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The study of mixture component effects in the presence of process variables has been of interest since the work of Scheffé. A key advantage of designed experiments in general is the ability to estimate and interpret interactions. A unique feature of mixture-process experiments is the potential presence of interactions between the mixture components and the process variables. The classic approach to interpret these has been to use contour plots and evaluate individual interaction coefficients in Scheffé mixture-process models. It is proposed to study the interactions along the Cox component axes, which greatly enhances the insight into the nature of these interactions that can be obtained from contour plots. Further, we propose an alternative analysis that produces estimates of the process variable main effects in mixture-process models. Both graphical and analytical methods are presented. This approach provides an overall view of the main effects and interactions that is consistent with how these terms are evaluated in factorial and response surface experiments with only process variables. Limitations of the classic approach are identified and discussed. Three examples are included to illustrate the approach. [ABSTRACT FROM AUTHOR]

Meta-analytic datasets can be large, especially when in primary studies multiple effect sizes are reported. The visualization of meta-analytic data is therefore useful to summarize data and understand information reported in primary studies. The gold standard figures in meta-analysis are forest and funnel plots. However, none of these plots can yet account for the existence of multiple effect sizes within primary studies. This manuscript describes extensions to the funnel plot, forest plot and caterpillar plot to adapt them to three-level meta-analyses. For forest plots, we propose to plot the study-specific effects and their precision, and to add additional confidence intervals that reflect the sampling variance of individual effect sizes. For caterpillar plots and funnel plots, we recommend to plot individual effect sizes and averaged study-effect sizes in two separate graphs. For the funnel plot, plotting separate graphs might improve the detection of both publication bias and/or selective outcome reporting bias.

Abstract Background Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable. Methods We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes. Results We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features. Conclusions The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

Conventional electronic health record information displays are not optimized for efficient information processing. Graphical displays that integrate patient information can improve information processing, especially in data-rich environments such as critical care. We propose an adaptable and reusable approach to patient information display with modular graphical components (widgets). We had two study objectives. First, reduce numerous widget prototype alternatives to preferred designs. Second, derive widget design feature recommendations. Using iterative human-centered design methods, we interviewed experts to hone design features of widgets displaying frequently measured data elements, e.g., heart rate, for acute care patient monitoring and real-time clinical decision-making. Participant responses to design queries were coded to calculate feature-set agreement, average prototype score, and prototype agreement. Two iterative interview cycles covering 64 design queries and 86 prototypes were needed to reach consensus on six feature sets. Interviewers agreed that line graphs with a smoothed or averaged trendline, 24-h timeframe, and gradient coloring for urgency were useful and informative features. Moreover, users agreed that widgets should include key functions: (1) adjustable reference ranges, (2) expandable timeframes, and (3) access to details on demand. Participants stated graphical widgets would be used to identify correlating patterns and compare abnormal measures across related data elements at a specific time. Combining theoretical principles and validated design methods was an effective and reproducible approach to designing widgets for healthcare displays. The findings suggest our widget design features and recommendations match critical care clinician expectations for graphical information display of continuous and frequently updated patient data. [ABSTRACT FROM AUTHOR]

Abstract Background Presenting results of diagnostic test accuracy research so that it is accessible to users is challenging. Commonly used accuracy measures (e.g. sensitivity and specificity) are poorly understood by health professionals and the public. Evidence suggests that presenting probabilities as natural frequencies rather than percentages facilitates understanding. We present a test consequence graphic to display results based on natural frequencies and test consequences. Methods The graphic was developed as part of a project to develop guidance for writing plain language summaries for Cochrane diagnostic test accuracy (DTA) reviews. Using a mixed methods approach (focus groups, user testing, web-based surveys, public engagement and piloting), the graphic emerged as a clear preference out of a range of methods for presenting probabilistic information (text only, numbers embedded in text, annotated graphic) across patient representatives, media representatives and health professionals. The structure of the graphic was refined during the research process. Results The test consequence graphic displays the results of diagnostic test accuracy study or review as natural frequencies based on a hypothetical cohort of 1000 patients receiving the test. Conclusions The test consequence graphic provides a tool to help researchers communicate the results of diagnostic research in a simple, easy to access format and encourage meaningful application of research findings to practice. Key to this is linking estimates of test accuracy to potential downstream consequences of testing.

Based on simultaneous voice and electroglottograph (EGG) signals, to gain a better understanding of human voice production process, to make pitch-synchronous segmentation of voice signals, and to make visual representations of pitch marks and timbre spectra with high resolution. The traditional spectrogram segments the voice signals with a process window of fixed size and fixed shift, then performs fast Fourier transformation after multiplied with a window function, typically a Hamming window. Then display power spectrum in both frequency and time. Pitch information and timbre information are mixed. The new design segments the signals into pitch periods, either using the derivatives of the EGG signals or based on the voice signals, then performs Fourier analysis to the segment of signals in each pitch period without using a window function. The pitch information and the timbre information are cleanly separated. The graphical representations of both pitch marks and timbre spectra exhibit high resolution and high accuracy. Detailed analysis of simultaneously acquired voice and EGG signals provides a more precise understanding of human-voice production process. The transient theory of voice production, proposed by Leonhard Euler in early 18th century, is substantiated with modern data. Based on the transient theory of voice production, a pitch-synchronous spectrogram software is developed, which makes a visual representation of pitch marks and timbre spectra. In addition, the timbre spectrum and the power evolution pattern in each pitch period can be displayed individually. Simultaneously acquired voice and EGG signals indicates that each glottal closing triggers a decaying elementary wave in the vocal tract. A superposition of those elementary waves constitutes voice. Based on that concept and using EGG data, a pitch-synchronous voice signal processing method is developed. The voice signal is first segmented into pitch periods, then the two ends are equalized. Fourier analysis is applied to obtain the timbre spectrum of each pitch period. High resolution display of timbre spectrum is generated. The power evolution pattern in each pitch period is also displayed. [ABSTRACT FROM AUTHOR]

Background: Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable.Methods: We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes.Results: We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features.Conclusions: The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis. [ABSTRACT FROM AUTHOR]

Background/Aims: Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes memory loss and a decline in cognitive abilities. AD is the sixth leading cause of death in the USA, affecting an estimated 5 million Americans. To assess the association between multiple genetic variants and multiple measurements of structural changes in the brain, a recent study of AD used a multivariate measure of linear dependence, the RV coefficient. The authors decomposed the RV coefficient into contributions from individual variants and displayed these contributions graphically. Methods: We investigate the properties of such a "contribution plot" in terms of an underlying linear model, and discuss shrinkage estimation of the components of the plot when the correlation signal may be sparse. Results: The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Conclusions: The contribution plot with shrinkage estimation can reveal truly associated explanatory variables. [ABSTRACT FROM AUTHOR]

We will analyze variations in brain anatomy and create the first integrated software environment to extract patterns from brains and target differences related to inter-individual variability, pathology, development, or degeneration. We will evaluate how well these differences can help diagnose and predict treatment outcome for major depressive disorder, which affects millions of Americans, but our work is intended to be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain anatomy between any two populations.

The multipath effect is a crucial error source caused by the environment around the station and cannot be eliminated or mitigated by differential algorithms. Theoretically, the maximum value for the carrier phase is a quarter the wavelength, i.e., about 4.8 cm for the GPS L1 signal. Considering the increasing demands of high-precision applications, the multipath error has become a major factor affecting the accuracy and reliability of GPS millimeter-level data processing. This paper proposes a multi-point hemispherical grid model (MHGM) to mitigate the multipath effect. In this method, the hemisphere centered on each station is divided into a grid, and the multipath error at the station is estimated based on the parameterization of the grid points. The double-differenced (DD) observed-minus-calculated (OMC) values on some previous days are treated as the observation values to model the present multipath error. Contrary to the present methods which rely much on the platform of data collection and processing, MHGM can be potentially applied to GPS data processing with the existing hardware and software. Experiments in high-multipath and low-multipath environments are designed by mounting a baffle or not. The experimental results show that MHGM is effective in mitigating the multipath effect. When using data from the previous day, an average improvement of about 63.3% in the RMS of DD OMC can be made compared with that without correction, and this is basically consistent with the sidereal filtering (SF) method which is 63.0%. Furthermore, the effectiveness of the above two methods is better than that of the empirical site model (ESM). The kinematic positioning results are also basically consistent with the statistical results of the RMS values of DD OMC. Historical data from more than one day can more explicitly and effectively model the MHGM. Furthermore, compared with the SF method, the MHGM can be used not only to mitigate the multipath error, but also to orientate the sources of the multipath error around the station, and give guidance in the physical elimination of these sources.

Assessment and communication of toxicology data are fundamental components of the work performed by veterinary anatomic and clinical pathologists involved in toxicology research. In recent years, there has been an evolution in the number and variety of software tools designed to facilitate the evaluation and presentation of toxicity study data. A working group of the Society of Toxicologic Pathology Scientific and Regulatory Policy Committee reviewed existing and emerging visualization technologies. This Points to Consider article reviews some of the currently available data visualization options, describes the utility of different types of graphical displays, and explores potential areas of controversy and ambiguity encountered with the use of these tools. [ABSTRACT FROM AUTHOR]

Cardiopulmonary exercise testing is a well-known, valuable tool in the clinical evaluation of patients with different causes of exercise limitation and unexplained dyspnea. A wealth of data is generated by each individual test. This may be challenging regarding a comprehensive and reliable interpretation of an exercise study in a timely manner. An optimized graphical display of exercise data may substantially help to improve the efficacy and reliability of the interpretation process. However, there are limited and heterogeneous recommendations on standardized graphical display in current exercise testing guidelines. To date, a widely used three-by-three array of specifically arranged graphical panels known as the "nine-panel plot" is probably the most common method of plotting exercise gas exchange data in a standardized way. Furthermore, optimized scaling of the plots, the use of colors and style elements, as well as suitable averaging methods have to be considered to achieve a high level of quality and reproducibility of the results. Specific plots of key parameters may allow a fast and reliable visual determination of important diagnostic and prognostic markers in cardiac and pulmonary diseases. [ABSTRACT FROM AUTHOR]

In demography, a detailed study of the temporal dynamics of the structure of a population is often required to better understand the processes that underline its overall dynamics and the individual’s life histories. Heatmaps, using time and structure (such as size-structure) as x and y coordinates and density as colours, are efficient tools for displaying the dynamics of a structured population. Such representations (structure-time diagrams) reveal the data at several levels, from general outlook to fine details. Despite its efficiency, this type of visual display has been scarcely used in ecology and demography. Using the example of springtail populations maintained in the laboratory and a woodlouse population studied in the field, we explain why this type of representation can be used to analyse the population dynamics of soil organisms and why it should be more widely used in demography. We also present the R package STdiag (for ‘Structure Time diagram’), an interface to complex graphical functions to easily produce and analyse such ‘structure-time diagrams’ from raw datasets. This package is available for all operating systems via R-Forge. Its syntax and options are described, discussed and illustrated using our case studies. This graphical display is a simple and efficient way to make large demographic datasets coherent and to disclose the underlying, often hidden, demographical processes.

Introduction: Guidelines report the quality of the evidence used when formulating recommendations to ensure transparency and allow end-users to assess the estimates of effect that underlie the recommendation. The aim of this study was to investigate the differences in grading of evidence quality between the Scottish Intercollegiate Guideline Network (SIGN) model and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method.Materials and Methods: The GRADE system was used to create evidence profiles for recommendations from a guideline that used the SIGN method to assess the quality of evidence. Scores were allocated to the quality assessment in its GRADE and SIGN forms, so that the difference between the two methods could be quantified.Results: The SIGN grading system led to quality ratings of the evidence of high and moderate but using the GRADE system, the majority of ratings were graded as low or very low. All of the recommendations were graded as higher quality by SIGN than by GRADE. The points' difference ranged from one to three points (maximum difference possible was four points). The most common reasons for downgrading evidence were "study limitations", "imprecision"' and "publication bias". "Indirectness" and "inconsistency" were rarely selected as reasons to downgrade the evidence.Conclusions: The GRADE and SIGN methods produce varying estimates of the quality of evidence. [ABSTRACT FROM AUTHOR]

Functional data are occurring more and more often in practice, and various statistical techniques have been developed to analyze them. In this paper we consider multivariate functional data, where for each curve and each time point a $$p$$ -dimensional vector of measurements is observed. For functional data the study of outlier detection has started only recently, and was mostly limited to univariate curves $$(p=1)$$ . In this paper we set up a taxonomy of functional outliers, and construct new numerical and graphical techniques for the detection of outliers in multivariate functional data, with univariate curves included as a special case. Our tools include statistical depth functions and distance measures derived from them. The methods we study are affine invariant in $$p$$ -dimensional space, and do not assume elliptical or any other symmetry. [ABSTRACT FROM AUTHOR]

The presentations of the well-known likelihood ratio, Wald and score test statistics in textbooks appear to lack a unified graphical and geometrical interpretation. We present two simple graphical representations on a common scale for these three test statistics, and also the recently proposed gradient test statistic. These unified graphical displays may favor better understanding of the geometrical meaning of the likelihood-based statistics and provide useful insights into their connections. [ABSTRACT FROM AUTHOR]

Classical and GGE biplot methods are graphical procedures that allow multivariate data to be analyzed. In particular, the GGE biplot displays the genotype main effect (G) and the genotype by environment interaction (GE) in two-way data. The GGE biplot originates from data graphical analysis of multi-environment trials (MET). Thus, agronomists, crop scientists and geneticists are potential users of this method. However, it can also be used to visualize and analyze other types of data. In this paper, we propose a new interactive computational implementation in language to perform the main functions of the classical and GGE biplot methods, so it is also useful for MET data visual analysis. This implementation is organized in an package named . This package is the only interactive, noncommercial and open source software that currently exists, offering a free alternative to available commercial software. In addition, it can be used without to practically have knowledge of the programming language. Here, we present and discuss the capabilities and features of the package and illustrate them by using real data. The package graphically addresses the questions that a researcher likely asks. This package is not only a tool for visual data analysis of multi-environment trials, useful for plant breeders and geneticists, in order to study yields from genotypes and interactions between genotype and environment, but also data from other areas can be analyzed by the package. [ABSTRACT FROM AUTHOR]

Background: Inadequate understanding of risk among counselees is a common problem in familial cancer clinics. It has been suggested that graphical displays can help counselees understand cancer risks and subsequent decision-making. We evaluated the effects of a graphical presentation in addition to a frequency format on counselees' understanding, psychological well-being, and preventive intentions. Design: Multicenter controlled trial. Setting: Three familial cancer clinics in the Netherlands. Methods: Participants: Unaffected women with a breast cancer family history (first-time attendees). Intervention: Immediately after standard genetic counseling, an additional consultation by a trained risk counselor took place where women were presented with their lifetime breast cancer risk in frequency format (X out of 100) (n = 63) or frequency format plus graphical display (10 × 10 human icons) (n = 91). Main outcome measures: understanding of risk (risk accuracy, risk perception), psychological well-being, and intentions regarding cancer prevention. Measurements were assessed using questionnaires at baseline, 2-week and 6-month follow-up. Results: Baseline participant characteristics did not differ between the two groups. In both groups there was an increase in women's risk accuracy from baseline to follow-up. No significant differences were found between women who received the frequency format and those who received an additional graphical display in terms of understanding, psychological well-being and intentions regarding cancer prevention. The groups did not differ in their evaluation of the process of counseling. Conclusion: Women's personal risk estimation accuracy was generally high at baseline and the results suggest that an additional graphical display does not lead to a significant benefit in terms of increasing understanding of risk, psychological well-being and preventive intentions. [ABSTRACT FROM AUTHOR]

Abstract: Objective: We aim to illustrate the potential impact of a new study on a meta-analysis, which gives an indication of the robustness of the meta-analysis. Study Design and Setting: A number of augmentations are proposed to one of the most widely used of graphical displays, the funnel plot. Namely, 1) statistical significance contours, which define regions of the funnel plot in which a new study would have to be located to change the statistical significance of the meta-analysis; and 2) heterogeneity contours, which show how a new study would affect the extent of heterogeneity in a given meta-analysis. Several other features are also described, and the use of multiple features simultaneously is considered. Results: The statistical significance contours suggest that one additional study, no matter how large, may have a very limited impact on the statistical significance of a meta-analysis. The heterogeneity contours illustrate that one outlying study can increase the level of heterogeneity dramatically. Conclusion: The additional features of the funnel plot have applications including 1) informing sample size calculations for the design of future studies eligible for inclusion in the meta-analysis; and 2) informing the updating prioritization of a portfolio of meta-analyses such as those prepared by the Cochrane Collaboration. [Copyright &y& Elsevier]

Background: Variation in graphic format can substantially influence interpretation of data. Despite a large body of literature on the optimal design of graphs, little attention has been paid to the format of charts for health monitoring. Aims: This study assessed the effect of aspect ratio (x:y ratio) and interconnecting lines on visual identification of change in biological data, such as during asthma exacerbations. Methods: Eighty volunteers viewed 72 sets of six consecutive blocks of unidentified biological data, recording if each block of data was increasing, decreasing, or the same as the previous block. Three chart aspect ratios were examined (A, 5.2:1; B, 3.0:1; C, 1.1:1), with or without lines between data points. Datasets from lung function monitoring by asthma patients included a mild/moderate/severe fall ('exacerbation') or no exacerbation. False negative (missing true exacerbations) and false positive (identifying non-existent exacerbations) responses were calculated. Results: 84% of exacerbation blocks were correctly identified. There was a significant interaction between exacerbation severity and aspect ratio (p=0.0048). The most compressed chart (C) had the fewest false negative responses. Moderate falls were missed in 24%, 12%, and 5% of trials on charts A, B, and C, respectively (C vs A: adjusted relative risk 0.19 (95% CI 0.12 to 0.30)). False positive responses were infrequent (A, 2.5%; B, 3.8%; C, 8.3%), increasing slightly if data points were joined with lines (4.3% vs 5.1%, p=0.004) . Conclusions: Compressed charts can improve the visual detection of change in biological data by up to 80%. The aspect ratio of charts should be standardised to facilitate clinical pattern recognition. [ABSTRACT FROM AUTHOR]

Biological data produced by high throughput technologies are becoming more and more abundant and are arousing many statistical questions. This paper addresses one of them; when gene expression data are jointly observed with other variables with the purpose of highlighting significant relationships between gene expression and these other variables. One relevant statistical method to explore these relationships is Canonical Correlation Analysis (CCA). Unfortunately, in the context of postgenomic data, the number of variables (gene expressions) is usually greater than the number of units (samples) and CCA cannot be directly performed: a regularized version is required. We applied regularized CCA on data sets from two different studies and show that its interpretation evidences both previously validated relationships and new hypothesis. From the first data sets (nutrigenomic study), we generated interesting hypothesis on the transcription factor pathways potentially linking hepatic fatty acids and gene expression. From the second data sets (pharmacogenomic study on the NCI-60 cancer cell line panel), we identified new ABC transporter candidate substrates which relevancy is illustrated by the concomitant identification of several known substrates. In conclusion, the use of regularized CCA is likely to be relevant to a number and a variety of biological experiments involving the generation of high throughput data. We demonstrated here its ability to enhance the range of relevant conclusions that can be drawn from these relatively expensive experiments. [ABSTRACT FROM AUTHOR]

Objectives To produce a graphical method to represent the performance of a screening test that illustrates the prevalence of the disorder being screened for, as well as its discriminatory potential. Concept A target plot was constructed in which the risk of the disorder is represented by a series of concentric circles of constant risk (isorisks) equivalent to specified false positive rates, with the highest risk in the centre and lower risks spreading outwards towards the circumference. Dots were drawn to represent cases of the disorder; these were of a size such that their total area as a proportion of the area of the whole target plot equalled the prevalence of the disorder in the screened population. The discriminatory power of the test was seen as the clustering of dots around the centre or bull's eye of the target. The detection rate could be estimated as the proportion of dots which fell within the isorisk corresponding to a specified false positive rate. Application The target plot was applied to second trimester antenatal screening for Down's syndrome using different combinations of screening markers, and also to screening for ischaemic heart disease using protein components of cholesterol (apolipoproteins A I and B and Lp(a) lipoprotein), systolic blood pressure, and smoking status. Discussion The efficacy of the different methods of screening for Down's syndrome is readily apparent using the target plot, as is the poorer performance of screening for ischaemic heart disease. Conclusions The target plot is a simple and quantitative way of displaying the performance of a screening test that may be useful in teaching and educational material. [ABSTRACT FROM AUTHOR]

One-dimensional statistical methods of scaling have been employed to present a distinct subjective criterion that is related to a measurable aspect of a software component. However, different aspects being measured and different software components being analyzed usually have some characteristics in common. Selected techniques for graphical representation permit a brief but nevertheless, a thorough view of complex relations among complicated sets of data. Several methods of visualizing and analyzing multidimensional data sets are presented and discussed. The underlying goals of such techniques are to find unknown structures and dependencies among measures, to represent different data sets in order to improve communication and comparability of distinct analyses, and to decrease visual complexity. For improved understandability of the statistical and related graphical concepts, a small set of design aspects from a real-world example is introduced. All the techniques illustrated in this article will be applied to the same set of data in order to allow them to be compared. [ABSTRACT FROM AUTHOR]

This paper describes a hybrid monitor for measuring the performance and observing the behavior of distributed systems during execution. Major problems are the interference of the monitoring system with the system to be monitored and the presentation of meaningful data to users. Monitoring becomes even more difficult in distributed systems since these systems feature asynchronous concurrent activities, nondeterministic behavior and lack central control, precise global time and accurate global state. We emphasize the data collection, analysis and presentation of execution data. A special hardware support, which consists of a test and measurement processor (TMP), was designed and has been implemented in the nodes of an experimental multicomputer system consisting of eleven nodes. The operations of the TMP are completely transparent with a minimal, less than 0.1 percent, overhead to the measured system. In the experimental system, all the TMP's were connected with a central monitoring station, using an in- dependent communication network, in order to provide a global view of the monitored system. The central monitoring station displays the resulting information in easy-to-read charts and graphs. Our experience with the TMP shows that it promotes an improved understanding of run-time behavior and performance measurements, to derive qualitative and quantitative assessments of distributed systems. [ABSTRACT FROM AUTHOR]

A radial plot is a graphical display for comparing estimates that have differing precisions It is a scatter plot of standardized estimates against reciprocals of standard errors, possibly with respect to a transformed scale, designed so that the original estimates can be compared and interpreted The estimates may be means, regression coefficients, proportions, rates, odds ratios, random effects, or indeed any parameter estimates that merit comparison between individuals or groups This article illustrates some uses of radial plots by discussing a variety of data examples taken from the literature, The statistical application areas include interlaboratory trials, point process event rates, empirical Bayes estimation, modeling of counting data, analysis of overdispersed and underdispersed binomial and Poisson data, mixture modeling and meta-analysis. [ABSTRACT FROM AUTHOR]

Correspondence analysis is an exploratory multivariate technique that converts a data matrix into a particular type of graphical display in which the rows and columns are depicted as points. The method has a long and varied history and has appeared in different forms in the psychometric and ecological literature, among others. In this article we review the geometry of correspondence analysis and its geometric interpretation. We also discuss various extensions of correspondence analysis to multivariate categorical data (multiple correspondence analysis) and a variety of other data types. [ABSTRACT FROM AUTHOR]

This article reports three experiments that examined the effects of photographic method, computerized visualization scheme, and posture complexity on posture perception and specification for computer-aided ergonomic analysis. The subjects were presented with photographs of working postures, and were required to manipulate human forms generated by an ergonomics software program to match the postures in the photographs. The first experiment showed a clear advantage of using a three-dimensional (3-D) human form graphic with two photographs when complex, asymmetric postures were analysed. However, the use of a 3-D human graphic display and two photographs jeopardized the subjects' posture specification performance when simple, symmetric postures were analysed. The results of the second and the third experiment demonstrated the importance of achieving congruency between photographic and computer display perspectives in improving posture specification performance. Implications for ergonomic job analysis and ergonomics software design are discussed. [ABSTRACT FROM AUTHOR]

A method of plotting data of more than two dimensions is used to analyze a paleobiogeographical problem relating to the lateral movement of continents. The method is suitable for the small samples common in paleontology; it seems to be effective in extracting more information of paleobiological and stratigraphical significance from the data than the usual graphical procedures of multivariate analysis. Brachycythere sapucariensis is a Lower Turonian (Cretaceous) species of ostracod found in the coastal sedimentary basins of Brazil (Sergipe), Gabon, and Nigeria. If the present positions of South America and Africa had been the same in the Cretaceous as today, one would expect the Nigerian and Gabonese individuals to be more alike than either of the African occurrences would be to the Brazilian ones. If, however, the drift interpretation of the origin of the South Atlantic Ocean is correct, Gabonese and Brazilian individuals should be more similar than either of these groups with Nigerian individuals. The results obtained do not disagree with the latter alternative. [ABSTRACT FROM AUTHOR]

The multipath effect is a crucial error source caused by the environment around the station and cannot be eliminated or mitigated by differential algorithms. Theoretically, the maximum value for the carrier phase is a quarter the wavelength, i.e., about 4.8 cm for the GPS L1 signal. Considering the increasing demands of high-precision applications, the multipath error has become a major factor affecting the accuracy and reliability of GPS millimeter-level data processing. This paper proposes a multi-point hemispherical grid model (MHGM) to mitigate the multipath effect. In this method, the hemisphere centered on each station is divided into a grid, and the multipath error at the station is estimated based on the parameterization of the grid points. The double-differenced (DD) observed-minus-calculated (OMC) values on some previous days are treated as the observation values to model the present multipath error. Contrary to the present methods which rely much on the platform of data collection and processing, MHGM can be potentially applied to GPS data processing with the existing hardware and software. Experiments in high-multipath and low-multipath environments are designed by mounting a baffle or not. The experimental results show that MHGM is effective in mitigating the multipath effect. When using data from the previous day, an average improvement of about 63.3% in the RMS of DD OMC can be made compared with that without correction, and this is basically consistent with the sidereal filtering (SF) method which is 63.0%. Furthermore, the effectiveness of the above two methods is better than that of the empirical site model (ESM). The kinematic positioning results are also basically consistent with the statistical results of the RMS values of DD OMC. Historical data from more than one day can more explicitly and effectively model the MHGM. Furthermore, compared with the SF method, the MHGM can be used not only to mitigate the multipath error, but also to orientate the sources of the multipath error around the station, and give guidance in the physical elimination of these sources. [ABSTRACT FROM AUTHOR]

Background and Objective: To introduce potential static tabular and graphical techniques for visually presenting overlap between systematic reviews (SRs) included in overviews of systematic reviews (OoSRs)., Methods: The graphical approaches described include Venn and Euler diagrams, as well as matrix-based, node-link, and aggregation-based techniques. We used fundamental concepts of mathematics from set and network theory to develop our novel graphical approaches. The graphical displays were created using R., Results: Overview authors have the flexibility to choose from a variety of visualizations, depending on the characteristics of their study. If the OoSRs include few SRs, a Venn or an Euler diagram can be used. In case of OoSRs with more SRs, Upset plots, heatmaps, and node-link graphs are more appropriate for visualizing overlapping SRs. Stacked bar plots constitute an aggregation-based technique of illustrating overlap. Strengths and limitations of each graphical approach are presented., Conclusion: The degree of overlap should be explored for the entire study and for specific outcomes of interest. The proposed graphical techniques may assist methodologists and authors in identifying overlap, which in turn may improve validity and transparency in OoSRs. More research is needed to understand which technique would be most useful and easiest to understand., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: The patient monitor (PM) is one of the most commonly used medical devices in hospitals worldwide. PMs are used to monitor patients' vital signs in a wide variety of patient care settings, especially in critical care settings, such as intensive care units. An interesting observation is that the design of PMs has not significantly changed over the past 2 decades, with the layout and structure of PMs more or less unchanged, with incremental changes in design being made rather than transformational changes. Thus, we believe it well-timed to review the design of novel PM interfaces, with particular reference to usability and human factors., Objective: This paper aims to review innovations in PM design proposed by researchers and explore how clinicians responded to these design changes., Methods: A literature search of relevant databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, identified 16 related studies. A detailed description of the interface design and an analysis of each novel PM were carried out, including a detailed analysis of the structure of the different user interfaces, to inform future PM design. The test methodologies used to evaluate the different designs are also presented., Results: Most of the studies included in this review identified some level of improvement in the clinician's performance when using a novel display in comparison with the traditional PM. For instance, from the 16 reviewed studies, 12 studies identified an improvement in the detection and response times, and 10 studies identified an improvement in the accuracy or treatment efficiency. This indicates that novel displays have the potential to improve the clinical performance of nurses and doctors. However, the outcomes of some of these studies are weakened because of methodological deficiencies. These deficiencies are discussed in detail in this study., Conclusions: More careful study design is warranted to investigate the user experience and usability of future novel PMs for real time vital sign monitoring, to establish whether or not they could be used successfully in critical care. A series of recommendations on how future novel PM designs and evaluations can be enhanced are provided., (©Evismar Andrade, Leo Quinlan, Richard Harte, Dara Byrne, Enda Fallon, Martina Kelly, Siobhan Casey, Frank Kirrane, Paul O'Connor, Denis O'Hora, Michael Scully, John Laffey, Patrick Pladys, Alain Beuchée, Gearóid ÓLaighin. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 03.07.2020.)