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Objectives. This study aimed to investigate 12-month and 7-day prevalence of musculoskeletal symptoms among workers of the pulp and paper industry, and assess the contribution of age, gender, body mass index (BMI) and physical activity (PA). Methods. The sample comprised 904 workers. Musculoskeletal symptoms were assessed using the Nordic musculoskeletal questionnaire. χ2 tests assessed the association between musculoskeletal symptoms and age/gender/BMI/PA, and multivariate logistic regression models predicted the 12-month occurrence of symptoms in the five most prevalent regions. Results. The five most affected body regions were the lower back, shoulders, neck, knees and wrists/hands. Older workers presented more symptoms in all body regions, except dorsal; female gender was associated with neck symptoms (p = 0.001); overweight was associated with a higher prevalence of lower back (p = 0.01), knee (p = 0.017) and ankle/foot (p = 0.037) symptoms; and a moderate PA level was associated with thigh/hip symptoms (p = 0.006). Age was a significant predictor in all five most affected regions; gender was a significant predictor for shoulders, neck and wrists/hands; and BMI was a significant predictor for lower back symptoms. Conclusion. The 12-month and 7-day prevalence of musculoskeletal symptoms among workers of the pulp and paper industry is higher in the lower back, shoulders, neck, knees and wrists/hands. [ABSTRACT FROM AUTHOR]

Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses., Competing Interests: The authors have declared that no competing interests exist.

In recent years, due to environmental awareness regarding the harmfulness of polymeric materials, there has been a growing interest in using papers, especially those from alternative raw materials. The importance of using all available raw materials is imperative today. Raw materials that were once viewed as waste are now of great importance because they have the ability to replace raw materials that are used irrationally or are lacking. Cereal straw is a lignocellulosic material that could be used in the paper industry and in the production of increasingly prevalent paper packaging. The aim of this research was to analyze the relationships between the qualitative parameters of straw-based printed papers containing 30% agricultural residues (wheat, barley, or triticale). The influence of two qualitative parameters (ink penetration depth and optical ink density) on the print-through was observed using multiple regression analysis on straw-based papers produced at the laboratory level. Throughout the research, 100% recycled wood paper was used as a reference sample. The results of the regression analysis showed that none of the variables individually make a statistically significant contribution to the prediction of the dependent variable in a linear context, that is, they indicate a non-linear interaction between the variables and the specific conditions under which the dependent variable reaches local extremes and changes in the gradient. Considering the results of the regression analysis and the visualization of the relationship, the model was additionally tested with other independent variables (paper type). From the results obtained, it can be concluded that the alternative straw-based paper with 30% barley pulp has the best interaction between ink and paper, which is the most similar to the reference sample in terms of printability, while the alternative straw-based paper with 30% wheat generally differs significantly from the reference paper when all three prints are considered. [ABSTRACT FROM AUTHOR]

Identifying factors that associate with more cited or higher quality research may be useful to improve science or to support research evaluation. This article reviews evidence for the existence of such factors in article text and metadata. It also reviews studies attempting to estimate article quality or predict long‐term citation counts using statistical regression or machine learning for journal articles or conference papers. Although the primary focus is on document‐level evidence, the related task of estimating the average quality scores of entire departments from bibliometric information is also considered. The review lists a huge range of factors that associate with higher quality or more cited research in some contexts (fields, years, journals) but the strength and direction of association often depends on the set of papers examined, with little systematic pattern and rarely any cause‐and‐effect evidence. The strongest patterns found include the near universal usefulness of journal citation rates, author numbers, reference properties, and international collaboration in predicting (or associating with) higher citation counts, and the greater usefulness of citation‐related information for predicting article quality in the medical, health and physical sciences than in engineering, social sciences, arts, and humanities. [ABSTRACT FROM AUTHOR]

This study measured the decrease in some mechanical properties of kraft paper used in packaging and bag production by examining the colour changes occurring in accordingly prepared standard samples subjected to UV influence. Thus, it is aimed to predict the mechanical properties of packages that have been exposed to sunlight over long periods of time with the colour change measurement, which is a non-destructive test, without the packaging deformation. Since there are mechanical differences between the cross-machine width and machine length of fabricated papers, these were measured separately. Measurements were made on papers exposed to UV for 0, 120, 240, 360, 480, 600, and 720 h. Tensile strength, strain at break, and tensile energy absorption (TEA) values were measured and also predicted for the kraft paper. For the prediction, the time and colour change values were used as independent variables, whereas the mechanical properties were taken as the dependent variables. The relationship between the time and colour change and the tensile strength, strain at break, and TEA variables in both the crossmachine and the machine directions were significant at the 5% level, and the R2 values were within acceptable limits. The results showed that the mechanical properties of the papers under UV effect decreased proportionally with the increase of the UV time. [ABSTRACT FROM AUTHOR]

The focus of this research project was to assist a local business, Krispy Kreme, to improve their marketing tactics and strategies. Also, to see customer perceptions of Krispy Kreme based on prior knowledge and experience of service; our research group used research tools like Likert and demographic questions all to analyze customer's answers towards Krispy Kreme. Using Qualtrics to easily disperse a questionnaire, we were able to collect a sample of 385 usable responses from a random sample. We analyzed the data through an application known as SPSS (Statistical Package for the Social Sciences) to develop meaningful conclusions and recommendations. Approximately, 81 percent (80.73%) of respondents agree or strongly agree on recommending Krispy Kreme to a friend. This led to a recommendation that Krispy Kreme rely on the customer to expand their market by word of mouth. T-Tests showed that there was no significant difference between males and females with any of the compare data. This is a significant finding for Krispy Kreme. Regression analysis showed that overall satisfaction of Krispy Kreme is good place to eat was dependent on the company's donut quality, the variety, the idea of creating a donut, and the idea of collaboration with other companies. Another regression showed that Krispy Kreme being perceived as a good donut place to be recommend to a friend, was determined by Krispy Kreme's donut quality, the response by respondents to have more Krispy Kreme locations, the preference for Krispy Kreme's donuts rather than Dunkin donuts, and respondents buying misshapen donuts at a discount price if Krispy Kreme sold them. Recommendations included that Krispy Kreme should make more marketing efforts towards attracting more diverse customers. Krispy Kreme also needs to have more locations to improve in sales as well as convenience, for customers to enjoy the Krispy Kreme donut experience. [ABSTRACT FROM AUTHOR]

Objective: To identify the 100 most-cited articles in DH and analyze their characteristics. Material and Methods: A search was performed on the Web of Science (WoS) and the 100 most-cited articles were selected. The following data were extracted: citations, year of publication, authorship, institution, country, journal, language, study design, topic of interest, conflict of interest (COI), and sponsorship. The VOSviewer software was used to visualize bibliometric networks. Poisson regression analysis was performed to measure associations between several citations and the characteristics of the studies. Results: The number of citations ranged from 346 to 48. The most-cited article was published in 1997 by Holland in the Journal of Clinical Periodontology. This journal published the most papers, followed by the Journal of Oral Rehabilitation and Journal of Dentistry. Laboratory research, review, and clinical trial were the study designs most prevalent. Reviews (p<0.05; PR= 1.853) and method development studies (p<0.05; PR= 1.853) had a more chance to present more citations. The main topics of interest were the clinical effectiveness of desensitizers and in vitro analysis of dentin morphology. Sponsorship and COI were underreported. England and USA presented the greatest number of citations and connections in the coauthorship network map. Conclusion: Most of the articles were original research, and their topics of interest were mainly the clinical effectiveness of desensitizing agents and dentin morphology. [ABSTRACT FROM AUTHOR]

Abstract In recent years, frequency domain spectroscopy (FDS) is often used to evaluate oil paper insulation state in power transformer bushing. But it is still very difficult to evaluate the moisture content accurately and quickly. In order to solve this problem, this paper proposes an intelligent algorithm based on random forest regression (RFR) to construct an efficient evaluation method through segmented FDS curves. Furthermore, the characteristics of FDS curves were studied and the intelligent method was compared with support vector regression (SVR) and deep neural networks (DNN). The results show that the dielectric loss, the real part and imaginary part of complex capacitance all move upward with the moisture increasing, so they can be used as the input feature of the evaluation model; The moisture content evaluation accuracy of the RFR model in the whole frequency band is higher than that of SVR and DNN models; With the increase of lower cut off frequency (FDS test stop frequency), the FDS test time is greatly shortened, and the accuracy of the RFR model can still meet the evaluation requirements. Therefore, the data in a compromise frequency band can be used to evaluate the moisture content of oil paper insulation accurately and quickly.

Near Infrared Spectroscopy (NIRS), as one of analysis technologies, has shown promisingly industrial applications for significant properties for recent decades such as fast response, preciseness, non-intrusion etc. Here, the authors employed NIRS coupled with a series of physical and chemical tests to assess the ageing condition of oil-paper insulation, which is responsible for the main insulation type of high-voltage power transformer. Among these procedures, the data analytic algorithms are of utmost importance to determine the evaluation precision. After plenty of trial-and-error, Savitzky-Golay (S-G) convolution was finally utilised to de-noise samples and improve the spectral data quality. The competitive adaptive reweighted sampling (CARS) was used to select the optimal wavelength combination of NIRS, which is found able to fully extract the effectively spectral information and reduce dimensions of spectral data. Based on the above-mentioned techniques, the quantitative analysis model of NIRS was established by partial least squares (PLS), which could synthetically process the spectral data and the degree of polymerisation (DP) of paper samples. The results indicated that compared with the traditional detection methods, the NIRS analysis is a powerful and informative tool to characterise the condition of oil-paper insulation without intrusion or damage to transformers.

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]

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

The article discusses the navigating manuscript rejection. Topics include the study demonstrating how common manuscript rejection is among faculty of all academic ranks in hopes to normalize rejection and open conversations about rejection in mentorship; approach, innovations related to the study design, and reason for the rejection of the study to quantify on the fate of manuscripts.

This study aimed to identify the performance properties that drive the shelf price of kitchen paper towels and evaluate whether sustainability is an important driver for pricing. Nineteen products were compared according to their performance (softness, absorbency, and strength), technology, and fiber morphology. Metrics to rank the products in different grades (economy, premium, and ultra) according to their performance were provided. A multiple linear regression showed that absorbency and softness are the most significant properties used to predict the price. Market data indicates that there is a segment of consumers willing to pay a premium price for products marketed as "sustainable", even though their performance is comparatively inferior to the majority of the samples. Sustainable products are up to 85% more expensive than regular products. The metrics obtained in this work can contribute to improvement in market transparency, and aid companies in deciding strategies for product development and new investments. [ABSTRACT FROM AUTHOR]

• Combined effects of extruded brick compositions with paper sludge and expanded perlite were characterized. • The use of additives in bricks has significantly affected their thermo-physical and mechanical properties. • The effects of process parameters on brick properties were investigated with a new mathematical modeling approach. • Fired brick samples showed good thermal insulation performance. • Produced exruded bricks have good potential for energy saving. Lightweight bricks produced with addition of different additive materials such as expanded perlite (EP) and recycling paper sludge (RPS) waste have been accomplished by a pug mill. Porous fired bricks were produced by using organic and inorganic additives such as paper waste and expanded perlite as pore-makers. In the experimental study, the seventeen mixture sets were prepared with Box-Behnken experimental design procedure of two additive ratios and firing temperatures. The brick samples were produced with shaping by extrusion method and firing at 850, 950 and 1050 °C for two hours after drying. Physical characteristics such as bulk density, apparent porosity and water absorption, and also, compressive strength, thermal conductivity and microstructural properties of brick samples were studied. Influences of process parameters on brick properties were investigated with a detailed neuro-regression analysis. In conclusion, the best brick composition (10 % EP + 10 %RPS by weight) in terms of thermal conductivity corresponding to acceptable mechanical properties was optimized. Accordingly, the compressive strength values of all samples are around 10 to 27 MPa. Also, the samples with high additive content (especially 10 %RPS + 5 %EP sample) exhibited the lowest thermal conductivity value (0.432 W/mK) due to the low firing temperature. [ABSTRACT FROM AUTHOR]

Under long-term synergy effect of multi-factors, especially the thermal stress, insulating paper will be degraded and its insulation performance will decline due to carbonization and degradation of cellulose. This paper presents an optical approach for aging feature extraction of the insulating paper, where one of the image processing methods called texture analysis is utilized. By conducting laboratory accelerated thermal aging tests, insulating paper samples with different aging conditions for both Nomex and Kraft, evaluated with the aging time, are prepared. After taking optical microscopic images of insulating paper samples belong to different aging groups, up to 14 texture features are extracted using the gray-level co-occurrence matrix (GLCM). With different feature selection methods applied, several of them are finally selected to represent the aging condition of insulating. Numerical tests with both supervised and unsupervised algorithms, as well as a linear regression method verifies the validity of these features in characterizing the aging condition of the insulating paper. [ABSTRACT FROM PUBLISHER]

Predicting paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kernel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris) and cypress species (Cupressus lusitanica, C. sempervirens, and C. arizonica) beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination) with two variables: the numerical variable density and the categorical variable species. [ABSTRACT FROM AUTHOR]

Drawing on language expectancy theory, we predict that linguistic errors in ICO white papers negatively impact investors' willingness to financially contribute to ICO projects. We manually annotate a sample of 546 ICO white papers according to 13 different error subcategories related to spelling and grammar. The error-annotated data are subsequently submitted to regression analyses which confirm that linguistic errors discourage potential investments in ICOs. Specifically, our analyses reveal the presence of 'high penalty' vs. 'low penalty' errors which result in higher vs. lower financial investment losses for the ICOs. The negative impact of language errors is stronger when ICO white papers are (1) written in native English-speaking countries and (2) from countries without cryptocurrency regulation. Results from an experiment confirm that this relationship is not driven by the entrepreneur- or investor-specific characteristics. Overall, we highlight that the reader identifies linguistic errors as a major 'red flag' that ultimately affects financial decision-making. [ABSTRACT FROM AUTHOR]