Visualization Techniques Research Articles

Experimental investigation of the wake of tandem cylinders using pivoted flapping mechanism for piezoelectric flag

In this paper, the current of ocean at a depth with the velocity of 0.1–0.3 m/s has been studied. The effect of the mounting mechanism of the piezoelectric flag on the energy efficiency of the energy harvester is investigated. The flag is mounted uniquely, and the results are analyzed to explain the underlying mechanism associated with variations in power output. The experiments described extend our previous work and it is the experimental investigation of the baseline case for our previous work. It was found that using a pivoted mounting mechanism caused a significant improvement in flapping amplitude (51%) and dominant frequency (23%). The variation in the flag's distance from the bluff body (Gd) is also investigated. The maximum output for a passively mounted flag was obtained at Gd=2D due to the existence of a vortex core position at the same point where the flag's head was positioned. The same mechanism is implemented for tandem cylinder arrangement. The study found that increasing the distance between the bluff bodies resulted in the suppression of vortex shedding of the upstream cylinder by the rear cylinder. However, this behavior was found to be random and was thoroughly explored in this study. The obtained results were also verified through the flow visualization technique. A comparative analysis is made to show the optimal performance of the harvester by fine-tuning parameters such as the gap between cylinders and piezo flag, flow rate, and flag-mounting technique. The comparative analysis with benchmark cases demonstrated a rise in the A/L ratio by 30.25% and in the flapping frequency by 19.09% in the case of 120° cut C-shaped inverted cylinder upstream, indicating a higher percentage gain of 66% more energy harvesting. An increase of 51.18% in the A/L ratio and 22.79% in flapping frequency with the use of a pivot-mounted piezoelectric flag was observed, indicating higher energy harvesting performance compared to the baseline case (circular cylinder with fixed flag head).

NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics

Stroke is a severe illness, that requires early stroke detection and intervention, as this would help prevent the worsening of the condition. The research is done to solve stroke prediction problem, which may be divided into a number of sub-problems such as an individual's predisposition to develop stroke. To attain this objective, a multiturn dataset consisting of various health features, such as age, gender, hypertension, and glucose levels, takes a central role. A multiple approach was put forward concentrating on integrating the machine learning techniques, such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine (SV), together to develop an ensemble machine called Neuro-Health Guardian. The hypothesis "Neuro-Health Guardian Model" integrates these algorithms into one, purported to make stroke prediction more accurate. The topic dives into each instance of preparation of data for analysis, data visualization techniques, selection of the right model, training, testing, ensembling, evaluation, and prediction. The models are validated with error rate accounted from their accuracy, precision, recall, F1 score, and finally confusion matrices for a look. The study's result is showing that the ensemble model that combines the multiple algorithms has the edge over them and this is evidently by the fact that it can predict stroke rises. Additionally, accuracy, precision, recall, and F1 scores are measured in all models and the comparison is done to provide a clear comparison of the models' performance. In short, the article presented the formation of the ongoing stroke prediction that revealed the ensemble model as a good anticipation. Precise stroke predisposition forecasting can assist in early intervention thereby preventing stroke-related deaths, and limiting disability burden by stroke. The conclusions that have come out of this study offer a great action item for the development of predictive models related to stroke prevention and treatment.

Multivariate technique for the prediction and classification of brain tumor using deep shallow network

Brain tumors, a major cause of death, carry a substantial socio-economic burden. It is essential to distinguish between various types of tumors, such as gliomas, meningiomas, and pituitary tumors, using MRI data. This assists radiologists and eliminates the need for risky histology biopsies. The objective of the proposed work is to create an automated system capable of distinguishing tumor types. This system integrates a deep, shallow network with the ResNet18 model for feature extraction and machine learning methods for classification on images from the Kaggle dataset. The standalone ResNet18 model produced an accuracy of 91.42 % and to enhance the accuracy, features extracted from the pool5 layer of the ResNet18 architecture were examined by chi-square algorithm. The optimal deep features extracted using the chi-square algorithm were integrated with 17 machine learning classifier techniques comprising variants of SVM, KNN, and ensemble techniques. The proposed method predicted the classes, yielding an accuracy of 99.9 % for cubic SVM among the variants of SVM, 94 % for cosine KNN among the variants of KNN, and 99.8 % for the Adaboost algorithm only, with a maximum of the top 10 features justified using local interpretable model-agnostic (LIME) and gradient-weighted class activation mapping (Grad-CAM) visualization techniques.

Tasks and Visualizations Used for Data Profiling: A Survey and Interview Study.

The use of good-quality data to inform decision making is entirely dependent on robust processes to ensure it is fit for purpose. Such processes vary between organisations, and between those tasked with designing and following them. In this article we report on a survey of 53 data analysts from many industry sectors, 24 of whom also participated in in-depth interviews, about computational and visual methods for characterizing data and investigating data quality. The paper makes contributions in two key areas. The first is to data science fundamentals, because our lists of data profiling tasks and visualization techniques are more comprehensive than those published elsewhere. The second concerns the application question "what does good profiling look like to those who routinely perform it?", which we answer by highlighting the diversity of profiling tasks, unusual practice and exemplars of visualization, and recommendations about formalizing processes and creating rulebooks.

Development of Situation Awareness Model in Robotic Spot-welding (RSW) System based on Sensor Data Visualization

The advancement of prognostics and health management (PHM) using the industrial internet of things (IIoT) and artificial intelligence (AI) has enabled stable mass production with assured quality. However, the manufacturing industry often faces variable conditions like design and material changes. Before applying PHM systems, it is crucial to design a system that recognizes these changes. This study developed a robust situation awareness model for a robotic spot-welding (RSW) system, resilient to reduced training data, using sensor data visualization and convolutional neural networks (CNN). Four variable situations were established based on thickness and material changes: GI steel 0.6 t, GI steel 1.0 t, mild steel 0.8 t, and GA steel 0.8 t, with GI steel 0.8 t as the reference. Data were collected using process parameters (welding current and electrode force) for each situation. A fuzzy-based energy pattern image (FEPI) visualization technique was applied to visualize energy differences in the spot-welding process from four sensors (current, voltage, electrode force, displacement). Using these techniques, thickness and material variation awareness models were constructed. The classification accuracy of CNN models trained on time-series data was evaluated to verify the effectiveness under reduced training data conditions.

Scalability in Visualization.

We introduce a conceptual model for scalability designed for visualization research. With this model, we systematically analyze over 120 visualization publications from 1990 to 2020 to characterize the different notions of scalability in these works. While many article have addressed scalability issues, our survey identifies a lack of consistency in the use of the term in the visualization research community. We address this issue by introducing a consistent terminology meant to help visualization researchers better characterize the scalability aspects in their research. It also helps in providing multiple methods for supporting the claim that a work is "scalable." Our model is centered around an effort function with inputs and outputs. The inputs are the problem size and resources, whereas the outputs are the actual efforts, for instance, in terms of computational run time or visual clutter. We select representative examples to illustrate different approaches and facets of what scalability can mean in visualization literature. Finally, targeting the diverse crowd of visualization researchers without a scalability tradition, we provide a set of recommendations for how scalability can be presented in a clear and consistent way to improve fair comparison between visualization techniques and systems and foster reproducibility.

Visual Cue Based Corrective Feedback for Motor Skill Training in Mixed Reality: A Survey.

When learning a motor skill it is helpful to get corrective feedback from an instructor. This will support the learner to execute the movement correctly. With modern technology, it is possible to provide this feedback via mixed reality. In most cases, this involves visual cues to help the user understand the corrective feedback. We analyzed recent research approaches utilizing visual cues for feedback in mixed reality. The scope of this article is visual feedback for motor skill learning, which involves physical therapy, exercise, rehabilitation etc. While some of the surveyed literature discusses therapeutic effects of the training, this article focuses on visualization techniques. We categorized the literature from a visualization standpoint, including visual cues, technology and characteristics of the feedback. This provided insights into how visual feedback in mixed reality is applied in the literature and how different aspects of the feedback are related. The insights obtained can help to better adjust future feedback systems to the target group and their needs. This article also provides a deeper understanding of the characteristics of the visual cues in general and promotes future, more detailed research on this topic.

Multilevel Visual Analysis of Aggregate Geo-Networks.

Numerous patterns found in urban phenomena, such as air pollution and human mobility, can be characterized as many directed geospatial networks (geo-networks) that represent spreading processes in urban space. These geo-networks can be analyzed from multiple levels, ranging from the macro-level of summarizing all geo-networks, meso-level of comparing or summarizing parts of geo-networks, and micro-level of inspecting individual geo-networks. Most of the existing visualizations cannot support multilevel analysis well. These techniques work by: 1) showing geo-networks separately with multiple maps leads to heavy context switching costs between different maps; 2) summarizing all geo-networks into a single network can lead to the loss of individual information; 3) drawing all geo-networks onto one map might suffer from the visual scalability issue in distinguishing individual geo-networks. In this study, we propose GeoNetverse, a novel visualization technique for analyzing aggregate geo-networks from multiple levels. Inspired by metro maps, GeoNetverse balances the overview and details of the geo-networks by placing the edges shared between geo-networks in a stacked manner. To enhance the visual scalability, GeoNetverse incorporates a level-of-detail rendering, a progressive crossing minimization, and a coloring technique. A set of evaluations was conducted to evaluate GeoNetverse from multiple perspectives.

Enhancing deep orebody prediction and localization through the revelation of geochemical primary halo patterns in drill holes

Geochemical primary halos play a crucial role in mineral exploration as they provide valuable insights into the dispersion of ore materials, identification of concealed deposits, and determination of ore deposit dimensions. The Naruo mining area in the Duolong mineral district, northern Tibet, China well-known for its worldclass porphyry-epithermal Cu–Au deposits is currently chosen as the study area. We employ statistical and nonlinear methods to analyze geochemical data from drill holes. Specifically, R-type cluster analysis is utilized to evaluate element affinity and concentration index, while an improved Grigorian zoning index is employed to delineate the vertical zoning sequence of the primary halo. These methodologies significantly enhance our understanding of element enrichment within geological contexts and greatly contribute to mineral resource evaluation. Based on these results, we have constructed a vertical zonation index model of denudation coefficient tailored to the geological characteristics of mineralization within the study area. This model effectively represents the vertical variation of the ore body. Our findings reveal a decrease in this zoning index towards the central region of deep ore bodies which correlates with deep granodiorite porphyry occurrences. Furthermore, we observe increasing concentrations of Cu, Ag, and Au with depth in specific sections of drill holes indicating promising exploration potential. Supplementary short-wave infrared data suggests the presence of a deep hydrothermal center in drill hole RNZK2404 which tentatively infers concealed porphyry bodies. Notably, the reversal observed at 4200 m on RNZK2404's zoning index hints at a possible hidden ore body at great depths. Finally, three-dimensional visualization techniques effectively illustrate spatial patterns for elements thereby paving way for future endeavors in mineral exploration.

Visualization and classification of mushroom species with multi-feature fusion of metaheuristics-based convolutional neural network model

Determining the correct mushroom species with the necessary ecological characteristics is critical to continue mushroom production, which is essential in gastronomy. The mushroom farmers and collectors technique may help identify toxic mushrooms by detecting poisonous mushrooms using images of different mushroom species with distinctive morphological features. However, it can be not easy to distinguish between species. This paper used a dataset of 6714 mushroom images obtained from nine different mushroom species to classify the mushroom species. For a more straightforward comprehension of mushroom images and feature extraction by reanalysis of data sets, data visualization was performed using Grad-CAM, LIME, and Heatmap methods. Residual block-based Convolutional Neural Network (CNN) architectures are trained to automatically classify the concatenated feature map obtained from the Grad-CAM, LIME, and Heatmap methods. After extracting the deep features of the images from each architecture, the Atom Search Optimization (ASO) algorithm has been used to select the most distinctive features. The 6714×9000 size of the concatenated feature map was reduced to 6714×600 using the ASO algorithm. Classification results were evaluated using six different classifiers based on the feature map obtained to determine the mushroom species. The nine classes of mushroom species were classified successfully with 95.45 % accuracy using the proposed model with the ASO algorithm and KNN classifier. The methodology introduces novel visualization techniques for interpreting CNN-based models’ decisions in mushroom species classification tasks. Using metaheuristics-based CNN models with multi-feature fusion techniques allows the model to leverage diverse sources of information, potentially enhancing its ability to discriminate between mushroom species and achieve higher classification accuracy than existing methods. This study can advance the mushroom species classification field by introducing new methodologies, improving classification accuracy, providing insights into model interpretability, and facilitating knowledge transfer to related fields.

Experimental study of flow control on tip leakage flow in variable geometry linear turbine cascades with different pivot layouts

Variable geometry turbines are essential for adjusting operational conditions in industrial gas turbines and variable cycle engines. These adjustments necessitate partial gaps at both ends of the variable guide vanes to alter the turning angle, consequently introducing an aerodynamic performance penalty. Moreover, the pivot layout profoundly influences aerodynamic losses. Research on turbine cascades that considers various partial gap layouts is limited, particularly in terms of experimental studies, which are rarely conducted. This study aims to diminish aerodynamic losses and augment the efficiency of gas turbines by examining the impact of pivot layouts on partial gap clearance and secondary flow. It further investigates the effectiveness of flow control strategies at the blade tip across different pivot configurations within a variable geometry turbine cascade, utilizing pneumatic probe scanning and surface oil flow visualization techniques. The results reveal that employing a cavity at the tip can significantly reduce aerodynamic losses in schemes both with and without a pivot, achieving maximum loss reductions of 15.8% and 3.7%, respectively. Additionally, a narrower squealer width can further decrease these losses. However, with a pivot located at the tip, the resulting separation flow and wake vortex become predominant sources of losses. The presence of the pivot weakens the tip leakage flow rate and the intensity of the tip leakage vortex (TLV), thus diminishing the effectiveness of cavity tip flow control. The cavity moderates TLV and enhances the interaction between TLV and the wake vortex, leading to increased aerodynamic losses.

Searching for Evidence of Subchromospheric Magnetic Reconnection on the Sun

Within the coronae of stars, abundances of those elements with low first ionization potential (FIP) often differ from their photospheric values. The coronae of the Sun and solar-type stars mostly show enhancements of low-FIP elements (the FIP effect) while more active stars such as M dwarfs have coronae generally characterized by the inverse-FIP (I-FIP) effect. Highly localized regions of I-FIP effect solar plasma have been observed by Hinode's EUV Imaging Spectrometer in a number of highly complex active regions (ARs), usually around strong light bridges of the umbrae of coalescing/merging sunspots. These observations can be interpreted in the context of the ponderomotive force fractionation model, which predicts that plasma with I-FIP effect composition is created by the refraction of waves coming from below the plasma fractionation region in the chromosphere. A plausible source of these waves is thought to be reconnection in the (high-plasma-β) subchromospheric magnetic field. In this study, we use the 3D visualization technique of Chintzoglou & Zhang combined with observations of localized I-FIP effect in the corona of AR 11504 to identify potential sites of such reconnection and its possible consequences in the solar atmosphere. We found subtle signatures of episodic heating and reconnection outflows in the expected places, in between magnetic flux tubes forming a light bridge, within the photosphere of the AR. Furthermore, on either side of the light bridge, we observed small antiparallel horizontal magnetic field components, supporting the possibility of reconnection occurring where we observe I-FIP plasma. When taken together with the I-FIP effect observations, these subtle signatures provide a compelling case for indirect observational evidence of reconnection below the fractionation layer of the chromosphere, however direct evidence remains elusive.

From Imaging to Visualization: Seeing the Future of Endometriosis Care

ObjectiveTo describe how the knowledge from standard imaging practices can be translated into three-dimensional visualization techniques and utilized in the surgical planning and management of endometriosis. SettingTertiary care academic centre. ParticipantsTwo case studies of patients with endometriosis are described. InterventionsTransvaginal ultrasound (1), magnetic resonance imaging, three-dimensional printing (2), and three-dimensional virtual reality modeling (3) were utilized during patient workup and preparation. Three-dimensional modeling was performed by a virtual reality technician and verified for accuracy by a fellowship trained radiologist. Surgical management for endometriosis was performed. ConclusionWhile expert transvaginal ultrasound and/or magnetic resonance imaging suffice for the majority of cases, three-dimensional printing and virtual reality modeling are a novel adjunct to standard imaging modalities. Rendering two-dimensional images into a three-dimensional representation allows users to interact with the anatomy and is particularly useful when distorted by complex pathology. These techniques contributed to improved patient understanding and experience, and helped medical learners better grasp regular imaging techniques and its translation to pelvic anatomy. Last, it augmented surgeon comprehension of the relationship between the pelvic structures, allowing for enhanced surgical planning and intraoperative decision making. Further study is being performed to quantify these effects.

Evaluation of Visual ConOps in Early Solution Validation in a Small and Medium‐sized Enterprise

AbstractThis paper focuses on the design, implementation, and assessment of the visual Concept of Operations (ConOps) as an informal visualization technique employed for early solution validation in Small and Medium‐sized Enterprises (SMEs). SMEs face significant challenges in early solution validation due to the complex nature of modern systems and the constantly changing market demands. These challenges may be further intensified by immature leadership and ineffective communication within the organization. By applying an industry‐as‐laboratory approach in an SME industry case, this study aims to reduce the negative impacts of miscommunication between internal and external stakeholders and contribute to needs elicitation and system validation process. The results show that visual ConOps can effectively support the need elicitation process, which is crucial for early validation, however, it may not independently serve as a comprehensive communication tool between the developer team and stakeholders. It is essential to supplement visual ConOps with complementary tools to effectively convey stakeholder input to the developer team.

The efficacy and safety of the training process in sumo through autogenic training for professional wrestlers

Background and purpose of the study. Considering the high demands for physical and psychological preparedness in sumo, the implementation of comprehensive approaches to training becomes relevant. Such an approach ensures the improvement of sports results and the health maintenance of athletes. The study is aimed at analyzing the effectiveness of autogenic training in the educational and training process among experienced sumo wrestlers. Materials and Methods. The analysis is based on the observation of 10 experienced sumo wrestlers from the "Adrenaline" sports club in Kharkiv, Ukraine. Methods used to assess the psycho-emotional and physical state of athletes included the SAN test, heart rate measurement, the Stange and Genchi tests, reaction time measurement, and the Romberg test. Mastery of autogenic training techniques took place in a group over 5 weeks at the end of each training session. After the first week, a visualization technique was added to the autogenic training. For the analysis and visualization of the obtained data it was used the integrated development environment PyCharm Community Edition. The programming code was written in Python. The experiment was conducted in Kharkiv (Ukraine), which has been located in a war zone since February 2022. The requirements of the military administration for ensuring the safety of the experiment participants were observed. Results. Significant improvements were observed in the psycho-emotional state of athletes, including a reduction in anxiety levels by 6.5 points (p<0.01) and an improvement in reactionmetry indicators by 3.4 cm (p < 0.05). Improvements were also noted in the well-being, mood, and activity of athletes. At the end of the experiment, no significant changes were observed in heart rate and the Stange and Genchi tests. A noticeable improvement was shown in the Genchi test, although these changes did not reach statistical significance. The results of the reactionmetry significantly improved, confirming the effectiveness of the training in enhancing the athletes' reaction speed. Conclusions. The study confirmed that autogenic training is an effective means of improving not only the psycho-emotional state but also the physical readiness of sumo wrestlers. The implementation of such methods can contribute to increasing the safety and effectiveness of the educational and training process, as well as achieving higher sports results while maintaining the health of athletes.

Applying Artificial Intelligence in Malaria Mosquito Research: A Bibliometric Study on Species Identification and Automated Detection

This study presents a bibliometric analysis on the integration of Artificial Intelligence in identifying and automatically detecting malaria mosquito species. The research explores trends and patterns in malaria research literature using a bibliometric data analysis approach. Data from 321 malaria research publications were collected and analyzed to identify frequently occurring keywords, authors, institutions, and collaborations among researchers. The analysis process involved sorting 1304 keywords by frequency, with "malaria," "artificial intelligence," and "machine learning" emerging as dominant keywords. Visualization techniques such as bar charts, word clouds, and network graphs were employed to understand keyword distribution, relationships, and evolution over time. The study highlights the interdisciplinary approach in current malaria research, combining medical, biological, and computational sciences to address this global health issue. By utilizing Python programming for analysis and visualization, this research demonstrates the effective processing and visualization of bibliometric data, providing deep insights into the patterns and trends in malaria research.

Real-time collection of the functional parameters for a passive house

This paper explores the concept of a real-time collection of functional parameters for passive houses and its significance in achieving energy efficiency and occupant comfort. In this case, both the specific parameters of the HVAC system and the electrical parameters will be analyzed with high accuracy. In addition, the microgrid power system application includes wind power, solar power and batteries connected online to the grid to help ensure continuous power supply for the house. The implemented solution aims at various technologies, sensors, and smart meters used for collecting meaningful information in passive houses. It also discusses the role of data analytics and visualization techniques with the purpose of providing a user-friendly interface. For straightforward analysis, all collected information will be stored both in the cloud and on a personal server.

Evaluating the feasibility of using augmented reality for tooth preparation

ObjectivesTooth preparation is complicated because it requires the preparation of an abutment while simultaneously predicting the ideal shape of the tooth. This study aimed to develop and evaluate a system using augmented reality (AR) head-mounted displays (HMDs) that provide dynamic navigation capabilities for tooth preparation. MethodsThe proposed system utilizes optical see-through HMDs to overlay digital information onto the real world and enrich the user's environment. By integrating tracking algorithms and three-dimensional modeling, the system provides real-time visualization and navigation capabilities during tooth preparation by using two different visualization techniques. The experimental setup involved a comprehensive analysis of the distance to the surface and cross-sectional angles between the ideal and prepared teeth using three scenarios: traditional (without AR), overlay (AR-assisted visualization of the ideal prepared tooth), and cross-sectional (AR-assisted visualization with cross-sectional views and angular displays). ResultsA user study (N = 24) revealed that the cross-sectional approach was more effective for angle adjustment and reduced the occurrence of over-reduction. Additional questionnaires revealed that the AR-assisted approaches were perceived as less difficult, with the cross-sectional approach excelling in terms of performance. ConclusionsVisualization and navigation using cross-sectional approaches have the potential to support safer tooth preparation with less overreduction than traditional and overlay approaches do. The angular displays provided by the cross-sectional approach are considered helpful for tooth preparation. Clinical significanceThe AR navigation system can assist dentists during tooth preparation and has the potential to enhance the accuracy and safety of prosthodontic treatment.

MODERN STATE OF APPLICATION OF AI TECHNOLOGIES IN CHEMICAL EDUCATION: PROBLEMS AND APPROACHES

The integration of artificial intelligence (AI) technologies into education systems has been a transformative force across multiple disciplines, with chemistry education proving particularly fertile ground for innovation. As we consider the prospects of AI in chemistry education, it is important to recognize the unique opportunities and challenges that this integration brings. AI technologies, ranging from machine learning algorithms to advanced simulation tools, offer unprecedented opportunities to improve the way chemistry is taught and learned. By introducing interactive content, personalized learning experiences, and improved visualization techniques, these technologies not only enable deeper understanding of complex chemical concepts but also transform traditional teaching methods. This article examines the current state of AI applications in chemistry education and highlights the significant benefits and potential future developments that could further transform this field. As AI continues to advance, its role in educational contexts, particularly in chemistry, promises to be both transformative and far-reaching, paving the way for more effective educational technologies. Keywords: chemistry, artificial intelligence, chemistry education, modern education trends, methods of teaching chemistry.

Visualization techniques of grease fluidity

Energy-saving technology has become increasingly significant as one of the carbon-neutral options for suppressing recent global warming. Grease-lubricated bearings have been used in various automotive and industrial machinery, requiring low torque and long service life for energy-saving performance, which is greatly influenced by grease fluidity. A numerical approach for understanding grease fluidity is very complex since grease is a non-Newtonian fluid with thixotropic properties. Visualization technique is one of the helpful methods to understand the complex grease fluidity and apply it to practical use. This paper describes state-of-the-art visualization techniques, such as fluorescence method, particle imaging velocimetry, infrared spectroscopy, X-rays, and neutron beams.