Types Of Charts Research Articles

The effectiveness of "ILU" massage in infants as a complementary therapy reduce constipation in the Public Health Center

Abstract
 Background: Giving MP-ASI is one of the supporting factors for growth and development, but nutritional intake is one of the factors that affect constipation. One alternative to the use of non-pharmacological therapy, which is administration to reduce constipation, is the "I L U" massage.
 Research methods : This research is a descriptive type of research, 5 babies who experience constipation were studied for 2 weeks using the bristol stool chart and Rome IV criteria instruments before and after giving the "I L U" massage.
 Results and Discussion : Babies who experience constipation are given "I L U" massage for 2 weeks with massage every 1 week 3 times which is observed based on the bristol stool chart and rome IV. Before the massage, the Bristol stool chart was 1-2 while Rome IV was 2-3 which indicates constipation. After 1 week after the massage, there was an increase in the Bristol stool chart and a decrease in Rome IV, and even 2 weeks after the “I L U” massage, the Bristol stool chart was 2,3,4 and Rome IV 0, indicating reduced constipation.
 Conclusion : There is an effect of giving "I L U" massage for 2 weeks on a decrease in the type of Bristol stool chart and a decrease in the value of Rome IV.
 Keywords: Complementary,Massage "I L U", constipation

How to visualize electricity consumption anomalies: The impact of chart types on triggered emotions and eye movements

Home Energy Management Systems (HEMS) show anomalies and unusual patterns of the electricity consumption of households, usually in the form of charts. Charts that show anomalies are very difficult to design in a way that is both comprehensible and visually appealing to users. However, visually appealing charts direct the users’ gaze to the central chart elements and thus trigger positive emotions, which in turn increase the intention to use HEMS. Therefore, this study focuses on the emotions and eye movements that different chart types showing anomalous electricity consumption trigger. An experimental eye-tracking and facial expression study was carried out with 50 participants that were randomly assigned to one of three commonly used chart types (bar, line or rose chart). The participants had to complete three search tasks during which the eye movements and the facial expressions were observed. The results show that different emotions are triggered depending on the chart type. Participants showed the highest number of sad and contempt facial expressions while viewing bar charts. Line charts triggered the most positive and, overall, the least negative facial expressions, followed by the rose charts. Bar charts triggered the most negative emotions among all three chart types. Although most eye-tracking metrics in this study differed by chart type, most results did not show significant differences. However, the later participants saw the area in demand in the rose charts, the sadder facial expressions occurred. HEMS providers can use these findings to design emotionally appealing charts for potential users, which in turn can increase their motivation to use these systems.

Building color palettes in your data visualization style guides.

Data visualization style guides are standards for formatting and designing representations of information, like charts, graphs, tables, and diagrams. To assist researchers communicate their visual content in better and more effective ways, this article accomplishes two tasks. First, we take a detailed look at a data visualization style guide and its components-what it is and what it should include. Second, we create a detailed template for the color section of a data visualization style guide. Creating a data visualization style guide as described here should help researchers across multiple disciplines create better and more consistent charts, graphs, and diagrams. Such style guides are useful for individuals and organizations in their efforts to be more efficient and consistent in their data communication products. Data visualization style guides often include explaining the what (eg, types of charts), the why (eg, reasons for using specific colors), and the how (eg, tools or templates) of creating consistent and effective visuals that can also fit within an individual's or organization's larger design system. We use a variety of tools to create, test, and implement a data visualization color palette. We provide sample color palettes and provide step-by-step instructions on how to import those palettes into six popular data visualization tools: Microsoft Excel and PowerPoint, Tableau, PowerBI, and Adobe Illustrator and InDesign.

WYTIWYR: A User Intent‐Aware Framework with Multi‐modal Inputs for Visualization Retrieval

AbstractRetrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations. The retrieved results are expected to conform to both explicit visual attributes (e.g., chart type, colormap) and implicit user intents (e.g., design style, context information) that vary upon application scenarios. However, existing example‐based chart retrieval methods are built upon non‐decoupled and low‐level visual features that are hard to interpret, while definition‐based ones are constrained to pre‐defined attributes that are hard to extend. In this work, we propose a new framework, namely WYTIWYR (What‐You‐Think‐Is‐What‐You‐Retrieve), that integrates user intents into the chart retrieval process. The framework consists of two stages: first, the Annotation stage disentangles the visual attributes within the query chart; and second, the Retrieval stage embeds the user's intent with customized text prompt as well as bitmap query chart, to recall targeted retrieval result. We develop aprototype WYTIWYR system leveraging a contrastive language‐image pre‐training (CLIP) model to achieve zero‐shot classification as well as multi‐modal input encoding, and test the prototype on a large corpus with charts crawled from the Internet. Quantitative experiments, case studies, and qualitative interviews are conducted. The results demonstrate the usability and effectiveness of our proposed framework.

Reevaluating the performance of control charts based on ranked-set sampling

Nearly one hundred types of control charts have been proposed that incorporate ranked-set sampling (RSS) methods. The performance of these charts has been evaluated with comparisons to existing charts based on simple random sampling. The reduction of the standard error in estimating the parameter being monitored with RSS leads to uniformly better average run length performance. We show, however, that these performance comparisons can be very misleading once the sampling strategy over time is considered more carefully with the benefits of RSS being considerably overstated. We consider the most basic RSS method when monitoring the mean of the process, but the approach can be applied to evaluate other RSS monitoring methods.

Design of adaptive EWMA control charts using the conditional false alarm rate

AbstractDynamic control limits can be useful in designing control charts, especially when sample sizes, risk scores, or other covariate values change over time. Computer simulation can be used to control the conditional false alarm rate and thus the in‐control run length properties. We show that this approach can be useful in designing adaptive exponentially weighted moving average (AEWMA) control charts for which the control chart smoothing parameter at a given time point depends on the observed value at that time point. We use AEWMA charts as examples, but the approach can be applied to the adaptive cumulative sum (CUSUM) chart and other types of adaptive charts.

ON THE COMPARATIVE EFFICIENCY OF CHANGE POINT DETECTION IN MULTIVARIATE TECHNOLOGICAL PROCESSES USING MULTIDIMENSIONAL DOUBLE CONTROL CHARTS

The problem of change point detection in multiparametric technological processes having a normal distribution and consisting in a shift from a given value of the sample mean and sample variance is investigated. Various types of control charts are considered, which make it possible to effectively detect simultaneous changes in the mean value and variance in multiparametric technological processes. By the method of statistical modeling, an analysis of the comparative effectiveness of control charts is carried out, practical recommendations are given.

On efficient change point detection using a step cumulative sum control chart

Control charts are widely used to monitor the stability of processes and Shewhart is the most commonly used type of chart because of the simplicity of its structure. To improve the performance, there are several modifications on control chart suggested since its proposal like cumulative sum and exponentially weighted moving average control charts. The cumulative sum chart accumulates all the deviations from target mean higher than a specific design parameter which can be used to controls the sensitivity of this chart. The optimal choice of this design parameter is half of the target shift size. This results into an optimal performance in terms of quick detection of shift that is equal to the target shift size. For shift sizes other than target, the cumulative sum chart loses its performance and becomes inferior to other control charts in terms of average run length. Since we rarely know the process shift size that is going to occur in future, this study proposes a revision in the structure of cumulative sum chart. The adjusted cumulative sum chart is shown to be very flexible for a wide range of shifts, when designed against a target shift size. The proposed adjusted cumulative sum control chart is compared with cumulative sum, exponentially weighted, combined Shewhart-CUSUM, progressive mean, double progressive mean and mixed EWMA-CUSUM control charts in terms of zero- and steady-state average run lengths. The superiority zones are identified and the results are supported by a real-life implementation of the proposed scheme.

Graph-Based Information Block Detection in Infographic With Gestalt Organization Principles.

An infographic is a type of visualization chart that displays pieces of information through information blocks. Existing information block detection work utilizes spatial proximity to group elements into several information blocks. However, prior studies ignore the chromatic and structural features of the infographic, resulting in incorrect omissions when detecting information blocks. To alleviate this kind of error, we use a scene graph to represent an infographic and propose a graph-based information block detection model to group elements based on Gestalt Organization Principles (spatial proximity, chromatic similarity, and structural similarity principle). We also construct a new dataset for information block detection. Quantitative and qualitative experiments show that our model can detect the information blocks in the infographic more effectively compared with the spatial proximity-based method.

Adjustment of Measurement Error Effects on Dispersion Control Chart with Distribution-Free Quality Variable

In industrial processes, control charts are useful tools to monitor the quality of products and detect possibly out-of-control processes. While many types of control charts have been available for data analysts, they were developed by assuming that the variables are precisely measured. In applications, however, measurement error is ubiquitous when data are falsely recorded by investigators or imprecisely collected by unadjusted machines. Even though the impacts of measurement error for different types of control charts have been explored, error-corrected control charts are still unavailable. In this study, we propose a new dispersion control chart with error correction to fill out this research gap. Our key idea is to convert the observed distribution-free process variables into a flexible sign statistic, and then adopt a function to adjust the measurement error effects on the sign statistic. Finally, we develop an exponentially weight-moving average dispersion control chart with measurement error correction based on the corrected sign statistic. The proposed error-corrected dispersion control chart not only eliminates measurement error effects, but also provides more reliable control limits for monitoring process dispersion. Throughout the numerical examination, we find that the proposed error-corrected dispersion control chart is effective in handling moderate and large levels of measurement error and shows good out-of-control detection performance. Finally, the proposed error-corrected dispersion control chart is implemented in the semiconductor data.

Kidney Biopsy and Type IV Collagen Gene Sequencing Fail to Explain Hematuria in Loin Pain Hematuria Syndrome

Loin pain hematuria syndrome (LPHS) is a rare clinical syndrome with a reported prevalence of 1 in 10,000. The syndrome is characterized by severe pain localized to the kidney in the absence of identifiable urinary tract disease. Because of an inadequate understanding of the pathophysiology of the disease, the goal of management has been limited to symptomatic pain management. Through detailed phenotype and genotype assessment we sought to identify possible underlying etiologies. We completed a chart review, ultrasound imaging, kidney biopsy, and type IV collagen (COL4A3, COL4A4, and COL4A5) gene sequencing in 14 patients with loin pain hematuria recruited from a single center. Red blood cells and red cell casts were observed within the tubules in 10 of 14 patients. The glomerular basement membrane (GBM) was normal in 11 patients and thickened in 1 patient. Staining for IgA kappa was present in 1 patient. C3 deposition without any inflammation was present in 7 patients. Arteriolar hyalinosis was present in 4 patients and endothelial cell injury was present in 6 patients. No pathogenic COL4A3, COL4A4, or COL4A5 variants were identified. Conventional histopathology and genetic testing for type IV collagen variants failed to identify the cause of hematuria in 14 patients with LPHS.

Eye-tracking detection of the area of interest in data visualization

This study examines the features of forming in data visualization. To do this, the authors hypothesize that there are special areas of interest on the charts. The user pays attention to them in order to decode data encrypted with graphics. The presence of such areas, and in some cases, points, are intuitively determined during the formation of the design rules of information graphics. To verify them, the study used the eye-tracking method and the cluster analysis method. The application of interdisciplinary principles and rules of infographics design has been studied on the example of horizontal and vertical bar charts, pie, pictorial and flow charts. The result of experiments with various types of charts showed the presence of additional areas of interest not previously indicated by data visualization specialists. This makes it possible to clarify the features of graphic forms and the formation of diagrams, allows you to verify the use of design rules formulated by the efforts of domestic and foreign specialists in the late XIX — early XX centuries. Such verification can be done using a methodology that includes statistical methods and the tracking method, which allows us to take into account the valuable experience of the past in modern information design. The presented procedure can be extended to other types of charts, diagrams and thematic maps, and have practical application in the analysis of big data visualization.

A review and comparison of control charts for ordinal samples

Qualitative, more specifically, ordinal data generating processes are common in real-world process control implementations. In this study, a survey of control charts for the sample-based monitoring of independent and identically distributed ordinal data is provided together with critical comparisons of the control statistics, for memory-less Shewhart-type and for memory-utilizing exponentially weighted moving average (EWMA) and cumulative-sum types of control charts. New results and proposals are also provided for process monitoring. Using some real-world quality scenarios from the literature, a simulation study for performance comparisons is conducted, covering sixteen different types of control chart. It is shown that demerit-type charts used in combination with EWMA smoothing generally perform better than the other charts, which may rely on quite sophisticated derivations. A real-world data example for monitoring flashes in electric toothbrush manufacturing is discussed to illustrate the application and interpretation of the control charts in the study.

RETRACTED: A Perception Study for Unit Charts in the Context of Large-Magnitude Data Representation

Unit charts are a common type of chart for visualizing scientific data. A unit chart is a chart used to communicate quantities of things by making the number of symbols on the chart proportional to the number of items represented. An accurate perception of the order of magnitude is essential to evaluating whether a unit chart can effectively convey information. Previous studies have primarily focused on perceptual properties at small order-of-magnitude scales or the efficacy of pictographs in unit charts. However, few researchers have explored the perceptual effectiveness of unit charts when representing large orders of magnitude. In this study, we performed a series of sampling measurements to investigate the visual–perceptual characteristics of unit charts when representing asymmetric interactions such as large-scale numbers. The results showed that under the restriction of the current conventional display medium, unit charts still offer a significant advantage over bar charts in a single-scale visual overview. However, this comes at the cost of a longer response time. Although this study constitutes basic research, accumulating evidence about how people reason about magnitudes beyond human perception is critical to the field of information science. This study may contribute to understanding how viewers perceive unit charts and the factors that influence graphical perception. This article provides some specific guidelines for designing unit charts that may be useful to visualization designers.

Design Characterization for Black-and-White Textures in Visualization.

We investigate the use of 2D black-and-white textures for the visualization of categorical data and contribute a summary of texture attributes, and the results of three experiments that elicited design strategies as well as aesthetic and effectiveness measures. Black-and-white textures are useful, for instance, as a visual channel for categorical data on low-color displays, in 2D/3D print, to achieve the aesthetic of historic visualizations, or to retain the color hue channel for other visual mappings. We specifically study how to use what we call geometric and iconic textures. Geometric textures use patterns of repeated abstract geometric shapes, while iconic textures use repeated icons that may stand for data categories. We parameterized both types of textures and developed a tool for designers to create textures on simple charts by adjusting texture parameters. 30 visualization experts used our tool and designed 66 textured bar charts, pie charts, and maps. We then had 150 participants rate these designs for aesthetics. Finally, with the top-rated geometric and iconic textures, our perceptual assessment experiment with 150 participants revealed that textured charts perform about equally well as non-textured charts, and that there are some differences depending on the type of chart.

Medication Transcription Errors at a Tertiary Healthcare Facility in Uva Province, Sri Lanka: A Retrospective study.

Medication transcription is an error-prone process in healthcare settings with paper-based documentation. However, it is often preventable. In Sri Lanka, a uniform medication chart is not currently in use. To describe transcription errors with the aim of designing a standardized drug chart to minimize the transcription errors at a tertiary care facility in Uva province, Sri Lanka. This cross-sectional study was conducted in selected units at Provincial General Hospital, Badulla. All discharged patients after a minimum of 72 hours hospital stay were included. The drug charts of bedhead tickets were scrutinized for transcription errors. At the time of study, four types of charts were in use. In total 272 drug charts, the median number of drugs was 9 (Interquartile range - IQR 6-12). Median length of patient's stay was 4 days (IQR 3-6). We encountered at least one transcription error of medication details in 88.6% charts. Amongst, medication name transcription error was the most common (220, 80.9%) followed by route (114, 41.9%) and frequency errors (70, 25.7%). During transcribing drug names, majority of charts had spelling errors (203, 74.6%). Although there was a statistically significant association between number of prescribed drugs and presence of at least one medication transcription error (p<.001), there was no significant association to number of days of patient stay (p=.99). The selected center has a significantly high prevalence of medication transcription errors. Hence, introducing a uniform medication administration chart is encouraged to minimize the opportunities for adverse patient outcomes.

Situating #MeToo. A Constructivist Perspective on Designing With Data

This document introduces the various questions, blockades, and off-centre paths encountered in researching and designing a project around tweets with hashtags related to the MeToo Movement. What the paper offers is a perspective on the process of designing with data from a constructivist perspective. Rather than asking what representation fits the data, we reverse the question on two levels. First, the data: we took authorship by selecting the data, and by making choices away from the algorithmic and the superlative. And second on the level of visualisation. Rather than finding the right chart type for the data, we ask what we want the data to be imagined as.

Time and magnitude monitoring using Weibull based Max-EWMA chart

To monitor time and magnitude, different types of control charts have been proposed in the literature. This article proposes a new maximum exponentially weighted moving average (Max-EWMA) control chart by assuming Weibull distribution. The test-statistic of the Max-EWMA chart consists of two EWMA statistics, where one statistic is used for monitoring magnitude while the other to monitor time interval between events. The performances of the proposed chart is assessed on the basis of run length characteristics like, average run length (ARL), standard deviation of the run length (SDRL), and quantiles of the run length distribution. Besides extensive simulation studies, real data sets are also considered to show the applications of the proposed chart. The results show that the proposed chart is efficient in detecting different size of small shifts.

Comparing the performance of Kernel PCA Mix Chart with PCA Mix Chart for monitoring mixed quality characteristics

Along with the development of information and technology, the quality characteristics of a product cannot be monitored separately in the different types of control charts. In the past, conventional control charts were developed to monitor only one type of quality characteristic. The variable control charts are used to observe the variable or metric quality characteristics. Meanwhile, in monitoring non-metric characteristics or categorical data, attribute control charts are employed. To accommodate these two types of data, the PCA Mix control chart is suggested to simultaneously monitor these two types of data in one chart. However, some drawbacks occur when this chart is applied to monitor non-metric data which has an imbalanced proportion. Therefore, the Kernel PCA Mix control chart is created to overcome the gaps that occurred in the PCA Mix chart. Similar to the previous chart, this chart is also constructed using Hotelling’s T2 statistics with Kernel Density Estimation control limit. Several simulations are used to evaluate the performance of the proposed control charts. The simulation results show that the proposed chart has a better result than the previous control chart, especially for a small mean shift with an imbalanced proportion of non-metric data. However, the PCA Mix chart has a similar performance to the proposed chart when it is applied to monitor the balanced proportion of categorical data with a large mean shift. The application with simulated data with various scenarios and the real-world case also shows that the Kernel PCA Mix chart performs better compared to the performance of the PCA Mix chart.

The best tool for the assessment of developmental disorders in children with down syndrome: comparison of standard and specialized growth charts - cross sectional study.

Down Syndrome (DS) is a chromosomal abnormality associated with a spectrum of cognitive and physical disabilities. Children with DS are exposed to both lower and excess body weight and follow distinct growth-curve patterns that deviate significantly from those of children without chromosomal defects. Anthropometric parameters are assessed in the pediatric population with the use of growth charts. The study is based on data from 411 children and adults with DS from Poland. Detailed information concerning children and online survey results were also analyzed. Centiles and standard deviation scores (SDS) of obtained anthropometric parameters were aligned with the data using the LMS method. The study aims to identify which type of growth chart (standard vs specialized) is a leading tool for earlier detection of developmental disorders in DS. The results obtained in the two types of growth charts differed. The advantage of the specialized growth charts over the standard ones cannot be unequivocally determined. Only the combination of both tools allows to detect the development disorders early in the broadest possible way.