern beliefs.

Evidences of localized coastal warming near major urban centres along the Indian coastline: past and future trends

Large-scale urbanization near the coasts is reported to directly impact physical and biogeochemical characteristics of near shore waters, through hydro-meteorological forcing, developing abnormalities such as coastal warming. This study attempts to understand the impact-magnitude of urban expansion on coastal sea surface temperature (SST) rise in the vicinity of six major cities along the Indian coastline. Different parameters such as air temperature (AT), relative humidity (RH), wind speed (WS), precipitation (P), land surface temperature (LST) and aerosol optical depth (AOD) representing the climate over the cities were analysed and AT was found to have highest correlation with increasing coastal SST values, specifically, along the western coast (R² > 0.93). Autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were employed to analyse past (1980-2019) and forecast future (2020-2029) SST trends off all urban coasts. ANN provided comparatively better prediction accuracy with RMSE values ranging from 0.40 to 0.76 K compared to the seasonal ARIMA model (RMSE: 0.60-1 K). Prediction accuracy further improved by coupling ANN with discrete wavelet transformation (DWT) which could reduce the data noise (RMSE: 0.37-0.63 K). The entire study period (1980-2029) revealed significant and consistent increase in SST values (0.5-1 K) along the western coastal cities which varied considerably along the east coast (from north to south), indicating the influence of tropical cyclones combined with increased river influx. Such unnatural interferences in the dynamic land-atmosphere-ocean circulation not only render the coastal ecosystems vulnerable to degradation but also potentially develop a feedback effect which impacts the general climatology of the region.

Exploring flexible polynomial regression as a method to align routine clinical outcomes with daily data capture through remote technologies

BACKGROUND

Clinical outcomes are normally captured less frequently than data from remote technologies, leaving a disparity in volumes of data from these different sources. To align these data, flexible polynomial regression was investigated to estimate personalised trends for a continuous outcome over time.

METHODS

Using electronic health records, flexible polynomial regression models inclusive of a 1st up to a 4th order were calculated to predict forced expiratory volume in 1 s (FEV₁) over time in children with cystic fibrosis. The model with the lowest AIC for each individual was selected as the best fit. The optimal parameters for using flexible polynomials were investigated by comparing the measured FEV₁ values to the values given by the individualised polynomial.

RESULTS

There were 8,549 FEV₁ measurements from 267 individuals. For individuals with > 15 measurements (n = 178), the polynomial predictions worked well; however, with < 15 measurements (n = 89), the polynomial models were conditional on the number of measurements and time between measurements. The method was validated using BMI in the same population of children.

CONCLUSION

Flexible polynomials can be used to extrapolate clinical outcome measures at frequent time intervals to align with daily data captured through remote technologies.

A polynomial regression-based approach to estimate relaxation rate maps suitable for multiparametric segmentation of clinical brain MRI studies in multiple sclerosis

BACKGROUND AND OBJECTIVE

Relaxation parameter maps (RPMs) calculated from spin-echo data have provided a basis for the segmentation of normal brain tissues and white matter lesions in multiple sclerosis (MS) MRI studies. However, Conventional Spin-Echo (CSE) sequences, once the core of clinical MRI studies, have been largely replaced by faster ones, which do not allow the calculation a-posteriori of RPMs from clinical studies. Aim of the study was to develop and validate a method to estimate RPMs (pseudo-RPMs) from routine clinical MRI protocols (including 3D-Gradient Echo T1w, FLAIR and fast-T2w sequences), suitable for fully automatic multiparametric segmentation of normal-appearing and pathological brain tissues in MS.

METHODS

The proposed method processes spatially normalized clinical MRI studies through a multistep pipeline, to collect a set of data points of matched signal intensities (from MRI studies) and relaxation parameters (from a CSE-derived digital template and an MS lesion database), which are then fitted by a multiple and multivariate 4-th degree polynomial regression, providing pseudo-RPMs. The method was applied to a dataset of 59 clinical MRI studies providing pseudo-RPMs that were segmented through a method originally developed for the CSE-derived RPMs. Results of the segmentation in 12 studies were used to iteratively optimize method parameters. Accuracy of segmentation of normal-appearing brain tissues from the pseudo-RPMs was assessed by comparing their age-related changes, as measured in 47 clinical studies, against those measured acquired using CSE sequences in a comparable dataset of 47 patients. Lesion segmentation was validated against manual segmentation carried out by three neuroradiologists.

RESULTS

Age-related changes of normal-appearing brain tissue volumes measured using the pseudo-RPMs substantially overlapped those measured using the RPMs obtained from CSE sequences, and segmentation of MS lesions showed a moderate-high spatial overlap with manual segmentation, comparable to that achieved by the widely used Lesion Segmentation Tool on FLAIR images, with a greater volumetric agreement.

CONCLUSIONS

The proposed approach allows calculation from clinical studies of pseudo-RPMs, which are equivalent to those obtainable from CSE sequences, avoiding the need for the acquisition of additional, dedicated sequences for segmentation purposes.

Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective

BACKGROUND AND OBJECTIVE

Diabetes mellitus is a metabolic disorder characterized by hyperglycemia, which results from the inadequacy of the body to secrete and respond to insulin. If not properly managed or diagnosed on time, diabetes can pose a risk to vital body organs such as the eyes, kidneys, nerves, heart, and blood vessels and so can be life-threatening. The many years of research in computational diagnosis of diabetes have pointed to machine learning to as a viable solution for the prediction of diabetes. However, the accuracy rate to date suggests that there is still much room for improvement. In this paper, we are proposing a machine learning framework for diabetes prediction and diagnosis using the PIMA Indian dataset and the laboratory of the Medical City Hospital (LMCH) diabetes dataset. We hypothesize that adopting feature selection and missing value imputation methods can scale up the performance of classification models in diabetes prediction and diagnosis.

METHODS

In this paper, a robust framework for building a diabetes prediction model to aid in the clinical diagnosis of diabetes is proposed. The framework includes the adoption of Spearman correlation and polynomial regression for feature selection and missing value imputation, respectively, from a perspective that strengthens their performances. Further, different supervised machine learning models, the random forest (RF) model, support vector machine (SVM) model, and our designed twice-growth deep neural network (2GDNN) model are proposed for classification. The models are optimized by tuning the hyperparameters of the models using grid search and repeated stratified k-fold cross-validation and evaluated for their ability to scale to the prediction problem.

RESULTS

Through experiments on the PIMA Indian and LMCH diabetes datasets, precision, sensitivity, F1-score, train-accuracy, and test-accuracy scores of 97.34%, 97.24%, 97.26%, 99.01%, 97.25 and 97.28%, 97.33%, 97.27%, 99.57%, 97.33, are achieved with the proposed 2GDNN model, respectively.

CONCLUSION

The data preprocessing approaches and the classifiers with hyperparameter optimization proposed within the machine learning framework yield a robust machine learning model that outperforms state-of-the-art results in diabetes mellitus prediction and diagnosis. The source code for the models of the proposed machine learning framework has been made publicly available.

Requested to do right things excessively: how citizenship pressure/future focus influence health-related work outcomes in health organizations during the pandemic

PURPOSE

Public health practitioners face citizenship pressure when requested to engage in more extra-roles behaviors during the pandemic. The purpose of the study is to reveal the potential influence mechanism of citizenship pressure on the health and work outcomes of practitioners.

DESIGN/METHODOLOGY/APPROACH

The authors completed a three-wave survey from a public healthcare organization during the coronavirus disease 2019 (COVID-19) delta-variant epidemic.

FINDINGS

Results of polynomial regression and response surface showed that increased (versus decreased) and consistently high (versus low) level of citizenship pressure induced citizenship fatigue, which in turn increases negative affect/turnover intention. These negative effects of citizenship pressure are weaker among practitioners with a higher level of future focus.

PRACTICAL IMPLICATIONS

Providing counseling service to health care practitioners in adopting a future time perspective of citizenship behaviors is important for public health organizations.

ORIGINALITY/VALUE

This study is among the earliest attempts to reveal the potential dark side of excessive request of conducting organization citizenship behavior which is more commonly seen within public health organizations in the context of pandemic.

A convolutional neural network based approach to financial time series prediction

Financial time series are chaotic that, in turn, leads their predictability to be complex and challenging. This paper presents a novel financial time series prediction hybrid that involves Chaos Theory, Convolutional neural network (CNN), and Polynomial Regression (PR). The financial time series is first checked in this hybrid for the presence of chaos. The chaos in the series of times is later modeled using Chaos Theory. The modeled time series is input to CNN to obtain initial predictions. The error series obtained from CNN predictions is fit by PR to get error predictions. The error predictions and initial predictions from CNN are added to obtain the final predictions of the hybrid model. The effectiveness of the proposed hybrid (Chaos+CNN+PR) is tested by using three types of Foreign exchange rates of financial time series (INR/USD, JPY/USD, SGD/USD), commodity prices (Gold, Crude Oil, Soya beans), and stock market indices (S&P 500, Nifty 50, Shanghai Composite). The proposed hybrid is superior to Auto-regressive integrated moving averages (ARIMA), Prophet, Classification and Regression Tree (CART), Random Forest (RF), CNN, Chaos+CART, Chaos+RF and Chaos+CNN in terms of MSE, MAPE, Dstat, and Theil’s U.

Predicting COVID-19 statistics using machine learning regression model: Li-MuLi-Poly

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

A comparison of Covid-19 cases and deaths in Turkey and in other countries

In this study, the characteristics of the Covid-19 pandemic in Turkey are examined in terms of the number of cases and deaths, and a characteristic prediction is made with an approach that employs artificial intelligence. The number of cases and deaths are estimated using the number of tests, the numbers of seriously ill and recovered patients as parameters. The machine learning methods used are linear regression, polynomial regression, support vector regression with different kernel functions, decision tree and artificial neural networks. The obtained results are compared by calculating the coefficient of determination (R ²), and the mean absolute percentage error (MAPE) values. When R ² and MAPE values are compared, it is seen that the optimal results for cases in Turkey are obtained with the decision tree, for deaths with polynomial regression method. The results reached for the United States of America and Russia are similar and the optimal results are obtained by polynomial regression. However, while the optimal results are obtained by neural networks in the Indian data, linear regression for the cases in the Brazilian data, neural network for the deaths, decision tree for the cases in France, polynomial regression for the deaths, neural network for the cases in the UK data and decision tree for the deaths are the methods that produced the optimal results. These results also give an idea about the similarities and differences of country characteristics.

Investigating the relationship between person-environment fit and safety behavior: A social cognition perspective

INTRODUCTION

This study explored the relationship between person-job fit and safety behavior, as well as the mediating role played by psychological safety, from the perspective of social cognitive theory and person-environment fit theory.

METHOD

A total of 800 employees from petroleum enterprises were recruited, with cluster random sampling used to collect data in two stages.

RESULTS

The results showed that employees' safety behavior is higher under the condition of "high person-job fit-high person-organization fit" than under that of "low person-job fit-low person-organization fit." In other words, the more congruent the level of person-job fit and person-organization fit for a given employee, the higher their level of safety behavior. Practical Applications: Psychological safety plays a mediating role between the congruence of both person-job fit and person-organization fit and employees' safety behavior.

Validating the response surface method in entrepreneurship management research

This study adapts an existing methodology in psychology to assess congruence relationships in entrepreneurship management. More specifically, it describes the application of a response surface method to examine the congruence effect of two predictor variables on an outcome variable. The study presents both visual and text presentations to serve as a guideline that can aid management researchers in adapting the method. The paper underscores three strengths of using the response surface method as a robust analytical approach to evaluating congruent and incongruent relationships.•

The response surface method can be used to examine congruency and incongruency between variables in the field of management in general and entrepreneurship management in particular.

•

The results can be visualized as two- and three-dimensional graphs.

•

Compared with a traditional approach, the response surface method offers a clearer visual representation of a focal relationship.

Towards a mathematical framework to inform neural network modelling via polynomial regression

Even when neural networks are widely used in a large number of applications, they are still considered as black boxes and present some difficulties for dimensioning or evaluating their prediction error. This has led to an increasing interest in the overlapping area between neural networks and more traditional statistical methods, which can help overcome those problems. In this article, a mathematical framework relating neural networks and polynomial regression is explored by building an explicit expression for the coefficients of a polynomial regression from the weights of a given neural network, using a Taylor expansion approach. This is achieved for single hidden layer neural networks in regression problems. The validity of the proposed method depends on different factors like the distribution of the synaptic potentials or the chosen activation function. The performance of this method is empirically tested via simulation of synthetic data generated from polynomials to train neural networks with different structures and hyperparameters, showing that almost identical predictions can be obtained when certain conditions are met. Lastly, when learning from polynomial generated data, the proposed method produces polynomials that approximate correctly the data locally.

A forecasting method with efficient selection of variables in multivariate data sets

Regression is a kind of data analysis technique in which the relationship between the independent variable(x) and dependent variable(y) is modeled and for polynomial regression it is up to the nth degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted by E (y|x). In this paper polynomial regression analysis has been improved through efficient selection of variables that is coefficient of determination. Coefficient of determination is a square of the correlation between new predicted y values and actual y values and its values are in the range from 0 to 1. The main purpose of regression analysis is to discover the relationship among the independent and dependent variables or in other words it is an explanation of variation in one variable with another variable. In this paper, the main focus is on Multivariate data sets that have many attributes and it is not necessary that all variables are required for data analysis purposes. Using coefficient of determination (COD) irrelevant attributes get eliminated during analysis. The main objective of research is to reduce the cost of data maintenance, reduce the execution time and improve the prediction accuracy rate. COD helps in selecting suitable independent variables. It is a notch that is used in statistical analysis that assesses how well a model explains and forecasts upcoming outcomes. This method also helps in eliminating the irrelevant variables which are not required for the prediction model by this maintenance cost and size of data sets can be reduced.

Development of the Birthweight Appropriateness Quotient: A New Measure of Infant's Size

INTRODUCTION

The customised birthweight model can be used to improve detection of babies that may be at risk of adverse outcomes associated with abnormal growth, however it is currently used in conjunction with either an intrauterine growth standard or the individualised birthweight ratio (IBR), both of which have significant methodological flaws. Our aim was to investigate the statistical validity of the IBR and attempt to develop a new measurement to represent the appropriateness of an infant's size at birth that will support clinicians in identifying infants requiring further attention.

METHODS

Routinely collected hospital maternity and neonatal data on singleton, term births from a tertiary Australian hospital were extracted for the time period 1998-2009. The relationships between birthweight, customised birthweight and IBR are investigated using correlation, regression analysis and division of births into groups of < 2500 g, 2500-4000 g and > 4000 g. A new measure, the Birthweight Appropriateness Quotient (BAQ), is developed. The utility of the BAQ is compared with IBR and birthweight to identify infants with a composite neonatal morbidity outcome.

RESULTS

Statistical flaws with the IBR due to significant correlation between birthweight and customised birthweight and a heterogenous relationship between these two measurements across the range of birthweight are present. BAQ is uncorrelated with birthweight. Comparison of BAQ and IBR as indicators of adverse neonatal outcome demonstrates that BAQ identifies babies at risk due to their small size and those babies at risk due to inappropriate size.

CONCLUSIONS FOR PRACTICE

BAQ is a customised measurement of an infant's size free of the statistical flaws experienced by the IBR with the ability to identify at-risk infants.

Analysis on novel coronavirus (COVID-19) using machine learning methods

In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy.

Detection of ventilatory thresholds using near-infrared spectroscopy with a polynomial regression model

Whether near-infrared spectroscopy (NIRS) is a convenient and accurate method of determining first and second ventilatory thresholds (VT₁ and VT₂) using raw data remains unknown. This study investigated the reliability and validity of VT₁ and VT₂ determined by NIRS skeletal muscle hemodynamic raw data via a polynomial regression model. A total of 100 male students were recruited and performed maximal cycling exercises while their cardiopulmonary and NIRS muscle hemodynamic data were measured. The criterion validity of VT_1VET and VT_2VET were determined using a traditional V-slope and ventilatory efficiency. Statistical significance was set at α = . 05. There was high reproducibility of VT_1NIRS and VT_2NIRS determined by a NIRS polynomial regression model during exercise (VT_1NIRS, r = 0.94; VT_2NIRS, r = 0.93). There were high correlations of VT_1VET vs VT_1NIRS (r = 0.93, p < .05) and VT_2VET vs VT_2NIRS (r = 0.94, p < .05). The oxygen consumption (VO₂) between VT_1VET and VT_1NIRS or VT_2VET and VT_2NIRS was not significantly different. NIRS raw data are reliable and valid for determining VT₁ and VT₂ in healthy males using a polynomial regression model. Skeletal muscle raw oxygenation and deoxygenation status reflects more realistic causes and timing of VT₁ and VT₂.

Diffusion tensor imaging of the corpus callosum in healthy aging: Investigating higher order polynomial regression modelling

Previous diffusion tensor imaging (DTI) studies confirmed the vulnerability of corpus callosum (CC) fibers to aging. However, most studies employed lower order regressions to study the relationship between age and white matter microstructure. The present study investigated whether higher order polynomial regression modelling can better describe the relationship between age and CC DTI metrics compared to lower order models in 140 healthy participants (ages 18-85). The CC was found to be non-uniformly affected by aging, with accelerated and earlier degradation occurring in anterior portion; callosal volume, fiber count, fiber length, mean fibers per voxel, and FA decreased with age while mean, axial, and radial diffusivities increased. Half of the parameters studied also displayed significant age-sex interaction or intracranial volume effects. Higher order models were chosen as the best fit, based on Bayesian Information Criterion minimization, in 16 out of 23 significant cases when describing the relationship between DTI measurements and age. Higher order model fits provided different estimations of aging trajectory peaks and decline onsets than lower order models; however, a likelihood ratio test found that higher order regressions generally did not fit the data significantly better than lower order polynomial or linear models. The results contrast the modelling approaches and highlight the importance of using higher order polynomial regression modelling when investigating associations between age and CC white matter microstructure.

Three Gorges Dam: polynomial regression modeling of water level and the density of schistosome-transmitting snails Oncomelania hupensis

Background

Schistosomiasis remains a major public health concern in China. Oncomelania hupensis (O. hupensis) is the sole intermediate host of Schistosoma japonicum, and its change in distribution and density influences the endemic S. japonicum. The Three Gorges Dam (TGD) has substantially changed the downstream water levels of the dam. This study investigated the quantitative relationship between flooding duration and the density of the snail population.

Methods

Two bottomlands without any control measures for snails were selected in Yueyang City, Hunan Province. Data for the density of the snail population and water level in both spring and autumn were collected for the period 2009–2015. Polynomial regression analysis was applied to explore the relationship between flooding duration and the density of the snail population.

Results

Data showed a convex relationship between spring snail density and flooding duration of the previous year (adjusted R², aR² = 0.61). The spring snail density remained low when the flooding duration was fewer than 50 days in the previous year, was the highest when the flooding duration was 123 days, and decreased thereafter. There was a similar convex relationship between autumn snail density and flooding duration of the current year (aR² = 0.77). The snail density was low when the flooding duration was fewer than 50 days and was the highest when the flooding duration was 139 days.

Conclusions

There was a convex relationship between flooding duration and the spring or autumn snail density. The snail density was the highest when flooding lasted about four to 5 months.

Statistical inference in a growth curve quantile regression model for longitudinal data

This article describes a polynomial growth curve quantile regression model that provides a comprehensive assessment about the treatment effects on the changes of the distribution of outcomes over time. The proposed model has the flexibility, as it allows the degree of a polynomial to vary across quantiles. A high degree polynomial model fits the data adequately, yet it is not desirable due to the complexity of the model. We propose the model selection criterion based on an empirical loglikelihood that consistently identifies the optimal degree of a polynomial at each quantile. After the parsimonious model is fitted to the data, the hypothesis test is further developed to evaluate the treatment effects by comparing the growth curves. It is shown that the proposed empirical loglikelihood ratio test statistic follows a chi-square distribution asymptotically under the null hypothesis. Various simulation studies confirm that the proposed test successfully detects the difference between the curves across quantiles. When the empirical loglikelihood is employed, we incorporate the within-subject correlation commonly existing in longitudinal data and gain estimation efficiency of the quantile regression parameters in the growth curve model. The proposed process is illustrated through the analysis of randomized controlled longitudinal depression data.

Sonographic Estimation of Umbilical Cord Cross-section Area and its Reference Value in Normal Pregnancy

INTRODUCTION

The routine antenatal sonographic investigations of the umbilical cord are limited for assessment of number of umbilical vessels and doppler evaluation of umbilical blood flow. With the advancements of the sonographic techniques it is now possible to have more detailed evaluation of umbilical cord. There exist only few literatures on assessment of umbilical cord cross-sectional area during pregnancy to provide a baseline reference value.

AIM

To establish the reference intervals of cross-sectional area of the umbilical cord during gestation and to find the correlation of umbilical cord cross-sectional area with the foetal anthropometric measurements.

MATERIALS AND METHODS

This study was conducted among 214 normal pregnant women who underwent a routine antenatal sonogram during gestational age ranging from 24-39 weeks in the Department of Radiodiagnosis. The umbilical cord cross-sectional area was calculated at a plane immediately close to the umbilical cord insertion to the foetal abdomen. The following foetal parameters were studied: Biparietal Diameters (BPD), Head Circumference (HC), Abdominal Circumference (AC), Femur Length (FL), and Estimated Foetal weight (EFW). The relationship between foetal anthropometric measurements and umbilical cord cross sectional area was assessed using spearman rank correlation. The 5^th, 10^th, 50^th, 90^th and 95^th percentiles of umbilical cord cross-sectional area were calculated for each gestational groups using polynomial regression analysis.

RESULTS

A statistically significant correlation was observed between cross-sectional area of umbilical cord with both gestational age and foetal anthropometric parameters. The mean age of study population was 25.08±3.5 years and the mean gestational age was 34.42±2.5 weeks. We observed a strong correlation between head circumference and umbilical cord cross-sectional area.

CONCLUSION

The mean umbilical cord cross-section area increases steadily with gestational age for up to 34 weeks and then it declines. Umbilical cord cross-sectional area can be easily measured and hence it can be included in routine antenatal sonographic evaluations to predict the perinatal outcome. Careful monitoring of the pregnancy is needed in case of abnormal cross-sectional area measurements.

Status inconsistency in groups: How discrepancies between instrumental and expressive status result in symptoms of stress

This study examines whether a mismatch between the positions that individuals hold in different status hierarchies results in symptoms of stress. Prior research has focused on inconsistencies between socioeconomic status dimensions (e.g., education and income) and did not find a significant relation between status inconsistency and stress. In this paper, we build on research on role differentiation and propose to study the effect of inconsistencies between instrumental status and expressive status in group contexts. We hypothesize that people with an inconsistency between these status dimensions experience feelings of uncertainty and frustration in their interactions with others and this manifests in stress-related symptoms. We test this hypothesis with data collected in a medium-sized Dutch childcare organization (N = 93). Polynomial regression analysis, visualized in response surface plots, suggests that status inconsistent employees report higher levels of stress.

Developmental Changes in Discrepancies Between Adolescents' and Their Mothers' Views of Family Communication

Prior work indicates that adolescents perceive the family more negatively than do their parents. These discrepant views comprise some of the most robust observations in psychological science, and are observed on survey reports collected in vastly different cultures worldwide. Yet, whether developmental changes occur with these discrepant views remains unclear. In a sample of 141 adolescents and their mothers, we examined 1-year developmental changes in discrepancies between parents' and adolescents' views of family functioning. We focused on discrepant views about a relatively covert domain of family functioning (i.e., internal views of open communication) and a relatively overt domain of such functioning (i.e., views about observable communication problems). We observed significant developmental changes in discrepant views for open communication, but not for communication problems. These findings have important implications for research examining links between discrepant views of family functioning and whether these discrepancies serve as risk or protective factors for adolescent psychosocial functioning.

RP-HPTLC Retention Data in Correlation with the In-silico ADME Properties of a Series of s-triazine Derivatives

The properties relevant to pharmacokinetics and pharmacodynamics of four series of synthesized s-triazine derivatives have been studied by Quantitative structure-retention relationship (QSRR) approach. The chromatographic behavior of these compounds was investigated by using reversed-phase high performance thin-layer chromatography (RP-HPTLC). Chromatographic retention (R M (0)) was correlated with selected physicochemical parameters relevant to pharmacokinetics, i.e. ADME (absorption, distribution, metabolism and excretion). In addition, the ability to act as kinase inhibitors and protease inhibitors was predicted for all investigated triazine classes. Also, in order to confirm similarities/dissimilarities between series of examined compounds, principal component analysis (PCA) based on calculated ADME properties was conducted. The R M (0) values of the s-triazine derivatives have been recommended for description and evaluation of pharmacokinetic properties. According to results of this study, the synthesized s-triazine derivatives meet pharmacokinetic criteria of preselection for drug candidates.

Prediction of In-silico ADME Properties of 1,2-O-Isopropylidene Aldohexose Derivatives

Retention behaviour of molecules mostly depends on their chemical structure. Retention data of biologically active molecules could be an indirect relationship between their structure and biological or pharmacological activity, since the molecular structure affects their behaviour in all pharmacokinetic stages. In the present paper, retention parameters (R M (0)) of biologically active 1,2-O-isopropylidene aldohexose derivatives, obtained by normal-phase thin-layer chromatography (NP TLC), were correlated with selected physicochemical properties relevant to pharmacokinetics, i.e. absorption, distribution, metabolism, and elimination (ADME) properties. Conducted correlation analysis showed high dependence between R M (0) and blood brain barrier penetration, skin permeability, enzyme inhibition, binding affinity to nuclear receptor ligand and G protein-coupled receptors, as well as lipophilicity (expressed as Hansh-Leo's parameter Clog P). The statistical validity of the established polynomial dependence of the second degree between R M (0) and mentioned ADME properties was confirmed by standard statistical measures and leave-one-out cross-validation method. On the basis of in-silico calculated ADME properties and retention data, the similarity between studied molecules was examined using principal component analysis (PCA). The obtained results indicate the possibility of predicting ADME properties of studied compounds on the basis of their retention data (R M (0)). These preliminary results could be treated as guideline for selecting new 1,2-O-isopropylidene aldohexose derivatives as drug candidates.

A single-ensemble-based hybrid approach to clutter rejection combining bilinear Hankel with regression

PURPOSE

Clutter regarded as ultrasound Doppler echoes of soft tissue interferes with the primary objective of color flow imaging (CFI): measurement and display of blood flow. Multi-ensemble samples based clutter filters degrade resolution or frame rate of CFI. The prevalent single-ensemble clutter rejection filter is based on a single rejection criterion and fails to achieve a high accuracy for estimating both the low- and high-velocity blood flow components.

METHODS

The Bilinear Hankel-SVD achieved more exact signal decomposition than the conventional Hankel-SVD. Furthermore, the correlation between two arbitrary eigen-components obtained by the B-Hankel-SVD was demonstrated. In the hybrid approach, the input ultrasound Doppler signal first passes through a low-order regression filter, and then the output is properly decomposed into a collection of eigen-components under the framework of B-Hankel-SVD. The blood flow components are finally extracted based on a frequency threshold.

RESULTS

In a series of simulations, the proposed B-Hankel-SVD filter reduced the estimation bias of the blood flow over the conventional Hankel-SVD filter. The hybrid algorithm was shown to be more effective than regression or Hankel-SVD filters alone in rejecting the undesirable clutter components with single-ensemble (S-E) samples. It achieved a significant improvement in blood flow frequency estimation and estimation variance over the other competing filters.