Visualization Process Research Articles

Application for polymer industry to hold nearly 40% of the bio-based platform chemicals market; demand to grow by 8% CAGR through 2031

Today, more leaders, managers, and directors can decide, even though they cannot decide for every situation. When decisions are scattered and unstructured, decisions become complicated and so that a single decision-maker based on expertise alone cannot address them adequately. This research proposes the Electronic Decision System (EDS) for effective data visualization and analysis process. EDS helps to decide during a complicated situation using Big Data Analytics-based Artificial Neural Network Framework (BDA-ANNF). The BDA-ANNF collects and analyses the data provided, and Artificial Neural Network (ANN) is used to predict and take accurate decisions in every complicated situation. These systems allow decision-makers to understand the market, direct and indirect climate, analyze the numerous trade-offs in the sector, evaluate costs linked to the various choices for decision-making, and eventually help people improve their decision-making outcome. EDS's approaches and techniques share one key challenge that must successfully provide consumers and policymakers with an appropriate and understandable simulation solution. The finding indicates that the BDA-ANNF decides the complicated situation up to the accuracy rate of 95.23%, which is not attainable by existing methods, and the error rate is 4.77%.

Shadowfocimetry: adapting the holographic principle to a manual focimeter for visualization/marking of permanent engravings in progressive addition lenses

Focimeters, especially manual versions, are the most used ophthalmic devices for dioptric power measurement in optometric clinical care. In the particular case of progressive addition lenses (PALs), they are used to determine far/near vision correction powers, but the user/clinician needs to know at which part of the PAL the measurement must be taken. For this reason, PALs have permanent engravings acting as reference marks to define the far/near vision areas for every PAL design. However, for several reasons these engravings are often difficult to localize and identify, making an accurate dioptric power determination difficult. In this Letter, we present an adaptation of the Gabor holographic principle to a manual focimeter and describe the methodology for the correct localization, visualization, and marking process of the reference engravings in PALs. Experimental results considering different types of PALs are included and the main limitations of the technique are also discussed.

Interactive and Reproducible Workflows for Exploring and Modeling RNA-seq Data with pcaExplorer, Ideal, and GeneTonic.

The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory Data Analysis with pcaExplorer Basic Protocol 2: Differential Expression Analysis with ideal Basic Protocol 3: Interpretation of RNA-seq results with GeneTonic Support Protocol: Downloading and installing pcaExplorer, ideal, and GeneTonic Alternate Protocol: Using functions from pcaExplorer, ideal, and GeneTonic in custom analyses.

Research on visualization and diagnosis method of civil aircraft WQAR data

Abstract WQAR data has the characteristics of large scale, high dimension and many random interference factors, which makes it extremely difficult to use WQAR operational data for flight quality monitoring, expert unknown anomaly detection and PHM. This paper proposes a civil aircraft WQAR data visualization display scheme and the overall framework of fault diagnosis adopts a two-stage cycle to research and recycle and improve the data preprocessing requirements, visualization and diagnostic process methods, etc., and use the civil aircraft operation data to verify the proposed theoretical model and diagnose the failure status. Results shown that the theoretical framework and method can accurately predict the health status of aircraft engines, and can effectively solve the problems of high dimension and many interference factors of WQAR data.

Batik Jlamprang with Koch Snowflake and Koch Anti Snowflake Fractal Geometry using Desmos

Batik Jlamprang is a cultural heritage from Pekalongan. This batik has a round shape and floral ornaments. The motif of batik Jlamprang is similar to the Koch snowflake. Mathematically, batik Jlamprang is one of the shapes of fractal geometry. There are many known shapes of fractals, some of which are Koch Snowflake and Koch Anti-Snowflake. The difference between Koch Snowflake and Koch Anti-Snowflake lies in the generating process. Koch Anti-Snowflake is the opponent of Koch Snowflake. The main step of the process is generated by compiling Koch Snowflake and Koch Anti-Snowflake function formulas, followed by iteration. The making of the batik motif is initially carried out traditionally, which has disadvantages in terms of time and cost. However, nowadays, the motif of batik Jlamprang can be made mathematically with the help of Desmos software. This will definitely shorten the time and reduce production costs. Desmos software was chosen because it has several advantages, including being easy to operate via mobile phone or computer. This paper examines the function formula, iteration, and application of Koch Snowflake and Koch Anti-Snowflake fractal geometry in designing batik Jlamprang assisted by Desmos. The method used was library research by collecting several relevant sources. The generating process produced a function formula of Pn (perimeter) and An or Sn (area) which is useful for designing the motif of batik Jlamprang. The visualization process was carried out on Desmos, followed by geometry transformation and cloning that were able to produce the motif of batik Jlamprang as desired.

Industrial process data visualization based on a deep enhanced t-distributed stochastic neighbor embedding neural network

PurposeThe purpose of this paper is to propose a approach for data visualization and industrial process monitoring.Design/methodology/approachA deep enhanced t-distributed stochastic neighbor embedding (DESNE) neural network is proposed for data visualization and process monitoring. The DESNE is composed of two deep neural networks: stacked variant auto-encoder (SVAE) and a deep label-guided t-stochastic neighbor embedding (DLSNE) neural network. In the DESNE network, SVAE extracts informative features of the raw data set, and then DLSNE projects the extracted features to a two dimensional graph.FindingsThe proposed DESNE is verified on the Tennessee Eastman process and a real data set of blade icing of wind turbines. The results indicate that DESNE outperforms some visualization methods in process monitoring.Originality/valueThis paper has significant originality. A stacked variant auto-encoder is proposed for feature extraction. The stacked variant auto-encoder can improve the separation among classes. A deep label-guided t-SNE is proposed for visualization. A novel visualization-based process monitoring method is proposed.

Digital twins: A brief overview of applications, challenges and enabling technologies in the last decade

The concept of Digitals Twins (DTs) is an evolving idea, which is becoming the center of attention for the industry and the scientific community. It can be described as the pairing of the digital and the physical, interconnected for data sharing. DT is a key enabler for Industry 4.0, especially on the digitalization and visualization processes. Even though the idea of DT has been defined thoroughly throughout the recent years, there are still many different interpretations which are the result of different viewpoints of the involved professionals. The digitalization process requires huge amounts of data, which is generated, collected, handled and processed by various sources as well as digital twins themselves, thus offering new challenges that need to be addressed. In this paper, we attempt to i) assess the current state of digital twins, ii) describe the terms digital model and digital shadow as they are often misused as synonyms and iii) review the concepts of Internet of Things (IoT) and Industry 4.0 and discuss how these are connected. Moreover, some applications of DTs and their enabling technologies will be presented.

A supervised biosensor-based non-variant structuring approach for analyzing infectious disease data

Data modelling and analysis have become a recent trend in medical and healthcare applications for their ease of visualization and handling. To keep up with the vast amount of information generated by such medical and healthcare applications, the need for computer-aided modelling and intelligent data handling is expected to increase the quality of assessment and visualization. Moreover, reliable modelling requires structured data handling for achieving better data visualization. The un-ordered and raw disease/medical data require formal structuring and grouping for improving the visualization process. Existing models consume too much time for processing a huge volume of data. This, in turn, causes a high error rate in classification, which directly affects system performance. In this paper, biosensors gather patient health information and examine the infectious with a high prediction rate. A biosensor rapidly collects patient health data changes and reduces the time complexity of data modelling and analysis. Moreover, a supervised Non-Variant Structuring (NVS) approach for grouping infectious disease data is introduced. This approach helps improve the visualization of sensor-based acquired raw data. In this structuring process, the associativity and disparity features of the infectious disease data are identified for grouping and analyzing the disease-related features. The introduced structuring method employs a supervised learning technique for identifying the associativity and disparity in different instances of accumulation based on a Hidden Markov Model (HMM). This learning technique reduces the chances of non-partial organization of infectious disease data for better modelling and analysis. The performance of the suggested approach is verified with a sensitivity ratio of 98.2%, a specificity ratio of 96.7%, and accuracy ratio of 95.5%, a prediction error rate of 7.8% less, and a classification time of 10.1% less compared to other existing methods.

Visualization of media content as a way to gain reader loyalty for media sustainability

Visualization is one of the trends in modern journalism. More and more media are beginning to pay equal attention to both text and visual content. This is due not only to the growing trend, but also to the demands of the audience. Therefore, the peculiarities of the perception of visual content is a topical issue that will be disclosed in this article. The main goal of the study is to determine the characteristics of the audience's perception of visual media material, in particular, the press, and ways of manipulative influence by the media on the audience through the visualization of content. In order to achieve the goal in the course of the study, the tasks will be solved to identify the characteristic types of perception of visual content by the audience, the key factors affecting the choice of visual content by the audience, and the comparison of theoretical knowledge with the results of practical research. The theoretical basis of the article is the results of scientific research in the field of visual communication. Practical research was carried out using a sociological survey, which revealed the directions of the audience's interests. The theoretical significance of the study is to determine the characteristics of the perception of visual content. The practical significance lies in the further application of the results obtained in the educational process of higher educational institutions with a degree in "Journalism" by practicing journalists of periodicals and Internet editions. The results of the study showed that today the visual part is one of the integral components of journalistic material. Especially for print publications, their electronic versions and independent Internet editions, in which it is important to tell about an event or problem not only through the text. The visualization process is significant both for the audience and for the journalists themselves, since the visual part makes it easier to understand the content. The image attracts a new media audience thanks to the creative approaches that designers or journalists use when presenting material. Keywords: mass media, visualization, image, periodicals, Internet, psychological influence, manipulative influence.

Methodological features of the study of «combat films» in the United States of 1941-1945

In modern studies on the history of wars, a fairly popular direction is the study of information discourse specifics. A special role in this discourse is played by propaganda through cinematography, which uses images, historical symbols and stable metaphors, the appeal to which can form a certain public reaction. The study of the main aspects of this topic is impossible without recourse to interdisciplinary methods of the humanities, developed as a result of a number of turns in the development of modern humanitarian knowledge, including anthropological, linguistic, cultural, calling to study the perception of the world, human behavior in the past in the totality of socio-economic, political, cultural practices adopted in the studied society at a given time. In addition, an appeal to the methods of historical imagology will make it possible to trace the evolution of the process of visualization and mythologization of the Second World War in US cinematography more accurately. Without the study of the basic principles, methods, mechanisms and tools of this process it is extremely difficult to understand the peculiarities of the development and interaction of cinema and US propaganda at subsequent historical stages and the present. American scientists have achieved significant results in the study of directors creative biographies, in the specifics of their interaction with federal government bodies, and in the analysis of the activities of the Office of War Information in the field of cinematography.

A Teaching Methods of Structural Mapping as a Visualization Process of Summary Thinking

The purpose of this thesis is to propose a method of teaching abstract thinking through structural drawing that visualizes the macro structure in a liberal arts writing class at a university. First, by analyzing the results of the learner’s structure diagram from 2018 to 2021, we calculated notable issues for summary thinking training, and based on this, we proposed a practical class plan. First, it was important to clearly maintain the distance between the learner, who is the subject of the text summary, and the original author of the text. This is an important principle for accurately understanding the text and also for understanding the learners themselves, who again are the subjects of the summary. Second, it was important to adjust the level of abstraction and generalization of text information. This abstraction generalization process is a process of considering abstract thinking and the relationship between texts by reflecting the key elements constituting the text in the structural diagram. Third, it was important to understand the text composition principle through the activity to check the hierarchy among the core information constituting the text. Some examples of possible classes to help us achieve the main goal for this summary thinking were introduced. First, we presented the process of internalizing the thinking process involved in abstract thinking through multi-faceted feedback activities on the structural diagram using abstract thinking. Second, in order to strengthen the process of understanding the internal relationship of the text structure, a method for internalizing the concept and thought process used in actual thinking was suggested by comparatively analyzing the meta-activities identified in the previous feedback process. Finally, a method to strengthen the openness of summary thinking was suggested by imparting that the structure drawing activity itself has an open possibility.

Introducing the combined atlas framework for large-scale web-based data visualization: The GloNAF atlas of plant invasion.

Large‐scale biodiversity data, for example, on species distribution and richness information, are being mobilized and becoming available at an increasing rate. Interactive web applications like atlases have been developed to visualize available datasets and make them accessible to a wider audience. Web mapping tools are changing rapidly, and different underlying concepts have been developed to visualize datasets at a high cartographic standard.Here, we introduce the Combined Atlas Framework for the development of interactive web atlases for ecological data visualization. We combine two existing approaches: the five stages of the user‐centred design approach for web mapping applications and the three U approach for interface success.Subsequently, we illustrate the use of this framework by developing the Atlas of Plant Invasions based on the Global Naturalized Alien Flora (GloNAF) database. This case study illustrates how the newly developed Combined Atlas Framework with a user‐centred design philosophy can generate measurable success through communication with the target user group, iterative prototyping and competitive analysis of other existing web mapping approaches.The framework is useful in creating an atlas that employs user feedback to determine usability and utility features within an interactive atlas system. Finally, this framework will enable a better‐informed development process of future visualization and dissemination of biodiversity data through web mapping applications and interactive atlases.

A Bounded Measure for Estimating the Benefit of Visualization (Part II): Case Studies and Empirical Evaluation.

Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss due to a variety of many-to-one mappings. At a glance, there seem to be numerous opportunities for viewers to misinterpret the data being visualized, hence, undermining the benefits of these visual representations. In practice, there is little doubt that these visual representations are useful. The recently-proposed information-theoretic measure for analyzing the cost–benefit ratio of visualization processes can explain such usefulness experienced in practice and postulate that the viewers’ knowledge can reduce the potential distortion (e.g., misinterpretation) due to information loss. This suggests that viewers’ knowledge can be estimated by comparing the potential distortion without any knowledge and the actual distortion with some knowledge. However, the existing cost–benefit measure consists of an unbounded divergence term, making the numerical measurements difficult to interpret. This is the second part of a two-part paper, which aims to improve the existing cost–benefit measure. Part I of the paper provided a theoretical discourse about the problem of unboundedness, reported a conceptual analysis of nine candidate divergence measures for resolving the problem, and eliminated three from further consideration. In this Part II, we describe two groups of case studies for evaluating the remaining six candidate measures empirically. In particular, we obtained instance data for (i) supporting the evaluation of the remaining candidate measures and (ii) demonstrating their applicability in practical scenarios for estimating the cost–benefit of visualization processes as well as the impact of human knowledge in the processes. The real world data about visualization provides practical evidence for evaluating the usability and intuitiveness of the candidate measures. The combination of the conceptual analysis in Part I and the empirical evaluation in this part allows us to select the most appropriate bounded divergence measure for improving the existing cost–benefit measure.

Conception about the Data Visualization Techniques Including Data Stream Mining and Bioinformatics

Data visualisation techniques are effectively beneficial to for data streaming in a significant way. Graphical representation is also measured with help of this data visualisation process. Several types of graphs, images, charts and maps are used to maintain data visualisation process. Decisions making process and problem-solving ability is also enhanced with help of these data visualisation procedures. Pie charts, bar charts, waterfall charts, Gantt chart and heat map are used to maintain the data visualisation process. Bullet graphs are also useful for this conception about data visualisation process. Researcher uses “interpretivism” research philosophy, “inductive” research approach and “qualitative” research method to gather information related to this study. Creative and innovative ideas and thoughts related to data are also used with help of this particular data stream mining in a significant way. Data visualisation market value and big data market size is also mentioned in this study. Constant cycles of news and entertainment are also gathered with help of this data visualisation techniques including data stream mining.

Juxtaposing GIS and Archaeologically Mapped Ancient Road Routes

Mapping ancient roads is crucial to tell credible geospatial stories about where, how, or why different people might have travelled or transported materials within and between places in the distant past. Achieving this process is challenging and commonly accomplished by means of archaeological and GIS methods and materials. It is not uncommon for different experts employing these methods to generate inconsistent delineations of the same ancient roads, creating confusion about how to produce knowledge and decisions based on multiple geospatial perspectives. This yet to be adequately addressed problem motivates our desire to enrich existing literature on the nature and extents of these differences. We juxtapose GIS and archaeologically generated road maps for northern Etruria, a region of ancient Italy with a well-developed road network built by the Etruscans and Romans. We reveal map differences through a map comparison approach that integrates a broad set of qualitative and quantitative measures plus geospatial concepts and strategies. The differences are evident in route locations, sinuosities, lengths, and complexities of the terrains on which the routes were set as defined by subtle variations in elevation, slope, and ruggedness. They ranged from 11.2–34.4 km in road length, 0–65.7 m in road relief, 1.0–13.5% in mean road grade, 0.07–0.79 in detour indices and 0.19–3.08 for mean terrain roughness indices, all of which can be considerable depending on application. Taken together, the measures proved effective in furthering our understanding of the range of possible disagreements between ancient linear features mapped by different experts and methods and are extensible for other application areas. They point to the importance of explicitly acknowledging and maintaining all usable perspectives in geospatial databases as well as visualization and analysis processes, regardless of levels of disagreement, and especially where ground-truth informed assessments cannot be reliably performed.

Ways of seeing: Peace process data-viz as a research practice

This article uses John Berger’s idea (1972) that images are connected to ‘ways of seeing’ to reflect on the creation of interactive visualizations of peace agreement and peace process data. We reflect on three visualizations created during a three-year long collaboration. We first describe our data, the peacebuilding ambitions for its use, and why we produced interactive forms of visualization. Second, we describe how the process of producing these visualizations created an interdisciplinary conversation and collaboration, which also connected different epistemic and geographic communities involved in peace processes. We term this ‘visualization-as-scoping’. Third, we reflect on both ‘what we saw’, through the process of visualization, how it affected policy, and the lessons we learned regarding visualization in the peacebuilding field. In the article, we argue that our experience of ‘visualization-as-scoping’ inverts traditional assumptions about the connection of data visualization to policy influence. In place of the notion of visualization-as-communication, focused on transmitting clear policy ‘messages’, we point to visualization-as-scoping as a practice of interchange, critique and re-iteration. Using John Berger as inspiration, we suggest that the ‘ways of seeing’ that result can usefully disrupt the idea of a data producing singular policy prescriptions, and rather enable people to grapple better with the complex political processes they are involved in.

Modeling of Internet of things for sustainable agricultural systems development on the base of different approaches

The paper deals with the problems of modelling Internet of Things systems in agricultural systems on the base of results of monitoring. The use of expert analysis in agricultural systems is considered. The main sets of parameters are shown. Illustration of the visualization process of indicators is discussed. The characteristics of expert analysis of objects in Internet of Things systems is considered. The integral indicators of the quality of the Internet of Things system are considered. In the course of implementing a dialogue with an expert, one has to implement a procedure related to dividing objects into classes. The results of parameters estimation in agricultural systems are shown.

New Approach to Accelerated Image Annotation by Leveraging Virtual Reality and Cloud Computing.

Three-dimensional imaging is at the core of medical imaging and is becoming a standard in biological research. As a result, there is an increasing need to visualize, analyze and interact with data in a natural three-dimensional context. By combining stereoscopy and motion tracking, commercial virtual reality (VR) headsets provide a solution to this critical visualization challenge by allowing users to view volumetric image stacks in a highly intuitive fashion. While optimizing the visualization and interaction process in VR remains an active topic, one of the most pressing issue is how to utilize VR for annotation and analysis of data. Annotating data is often a required step for training machine learning algorithms. For example, enhancing the ability to annotate complex three-dimensional data in biological research as newly acquired data may come in limited quantities. Similarly, medical data annotation is often time-consuming and requires expert knowledge to identify structures of interest correctly. Moreover, simultaneous data analysis and visualization in VR is computationally demanding. Here, we introduce a new procedure to visualize, interact, annotate and analyze data by combining VR with cloud computing. VR is leveraged to provide natural interactions with volumetric representations of experimental imaging data. In parallel, cloud computing performs costly computations to accelerate the data annotation with minimal input required from the user. We demonstrate multiple proof-of-concept applications of our approach on volumetric fluorescent microscopy images of mouse neurons and tumor or organ annotations in medical images.

A Bounded Measure for Estimating the Benefit of Visualization (Part I): Theoretical Discourse and Conceptual Evaluation.

Information theory can be used to analyze the cost–benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost–benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson–Shannon divergence, its square root, and a new divergence measure formulated as part of this work. We describe the rationale for proposing a new divergence measure. In the first part of this paper, we focus on the conceptual analysis of the mathematical properties of these candidate measures. We use visualization to support the multi-criteria comparison, narrowing the search down to several options with better mathematical properties. The theoretical discourse and conceptual evaluation in this part provides the basis for further data-driven evaluation based on synthetic and experimental case studies that are reported in the second part of this paper.

Research on electronic decision system for effective data visualization and analysis process

Today, more leaders, managers, and directors can decide, even though they cannot decide for every situation. When decisions are scattered and unstructured, decisions become complicated and so that a single decision-maker based on expertise alone cannot address them adequately. This research proposes the Electronic Decision System (EDS) for effective data visualization and analysis process. EDS helps to decide during a complicated situation using Big Data Analytics-based Artificial Neural Network Framework (BDA-ANNF). The BDA-ANNF collects and analyses the data provided, and Artificial Neural Network (ANN) is used to predict and take accurate decisions in every complicated situation. These systems allow decision-makers to understand the market, direct and indirect climate, analyze the numerous trade-offs in the sector, evaluate costs linked to the various choices for decision-making, and eventually help people improve their decision-making outcome. EDS's approaches and techniques share one key challenge that must successfully provide consumers and policymakers with an appropriate and understandable simulation solution. The finding indicates that the BDA-ANNF decides the complicated situation up to the accuracy rate of 95.23%, which is not attainable by existing methods, and the error rate is 4.77%.