Search

Reproducibility, the ability to reproduce the results of published papers or studies using their computer code and data, is a cornerstone of reliable scientific methodology. Studies where results cannot be reproduced by the scientific community should be treated with caution. Over the past decade, the importance of reproducible research has been frequently stressed in a wide range of scientific journals such as Nature and Science and international magazines such as The Economist. However, multiple studies have demonstrated that scientific results are often not reproducible across research areas such as psychology and medicine. Statistics, the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data, prides itself on its openness when it comes to sharing both computer code and data. In this article, we examine reproducibility in the field of statistics by attempting to reproduce the results in 93 published papers in prominent journals using functional magnetic resonance imaging (fMRI) data during the 2010–2021 period. Overall, from both the computer code and the data perspective, among all the 93 examined papers, we could only reproduce the results in 14 (15.1%) papers, that is, the papers provide both executable computer code (or software) with the real fMRI data, and our results matched the results in the paper. Finally, we conclude with some author-specific and journal-specific recommendations to improve the research reproducibility in statistics. [ABSTRACT FROM AUTHOR]

In order to improve the effect of agricultural data mining and statistics in the Internet era, based on data mining technology, this paper combines data mining technology with modern agricultural information to solve the problems of poor timeliness and incomplete information in the collection and sorting of agricultural information. Moreover, this paper makes innovations in time series representation and time series measurement, and uses custom methods to improve agricultural data mining and statistical algorithms. In addition, this paper finds reasonable mining methods to acquire knowledge through data analysis. Finally, this paper combines the actual needs to construct a data mining and statistical analysis model of precision and intelligent agriculture based on big data analysis, and designs experiments to verify the performance of the model. The research results show that the model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

It is essential to develop infrared (IR) thermogram identification technologies to establish automatic diagnosis systems in power substations. The convolutional neural network (CNN) based methods show the highest accuracy in this field. The IR thermograms of electrical equipment are very different from general digital images, which means the present methods need further improvements. For data-driven CNN methods, it is necessary to study the characteristics of the IR data. This paper collected 11817 thermograms from substations and structured the dataset according to equipment types. The statistical features of mean, variance, skewness, kurtosis and contrast are analyzed and compared with other five image datasets. Several tricks are revealed from the analysis and tested on CNN models. Firstly, greycaling the Iron pseudo-color images extracts the temperature information and makes it possible to design models with fewer channels. The test shows it could reduce over 35% computational costs. Secondly, the sparse information of color and edges of thermograms makes it necessary to keep the original aspect ratio. The image preprocessing method of cropping shows better performance than padding and rescaling. Thirdly, the 0-1 normalization can boost the training process for about 100 epochs, which is related to the particular background of thermograms. [ABSTRACT FROM AUTHOR]

AbstractLow-rank approximation is a popular strategy to tackle the “big n problem” associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial and should be carefully specified. Predictive processes simplify the problem by inducing basis functions with a covariance function and a set of knots. The existing literature suggests certain practical implementations of knot selection and covariance estimation; however, theoretical foundations explaining the influence of these two factors on predictive processes are lacking. In this paper, the asymptotic prediction performance of the predictive process and Gaussian process predictions are derived and the impacts of the selected knots and estimated covariance are studied. The use of support points as knots, which best represent data locations, is advocated. Extensive simulation studies demonstrate the superiority of support points and verify our theoretical results. Real data of precipitation and ozone are used as examples, and the efficiency of our method over other widely used low-rank approximation methods is verified. [ABSTRACT FROM AUTHOR]

In many collegiate level statistics courses, the focus of the learning outcomes is often on the analysis of data after it has been collected. Students are provided with clean data sets from previous studies to practice statistical analysis, but receive little to no application as to the amount of time and effort that goes in to collecting good data. To account for these deficits at the author's institution, a design of experiments course was created that provided students with a more hands-on learning experience to the statistical process, especially as pertains to data collection. This paper focuses on five of the experiments that students designed and implemented during the course, and some suggestions to instructors that may wish to use these experiments in their own courses. [ABSTRACT FROM AUTHOR]

The Cash statistic, also known as the C statistic, is commonly used for the analysis of low-count Poisson data, including data with null counts for certain values of the independent variable. The use of this statistic is especially attractive for low-count data that cannot be combined, or re-binned, without loss of resolution. This paper presents a new maximum-likelihood solution for the best-fit parameters of a linear model using the Poisson-based Cash statistic. The solution presented in this paper provides a new and simple method to measure the best-fit parameters of a linear model for any Poisson-based data, including data with null counts. In particular, the method enforces the requirement that the best-fit linear model be non-negative throughout the support of the independent variable. The method is summarized in a simple algorithm to fit Poisson counting data of any size and counting rate with a linear model, by-passing entirely the use of the traditional χ 2 statistic. [ABSTRACT FROM AUTHOR]

The Journal of Statistics and Data Science Education regularly publishes interviews with notable statistics and data science educators. These interviews aim to provide insight into the development of statistics and data science education, offer a historical perspective, and humanize the field through personal anecdotes. Allan Rossman has been conducting these interviews since 2011, profiling a diverse range of individuals who have made significant contributions to the field. Taylor & Francis has made the complete set of interviews easily accessible through centralized lists. The current issue of the journal features papers on various topics, including the role of eugenics in the development of statistics and strategies for addressing this history in the classroom. [Extracted from the article]

Researchers in statistical shape analysis often analyze outlines of objects. Even though these contours are infinite-dimensional in theory, they must be discretized in practice. When discretizing, it is important to reduce the number of sampling points considerably to reduce computational costs, but to not use too few points so as to result in too much approximation error. Unfortunately, determining the minimum number of points needed to achieve sufficiently approximate the contours is computationally expensive. In this paper, we fit regression models to predict these lower bounds using characteristics of the contours that are computationally cheap as predictor variables. However, least squares regression is inadequate for this task because it treats overestimation and underestimation equally, but underestimation of lower bounds is far more serious. Instead, to fit the models, we use the LINEX loss function, which allows us to penalize underestimation at an exponential rate while penalizing overestimation only linearly. We present a novel approach to select the shape parameter of the loss function and tools for analyzing how well the model fits the data. Through validation methods, we show that the LINEX models work well for reducing the underestimation for the lower bounds. [ABSTRACT FROM AUTHOR]

In this paper, we consider the dynamical behaviour of a stochastic coronavirus (COVID-19) susceptible-infected-removed epidemic model with the inclusion of the influence of information intervention and Lévy noise. The existence and uniqueness of the model positive solution are proved. Then, we establish a stochastic threshold as a sufficient condition for the extinction and persistence in mean of the disease. Based on the available COVID-19 data, the parameters of the model were estimated and we fit the model with real statistics. Finally, numerical simulations are presented to support our theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents a machine-learning-based method for evaluating the internal value of talent in any organization and for evaluating the salary criteria. The study assumes the design and development of a salary predictor, based on artificial intelligence technologies, to help determine the internal value of employees and guarantee internal equity in the organization. The aim of the study is to achieve internal equity, which is a critical element a that directly affects employees' motivation. We implemented and validated the method with 130 employees and more than 70 talent acquisition cases with a Basque technology research organization during the years 2021 and 2022. The proposed method is based on statistical data assessment and machine-learning-based regression. We found that while most organizations have established variables for job evaluation as well as salary increments for staff according to their contribution to the organization, only a few employ tools to support equitable internal compensation. This study presents a successful real case of artificial intelligence applications where machine learning techniques help managers make the most equitable and least biased salary decisions possible, based on data. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the mean change-point models based on associated sequences. Under some weak conditions, we obtain a limit distribution of CUSUM statistic which can be used to judge the mean change-mount δ n is satisfied or dissatisfied n 1 / 2 δ n = o (1). We also study the consistency of sample covariances and change-point location statistics. Based on Normality and Lognormality data, some simulations such as empirical sizes, empirical powers and convergence are presented to test our results. As an important application, we use CUSUM statistics to do the mean change-point analysis for a financial series. [ABSTRACT FROM AUTHOR]

We agree that converting data to similar resolutions would lose some statistical information from our data. We thank gratefully Professors Wei Tu, Bei Jiang, Linglong Kong, and Professor Sudipto Banerjee for their insightful comments and discussion of our paper. The inference of the occupancy status of houses sharing one location might require more information at the individual level, which is the primary data barrier in social science. [Extracted from the article]

It should, however, be noted that such approximations have ultimately allowed the authors to conduct a comprehensive analysis of thousands of images from multiple studies, which further substantiates their findings. We congratulate the authors on an interesting paper which combines the ideas of shape analysis with longitudinal data analysis. This, of course, loses some of the elegance of the SRNF framework, but at a considerable ease of preserving the topology of the data and avoiding Euclidean approximations. [Extracted from the article]

Europe has experienced a major boom of new breweries over the last thirty years, with thousands of new breweries being set up, even in regions where brewing has no history. So far, however, this microbrewing wave has not been systematically mapped. This paper presents a unique database of European breweries from 1990–2020. Using a series of maps and statistical analyses, it shows how breweries have gradually spread across Europe. Initially, microbreweries were being established in countries that are in a declining stage of the beer life-cycle from industrial breweries. After 2005 (and particularly in the 2010s), breweries reached other regions through neighbouring and hierarchical spatial diffusion. [ABSTRACT FROM AUTHOR]

Rapid population growth has had a significant impact on society, economy and environment, which will challenge the achievement of the United Nations Sustainable Development Goals (SDGs). Spatially accurate and detailed population distribution data are essential for measuring the impact of population growth and tracking progress on the SDGs. However, most population data are evenly distributed within administrative units, which seriously lacks spatial details. There are scale differences between the population statistical data and geospatial data, which makes data analysis and needed research difficult. The disaggregation method is an effective way to obtain the spatial distribution of population with greater granularity. It can also transform the statistical population data from irregular administrative units into regular grids to characterize the spatial distribution of the population, and the original population count is preserved. This paper summarizes the research advances of population disaggregation in terms of methodology, ancillary data, and products and discusses the role of spatial disaggregation of population statistical data in monitoring and evaluating SDG indicators. Furthermore, future work is proposed from two perspectives: challenges with spatial disaggregation and disaggregated population as an Essential SDG Variable (ESDGV). [ABSTRACT FROM AUTHOR]

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset. [ABSTRACT FROM AUTHOR]

Mud pulse transmission technology is widely used in logging while drilling (LWD), while its low data transmission efficiency has restricted the development of LWD technology for a long time. In order to improve the equivalent data transmission efficiency of LWD system, a data compression method, namely, Frame Prediction Huffman Coding (FPHC) with a group of frames, is proposed in this paper. Several factors including the data accuracy, the need for real-time transmission, and the existence of transmission errors are comprehensively considered in the proposed method. Through the probability statistical analysis of LWD data, it is proved that the data have a relatively stable probability distribution after predictive coding. In addition, the Huffman code table generated from predictive coding is universally applicable to other LWD data. Through data compression experiments on logging data, it is verified that the proposed method has higher compression efficiency compared with Huffman coding, Lempel–Ziv–Welch algorithm, adaptive Huffman coding and adaptive arithmetic coding. This lossless compression method will have a good application prospect because of its ability to prevent error diffusion, simple implementation and easy to be embedded in down-hole equipment. [ABSTRACT FROM AUTHOR]

This paper presents the designing of the V-test test for testing the randomness of angles under the neutrosophic statistics. The proposed test applies when the decision-maker is uncertain about the sample size or in the measurement of angles. The operational procedure for testing the randomness of the radar angles under an indeterminate environment is given. An example of radar data is chosen for illustration purpose. From the radar example, it is concluded that the proposed test is suitable to apply for testing the radar angles randomness when uncertainty is present. In addition, the proposed test is more efficient, flexible, and informative than the existing test under classical statistics. [ABSTRACT FROM AUTHOR]

We often encounter situations in which data from the TCGA that have been analyzed in papers we read or reviewed cannot be reproduced, even when TCGA datasets are used, especially in survival analyses. Therefore, we attempted to confirm the data source for TCGA survival analysis and found that several websites used to analyze the survival data of TCGA datasets inappropriately handle the survival data, causing differences in statistical analyses. This causes the misinterpretation of results because figures of survival analysis results in several papers are sometimes exactly as generated by these sites, and the results depend on only the tools provided by these sites. We would like to make this situation widely known and raise the problem for scientific soundness. [ABSTRACT FROM AUTHOR]

Ebegil, Özdemir and Gökpinar [[14]] adopted the Ridge and Liu-type estimators into some shrinkage estimators using a median ranked set sample in the presence of multicollinearity. Sancar and Inan [[15]] proposed a Liu-type logistic estimator as a two-parameter estimator to overcome the multicollinearity problem in the logistic model to insist that using the two-phase approaches is not guaranteed to overcome multicollinearity. They applied those approaches to the 2017 Annual Industry and Service Statistics data and found that restricted calibration estimators were more efficient than the generalized regression estimator in estimating the variables with high variance such as turnover. For classical estimation methods, they used maximum likelihood estimators, moment's estimators, least squares estimators, and weighted least squares estimators, percentile estimators, the ordinary least squares estimators, best linear unbiased estimators, L-moments estimators, trimmed L-moments estimators, and Bain and Engelhardt [[3]] estimators. [Extracted from the article]

In these cases getting accurate confidence intervals is of first order importance, and the methods Ignatiadis and Wager develop are likely to be useful. I want to congratulate Nikolaos Ignatiadis and Stefan Wager on a very stimulating paper on a timely and important topic. [Extracted from the article]

Objective: To explore in a primary care setting the associations between patients' daily self-measured blood pressure (BP) during eight weeks and concurrent self-reported values of wellbeing, lifestyle, symptoms, and medication intake. We also explore these associations for men and women separately. Design and setting: The study is a secondary post-hoc analysis of the randomised controlled trial PERson-centeredness in Hypertension management using Information Technology (PERHIT). The trial was conducted in primary health care in four regions in Southern Sweden. Patients: Participants (n = 454) in the intervention group in the PERHIT-trial used an interactive web-based system for self-management of hypertension for eight consecutive weeks. Each evening, participants reported in the system their wellbeing, lifestyle, symptoms, and medication adherence as well as their self-measured BP and heart rate. Main outcome measures: Association between self-reported BP and 10 self-report lifestyle-related variables. Results: Self-reported less stress and higher wellbeing were similarly associated with BP, with 1.0 mmHg lower systolic BP and 0.6/0.4 mmHg lower diastolic BP (p < 0.001). Adherence to medication had the greatest impact on BP levels (5.2/2.6 mmHg, p < 0.001). Restlessness and headache were also significantly associated with BP, but to a lesser extent. Physical activity was only significantly associated with BP levels for men, but not for women. Conclusion: In hypertension management, it may be important to identify patients with high-stress levels and low wellbeing. The association between medication intake and BP was obvious, thus stressing the importance of medication adherence for patients with hypertension. KEY POINTS: Associations between daily home blood pressure (BP) and self-reports of lifestyle and symptoms have not been previously well explored. Self-reported higher wellbeing, lower restlessness, less stress, and higher medication adherence were significantly associated with lower same-day BP levels. Physical activity was significantly associated with same-day BP for men, but not for women. Using a hypertension management system may be a valuable tool for communication between the patient and physician. [ABSTRACT FROM AUTHOR]

To assess the cultural competence (CC) of GP trainees and GP trainers. Design and setting: A cross-sectional survey study was conducted at the GP Training Institute of Amsterdam UMC. We included 92 GP trainees and 186 GP trainers. We measured the three domains of cultural competency: 1) knowledge, 2) culturally competent attitudes and 3) culturally competent skills. Regression models were used to identify factors associated with levels of CC. Participants rated their self-perceived CC at the beginning and end of the survey, and the correlation between self-perceived and measured CC was assessed. Approximately 94% of the GP trainees and 81% of the GP trainers scored low on knowledge; 45% and 42%, respectively, scored low on culturally competent attitudes. The level of culturally competent skills was moderate (54.3%) or low (48.4%) for most GP trainees and GP trainers. The year of residency and the GP training institute were significantly associated with one or more (sub-)domains of CC in GP trainees. Having >10% migrant patients and experience as a GP trainer were positively associated with one or more (sub-) domains of cultural competence in GP trainers. The correlation between measured and self-perceived CC was positive overall but very weak (Spearman correlation coefficient ranging from −0.1–0.3). The level of cultural competence was low in both groups, especially in the knowledge scores. Cultural competence increased with experience and exposure to an ethnically diverse patient population. Our study highlights the need for cultural competence training in the GP training curricula. General practitioner (GP) trainees find cross-cultural consultations stressful due to a self-perceived lack of cultural competence (CC). The level of CC in general practice is as yet unknown. On average, the level of CC was low for the majority of GP trainees and GP trainers, especially for the scores on knowledge. CC increased with experience and exposure to an ethnically diverse patient population. GP trainees and trainers perceived a lack of covered education on various topics related to the care of migrants. Our study highlights the need for cultural competence training in the GP training curricula. [ABSTRACT FROM AUTHOR]

An anonymous survey was given to n = 73 students in an asynchronous online statistical literacy course at a mid-sized Hispanic Serving Institution. Informed by teaching experience, literature on lexical ambiguity, and everyday usage of statistics words and phrases, the first author designed the survey to yield insight into how students view phrases such as "average person," "random person," and "outlier person," and to explore possible connections or patterns with such phrases. Findings suggest that students view phrases such as "random person" in a way much further from standard usage than they do phrases such as "anonymous person." Considerations such as diversity, agency, and variable identification are identified as possibly affecting usage and meriting further investigation. Recommendations for teaching and future research are provided. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

In recent years, the discipline of statistics has begun reckoning with its difficult history. Institutions are reconsidering names that have honored key historical figures in statistics who have deep ties to eugenics movements and racial and class prejudice. These names, however, continue to appear in our classrooms, where we teach the methods created by these individuals, raising the question of how instructors should address their legacies. Three examples of famous statisticians and their work—Francis Galton's use of conditional probabilities to demonstrate "hereditary talent," Karl Pearson's attempt to quantify the intelligence of Jewish immigrant students, and Ronald A. Fisher's creation of the analysis of variance to de-emphasize environment in human development—highlight the intimate ties between statistics and eugenics. These examples, along with a discussion of the context of these men, eugenics movements, and the statisticians and scientists who opposed their eugenic programs, can humanize the field for students, teach them about the challenges in accurate and unbiased data collection and analysis, and connect historical mistakes to contemporary ethical issues. Confronting this history in the classroom can both improve the teaching of the statistical methodologies themselves and begin a broader conversation about the role of statistics in the world. for this article are available online. [ABSTRACT FROM AUTHOR]

In this article, we explore the drivers of students' success in a first-year university statistics course. Using a unique sample from Westminster International University in Tashkent, we discover that student engagement with their studies is reflected in their class attendance and in the use of online resources, which continue to play an important role in their academic performance. Moreover, we discovered that socio-demographic factors such as age, gender, marital status, employment, and language of instruction of prior schooling are significant in explaining student academic outcomes. These factors have important policy implications for both the university and the broader national level. [ABSTRACT FROM AUTHOR]

Objective: A study was undertaken to explore the differences in nutrition knowledge, attitudes, beliefs and practices and their correlations among adults in both urban and rural communities in the Free State province of South Africa. Design: This study forms part of the cross-sectional Assuring Health for All in the Free-State (AHA-FS) study. Setting: The AHA-FS study is conducted in urban and rural parts of the Free State province of South Africa. The rural and urban stages of the study were conducted in 2007 and 2009, respectively. Subjects: The sample included 846 adult household members, aged between 25 and 65 years, from both rural and urban areas of the Free State province. Outcome measures: Nutrition knowledge, attitudes, beliefs and practices were measured. Results: The sample included predominantly females (78.2%). Rural adults had significantly better nutrition knowledge (p < 0.001), positive attitudes (p < 0.001) and positive beliefs (p < 0.001) and their nutrition knowledge and attitudes (r = 0.27, p < 0.001), nutrition knowledge and beliefs (r = 0.16, p < 0.001), and nutrition attitudes and beliefs (r = 0.38, p < 0.001) were significantly correlated with each other. However, nutrition knowledge, attitudes and beliefs were not correlated with nutrition practices among our sample in either the urban or rural setting. Conclusion and implication: The results of the current study confirm that relevant and culturally acceptable nutrition education interventions for translating nutrition-related knowledge, attitudes and beliefs into practices are required. [ABSTRACT FROM AUTHOR]

Consider a finite sample from an unknown distribution over a countable alphabet. The missing mass refers to the probability of symbols that do not appear in the sample. Estimating the missing mass is a basic problem in statistics and related fields, which dates back to the early work of Laplace, and the more recent seminal contribution of Good and Turing. In this article, we introduce a generalized Good-Turing (GT) framework for missing mass estimation. We derive an upper-bound for the risk (in terms of mean squared error) and minimize it over the parameters of our framework. Our analysis distinguishes between two setups, depending on the (unknown) alphabet size. When the alphabet size is bounded from above, our risk-bound demonstrates a significant improvement compared to currently known results (which are typically oblivious to the alphabet size). Based on this bound, we introduce a numerically obtained estimator that improves upon GT. When the alphabet size holds no restrictions, we apply our suggested risk-bound and introduce a closed-form estimator that again improves GT performance guarantees. Our suggested framework is easy to apply and does not require additional modeling assumptions. This makes it a favorable choice for practical applications. for this article are available online. [ABSTRACT FROM AUTHOR]

Scientific journals may counter the misuse, misreporting, and misinterpretation of statistics by providing guidance to authors. We described the nature and prevalence of statistical guidance at 15 journals (top-ranked by Impact Factor) in each of 22 scientific disciplines across five high-level domains (N = 330 journals). The frequency of statistical guidance varied across domains (Health & Life Sciences: 122/165 journals, 74%; Multidisciplinary: 9/15 journals, 60%; Social Sciences: 8/30 journals, 27%; Physical Sciences: 21/90 journals, 23%; Formal Sciences: 0/30 journals, 0%). In one discipline (Clinical Medicine), statistical guidance was provided by all examined journals and in two disciplines (Mathematics and Computer Science) no examined journals provided statistical guidance. Of the 160 journals providing statistical guidance, 93 had a dedicated statistics section in their author instructions. The most frequently mentioned topics were confidence intervals (90 journals) and p-values (88 journals). For six "hotly debated" topics (statistical significance, p-values, Bayesian statistics, effect sizes, confidence intervals, and sample size planning/justification) journals typically offered implicit or explicit endorsement and rarely provided opposition. The heterogeneity of statistical guidance provided by top-ranked journals within and between disciplines highlights a need for further research and debate about the role journals can play in improving statistical practice. [ABSTRACT FROM AUTHOR]

This study compares and correlates young and elderly people's pinch force and endurance, and proposes inventive solutions contextualised from these statistical evaluations using TRIZ techniques. An experiment was performed with 64 young and old adults who were required to pinch an apparatus while wearing a glove with pressure sensors. Participants held their pinch until they could no longer sustain it. Pinch force and endurance time were measured and analysed using Pearson's correlation and T-Tests. The analyses confirmed that pinch force decreases with lower endurance and vice versa. Apart from the significant difference in elderly and young people's pinch force and endurance, researchers found that young people sustained pinches longer and at a higher force than elderly people. TRIZ strategies suggested that it is better for objects to 'grasp' the elderly's fingers in pinch activities rather than the usual way. Elderly people could attempt pinching periodically to conserve endurance and reduce injury risks from highly sustained exertions. This study provides an explicit pathway in developing innovative solutions that help prevent finger-related musculoskeletal risks among elderly people. These solutions have the potential in contributing to advances in ergonomics, mechanical design and biomechanics in the form of hand prosthetics and hand exoskeletons. [ABSTRACT FROM AUTHOR]

The objective of this study was to examine sexual orientation-based disparities in six self-reported health outcomes among millennial aged military veterans. We collected data using The Millennial Veteran Health Study, a cross-sectional internet-based survey with extensive quality control measures. The survey was fielded April through December 2020 and targeted millennial aged veterans across the United States. A total of 680 eligible respondents completed the survey. We assessed six binary health outcomes: alcohol use, marijuana use, frequent chronic pain, opioid misuse, high psychological distress, and fair or poor health status. Using logistic regression adjusted for a range of demographic, socioeconomic, and military-based covariates, we find that bisexual veterans consistently report worse health than straight veterans for all six health outcomes tested. Results for gay or lesbian, compared to straight veterans, were less consistent. Sensitivity models with continuous outcomes, and stratified by gender, found similar results. These results have implications for improving the health of bisexual individuals, including addressing discrimination, belonging, and social identity, particularly in institutional settings that have traditionally heteronormative and masculine cultures such as the military. [ABSTRACT FROM AUTHOR]

Can a "nudge" toward engaging, fun, and useful material improve student attitudes toward statistics? We report on the results of a randomized study to assess the effect of a "nudge" delivered via a weekly E-mail digest on the attitudes of students enrolled in a large introductory statistics course taught in both flipped and fully online formats. Students were randomized to receive either a personalized weekly E-mail digest with course information and a "nudge" to read and explore interesting applications of statistics relevant to the weekly course material, or a generic course E-mail digest with the same course information, and no "nudge." Our study found no evidence that "nudging" students to read and explore interesting applications of statistics resulted in better attitudes toward statistics. for this article are available online. [ABSTRACT FROM AUTHOR]

In K–12 statistics education, there is a call to integrate statistics content standards throughout a mathematics curriculum and to teach these standards from a data analytic perspective. Annotated lesson notes within a lesson plan are a freely available resource to provide teachers support when navigating potentially unfamiliar statistics content and teaching practices. We identified several types of annotated lesson notes, created two statistics lesson plans that contained various annotated lesson notes, and observed secondary mathematics teachers implement the lesson plans in their intermediate algebra courses. For this study, we qualitatively investigated how two teachers' instructional actions compared to what was prescribed in the annotated lesson notes. We found ways in which the teachers' instructional actions, across their differing contexts, aligned with, varied from, or adapted to the annotated lesson notes. From these results, we highlight affordances and limitations of annotated lesson notes for statistics instruction and offer recommendations for those who create statistics curricula with annotated lesson notes. [ABSTRACT FROM AUTHOR]

Being a teacher or a student in a class with a large enrollment can be intimidating. Often, teachers view comforts that are common to small classes as unattainable in a larger class, including knowing students' names, using active learning, employing group work, and creating group discussion. Students in large classes may find that the class size leads to isolation. At Utah State University, we offer introductory statistics classes for various audiences using a large lecture format. The authors have collectively led these large lectures dozens of times and found that, despite its shortcomings, the large lecture format can be an asset when teaching statistics. With an active learning approach such as recommended by the revised GAISE College report, large class sizes permit realistic sampling, facilitate student-driven simulation, and provide bountiful opportunities for experimentation. In this article, we discuss the benefits of large classes for statistics teaching and present examples of using large class sizes to create an engaging environment that where students are involved in active learning and collecting real data to foster statistical thinking. [ABSTRACT FROM AUTHOR]

As it is known that the hybrid Fibre Reinforced Polymer (FRP) composite laminate is a recently evolved class of structural material. Hence, the present work deals with secondary processing ability like hole drilling on hard to machine FRP laminate. The influence of drilling attributes on the delamination factor and surface roughness contours are studied for a high thickness hybrid (carbon/glass FRP) laminates. Here, the experimentation was performed utilising Taguchi's L27 design of experiments array. Later, on post-drilling, the predominant and optimum variables were studied through taguchi and variance analysis to highlight their contribution on the response functions. Taguchi results indicate that the combination of the 90՞tungsten carbide tool, speed of 800 rpm, and rate of feed 50 mm/min gives the best performance concerning the delamination. Also, it was observed that the combination of the 118՞tungsten carbide tool, cutting speed of 900 rpm and the rate of feed 60 mm/min give the best performance concerning surface roughness. Whereas, as per ANOVA, the highest percentage contribution factor was concerned to a tool material followed by other factors and analysed data lie with the confidence level of 95%. The work also indicates that tungsten carbide tool yield better results compared to high-speed steel tool. Further, fibre morphology has been studied, which indicates optimal structure with minimal damage. [ABSTRACT FROM AUTHOR]

There is an ongoing controversy about women's sexuality and the existence of different orgasms. The debate is tilted toward anatomical and physiological evidence, which often leaves subjective experiences out of the picture. The aim of the current mixed-methods study was to capture women's accounts of their experiences of orgasmic states. As part of a larger online survey, 513 women (M = 25.89 years, SD = 5.60) from a community sample filled in open-ended questions on their experience of different kinds of orgasms. Additionally, women rated semantic differentials with bipolar adjectives characterizing vaginal and clitoral orgasms. A sub-sample of n = 257 women (50%) had experienced both, vaginal and clitoral orgasms and rated both separately on the semantic differential. Wilcoxon signed-rank test showed significant differences in that clitoral orgasms were, amongst others, rated as sharper, easier, and more controllable, while vaginal orgasms were rated as wilder, deeper, more pulsating, and extending. In open-ended questions, women talked about various other orgasmic experiences, such as mixed clitoral/vaginal orgasms, whole body, cervical, anal, or mental orgasms. Some women were uncertain about their orgasmic experiences. It is time to integrate anatomical, psychophysiological, and experiential data and conclude that either "all clitoral" or "clitoral and vaginal" falls short to do justice to the complexity of women's orgasms. Understanding and defining these various types of orgasms and allowing for the apparent diversity to have its place in research and in social discourse is a task for future research and pleasure-positive sex education to increase pleasure literacy. [ABSTRACT FROM AUTHOR]

Preeclampsia (PE) presence could lead to hemodynamic changes. Previous research suggested that morphological parameters based on photoplethysmographic pulse waves (PPGW) could help diagnose PE. To investigate the performance of a novel PPGPW-based parameter, falling scaled slope (FSS), in distinguishing PE. To investigate the advantages of the machine learning algorithm over the conventional statistical methods in the analysis. Eighty-one pieces of PPGPW data were acquired for the study (PE, n = 44; normotensive, n = 37). The FSS values were calculated and used to construct a PE classifier using the K-nearest neighbors (KNN) algorithm. A predicted PE state varying from 0 to 1 was also calculated. The classifier's performance in distinguishing PE was evaluated using the ROC and AUC. A comparison was conducted with previously published PPGPW-based models. Compared to the previous PPGPW-based parameters, FSS showed a better performance in distinguishing PE with an AUC value of 0.924, the best threshold of 0.498 could predict PE with a sensitivity of 84.1% and a specificity of 89.2%. As for the analysis method, training a classifier using the KNN algorithm had an advantage over the conventional statistical methods with the AUC values of 0.878 and 0.749, respectively. The result indicated that FSS might be an effective tool for identifying PE. Moreover, the machine learning algorithm could further help the data analysis and improve performance. [ABSTRACT FROM AUTHOR]

Probability distribution functions (PDFs) of atmospheric reentry latitude predictions are shown to be bimodal for spacecraft in low-eccentricity, prograde low Earth orbits at altitudes of 300 km and lower. Using two-line element (TLE) data for initial orbit conditions, coupled with coarse estimates for spacecraft aerodynamic characteristics, parametric simulations produce bimodal distributions that suggest a greater likelihood of reentry near the latitudinal maxima of a given spacecraft's ground track. Various computational measures are used to test for and quantify bimodality in the reentry latitude data sets. Also, a method for approximating bandwidth is introduced for the kernel estimation of reentry latitude probability density. Overall, statistical analysis indicates that actual reentry latitudes are generally within 1-σ of observed hemisphere means as demonstrated by six historical reentry cases. [ABSTRACT FROM AUTHOR]

In the present article, Dai et al. ([5]) studies FDR control of the GM and DS methods for generalized linear models (GLMs) under a unified framework. Specifically, Xing, Zhao, and Liu ([16]) develops the Gaussian Mirror (GM) method for controlling FDR in high-dimensional linear regressions. Wang and Ramdas ([15]) uses e-value and develops the e-BH procedure to control FDR. With the maturity of data collection techniques, the analysis of data with high-dimensional input is now routinely conducted. [Extracted from the article]

Copyright of Journal of Natural Fibers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Prevention capacity of local health organisations is associated with the performance and outcomes in public health. In Colombia, where cardiovascular disease is the leading cause of morbidity and mortality, there is limited knowledge about the capacity of local health departments to prevent this condition. Efforts are needed to address problems, potential solutions and expected outcomes regarding cardiovascular disease. In this study, a conceptual model for cardiovascular disease prevention capacity in Colombian local health departments was developed, a questionnaire based on this model was validated, the overall cardiovascular disease prevention capacity in a subsample of these organisations was measured, and the association between cardiovascular disease prevention capacity and political, population, and organisational factors was examined. Once the acceptable performance of the questionnaire was verified, variability in cardiovascular prevention capacity was found among a subsample of local health departments. Furthermore, this study provides primary evidence regarding the association between the size of local health departments and overall cardiovascular disease prevention capacity in Colombia. Future studies should focus on measuring this capacity on a larger scale and developing, implementing, and evaluating interventions aimed at strengthening cardiovascular prevention capacity in Colombian local jurisdictions. [ABSTRACT FROM AUTHOR]

Globally, anaemia prevails as a public health issue, being also a concern in Mozambique, where about two-thirds of children 6–59 months of age are affected by this condition. We carried out this study to estimate anaemia prevalence and evaluate structural determinants and haematological parameters association among children aged 6–59 months attending pediatric inpatient and outpatient services in a Quaternary Health Facility in Maputo City Province, Mozambique. We collected data from 637 inpatients or outpatients who attended pediatric consultations at the Maputo Central Hospital. The overall rate of anaemia in children aged 6–59 months was 62.2% (396/637), with 30.9% moderate anaemia (197/637), 23.9% mild anaemia (152/637), and 7.4% severe anaemia (47/637). Among our study participants, critical factors for anaemia were those concerning the age group, child´s caregiver schooling, malaria and size of the liver. [ABSTRACT FROM AUTHOR]

Brazil has a historical gap regarding information on violence against women. Herein we aimed to evaluate the association of socioeconomic and demographic characteristics with physical violence against women in Brazil, as well as the possible escalation of violence to severe patterns of violence. We analysed data from the 3rd Brazilian Household Survey on Substance Use, in 2015. The main outcomes were reporting any physical violence and being stabbed/shot in the last 12-months. Logistic regressions were fitted to assess the association between socioeconomic and demographic variables with the outcomes. We estimated 3.8 million women reported any physical violence (5.52%): 3.79% reported threats to beat/ push/kick, 1.87% threats with knife/gun, 2.49% were beaten/pushed/kicked, 0.63% were spanked/ choked, and 0.21% were stabbed/shot. The higher the severity of violence, the higher the number of types of violence experienced. The likelihood of reporting any violence was higher among women 18–24 years, without a stable partner, who were at an informal job or unemployed, and who live in urban areas. The sociodemographic characteristics associated with reporting any violence reinforce the importance of addressing gender inequalities. Evidence of escalation violence reinforces the need to protect and care for women who report any type of violence. [ABSTRACT FROM AUTHOR]

In this study, we used GPS-TEC measurements from six stations in East Africa located in low/equatorial latitudes to investigate medium-term ionospheric response to solar and geomagnetic circumstances during 2014-2016. Daily mean solar proxies (F10.7 and SSN) and geomagnetic activity indices (Kp and Dst) were used to determine solar and geomagnetic activity effects on ionospheric vertical Total-Electron-Content (vTEC). We have applied statistical analysis and quadratic fits with solar indices to find the type of trends, forecast vTEC, and describe the daily fluctuations of vTEC. Asab and Debark had the highest vTEC values among the six stations during the months of equinoxes in which they are located at 13.06° and 13° geographic latitudes, respectively. The highest vTEC values were 86, 80, and 75 TECU in March, October, and April 2014, respectively; 72 TECU in March and April 2015, and 70 TECU in February 2015. Within 24 hours, the magnitudes of vTEC were greater in February, March, and April 2014, 2015, and 2016 than in the remaining months. The changes in vTEC have demonstrated good agreement with the trend of solar parameters, and F10.7 has a stronger correlation with vTEC than SSN by 20%. The effects of solar activity on GPS-vTEC were positive, whereas the vTEC was disturbed during the storm's main and recovery phases, which led to positive and negative ionospheric effects, respectively. We found both linear and non-linear trends. Nevertheless, when we approximated (F10.7)² and (SSN)² coefficients, linear trends in vTEC were dominant. The anticipated and observed vTEC measurements are in moderately good agreement. [ABSTRACT FROM AUTHOR]

We explore the taxonomy of cryptocurrencies and integrate our analysis with traditional ways of understanding financial assets. We thus classify cryptocurrencies using the time series and distributional properties of returns. Cryptocurrencies appear inherently speculative in nature. The result is even more clear cut when time series measures of distance are used. Results tally with wider concerns raised regarding excessive volatility of stablecoins. [ABSTRACT FROM AUTHOR]

We assess the professional autonomy of the 13 principal U.S. federal statistical agencies. We define six components or measures of such autonomy and evaluate each of the 13 principal statistical agencies according to each measure. Our assessment yields three main findings: (a) Challenges to the objectivity, credibility, and utility of federal statistics arise largely as a consequence of insufficient autonomy. (b) There is remarkable variation in autonomy protections and a surprising lack of statutory protections for many agencies for many of the proposed measures. (c) Many existing autonomy rules and guidelines are weakened by unclear or unactionable language. We conclude that a lack of professional autonomy unduly exposes the principal federal statistical agencies to efforts to undermine the objectivity of their products and that agencies cannot completely rebuff these efforts. Our main recommendations are to strengthen the role of the OMB Chief Statistician and to legislate new statutory autonomy protections, including explicit authorization for the principal federal statistical agencies that currently have no recognition in statute. We also recommend periodic assessments of the health of the federal statistical system, including not only autonomy protections and resources, but also how well agencies are satisfying data needs for the public good and using best methods to do so. [ABSTRACT FROM AUTHOR]