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The histogram remains a widely used tool for visualization of the distribution of a continuous variable, despite the disruption of binning the underlying continuity into somewhat arbitrarily sized discrete intervals imposed by the simplicity of its pre-computer origins. Alternatives include three visualizations, namely a smoothed density distribution such as a violin plot, a box plot, and the direct visualization of the individual data values as a one-dimensional scatter plot. To promote ease of use, the plotting function discussed in this work, Plot(x), automatically integrates these three visualizations of a continuous variable x into what is called a VBS plot here, tuning the resulting plot to the sample size and discreteness of the data. This integration complements the information derived from the histogram well and more easily generalizes to a multi-panel presentation at each level of a second categorical variable.

The histogram remains a widely used tool for visualization of the distribution of a continuous variable, despite the disruption of binning the underlying continuity into somewhat arbitrarily sized discrete intervals imposed by the simplicity of its pre-computer origins. Alternatives include three visualizations, namely a smoothed density distribution such as a violin plot, a box plot, and the direct visualization of the individual data values as a one-dimensional scatter plot. To promote ease of use, the plotting function discussed in this work, Plot(x), automatically integrates these three visualizations of a continuous variable x into what is called a VBS plot here, tuning the resulting plot to the sample size and discreteness of the data. This integration complements the information derived from the histogram well and more easily generalizes to a multi-panel presentation at each level of a second categorical variable. [ABSTRACT FROM AUTHOR]

The study of groundwater samples from Patna, which is in the middle Ganga River basin in the state of Bihar, was done to evaluate the hydrogeochemical nature of groundwater and figure out the controlling factors that affect its ionic chemistry. This study's groundwater samples were taken seasonally from 20 Patna, Bihar locations from 2019 to 2020 and analyzed for their physical and chemical properties using APHA's standard methods. Multivariate statistical techniques, such as Correlation matrix, Principal Component Analysis and Hierarchical cluster analysis, were carried out on the analytical variables, followed by interpreting the groundwater's geochemical nature using Durov Plot, Piper plot, ion-scatter plots and Gibb's diagram. The results obtained were compared with water quality standards prescribed by the Bureau of Indian Standards. The study revealed that groundwater's alkaline nature and water's total hardness varied from hard to very hard categories. Total dissolved solids range from 158 to 575 mg/l, where 31.66% of samples have exceeded the desirable TDS limit for drinking water. The abundance of anions was observed in decreasing order (HCO3− > Cl− > SO42−) during all seasons and cations as Ca2+ > Na+ > Mg2+ > K+ in pre-monsoon and post-monsoon, whereas in monsoon season, it was observed as Mg2+ > Na+ > Ca2+ > K+. Ca2+–Mg2+–HCO3− type water was revealed as the significant hydrogeochemical facie, and the reverse ion exchange process was observed to be the governing factor in 77% of the total groundwater samples. Gibbs's diagram showed rock–water dominance as a major factor in groundwater ionic composition during all seasons. [ABSTRACT FROM AUTHOR]

Objective To explore the application value and advantages of combining electrocardiographic waterfall plot with scatter plot in diagnosing atrial arrhythmias. Methods The characteristics of changes in the electrocardiographic waterfall plot and scatter plot of typical atrial arrhythmias in ambulatory electrocardiography (AECG) were analyzed, and their application advantages were summarized. Results The electrocardiographic waterfall and scatter plots of different types of atrial arrhythmias were manifested by different characteristics. (ⅰ) Frequent atrial premature beats. The waterfall plot was characterized by intermittent P-peak band breaks while R- and T-peak bands remained unchanged; the scatter plot was featured by premature beat points located in the fast acceleration zone. (ⅱ) Atrial autonomic rhythm. The waterfall plot was characterized by a break in the P-peak band and changes of PR interval; the scatter plot feature was that this rhythm could be integrated into sinus rhythm cluster which was difficult to be distinguished from sinus rhythm. (ⅲ) Intermittent short paroxysmal atrial tachycardia. The waterfall plot was characterized by a P-peak band rupture and appearance of two or more R-peak bands; the feature of the scatter plot was that the cluster point could be located in the fast acceleration zone or on the 45° line, and its heart rate range could be distinguished from sinus heart rate, but the onset time can not be determined. ( ⅳ) Persistent atrial fibrillation. The waterfall plot was characterized by the disappearance and disorder of P-peak band while R- and T-peak bands remained unchanged; the scatter plot was featured by a fan shape. (ⅴ) Atrial flutter. The waterfall plot was characterized by a P-peak band fault, with the appearance of two or more P-peak bands and corresponding PR intervals; the scatter plot was featured by a grid-like pattern. Conclusion The type of atrial arrhythmias could be accurately and quickly determined by selecting the leads with significant changes of P-QRS-T complex in AECG, and observing the characteristics of P-, QRS- and T-peak band changes in the electrocardiographic waterfall plot combined with changes of ECG scatter plot. [ABSTRACT FROM AUTHOR]

The article presents approaches to monitoring and assessing social aspects of regional systems with identification of elements of the health system and demographic components that potentially affect the economic efficiency of functioning of territories. The relevance of the article is determined by objective features of the development of mesoterritories. The analysis of spatial features of the systems’ functioning was conducted on the materials of the Central Federal District. The purpose of the study is to assess the features of the regional genesis using regression analysis and the resulting indicator – gross regional product (hereinafter referred to as GRP), which allows us to give a more reliable characterisation of the mesoterritorial transformation. The subjects were grouped according to the quality of medical and demographic characteristics on the basis of the cluster analysis with further allocation of flagship, catching-up and lagging regions. To substantiate the reliability of the territories’ affiliation to a specific cluster group, a statistical analysis was performed on average values with consideration to identification of the level of medical and demographic development. The conclusion is made about the influence of some factor criteria on the regional space with the construction of a scatter plot. It displays the affiliation to the selected confidence interval and distribution of the elements of the set in the plane between the resulting and factor values, describing the dependence of the GRP on the medical and demographic indicator. These econometric and analytical models can be used by regional governments in the process of strategic planning.

This study aims to develop a fault detection system designed specifically for wind turbine gearboxes. It proposes a hybrid fault diagnosis algorithm that combines scatter plot analysis with the visual geometric group (VGG) technique to identify various fault types, including gear rust, chipping, wear, and aging. To capture vibration signals, a three-axis vibration sensor was integrated with a NI-9234 DAQ card. Digital signal processing techniques were employed to actively filter out noise from the captured signals. Gaussian white noise was incorporated into the training data to enhance the noise resistance of the network model, which was then utilized for scatter plot generation. The VGG technique was subsequently applied to identify faults. The testing data were collected at two different speeds, with 1500 samples taken at each speed, totaling 3000 samples. For both training and testing, 400 samples of each fault type were employed for training, while 200 samples were allocated for testing. The test results demonstrated an overall identification accuracy of 97.7% for both the no-fault gearbox and the four-fault states, underscoring the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This study assessed hourly electricity consumption profiles in railway signal boxes located in Poland. The analyses carried out consisted of assessing the correlation among the hourly demand profile, weather indicators, and calendar indicators, e.g., temperature, cloud cover, day of the week, and month. The analysis allowed us to assess which indicator impacts the energy consumption profile and would be useful when forecasting energy demand. In total, 15 railway signal boxes were selected for analysis and grouped according to three characteristic repeatability profiles. On this basis, six of the signal boxes and one that did not fit into any of the groups were selected for further analysis. Four correlation research methods were selected for analysis: Pearson's method, Spearman's method, scatter plots, and distance covariance. The possibility of forecasting electricity consumption based on previously aggregated profiles and determining correlations with indicators was presented. The given indicators vary depending on the facility. Analyses showed different dependencies of the electricity demand profile. The ambient temperature and time of day have the greatest impact on the profile. Regarding the correlation with temperature, the results of the Pearson's and Spearman's coefficients ranged from approximately −0.4 to more than −0.8. The highest correlation coefficients were obtained when comparing the demand profile with the previous day. In this case, the Pearson's and Spearman's coefficients for all analysed objects range from approximately 0.7 to over 0.9. [ABSTRACT FROM AUTHOR]

Coffee is an established plant for its flavor and has high commercial use. In Nepal, the popularity of coffee is increasing for its high economic value. However, its diversity and the status of its genetic mapping have not been studied in Nepal. In the present study, the genetic diversity of 28 coffee accessions was assessed by using twenty-four SSR markers with the aim of studying the variation of coffee in accord with the genetic markers from a molecular approach. With the use of DNA extraction and marker selection for its amplification using PCR tools, a total of 81 loci from SSR were identified. Of all SSR 63.22% showed for mean polymorphism. The mean polymorphic information content of SSR was 0.38, which showed low genetic diversity of SSR markers among Coffea genotypes. On the basis of the SSR marker, the unweighted pair group method with arithmetic mean (UPGMA) dendrogram constructed showed a similar group of distribution among 28 accessions, which was further supported by a principle coordinate analysis scatter plot. The phylogenetic relationships among the accessions were assessed by SSR marker, which also showed low diversity in coffee genotypes. Our study demonstrated the use of SSR markers in diversity analysis as the data were informative and highly reproducible for evaluating relationships among coffee cultivars in Nepal. The use of more markers systems and a high genotype pool would have been beneficial in accessing more accurately. Regardless, the information from the phylogenetic relationship study could be useful for breeding, varietal improvement, and for conservation programs. [ABSTRACT FROM AUTHOR]

AbstractIn a complex navigation environment, it is very important to solve the problems of multiple constraints and complex calculations in the route planning process of multiple reconnaissance unmanned aerial vehicles (UAVs) to improve the flight reconnaissance effect, and the bat algorithm (BA) with simple parameters has a certain effect on it. Aiming at the problems of unbalanced global optimization and local optimization and slow convergence speed in the later iteration of the BA in the path planning of multi-reconnaissance UAVs, a Levy-based flight search strategy and an improved bat algorithm (IBA) with changing speed are designed. In order to solve the multi-UAV route planning problem, the 3D space is reduced to a divided two-dimensional space based on the “scatter plot” method, and the spatial encoding method is used. Finally, it is proved that the IBA is theoretically more concise and effective than other algorithms.

Vertical vegetation differentiation is the most important form of spatial pattern in mountainous areas. It is of great significance to accurately divide vegetation into vertical zones for the study of mountain ecosystems and ecological protection. In order to accurately divide the vertical zone of mountain vegetation and determine the spatial distribution of mountain vegetation, the relationship between the vegetation index of various vegetation types and altitude was examined using remote sensing and geographic information technology. Taking Taibai Mountain, the main peak of the Qinling Mountains in China, as the study area, based on the difference in NDVI between summer and autumn (DNSA), this work constructed a DEM-NDVI scatter plot and quantified the boundary of the vertical zone by the half-peak width calculation method. The findings showed that: (1) the vertical distribution pattern of mountain vegetation may very well be reflected in the scatterplot that NDSA and DEM created; (2) Six vertical belts could be accurately identified to the meter level on Taibai Mountain's south slope. Up to the altitude, the oak forest zone from the bottom of the mountain to the elevation of 1919 m, the pine-oak mixed forest zone is distributed in 1919–2331 m, the birch forest is distributed in 2115–2585 m, the fir forest is distributed in 2516–3150 m, the redwood forest is distributed in 3109–3551 m, and the alpine scrub meadow is distributed in 3551 m to the peak. On the north slope, 1053–2087 m above sea level is oak forest, 2087–2693 is birch forest, 2562–3006 is fir forest, 2987–3513 m is redwood forest, and 3513 to the top of the mountain is alpine scrub meadow; and (3) the distribution pattern of the vegetation vertical belt on the DEM-NDVI scatter plot was essentially compatible with the vegetation classification results derived from remote sensing images. The DEM-NDVI scatter plot can reflect the average distribution of vegetation population and can more accurately express the characteristics of vegetation vertical zone changes with altitude. [ABSTRACT FROM AUTHOR]

A probabilistic neural network has been implemented to predict the malignancy of breast cancer cells, based on a data set, the features of which are used for the formulation and training of a model for a binary classification problem. The focus is placed on considerations when building the model, in order to achieve not only accuracy but also a safe quantification of the expected uncertainty of the calculated network parameters and the medical prognosis. The source code is included to make the results reproducible, also in accordance with the latest trending in machine learning research, named Papers with Code. The various steps taken for the code development are introduced in detail but also the results are visually displayed and critically analyzed also in the sense of explainable artificial intelligence. In statistical-classification problems, the decision boundary is the region of the problem space in which the classification label of the classifier is ambiguous. Problem aspects and model parameters which influence the decision boundary are a special aspect of practical investigation considered in this work. Classification results issued by technically transparent machine learning software can inspire more confidence, as regards their trustworthiness which is very important, especially in the case of medical prognosis. Furthermore, transparency allows the user to adapt models and learning processes to the specific needs of a problem and has a boosting influence on the development of new methods in relevant machine learning fields (transfer learning).

Parallel coordinates plots are popular tools for high-dimensional data visualization. To alleviate the difficulties caused by the inherent defects that arise when the dimensions are increased, this study attached two-dimensional kernel density scatter plots to parallel coordinates plot, which integrates the Cartesian and parallel coordinate systems, to combine their benefits and conveniently explore the relationships between paired attributes. The collaborative design combining the two visual types was realized through the corelative axis swap interaction method, which allows users to freely exchange parallel coordinate axes while also updating the associated kernel density scatter plots based on the Cartesian coordinate system. In this study, we obtained six cases of real datasets and performed a field trial study to assess the usefulness of our method in assisting users in evaluating correlations between paired attributes. The results show that our method is more useful for analyzing the relationships between paired attributes.

Eye-tracking technology is becoming more and more important in the field of evaluating human behavior. Because with this technology, features such as the position of unconscious eye movements, focusing time, pupil size, and the number of blinks are obtained. Based on these features, behavioral information such as consumption models, psychology, and interests can be reached. However, since eye movements produce data in milliseconds, it is a problem to evaluate and analyze these data. This study aims to make useful and confidential information more visible by applying visualization techniques in the analysis and evaluation phase of complex and dense data obtained with an eye tracker. For this purpose, spectrogram images are obtained by multiplexing the eye data, which can be taken in limited numbers due to cost, by non-linear interpolation. After the multiplexed data are deep learning and DVM (Support Vector Machine) processes, a classification is made about what kind of document the user reads. As a result, the type of document read with 95.24% accuracy was determined with the proposed classification algorithm. [ABSTRACT FROM AUTHOR]

Background: Heterozygous gain-of-function variants in SAMD9L are associated with ataxia-pancytopenia syndrome (ATXPC) and monosomy 7 myelodysplasia and leukemia syndrome-1 (M7MLS1). Association with peripheral neuropathy has rarely been described. Methods: Whole-exome sequencing (WES) from DNA extracted from peripheral blood was performed in a 10-year-old female presenting with demyelinating neuropathy, her similarly affected mother and the unaffected maternal grandparents. In addition to evaluation of single nucleotide variants, thorough work-up of copy number and exome-wide variant allele frequency data was performed. Results: Combined analysis of the mother's and daughter's duo-exome data and analysis of the mother's and her parents' trio-exome data initially failed to detect a disease-associated variant. More detailed analysis revealed a copy number neutral loss of heterozygosity of 7q in the mother and led to reanalysis of the exome data for respective sequence variants. Here, a previously reported likely pathogenic variant in the SAMD9L gene on chromosome 7q (NM_152703.5:c.2956C>T; p.(Arg986Cys)) was identified that was not detected with standard filter settings because of a low percentage in blood cells (13%). The variant also showed up in the daughter at 32%, a proportion well below the expected 50%, which in each case can be explained by clonal selection processes in the blood due to this SAMD9L variant. Conclusion: The report highlights the specific pitfalls of molecular genetic analysis of SAMD9L and, furthermore, shows that gain-of-function variants in this gene can lead to a clinical picture associated with the leading symptom of peripheral neuropathy. Due to clonal hematopoietic selection, displacement of the mutant allele occurred, making diagnosis difficult. [ABSTRACT FROM AUTHOR]

BackgroundArrhythmia is a common cardiovascular disease, which has a range of transient or paroxysmal conditions. Arrhythmia easily occurs outside of the hospital, but signals of its onset often could not be captured by traditional ECG devices since they can not be worn at any time.ObjectiveTo assess the effect of applying scatter diagram in prehospital screening for arrhythmia via analyzing patients' data monitored by the single lead, wearable remote ECG monitoring system.MethodsParticipants (n=1 076) were primary care patients who were selected from Yinchuan from September 2018 to September 2019. All of them used single lead, wearable remote ECG monitoring system to monitor cardiac rhythms prehospitally when they had palpitation, dizziness, chest tightness, shortness of breath and other symptoms, and real-timely uploaded 24-hour ambulatory ECG data to be used for screen for arrhythmia by different approaches: approach A (diagnosis made using scatter diagram analysis by primary care physicians) , approach B (diagnosis made using scatter diagram analysis by physicians from Remote ECG Center, the First People's Hospital of Yinchuan) , and approach C (diagnosis made using scatter diagram analysis and ECG analysis by physicians from Remote ECG Center, the First People's Hospital of Yinchuan) . Prevalence and types of arrhythmia detected by these approaches and diagnostic coincidence rate of these approaches were analyzed. The sensitivity, specificity, positive and negative predictive values of approaches A and B were assessed with those of approach C as the gold standard.Results(1) The frequencies of arrhythmias detected by approaches A, B and C were 1 301, 1 323, and 1 647, respectively. The types of arrhythmias detected by approaches A, B and C were 14, 14, and 15, respectively. And the prevalence of arrhythmias detected by approaches A, B and C were 80.9%, 81.2% and 87.5%, respectively. (2) The diagnoses made by approaches A and B were highly consistent〔Kappa=0.891, 95%CI (0. 711, 1.071) , P=0.617〕, and the diagnostic coincidence rate was 96.7%. The diagnoses made by approaches B and C were highly consistent〔Kappa=0.759, 95%CI (0.489, 1.029) , P

The article shows the possibilities of data visualization for various samples using examples of hydrological and water management information. The efficiency of using some techniques (graphical representation of data, Shewhart control maps, box-and-whiskers diagram, and scattering diagram) for analyzing the results of water quality control of the Moscow river and some of its tributaries is demonstrated. It is shown that the methods of visual analysis allow you to visually display any changes in the quality of surface water and can become an effective tool in the organization of water use in the metropolis.

Oscillation is ubiquitous and important in both bio-engineering and biosystems. We here analyze the features of oscillation in a well-studied biochemical model of glycolysis with autocatalytic kinetic step. This Selkov model is followed deterministically using mass action kinetics. We explore the sensitivity analysis technique to assess degree of susceptibility of the system with respect to different input parameters for a thorough understanding of the merit of individual parameters controlling the core dynamics. Emphasis is concentrated on the system's biological response via oscillation, bracketing the range of perturbation allowed to the kinetic parameters. An interesting observation is the switchover of the dynamics from non-oscillating to damping followed by stable sustained undulation with one or two controlling kinetic parameters which are quite appealing. The result may be illuminating in understanding the dynamics behind oscillations in biochemical systems in general. A key outcome of the study is the prioritization of most sensitive parameter for the network model controlling the dynamical features via scatterplot analysis. [ABSTRACT FROM AUTHOR]

This research focused on analyzing and classifying the survival of some breast cancer in-patients in Kwara State, Nigeria. The relationships between some variables that were measured to determine the survival of breast cancer in a cross-sectional study in Nigeria were explored. Data detailing information on gender, age, mode of diagnosis, length of stay, location of breast cancer and the outcome of the treatment on 300 patients admitted based on breast cancer issues were collected at the University of Ilorin Teaching Hospital (UITH), Ilorin, Nigeria between year 2011 and 2016. Descriptive analyses were performed; cluster analysis was performed using k-mean clustering method with the aid of Orange software. Analysis of the cluster is done to reveal the latent patterns that could lead to a better understanding on how some variables could combine to determine the extent of breast cancer survival in Nigeria The scatter plot was used for data visualization while the Silhouette plot showed validates the choice of using only three clusters. Breast cancer survival is associated with the age bracket of the patients. Also, breast cancer survival is dependent on the location of the cancer. In addition, mode of diagnosis was found to be associated with the location of the breast cancer. Furthermore, k-clustering algorithm identified three distinct clusters. The low Silhouette values of obtained in both plots showed that only few clusters are available for this type of data. Louvain clustering revealed the latent patterns that depicts the prevalence of breast cancer in Nigeria. [ABSTRACT FROM AUTHOR]

In the present study, a total of 155 desi cotton accessions (Gossypium arboreum L.) were evaluated using six qualitative traits in an augmented design. The distribution of genotypes categorized under the selected traits and their states were computed for all the genotypes. Principal Component Analysis (PCA) identified three principal components accounting for 56.91% of the variability in the arboreum germplasm with PC 1 contributing 20.51% and PC 2 contributing 18.62%. The results of the eigen vectors of six qualitative traits indicated that the traits namely anther colour, plant body colour and boll shape could serve as distinguishable morphological markers for characterization of G. arboreum genotypes. Correlation studies revealed significant positive association among the traits under study. Scatter plot between PC 1 and PC 2 showed the convex of the hull occupied by the genotypes namely DWDA 1701, NDLA 3086, JLA 0610, CAN 1038, RG5 21, HD 544, PA 778, GAM 261, FDK2 95, K 12 and CIsa 90 presenting their diverse nature among the genotypes. The clustering analysis separated 155 G. arboreum accessions into five major clusters and maximum genotypes were placed in Cluster V. Combined results of PCA and cluster analysis selected DWDA 1701, GAM2 60, TAS 130, CISA 1793 and RG 690 as promising genotypes with highest degree of divergence for future desi cotton breeding research.

The Pearson correlation coefficient can be recovered from the two least squares regression lines y = b0 + b1x and x = a0 + a1y without any data. This can be done both algebraically and geometrically. This can be done without data even when the scales of the variables are different. The authors conclude with ways to use such information during instruction to help scaffold student understanding, number sense and problem-solving skills. [ABSTRACT FROM AUTHOR]

—The goal of this paper is to create a security system to identify a specific person who wants to access private information or enter a building using their voice. To perform this system, we identified a database containing the audio files of the users who will be able to authenticate with this system. Several steps were sequentially performed in order to extract the characteristics of the Mel Frequency Cepstral Coefficients from the audio files. Based on the k-Nearest Neighbor algorithm with an Euclidean distance and 4 neighbors, a training model was created. Through experimental results we prove in two ways, using confusion matrix and scatter plot, that the overall voice fingerprint recognition is 100%, for this particular configuration. [ABSTRACT FROM AUTHOR]

Clustering is rapidly becoming a powerful data mining technique, and has been broadly applied to many domains such as bioinformatics and text mining. However, the existing methods can only deal with a data matrix of scalars. In this paper, we introduce a hierarchical clustering procedure that can handle a data matrix of scatter plots. To more accurately reflect the nature of data, we introduce a dissimilarity statistic based on "data depth" to measure the discrepancy between two bivariate distributions without oversimplifying the nature of the underlying pattern. We then combine hypothesis testing with hierarchical clustering to simultaneously cluster the rows and columns of the data matrix of scatter plots. We also propose novel painting metrics and construct heat maps to allow visualization of the clusters. We demonstrate the utility and power of our new clustering method through simulation studies and application to a microbe-host-interaction study.