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Scientometrics indicators are used to assess scientists, universities and research institutes for scientific policy-making. The aim of this research was to assess the status of top Iranian medical researchers using scientometric indicators. The study was carried out using scientometric methods. The statistical population included the top 500 Iranian researchers in the field of medicine who were ranked in the Iranian Scientometric Information Database (ISID) based on some scientometric indicators. The data were analyzed using SPSS 22 software and Pearson's correlation coefficient, stepwise regression analysis and Chi-square tests were applied for data analysis. Findings revealed that each researcher had an average h index of 24.04, g index of 40.15, and i10 index of 90.79. There was a positive significant relationship between the number of internationally-collaborated papers and the number of citations received (r = .606, p < .01). The stepwise multiple regression analysis showed that the three variables of paper number, citation counts and mean citation rate determined 72 % of the changes in the h-index. It can be concluded that researchers publishing more papers are more likely to be cited. However, their scientometric indexes are not always better than those of other scientists. [ABSTRACT FROM AUTHOR]

The real-time operational safety of in-service bridges has received wide attention in recent years. By fully utilizing the health monitoring data of bridges, a structural abnormal pattern detection method based on data mining can be established to effectively ensure the safety of in-service bridges. This paper takes a large-span arch bridge as the research object, analyzes the time-based variation of the main monitoring data of the structure, establishes Lasso regression models for load characteristic indicators and vertical bending fundamental frequency of the structure under different time scales, and uses the residuals of the Lasso model to indicate the structural state and identify abnormal patterns. Firstly, the monitoring data of bridge structural temperature, girder end displacement, and girder acceleration were analyzed, and the interrelationships were studied to extract characteristic parameters of structural load characteristics and structural frequency. Then, the time-varying patterns of structural response were analyzed, and Lasso regression models and their regression variables were discussed based on monitoring data under two different time scales: daily cycle and annual cycle. The abnormal pattern detection method for bridge structures was developed. Finally, the effectiveness of this method was verified by taking the bridge deck pavement replacement as the abnormal pattern. The research results indicate that the proposed bridge structure abnormal pattern detection method based on Lasso regression can effectively monitor changes in the state of the bridge, and the residual dispersion of the model established on the annual cycle scale is relatively smaller than that on the daily cycle scale, resulting in better abnormal detection performance. [ABSTRACT FROM AUTHOR]

Background: Diabetic autonomic neuropathy is a common complication of type 2 diabetes mellitus (T2DM), especially in patients with long-term, poorly controlled diabetes. This study investigates the effects of exercise on autonomic nervous system activity in T2DM patients over time. Methods: A literature review using MEDLINE, Embase, Cochrane Library, Scopus, and PubMed identified studies assessed via heart rate variability. Papers were categorized into three groups: immediate effects (within 60 min), short-term effects (2–3 months), and long-term effects (over 4 months). Results: Nine articles with 161 T2DM patients were included in the meta-analysis. RMSSD changes after exercise were −4.3 (p = 0.227), 8.14 (p < 0.001), and 4.17 (p = 0.002) for the immediate, short-term, and long-term groups, respectively. LF/HF ratio changes were 0.21 (p = 0.264), −3.04 (p = 0.102), and −0.05 (p = 0.006) for the respective groups. Meta-regression indicated age, male gender, and exercise duration were associated with increased RMSSD, with coefficients of 2.36 (p = 0.001), 13.76 (p = 0.008), and 1.50 (p = 0.007), respectively. Age positively correlated with the LF/HF ratio, with a coefficient of 0.049 (p = 0.048). Conclusions: Regular exercise (≥3 times per week) for over 2 months increases parasympathetic activity in T2DM patients, while sympathetic activity decreases significantly after 4 months. Further study is needed to validate these findings. [ABSTRACT FROM AUTHOR]

Background: The condition and correlation of fatigue, sleep and physical activity in postoperative patients with pituitary adenomas remain unclear. This survey aimed to evaluate the current status and influencing factors of fatigue, sleep and physical activity in postoperative patients with pituitary adenomas. Methods: Patients undergoing pituitary adenoma resection in two tertiary hospitals from November 2019 to November 2021 were included. The general data questionnaire, Multidimensional Fatigue Inventory (MFI‐20), Pittsburgh Sleep Quality Index (PSQI) and international physical activity questionnaire were used for data analysis. Results: In total, 184 patients with pituitary adenomas were included. The postoperative patients with pituitary adenomas had a high level of fatigue. In total, 34 (18.5%) patients had low level of physical activity, 76(41.3%) patients had medium level of physical activity and 74 (40.2%) had high level of physical activity. Postoperative time, PSQI, physical activity level and gender were the influencing factors of fatigue in patients with pituitary adenomas (all P < 0.05). Conclusions: Postoperative patients with pituitary adenomas have a higher level of fatigue, and it is related to reduced sleep quality and activity. Relevant nursing measures should be taken according to the influencing factors of fatigue to reduce the fatigue of postoperative patients with pituitary adenomas. Summary statement: What is already known about this topic? The postoperative nursing care of patients is very important to the quality of life in patients with pituitary adenomas.Fatigue, sleep and physical activity in postoperative patients with pituitary adenomas remain unclear. What this paper adds? There is a high level of fatigue in patients with pituitary adenomas within 1 year after operation.The degree of fatigue is negatively correlated with the course of disease and the level of physical activity. The implications of this paper: Interventions and nursing care strategies targeted on the factors affecting postoperative fatigue of patients with pituitary adenomas are needed. [ABSTRACT FROM AUTHOR]

Spherical data arise widely in various settings. Spherical statistics is an analysis of data on a unit hyper-spherical domain. In this paper, we mainly consider the local kernel estimators for regression models with a binary response and the predictors including spherical variables. We apply the random forests kernel to nonparametric binary regression models with spherical predictors. Simulation experiments and real examples are used to validate the performance of the new models. Compared with the classical von Mises–Fisher kernel and the linear-spherical kernel, the random forests kernel has better fitting effect and faster computation speed. Compared with other classifiers, the models proposed in this paper have better classification performance in both low and high dimensional cases. [ABSTRACT FROM AUTHOR]

Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification. [ABSTRACT FROM AUTHOR]

The Modern Data Analysis paradigm (Williams, 2021) advocates using multiple methods to address the same research question, which is rarely done in studies of advertising creative effects. In this paper, we apply the MDA paradigm to data from Hartnett, Kennedy, et al. (2016), which coded 158 creative variables for 312 television advertisements with commercially validated short-term sales effectiveness outcomes. We found that many models give higher classification accuracy than the ordinal regression model previously applied, some significantly higher. Importantly, by applying many alternative but equally plausible analytical methods, we can identify creative variables associated with commercial success and have evidence-based confidence that these creative variables are artefacts of the data, and not artefacts of any particular analytical method and its associated assumptions. The findings reveal several alternative creative variables that are consistently associated with sales success across methods, which relate to the timing aspects of visual branding. [ABSTRACT FROM AUTHOR]

This study aims to investigate green finance's impact on the synergy between ESG and green innovation (ESGGI) performance and examine the potential influence played by stakeholders' green concerns on this impact. Hence, we calculated the synergy of ESGGI performance based on the entropy method and the coupled coordination degree model and conducted regression analyses on the data of 1143 Chinese companies from 2012 to 2020. The results reveal a remarkable inverted U-shaped relationship between green finance and the synergy of ESGGI performance. Additionally, the green concerns of the government and the media can efficiently moderate green finance's impact on the synergy of ESGGI performance. Further tests show that green finance's impact on the synergy of ESGGI performance of SOEs, HPEs, and HTEs is more significant. This paper demonstrates an association between green finance and the synergy of ESGGI performance, which yields new insights for companies to implement green transformation and sustainable development. [ABSTRACT FROM AUTHOR]

In the count data set, the frequency of some points may occur more than expected under the standard data analysis models. Indeed, in many situations, the frequencies of zero and of some other points tend to be higher than those of the Poisson. Adapting existing models for analyzing inflated observations has been studied in the literature. A method for modeling the inflated data is the inflated distribution. In this paper, we extend this inflated distribution. Indeed, if inflations occur in three or more of the support point, then the previous models are not suitable. We propose a model based on zero, one, ... , and k inflated points with probabilities w 0 , w 1 , ... , and w k , respectively. By choosing the appropriate values for the weights w 0 , ... , w k , various inflated distributions, such as the zero-inflated, zero–one-inflated, and zero–k-inflated distributions, are derived as special cases of the proposed model in this paper. Various illustrative examples and real data sets are analyzed using the obtained results. [ABSTRACT FROM AUTHOR]

Analytic methods development, like many other disciplines, relies on experimentation and data analysis. Determining the contribution of a paper or report on a study incorporating data analysis is typically left to the reviewer's experience and good sense, without reliance on structured guidelines. This is amplified by the growing role of machine learning driven analysis, where results are based on computer intensive algorithm applications. The evaluation of a predictive model where cross validation was used to fit its parameters adds challenges to the evaluation of regression models, where the estimates can be easily reproduced. This lack of structure to support reviews increases uncertainty and variability in reviews. In this paper, aspects of statistical assessment are considered. We provide checklists for reviewers of applied statistics work with a focus on analytic method development. The checklist covers six aspects relevant to a review of statistical analysis, namely: (1) study design, (2) algorithmic and inferential methods in frequentism analysis, (3) Bayesian methods in Bayesian analysis (if relevant), (4) selective inference aspects, (5) severe testing properties and (6) presentation of findings. We provide a brief overview of these elements providing references for a more elaborate treatment. The robustness analysis of an analytical method is used to illustrate how an improvement can be achieved in response to questions in the checklist. The paper is aimed at both engineers and seasoned researchers. [ABSTRACT FROM AUTHOR]

Traditional methods have some problems such as low analysis accuracy and long time consumption in analyzing trend data of multimedia teaching and communication platform. Therefore, this paper designs a new trend analysis algorithm in multimedia teaching and communication platform.The linear regression model is used to segment the data flow of the multimedia teaching and communication platform, the inverse lemma of a matrix is introduced to modify the model parameters of the data trend analysis of the multimedia teaching and communication platform, and the recursive regression modeling is used to design the data trend analysis algorithm of the multimedia teaching and communication platform.To verify the effectiveness of this method in analyzing data trends in the multimedia teaching communication platform, a comparative experiment is designed.The results show that the algorithm in this paper has a significant clustering trend and shorter trend analysis time when analyzing the changing trend of multimedia teaching communication platform data and can effectively improve the accuracy of clustering trend judgment. [ABSTRACT FROM AUTHOR]

Thanks to technological advances, the administration of cognitive assessments via digital solutions continues to increase, both in research and clinical practice. "Guttmann Cognitest"® is a digital solution for cognitive assessment which includes seven computerized tasks designed to assess main cognitive functions requiring approximately 20 min to be completed. The purpose of the present study was to validate it against standard and more extensive in-person neuropsychological assessments in the context of the Barcelona Brain Health Initiative (BBHI) cohort study. We studied 274 participants of the BBHI (126 women, mean age = 56.14, age range 44-69), who underwent an extensive in-person assessment, including a classical paper-and-pencil neuropsychological assessment and a cognitive assessment via the "Guttmann Cognitest"®. Principal component analysis indicated that "Guttmann Cognitest"® measures four main cognitive domains and convergent validity analysis demonstrated that cognitive performance was associated with gold standard paper and pencil tests. Results also showed an expected negative correlation with age, a relation with educational level as well as a gender effect. Regression-based norming equations for the sample tested are also reported. Performing a cognitive assessment with this digital solution is feasible and potentially useful to gather information about cognitive functioning in large samples and experimental settings. [ABSTRACT FROM AUTHOR]

Enterprise Resource Planning systems represent those systems that have the role to significantly improve the flow of information between departments, but also to streamline the activity of companies. The purpose of this paper is to identify the main aspects through which Enterprise Resource Planning systems offer to a company the opportunity to grow sustainably, thus becoming a "green business". The research method used in this research was the survey, the questionnaire being an efficient tool for data collection. The questionnaire includes some demographic questions besides the questions specific to the subject of the paper, which has shown that Enterprise Resource Planning systems help an organization to become a green business. For data analysis we used the multiple regression model, using Microsoft Excel. The results showed that there is a significant influence between the independent variables (i.e. reducing costs, clarity and quality of information generated by the Enterprise Resource Planning system, increasing labor productivity, influencing production and services in general, influencing business infrastructure) and the dependent variable (attention to sustainability by companies). The conclusion of this paper demonstrated that Enterprise Resource Planning systems are useful in a company, because the information provided by Enterprise Resource Planning systems is useful in the decision-making process to ensure the desired sustainability, so that the company becomes a "green business". [ABSTRACT FROM AUTHOR]

Count data are often encountered in consumer behavior research. Normal regression, or ordinary least squares, has been used predominantly to analyze count data in experimental studies, while the appropriate models for count data analysis such as Poisson regression have not been fully embraced in consumer behavior research. The fact that only a small fraction of published papers in consumer behavior research with count data have used Poisson regression calls for a push to rethink the common approach of using normal regression for count data analysis. To demonstrate the importance and value of using Poisson regression for count data, we first discuss the parametric forms and properties of both normal regression and Poisson regression, and then show readers through large-scale simulated experiments that Poisson regression is the appropriate model to use for count data, not only because of better model fit but also because of lower error rates in hypothesis testing in various experimental settings, which is critical for consumer behavior researchers. [ABSTRACT FROM AUTHOR]

The introduction of long‐acting injectable buprenorphine preparations for opioid use disorder has been widely heralded as a breakthrough treatment, with several studies indicating positive results when using these medications. In many locations, nurse practitioners prescribe, administer, and monitor long‐acting injectable preparations. The objective of this paper is to explore whether a reduction in dispensed needles and syringes is attributable to increased nurse practitioner prescribing of LAIB. We used a retrospective audit of needles dispensed through the health service needle and syringe program vending machine, and individuals treated with long‐acting injectable buprenorphine by the nurse practitioner led model. In addition, we examined potential factors that may influence changes in the number of needles dispensed. Linear regression found that each individual with opioid dependence treated with long‐acting injectable buprenorphine was associated with 90 fewer needles dispensed each month (p < 0.001). The nurse practitioner led model of care for individuals with opioid dependence appears to have influenced the number of needles dispensed at the needle and syringe program. Although all confounding factors could not be discounted entirely, such as substance availability, affordability, and individuals obtaining injecting equipment elsewhere, our research indicates that a nurse practitioner led model of treating individuals with opioid use disorder influenced needle and syringe dispensing in the study setting. [ABSTRACT FROM AUTHOR]

This paper suggests an approach to process and model the data obtained in biomonitoring studies. The approach is validated on data obtained from biomonitoring studies performed in the Republic of Moldova in 2015. Using the preliminary data, the decomposition on the basis of the pollution spectrum for the most polluted and cleanest sites is first carried out. The deviations of model predictions from the actual measurements are considered. A correlation analysis is next performed to evidence the correlation of two geographical coordinates with chemical elements. Factor analysis and regression analysis are applied to highlight the nonlinear mechanisms specific to the obtained data. A multilayer neural network-based model is derived to describe the relationship of the pollution rank to the geographic coordinates. The predictive capabilities of the model are represented graphically. [ABSTRACT FROM AUTHOR]

Bootstrap is a simple, yet powerful method of estimation based on the concept of random sampling with replacement. The ridge regression using a biasing parameter has become a viable alternative to the ordinary least square regression model for the analysis of data where predictors are collinear. This paper develops a nonparametric bootstrap-quantile approach for the estimation of ridge parameter in the linear regression model. The proposed method is illustrated using some popular and widely used ridge estimators, but this idea can be extended to any ridge estimator. Monte Carlo simulations are carried out to compare the performance of the proposed estimators with their baseline counterparts. It is demonstrated empirically that MSE obtained from our suggested bootstrap-quantile approach are substantially smaller than their baseline estimators especially when collinearity is high. Application to real data sets reveals the suitability of the idea. [ABSTRACT FROM AUTHOR]

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc. [ABSTRACT FROM AUTHOR]

Purpose. This research aims to outline a methodology for accident forecasting and risk assessment in mining operations using the Stanterg Mine as a case study. It emphasises the crucial role of reliable mining accident reporting and accurate data processing in forecasting accidents and effectively managing risks during mining operations. Methods. The research paper analyzes various methodologies for forecasting mining accidents using Excel and Simple Linear Regression Method (SLRM) to analyse data selected from the Stanterg Mine accidents. Findings. The forecast indicates that on average there are about 3 accidents per month at the Stanterg Mine. The analysis, based on a one-month study of 42 reported accidents, and assuming a steady production rate, suggests an increased risk of accidents. This is supported by a thorough assessment using a 3x3 risk assessment matrix tailored to the Stanterg Mine. Stope mining is highlighted as the most hazardous area, associated with risks ranging from moderate to extreme levels. Originality. Mining accident analysis at the Stanterg Mine involves an examination of the incidents, including factors leading to accidents, encountered hazards and their consequences. Accident forecasting entails studying historical data, identifying patterns, and using predictive modelling to anticipate future incidents. This proactive approach enables mining companies to proactively address risks and take preventative measures, reducing the probability of accidents. Practical implications. The systematic processing and analysis of mining accidents has revealed valuable insights into the practical application of risk assessment in mining operations. The examination of accidents at the Stanterg Mine provides researchers with crucial knowledge for effective risk assessment and management in the mining sector. [ABSTRACT FROM AUTHOR]

Background: Gender dysphoria among transgender adolescents has predominantly been examined in relation to body dissatisfaction. While in adult transgender samples, body dissatisfaction is higher than in cisgender controls, this has so far rarely been investigated for adolescents. In the context of a cisnormative society, the impact of influences from the social environment on body dissatisfaction and gender dysphoria has been neglected in research. Therefore, this study aimed to (1) provide a detailed analysis of body dissatisfaction among young transgender people and (2) investigate whether body dissatisfaction and gender dysphoria are associated with experiences of minority stress such as trans hostility and poor peer relations (PPR). Methods: The paper presents a cross-sectional study among a sample of transgender adolescents, presenting at a specialized outpatient counseling clinic (N = 99; age M = 15.36, SD = 1.85). First, body dissatisfaction (assessed by the Body-Image-Scale; BIS), was explored and compared to data from a population-based control group of cisgender peers (N = 527; age M = 14.43, SD = 0.97). Second, within a clinic-referred transgender subsample (n = 74), associations between body dissatisfaction and gender dysphoria (measured by Utrecht Gender Dysphoria Scale; UGDS), PPR (measured by the Youth-Self-Report; YSR-R), and trans hostile experiences (assessed in clinical interview) were examined by correlations, t-tests and multivariate regression. Results: Transgender adolescents reported more body dissatisfaction than cisgender peers. The dissatisfaction with sex characteristics, non-hormonal reactive body regions and the total score for body dissatisfaction were positively related with gender dysphoria. The majority had experienced trans hostility in the present and/or past (54.1%) and PPR (63.5%). More body dissatisfaction was correlated with more PPR regarding visible body parts i.e., hair, overall appearance and muscles, whilst PPR and gender dysphoria were not associated. Transgender adolescents who experienced trans hostility showed higher gender dysphoria and PPR, but not more body dissatisfaction. In multiple regression, trans hostility predicted gender dysphoria, whilst age and PPR predicted body dissatisfaction. Discussion: Experiences of minority-stress differentially interact with body dissatisfaction and gender dysphoria among transgender adolescents. Social correlates of body dissatisfaction and gender dysphoria must be considered when working with young transgender people. [ABSTRACT FROM AUTHOR]

Background: When parents bring their child to appointments and then adhere to agreed speech and language therapy (SLT) recommendations, there is the potential to increase the intensity of the intervention, support generalization and improve outcomes. In SLT, however, little is known about factors that may promote attendance or adherence. Studies in other clinical areas such in medicine, psychology and physiotherapy have identified risk factors for non‐attendance or non‐adherence that are multifactorial and variable dependent on, for example, population and intervention. Aims: To identify rates of non‐attendance and non‐adherence, and to identify parent or child factors associated with parent involvement in intervention for children under 5 years of age receiving SLT. Methods: Parents completed questionnaires at two time points assessing the domains of parents' beliefs (problem perceptions, self‐efficacy), personal circumstances (socio‐demographics, family functioning), treatment experience and child factors. Predictors of parent attendance and adherence were identified through multiple regression analyses. Non‐attendance rates were identified via local health records and non‐adherence ascertained using a specific parent‐reported measure within the treatment experience domain. Results: Participants (N = 199) were predominantly mothers, and were ethnically and socio‐economically diverse, speaking a wide range of languages. Their children presented with a range of speech, language communication needs (SLCN). The rate of non‐attendance was 25% and the main predictors of non‐attendance were maternal age, education level and two factors within the parent beliefs domain. This model explained 40% of the variance in attendance. The rate of non‐adherence in this cohort was 26% with parental rating of the importance of a recommendation and self‐efficacy beliefs predicting adherence; this explained 56% of the variance in adherence to SLT recommendations at home. Conclusions & Implications: Our research has provided preliminary evidence of the influence of parents' beliefs, personal circumstances and treatment experiences on their involvement in their child's therapy. Speech and language therapists should consider factors impacting attendance and adherence to treatment and explore parental perceptions of their child's SLCN before embarking on an intervention, a foundation for collaborative practice. A possible limitation of this study is that the levels of attrition in our sample led to generally high measured rates of participation, which should be considered in future studies. Future research should explore adherence in treatments with varying doses, with different types of SLCN or interventions and in different settings. WHAT THIS PAPER ADDS: What is already known on the subject: It is acknowledged that parent involvement in their child's therapy, such as attending and adhering to recommendations, is important but little is known about the rates of involvement and what factors may be associated with attendance and adherence in SLT. Qualitative research has explored parental involvement suggesting that beliefs about an intervention may be pertinent. Extensive research in other clinical areas suggest multiple and varied factors are influential and further exploration of particular populations and interventions is necessary. What this paper adds to the existing knowledge: This study identified rates of parental non‐attendance and non‐adherence in a cohort of predominantly mothers of children under the age of 5 years. It is the first study to measure parent adherence in SLT and identify factors that are associated parental adherence to SLT recommendations. It adds to the small body of SLT specific research in understanding risk factors for non‐attendance. What are the potential or actual clinical implications of this work?: This study highlights the need for a speech and language therapist to consider and explore parents' perspectives of their child's SLCN as a part of achieving collaboration with a parent in order to achieve the best outcomes. It provides a foundation for further systematic research into parent involvement with the ultimate aim of enhancing outcomes for children with SLCN. [ABSTRACT FROM AUTHOR]

Control charts are widely used as a tool in process quality monitoring to detect anomalies and to improve the quality of a process and product. Nevertheless, their limitations have increased in the face of increasingly complex manufacturing processes. They do not have capability of handling large streams of non-normal and autocorrelated multivariate data, which is in most real applications. This may lead to an increase in false alarm signals and/or missed detection of anomalies. They are not designed to automatically identify the root causes of an anomaly when the process is out-of-control. Several machine-learning techniques were integrated with control charts to improve the sensitivity and specificity of anomaly detection. Nevertheless, some existing techniques still produce a high false alarm rate and/or missed detection. The root cause analysis is seldom performed. In this paper, we propose a new integration that combines the logical analysis of data regression technique (LADR) and the exponential weighted moving average (EWMA) as a new model-based control chart. LADR is based on the traditional LAD methodology, which is a supervised data mining technique for pattern generation. LADR transforms the original independent variables into pattern variables by using cbmLAD software to develop a regression model. The LADR–EWMA increases the sensitivity of anomaly detection in the process and uses the patterns to perform root cause analysis of that anomaly. We applied LADR–EWMA to a real application: a concrete manufacturing process. We compared its performance with Linear regression, Support vector regression, Partial Least Square regression, and Multivariate adaptive regression Spline. The results demonstrate that the LADR–EWMA, which is based on pattern recognition, performs better compared to the other techniques in terms of a reduction of false alarms and missed detection. In addition, LADR–EWMA facilitates interpretation and identification of the root cause of the detected anomaly. [ABSTRACT FROM AUTHOR]

Background: Decisions regarding maintenance therapy in patients with multiple myeloma should be based on both treatment efficacy and health-related quality of life (HRQL) consequences. In the CARFI trial, patients with first relapse of multiple myeloma underwent salvage autologous stem cell transplantation (salvage ASCT) before randomization to carfilzomib-dexamethasone maintenance therapy (Kd) or observation. The primary clinical endpoint was time to progression, which was extended by 8 months by Kd. The aim of this paper is to present the all HRQL endpoints of the CARFI trial including the HRQL effect of Kd maintenance therapy relative to observation. The primary HRQL endpoint was assessed by EORTC QLQ-C30 Summary score (QLQ-C30-sum) at 8 months follow-up. A key secondary HRQL endpoint was quality-adjusted progression-free-survival (QAPFS). Methods: HRQL was assessed with EORTC QLQ-C30, EORTC QLQ-MY20 and FACT/GOG-Ntx at randomization and every second month during follow-up. HRQL data were analyzed with linear mixed effect models until 8 months follow-up. QAPFS per individual was calculated by multiplying progression-free survival (PFS) by two quality-adjustment metrics, the QLQ-C30-sum and EORTC Quality of Life Utility Measure-Core 10 dimensions (QLU-C10D). The QAPFS per treatment group was estimated with the Kaplan-Meier method. P < 0.05 was used for statistical significance, and a between-group minimal important difference of 10 points was interpreted as clinically relevant for the QLQ-C30-sum. Results: 168 patients were randomized. HRQL questionnaire compliance was 93%. For the QLQ-C30-sum, the difference of 4.62 points (95% confidence interval (CI) -8.9: -0.4, p = 0.032) was not clinically relevant. PFS was 19.3 months for the Kd maintenance group and 16.8 months for the observation group; difference = 2.5 months (95% CI 0.5; 4.5). QAPFS based on the QLQ-C30-sum for the Kd maintenance group was 18.0 months (95% CI 16.4; 19.6) and for the observation group 15.0 months (95% CI 13.5; 16.5); difference = 3.0 months (95% CI 0.8–5.3). QAPFS based on the QLU-C10D for the Kd maintenance group was 17.5 months (95% CI 15.9; 19.2) and 14.0 months (95% CI 12.4; 15.5) for the observation group; difference = 3.5 months (95% CI 1.1–5.9). Conclusions: Kd maintenance therapy after salvage ASCT did not adversely affect overall HRQL, but adjustment for HRQL reduced the PFS compared to unadjusted PFS. PFS of maintenance therapy should be quality-adjusted to balance the benefits and HRQL impact. [ABSTRACT FROM AUTHOR]

The development of local government online questioning is a practical exploration to promote the digital governance of government. Exploring the influencing factors of local government online questioning response can provide guidance for the governance practice of online questioning. Based on the TOE framework, this paper constructs an analysis model of the influencing factors of local government online questioning response. By using SPSS tools to conduct regression analysis on data from 230 questionnaire samples, the results show five factors: technical competence, high-level support, perceived benefits, public readiness, and public satisfaction to the responsiveness of local government were 0.019, 0.332, 0.265, 0.156, and 0.048, respectively. Therefore, the research conclusion that technical ability, high-level support, perceived benefits, public readiness, and public satisfaction, have a positive impact on local government's response ability. The analysis of the influencing factors of government's online in this paper improves the existing research to a certain extent and provides a new path and perspective for the development of network politics in China. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this note is to describe the concept of regression trees (RTs) for hospitality data analysis. Design/methodology/approach: RT is an effective non-parametric predicting modelling approach that would free researchers from the need to force a certain functional form. The method does not require normalization or scaling of data. Findings: The authors illustrate how RTs can be used to find a model that would result in the best prediction. Research limitations/implications: A common challenge facing hospitality researchers is to estimate a regression model with the correct specification. RTs can help researchers identify the best explanatory model for prediction. Originality/value: This paper describes the concept of RTs for the modelling of hospitality data. [ABSTRACT FROM AUTHOR]

Symbolic data analysis is a research area related to machine learning and statistics, which provides tools to describe geo-objects, and enables several types of variables to be dealt with, including interval type variables. Moreover, despite the recent progress in understanding symbolic data, there are no studies in the literature that address this type of data in the context of spatial data analysis. Thus, in this paper, we propose two different approaches of the spatial regression model for symbolic interval-valued data. The first fits a linear regression model on the minimum and maximum values of the interval values and the second fits a linear regression model on the center and range values of the interval. In order to evaluate the performance of these approaches, we have performed Monte Carlo simulations in which we calculated the mean value of the performance metric of the models analyzed. Furthermore, we also analyzed two applications involving real data. In the first, we examined the performance of the models in the Brazilian State of Pernambuco. In the second application, we analyzed the performance of the models for the Brazilian Northeastern region. Both applications were related to socioeconomic variables. We observed that in areas with less spatial variability, the interval spatial regression model performs better when compared with a usual method. When considering areas with a higher spatial variability, both ways presented similar results. [ABSTRACT FROM AUTHOR]

The paper studies the link between yearly behavioural expectations and technology adoption decisions of entrepreneurs and managers in the temporal context. According to our hypothesis, the expectations of managers are of a heterogeneous nature, possessing both backward-looking and forward-looking properties, and the external conditions may influence the severity of these properties in the expressed expectations. In the paper, three types of correlations are analyzed in the framework of regression analysis, that is, a link between past and present expectations (the 'inertial' mechanism), a link between present expectations and current levels of technology adoption in an industry (the 'adaptive' mechanism), and a link between present expectations and future levels of technology adoption (the 'forward-looking' mechanism). The main conclusion of the work is that radical external unforeseen shocks strongly decrease inter-temporal links between past expectations and present expectations and behaviour, which are represented by the inertial and forward-looking mechanisms respectively, and promote a link between current behaviour and the present expectations, which is represented by the adaptive mechanism. [ABSTRACT FROM AUTHOR]

In order to obtain the change rule of surrounding rock structure displacement and supporting structure internal force with time during the construction of the low mountain ridge tunnel, this paper relies on the Xishan Tunnel Project as the background. During tunneling, the displacement around the tunnel, the subsidence of the surface, the internal force of the steel arch and the pressure between the two layers of support are monitored dynamically. According to the above monitoring and measurement data, and the monitoring data analysis and nonlinear regression fitting, the predicted trend curve is obtained, the displacement change rules and characteristics of various surrounding rocks of the tunnel are obtained, to ensure the construction safety and stability requirements of supporting structure, and to provide a reasonable opportunity for the construction of secondary lining. [ABSTRACT FROM AUTHOR]

BACKGROUND: A valid distribution of key anthropometric parameters among participants is often a perquisite of ergonomics research. OBJECTIVE: In this paper, we investigated the accuracy of self-reported stature and body mass of the population in the Netherlands. METHODS: Data from 4 experiments was synthesized where in each experiment, participants self-reported their stature and body mass prior to being measured, of which they were not notified before. RESULTS: Statistical analysis of 249 records indicated that on average, participants overreported their stature by 1.31 cm and underreported their mass by 1.45 kg. This is especially true for people with a BMI≥25. CONCLUSION: Two models were proposed to adjust the self-reported stature and body mass for ergonomic researchers in a survey or recruitment. Limitations in using the models are highlighted as well. [ABSTRACT FROM AUTHOR]

In this paper, we use The World Happiness Report to illustrate the use of linear regression. We perform some linear regression using the dataset values to pinpoint the effectiveness of linear regression in data analysis. The results show that linear regression can be used to precisely define trends in the data between the output variable and the input variables. [ABSTRACT FROM AUTHOR]

The present paper deals with the problem of estimation of finite population mean of study variable using two auxiliary variables in two-phase sampling scheme using predictive approach in case of missing values of the study variable and unknown population mean of first auxiliary variable. Four classes of such estimators have been proposed using this predictive approach. The expressions of bias and mean square errors are derived up to first order of approximation. The optimal values of the constants involved in the proposed classes of estimators have been obtained and thus minimum mean square errors of the proposed classes are obtained in this study. The empirical and graphical comparisons with regression type estimators (under single phase and double phase sampling scheme) and also among themselves have been made for evaluating the performance of the proposed classes for different choices of non-responding units. Five real data sets and three simulated data sets following normal distribution have been used to evaluate the performance of the proposed classes. Numerical findings confirm the theoretical results obtained regarding superiority of proposed classes of estimators over the conventional regression type estimators in terms of percent relative efficiencies. [ABSTRACT FROM AUTHOR]

Bilingual language development is different from monolingual language development. The lack of appropriate assessment tools geared to the bilingual population has led to inaccurate over-diagnosis of bilingual children with typical language development (TLD) as children with Developmental Language Disorder (DLD) and under-diagnosis of bilingual children with DLD. The present paper addresses this challenge by focusing on Hebrew as a second language (L2) of bilingual preschool children whose first language (L1) is either English or Russian, taking into consideration both chronological age (CA) and age of onset of bilingualism (AOB). This study aimed to generate bilingual standards for a monolingual screening test, Goralnik Screening Test for Hebrewby arriving at appropriate bilingual typical development cut-off points. A total of 443 bilingual Hebrew speaking children (397 with TLD and 46 with DLD), ages 61–78 months (M = 70; SD = 4), 199 with L1 English and 244 with L1 Russian, took part in the study. The results demonstrate low diagnostic accuracy when a monolingual test with monolingual norms is used for bilingual children, in contrast with increased diagnostic accuracy when bilingual standards are used for bilingual children. The paper concludes by showing the importance of bilingual standards when assessing clinical populations with varying ages of acquisition, and in particular, for those who were exposed to their second language after the age of four. [ABSTRACT FROM AUTHOR]

Aims: This study aims to measure any changes in the marital relationship during the first year after a diagnosis of prostate cancer, identify the demographic characteristics that influenced such changes, and measure changes related to health‐related quality of life (HRQoL). Background: Knowledge is limited on the impact of a diagnosis of prostate cancer on the marital relationship and HRQoL of patients and their spouses. Design: A 1‐year longitudinal study. Methods: Data were collected from five Finnish hospitals between October 2013 and January 2017. Of the 350 recruited couples (N = 700), 179 patients and 166 spouses completed the Marital Questionnaire and the RAND 36‐Item Health Survey 1 year after diagnosis. Results: No major changes were found in the marital relationship during the follow‐up period. The spouses reported statistically significant changes in their marital relationships, but the patients did not. Furthermore, changes in the marital relationship were not associated with the patients' HRQoL. Among spouses, emotional well‐being was associated with changes in the marital relationship. Conclusion: The marital relationship was relevant in terms of the spouses' HRQoL during the first year after a diagnosis of prostate cancer. Nurses and other healthcare providers should assess counselling and support provided to spouses individually. Summary statement: What is already known about this topic? Prostate cancer affects not only the individual but also the whole family's structure and hierarchy.Prostate cancer patients' quality of life is better if their spouses have positive beliefs about the disease's duration and treatment control. What does this paper add? Spouses reported statistically significant changes in the marital relationship, but the patients did not.Among spouses, emotional well‐being was associated with changes in the marital relationship. This paper's implications: By individually assessing counselling and support to spouses during prostate cancer care, nurses and other healthcare providers have the opportunity to use these findings. [ABSTRACT FROM AUTHOR]

Background: Interrupted time series (ITS) analysis is a time series regression model that aims to evaluate the effect of an intervention on an outcome of interest. ITS analysis is a quasi-experimental study design instrumental in situations where natural experiments occur, gaining popularity, particularly due to the Covid-19 pandemic. However, challenges, including the lack of a control group, have impeded the quantification of the effect size in ITS. The current paper proposes a method and develops a user-friendly R package to quantify the effect size of an ITS regression model for continuous and count outcomes, with or without seasonal adjustment. Results: The effect size presented in this work, together with its corresponding 95% confidence interval (CI) and P-value, is based on the ITS model-based fitted values and the predicted counterfactual (the exposed period had the intervention not occurred) values. A user-friendly R package to fit an ITS and estimate the effect size was developed and accompanies this paper. To illustrate, we implemented a nation population-based ITS study from January 2001 to May 2021 covering the all-cause mortality of Israel (n = 9,350 thousand) to quantify the effect size of Covid-19 exposure on mortality rates. In the period unexposed to the Covid-19 pandemic, the mortality rate decreased over time and was expected to continue decreasing had Covid-19 not occurred. In contrast, the period exposed to the Covid-19 pandemic was associated with an increased all-cause mortality rate (relative risk = 1.11, 95% CI = 1.04, 1.18, P < 0.001). Conclusion: For the first time, the effect size in ITS: was quantified, can be estimated by end-users with an R package we developed, and was demonstrated with data showing an increase in mortality following the Covid-19 pandemic. ITS effect size reporting can assist public health policy makers in assessing the magnitude of the entire intervention effect using a single, readily understood measure. [ABSTRACT FROM AUTHOR]

Employment equity has continued to occupy a centre stage in the field of human resource management in the current and forthcoming decades in various countries worldwide. It plays a critical role in promoting the elimination of unfair discrimination and consideration of people from previously disadvantaged groups at the workplace. The paper aims to establish the relationship between employment equity implementation and employee performance. The paper also examines the relationship between eliminating unfair discrimination and employee performance at the workplace. In addition, the paper also aims to evaluate the relationship between the maintenance of affirmative action and employee performance. This study adopted a descriptive research design and a quantitative research approach. The data employed regression analysis to test the hypotheses. Data was gathered from 172 employees in a leading organisation in South Africa. This paper's key findings revealed a positive relationship between employment equity implementation and employee performance. The statistical tests revealed a significant relationship between eliminating discrimination and improving employee performance in the workplace. However, the results of the statistical tests revealed an insignificant relationship between the maintenance of affirmative action and the improvement of employee performance at the workplace. In conclusion, organisations should ensure they eliminate unfair discrimination at all costs. This can improve employee performance. [ABSTRACT FROM AUTHOR]

Intimate partner violence (IPV) continues to be a global problem. The increasing international research, focussing on children who witness IPV between their parents, indicates that these circumstances might influence children in different ways. Despite these efforts, little evidence stems from Germany. Using data from a German school student survey from 2017 and 2019 (N = 21,382), this paper aims to examine the consequences of witnessing IPV. The present paper takes a closer look at children's violent behaviour, attitudes and quality of life while controlling for other potential influences (e.g. physical and psychological child abuse). Results indicate that IPV has a significant negative effect on children's quality of life, in particular on children's sense of security at home and children's life satisfaction. No other effects were significant. In discussing these complex dynamics, the results of the present study highlight the importance of considering children who witness IPV as direct victims. A home with personal threats cannot provide emotional and physical security. Coping with this absence of a secure base at home may lead to the neglect of important developmental tasks during that time, which can have possible far-reaching implications on different areas of children's lives (e.g. social competence). Further research examining the severity and duration of witnessing IPV within the context of a longitudinal design is recommended. [ABSTRACT FROM AUTHOR]

Background: Developmental language disorder (DLD) has clear functional ramifications in the areas of social competency, emotion recognition, emotional well‐being and literacy skill, which have been found to persist from childhood to adolescence. These domains are important factors during the transition from primary to secondary school in typical development (TD). Nevertheless, there is a paucity of research on the transition from primary to secondary school for adolescents with DLD and low language (LL) ability. Aims: To investigate the association between psychosocial domains, literacy skill and concerns about school during the transition to secondary school for adolescents with DLD, LL and their TD peers. The first research question examined whether levels of social competency, emotion recognition, emotional well‐being and literacy skill predicted school concern for the three groups. The second research question explored the longitudinal relationship between school concern and psychosocial and literacy indicators over the transition. Methods & Procedures: Participants (aged 10–11 years) with DLD (n = 30), LL ((n = 29) and TD (n = 42) were recruited for this longitudinal study from eight UK primary schools. Standardized language and psychosocial assessments were administered in the spring and summer terms of the final year of primary school, and the autumn and summer terms of the first year in secondary school. Outcomes & Results: Regression analyses showed pre‐transition school concern to be predicted by emotion recognition for the DLD group, by social competence and emotional well‐being for the LL group, and by social competence for the TD group. Post‐transition concern was predicted by emotional well‐being for the LL group; with no significant predictors for the DLD/TD groups. Path analyses revealed an association between literacy skill and emotion recognition for the DLD group. A developmental cascade illustrated a relationship between social competence and emotional well‐being for the TD group. Conclusions & Implications: This study concludes that transition interventions must be differentiated according to language ability; support for DLD adolescents should focus on emotion recognition skill, provision for LL adolescents should centre on emotional well‐being, and TD participants should be particularly supported in the domain of social competence in advance of the move to secondary school. Additionally, this study emphasizes the importance of the inclusion of a LL group, as their outcomes cannot be predicted using DLD/TD research. WHAT THIS PAPER ADDS: What is already known on the subject: There is limited research exploring the transition from primary school to secondary for adolescents with DLD or LL ability. Research on TD has highlighted vulnerable domains at this time. As these key areas are evidenced spheres of difficulty in DLD and LL, there was a strong impetus for this study. What this paper adds to existing knowledge: This study reveals differences between language ability groups in the predictors of school concern both pre‐ and post‐transition. Concerns of adolescents with DLD were predicted by emotion recognition, those of adolescents with LL were predicted by emotional well‐being, and a longitudinal link between social competence and emotional well‐being was evidenced for TD adolescents. What are the potential or actual clinical implications of this work?: This study has implications for researchers and clinicians, as a need has been identified for a new clinical group. It also has practical implications for transition support planning for educational practitioners, families and adolescents. [ABSTRACT FROM AUTHOR]

Functional time series model has been the subject of the most research in recent years, and since functional data is infinite dimensional, dimension reduction is essential for functional time series. However, the majority of the existing dimension reduction methods such as the functional principal component and fixed basis expansion are unsupervised and typically result in information loss. Then, the functional time series model has an urgent need for a supervised dimension reduction method. The functional sufficient dimension reduction method is a supervised technique that adequately exploits the regression structure information, resulting in minimal information loss. Functional sliced inverse regression (FSIR) is the most popular functional sufficient dimension reduction method, but it cannot be applied directly to functional time series model. In this paper, we examine a functional time series model in which the response is a scalar time series and the explanatory variable is functional time series. We propose a novel supervised dimension reduction technique for the regression model by combining the FSIR and blind source separation methods. Furthermore, we propose innovative strategies for selecting the dimensionality of dimension reduction space and the lags of the functional time series. Numerical studies, including simulation studies and a real data analysis are show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The number of genome assemblies has rapidly increased in recent history, with NCBI databases reaching over 41,000 eukaryotic genome assemblies across about 2,300 species. Increases in read length and improvements in assembly algorithms have led to increased contiguity and larger genome assemblies. While this number of assemblies is impressive, only about a third of these assemblies have corresponding genome size estimations for their respective species on publicly available databases. In this paper, genome assemblies are assessed regarding their total size compared to their respective publicly available genome size estimations. These deviations in size are assessed related to genome size, kingdom, sequencing platform, and standard assembly metrics, such as N50 and BUSCO values. A large proportion of assemblies deviate from their estimated genome size by more than 10%, with increasing deviations in size with increased genome size, suggesting nonprotein coding and structural DNA may be to blame. Modest differences in performance of sequencing platforms are noted as well. While standard metrics of genome assessment are more likely to indicate an assembly approaching the estimated genome size, much of the variation in this deviation in size is not explained with these raw metrics. A new, proportional N50 metric is proposed, in which N50 values are made relative to the average chromosome size of each species. This new metric has a stronger relationship with complete genome assemblies and, due to its proportional nature, allows for a more direct comparison across assemblies for genomes with variation in sizes and architectures. [ABSTRACT FROM AUTHOR]

As the alternative of Cox model, the accelerated failure time (AFT) model, which simply regresses the logarithm of the survival time over the covariates, is commonly used in the analysis of interval-censored data. In this paper, we propose a novel two-component mixture-cure model for the interval-censored failure time data in the presence of a cure fraction. Specifically, the first component is a logistic regression model that describes the cure rate, and the second component is a semiparametric accelerated failure time model that describes the failure time of interest for the uncured subjects. An efficient semiparametric procedure is developed to estimate parameters in the considered model. We propose a penalized sieve maximum likelihood estimation approach with Bernstein polynomials to estimate the regression parameters quickly and accurately and the proposed procedure does not rely on the assumption of the distribution of the measurement error. The asymptotic properties of the resulting estimators are established. Extensive simulation studies conducted indicate that the proposed procedure works well for practical situations. In addition, AIDS data analysis is provided for illustration of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we have compared some linear and nonlinear models for explaining and forecasting the productionof two different crops, Mango and the Sugarcane using area of fields as the auxiliary variable. The models under considerations are compared on the basis of different fitting measures such as, Coefficient of Determination (R²),Residual Mean Square (s²), Mean Absolute Error (MAE) and Akaike Information Criterion (A.I.C.). The two primary data sets have been collected, first for sugarcane production from the Sitapur district of Uttar Pradesh state in India and second for mango production from the Lucknow district of Uttar Pradesh state in India. The fitting measures are calculated for the collected primary data sets and the best fitted models are selected and recommended for further use on the basis of the fitting measures. From the data analysis for both the data sets, it is found that the Compound, Growth, Exponential and Logistic models are equally good for practical applications. [ABSTRACT FROM AUTHOR]

Background: Research in word‐finding difficulties is sparse, especially in languages other than English. As a result, the factors associated with the frequency of word‐finding behaviours are poorly understood, particularly in discourse. Aims: This exploratory study is the first to collect data relative to the expected frequency of word‐finding behaviours in narration for French speakers aged 7–12 years old with typical development; and (2) to identify the factors associated with the frequency of word‐finding behaviours in narration. We hypothesized that sociodemographic characteristics, language abilities and characteristics of the spoken productions could be linked to word‐finding behaviours. Methods & Procedures: The participants consisted of 61 French‐speaking children aged 7–12 years old (M = 9;09 years, SD = 1;06 years) with typical development. Children were asked to complete a receptive vocabulary task, a non‐word‐repetition task and two narrative tasks. The children's narrative samples were coded using a discourse analysis of word‐finding behaviours, as described by Diane J. German. Outcomes & Results: Participants produced 39% of T‐units with least one word‐finding behaviour, which included relatively frequent use of repetitions and word reformulations. Regression analyses revealed that the participants' gender was the only significant variable associated with the frequency of word‐finding behaviours in narration. Boys produced a higher percentage of T‐units with at least one word‐finding behaviour. Post‐hoc analyses suggested that gender differences reflected differences in repetitions and word reformulations. Boys and girls did not differ on any other language measures collected. Conclusions & Implications: Speech–language pathologists may want to take into account a child's gender when assessing word‐finding difficulties in narration. Additional studies are needed to understand whether this finding extends to other word‐finding tasks and to children with word‐finding difficulties. WHAT THIS PAPER ADDS: What is already known on the subject: Studies in English have identified that analyses of word‐finding behaviours in discourse can be used to correctly identify children with word‐finding difficulties. Studies are needed in other languages to determine if this observation holds true across languages. Moreover, the factors associated with the frequency of word‐finding behaviours in discourse are poorly understood. What this paper adds to existing knowledge: The frequency of word‐finding difficulties in narration was higher in our study of French‐speaking children than in previous studies of English. Gender was the only factor associated with the frequency of word‐finding difficulties. What are the potential or actual clinical implications of this work?: The language and task used to assess word‐finding skills as well as the child's gender may be important to take into account when assessing word‐finding difficulties based on word‐finding behaviours. [ABSTRACT FROM AUTHOR]

We present an R-package for predictive modelling, CARRoT (Cross-validation, Accuracy, Regression, Rule of Ten). CARRoT is a tool for initial exploratory analysis of the data, which performs exhaustive search for a regression model yielding the best predictive power with heuristic 'rules of thumb' and expert knowledge as regularization parameters. It uses multiple hold-outs in order to internally validate the model. The package allows to take into account multiple factors such as collinearity of the predictors, event per variable rules (EPVs) and R-squared statistics during the model selection. In addition, other constraints, such as forcing specific terms and restricting complexity of the predictive models can be used. The package allows taking pairwise and three-way interactions between variables into account as well. These candidate models are then ranked by predictive power, which is assessed via multiple hold-out procedures and can be parallelised in order to reduce the computational time. Models which exhibited the highest average predictive power over all hold-outs are returned. This is quantified as absolute and relative error in case of continuous outcomes, accuracy and AUROC values in case of categorical outcomes. In this paper we briefly present statistical framework of the package and discuss the complexity of the underlying algorithm. Moreover, using CARRoT and a number of datasets available in R we provide comparison of different model selection techniques: based on EPVs alone, on EPVs and R-squared statistics, on lasso regression, on including only statistically significant predictors and on stepwise forward selection technique. [ABSTRACT FROM AUTHOR]

In this paper, a new product relative error estimation method for the partially linear multiplicative regression model (PLMM) is proposed. Both parametric and nonparametric parts are estimated by the least product relative error (LPRE) criterion and local smoothing technique. Besides, regularization conditions are given to establish the consistent and asymptotic normal properties. Finally, several numerical simulations and a real data analysis are included to show the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

One of the problems that urban communities face is migrating from small towns to big cities. Low quality of life and lack of attachment are some of the reasons that cause such a problem. Therefore, in small towns, the need to pay attention to the quality of life and the sense of attachment increases. Given that the relationship between quality of life, attachment to the city and participation has not been sufficiently studied, the present paper tries to understand the relationships between these components in the Sangar Town and find the impact of individual characteristics (gender, age and education), the length of residence and the level of income on each of the components. The Mann-Whitney U test and Spearman correlation test were used to examine the effect of demographic characteristics (age, education level, income, level, gender and length of residence) on each of the components. Using exploratory factor analysis, 9 factors were extracted from the quality of life items, and to analyze the relationships between quality of life, attachment to City and participation structural equation modeling was used. Multivariate linear regression was also used to examine the variables affecting participation. Findings indicate that quality of life (consisting of 9 factors) affects attachment to the city and attachment to the city affect participation. Urban policymakers can use the results of the present study to evaluate the effects of their executive strategies on urban livability and the satisfaction of residents. [ABSTRACT FROM AUTHOR]

Generalized estimating equation (GEE) is widely adopted for regression modeling for longitudinal data, taking account of potential correlations within the same subjects. Although the standard GEE assumes common regression coefficients among all the subjects, such an assumption may not be realistic when there is potential heterogeneity in regression coefficients among subjects. In this paper, we develop a flexible and interpretable approach, called grouped GEE analysis, to modeling longitudinal data with allowing heterogeneity in regression coefficients. The proposed method assumes that the subjects are divided into a finite number of groups and subjects within the same group share the same regression coefficient. We provide a simple algorithm for grouping subjects and estimating the regression coefficients simultaneously, and show the asymptotic properties of the proposed estimator. The number of groups can be determined by the cross validation with averaging method. We demonstrate the proposed method through simulation studies and an application to a real data set. [ABSTRACT FROM AUTHOR]

Statistical data analysis and machine learning heavily rely on error measures for regression, classification, and forecasting. Bregman divergence (BD ) is a widely used family of error measures, but it is not robust to outlying observations or high leverage points in large- and high-dimensional datasets. In this paper, we propose a new family of robust Bregman divergences called "robust- BD " that are less sensitive to data outliers. We explore their suitability for sparse large-dimensional regression models with incompletely specified response variable distributions and propose a new estimate called the "penalized robust- BD estimate" that achieves the same oracle property as ordinary non-robust penalized least-squares and penalized-likelihood estimates. We conduct extensive numerical experiments to evaluate the performance of the proposed penalized robust- BD estimate and compare it with classical approaches, and show that our proposed method improves on existing approaches. Finally, we analyze a real dataset to illustrate the practicality of our proposed method. Our findings suggest that the proposed method can be a useful tool for robust statistical data analysis and machine learning in the presence of outliers and large-dimensional data. [ABSTRACT FROM AUTHOR]

Background: After two years of psychological, physical, social, economic, environmental, and societal challenges, this paper examines the psychological health and well-being of Portuguese students based on their socioemotional skills (SSES), positive youth development (PYD), depression, anxiety, and stress (DASS), as well as the relationship between these variables and their influence on perceived quality of life and life satisfaction. Methods: This study examined 3235 students from lower to upper secondary, half of whom were female (M = 14.46 ± 1.883 years old). Using SPSS software, descriptive statistics were determined for all variables; mean differences between age and gender were found using ANOVA and the post hoc Scheffe test. Linear regressions with the Enter method were used to study how to predict perceived quality of life and satisfaction with life. Results: Males had scores indicating more SSES|optimism, emotional control, resilience, confidence, sociability, creativity, energy, a sense of belonging to school, and PYD. Girls had better skills for cooperating and relating to teachers but more test anxiety and DASS. Younger adolescents had better psychological health, greater skills, and a better perception of quality of life and life satisfaction when compared to older adolescents. Age, gender, SSES, PYD, and the DASS variables can explain 69% of the variance in life satisfaction, while these variables can explain 60.5% of the variance in perceived quality of life. Conclusions: These results point to the relevance of SSES for psychological health and well-being, suggesting that interventions should focus on promoting these variables, paying special attention to female gender and age-related challenges. [ABSTRACT FROM AUTHOR]