Visualizing experimental investigation on gas-liquid replacements in a microcleat model using the reconstruction method

Cleats are the main channels for fluid transport in coal reservoirs.However,the microscale flow characteristics of both gas and water phases in primary cleats have not been fully studied as yet.Accordingly,the local morphological features of the cleat were determined using image processing technology and a transparent cleat structure model was constructed by microfluidic lithography using the multiphase fluid visualization test system.Besides,the effect of microchannel tortuosity characteristics on two‐phase flow was analyzed in this study.The results are as follows:(1)The local width of the original cleat structure of coal was strongly nonhomogeneous.The cleats showed contraction and expansion in the horizontal direction and undulating characteristics in the vertical direction.(2)The transient flow velocity fluctuated due to the structural characteristics of the primary cleat.The water‐driven gas interface showed concave and convex instability during flow,whereas the gas‐driven water interface presented a relatively stable concave surface.(3)The meniscus advanced in a symmetrical pattern in the flat channel,and the flow stagnated due to the influence of undulation points in a partially curved channel.The flow would continue only when the meniscus surface bypassed the stagnation point and reached a new equilibrium position.(4)Enhanced shearing at the gas-liquid interface increased the gas‐injection pressure,which in turn increased residual liquids in wall grooves and liquid films on the wall surface.

Method for visualizing the shear process of rock joints using 3D laser scanning and 3D printing techniques

This study presents a visualized approach for tracking joint surface morphology.Three-dimensional laser scanning(3DLS)and 3D printing(3DP)techniques are adopted to record progressive failure during rock joint shearing.The 3DP resin is used to create transparent specimens to reproduce the surface morphology of a natural joint precisely.The freezing method is employed to enhance the mechanical properties of the 3DP specimens to reproduce the properties of hard rock more accurately.A video camera containing a charge-coupled device(CCD)camera is utilized to record the evolution of damaged area of joint surface during the direct shear test.The optimal shooting distance and shooting angle are recommended to be 800 mm and 40?,respectively.The images captured by the CCD camera are corrected to quantitatively describe the damaged area on the joint surface.Verification indicates that this method can accurately describe the total sheared areas at different shear stages.These findings may contribute to elucidating the shear behavior of rock joints.

Computer-based sensing and visualizing of metal transfer mode in gas metal arc welding

Using Xenon lamp lights to overcome the strong interference from the welding arc, a computer-based system is developed to sense and visualize the metal transfer in GMAW. This system combines through-the-arc sensing of the welding current and arc voltage with high speed imaging of the metal transfer. It can simultaneously display the metal transfer processes and waveforms of electrical welding parameters in real-time The metal transfer videos and waveforms of electrical welding parameters can be recorded. Metal transfers under various welding conditions have been investigated with the system developed.

Visualizing Association Rules Using Linked Matrix,Graph, and Detail Views

Although association rule mining is an important pattern recognition and data analysis technique, extracting and finding significant rules from a large collection has always been challenging. The ability of information visualization to enable users to gain an understanding of high dimensional and large-scale data can play a major role in the exploration, identification, and interpretation of association rules. In this paper, we propose a method that provides multiple views of the association rules, linked together through a filtering mechanism. A visual inspection of the entire association rule set is enabled within a matrix view. Items of interest can be selected, resulting in their corresponding association rules being shown in a graph view. At any time, individual rules can be selected in either view, resulting in their information being shown in the detail view. The fundamental premise in this work is that by providing such a visual and interactive representation of the association rules, users will be able to find important rules quickly and easily, even as the number of rules that must be inspected becomes large. A user evaluation was conducted which validates this premise.

Visualizing diffusion tensor fields on streamsurfaces with merging ellipsoids

Streamsurfaces in diffusion tensor fields are used to represent structures with pri- marily planar diffusion. So far, however, no effort has been made on the visualization of the anisotropy of diffusion on them, although this information is very important to identify the problematic regions of these structures. We propose two methods to display this anisotropy information. The first one employs a set of merging ellipsoids, which simultaneously character- ize the local tensor details - anisotropy - on them and portray the shape of the streamsurfaces. The weight between the streamsurfaces continuity and the discrete local tensors can be inter- actively adjusted by changing some given parameters. The second one generates a dense LIC （line integral convolution） texture of the two tangent eigenvector fields along the streamsurfaces firstly, and then blends in some color mapping indicating the anisotropy information. For high speed and high quality of texture images, we confine both the generation and the advection of the LIC texture in the image space. Merging ellipsoids method reveals the entire anisotropy information at discrete points by exploiting the geometric attribute of ellipsoids, and thus suits for local and detailed examination of the anisotropy; the texture-based method gives a global representation of the anisotropy on the whole streamsurfaces with texture and color attributes. To reveal the anisotropy information more efficiently, we integrate the two methods and use them at two different levels of details.

Plant-based meat substitutes by high-moisture extrusion:Visualizing the whole process in data systematically from raw material to the products

High-moisture extrusion technology should be considered one of the best choices for producing plant-based meat substitutes with the rich fibrous structure offered by real animal meat products.Unfortunately,the extrusion process has been seen as a“black box”with limited information about what occurs inside,causing serious obstacles in developing meat substitutes.This study designed a high-moisture extrusion process and developed 10 new plant-based meat substitutes comparable to the fibrous structure of real animal meat.The study used the Feature-Augmented Principal Component Analysis(FA-PCA)method to visualize and understand the whole extrusion process in three ways systematically and accurately.It established six sets of mathematical models of the high-moisture extrusion process based on 8000 pieces of data,including five types of parameters.The FA-PCA method improved the R^(2) values significantly compared with the PCA method.The Way 3 was the best to predict product quality(Z),demonstrating that the gradually molecular conformational changes(Y^(n'))were critical in controlling the final quality of the plant-based meat substitutes.Moreover,the first visualization platform software for the high-moisture extrusion process has been established to clearly show the“black box”by combining the virtual simulation technology.Through the software,some practice work such as equipment installation,parameter adjustment,equipment disassembly,and data prediction can be easily achieved.

Measuring and Visualizing Research Collaboration and Productivity

Purpose： This paper presents findings of a quasi-experimental assessment to gauge the research productivity and degree of interdisciplinarity of research center outputs. Of special interest, we share an enriched visualization of research co-authoring patterns. Design/methodology/approach： We compile publications by 45 researchers in each of 1） the iUTAH project, which we consider here to be analogous to a ＂research center,＂ 2） CG1-a comparison group of participants in two other Utah environmental research centers, and 3） CG2--a comparison group of Utah university environmental researchers not associated with a research center. We draw bibliometric data from Web of Science and from Google Scholar. We gather publications for a period before iUTAH had been established （2010-2012） and a period after （2014-2016）. We compare these research outputs in terms of publications and citations thereto. We also measure interdisciplinarity using Integration scoring and generate science overlay maps to locate the research publications across disciplines. Findings： We find that participation in the iUTAH project appears to increase research outputs （publications in the After period） and increase research citation rates relative to the comparison group researchers （although CG 1 research remains most cited, as it was in the Before period）. Most notably, participation in iUTAH markedly increases co-authoring among researchers--in general; and for junior, as well as senior, faculty; for men and women： across organizations; and across disciplines. Research limitations： The quasi-experimental design necessarily generates suggestive, not definitively causal, findings because of the imperfect controls. Practical implications： This study demonstrates a viable approach for research assessment of a center or program for which random assignment of control groups is not possible. It illustrates use of bibliometric indicators to inform R＆D program management. Originality/value： New visualizations of researcher collaboration provide compelling comparisons of the extent and nature of social networking among target cohorts.

Visualizing complex pore structure and fluid flow in porous media using 3D printing technology and LBM simulation

Pore structures of porous media and properties of fluid flow are key factors for the study of non-Darcy groundwater flow. However, it is difficult to directly observe pore structures and flow properties, resulting in a "black box" problem of porous media. This problem has hindered the in-depth study of the groundwater flow mechanism at the pore scale. In recent years, 3D rapid prototyping technology has seen tremendous development. 3D printing provides digital models and printing models of porous media with clear internal structure. Thus, Lattice Boltzmann Method can be used to simulate the flow processes at the pore scale based on real pore structures. In this study, 3D printing cores and Lattice Boltzmann Method were coupled to conduct both laboratory and numerical experiments in spherical porous media with different sphere diameters and periodic arrays. The LBM simulation results show a good agreement with laboratory experimental results. With the advantages of LBM and 3D printing, this approach provides a visualization of the complex pore structure and fluid flow in pores, which is a promising method for studies of non-Darcy groundwater flow at the pore scale.

Visualizing Investment Decision on Decision Balls

Decision makers’ choices are often influenced by visual background information. This study uses open-ended equity funds in Taiwan to investigate three well-known optimal portfolio models, including the mean-variance, maximin, and minimization of mean absolute deviation. The optimal portfolios are then visualized on Decision Balls to assist investors in making investment decisions. By observing the Decision Balls, investors can see the optimal portfolios, compare the optimal weights provided by the different models, view the cluster of funds, and even find substitute funds if preferred funds are not available.

Geological Panorama Database:Digitizing and Visualizingthe Geological Outcrops

1 Introduction Geological outcrops or sections are the basis of geological research.However,the traditional methods for presenting them are mainly photos which fall short of delivering a true visual sense (Deng et al.,2009;Hou et al.,2014).With the continuous development of image acquisition technology using single-lens reflex camera (SLR camera),image synthesis,large file storage and acquisition,panoramic visualization and network technology.

Visualizing the Earth in Earthquake Education:Investigating the Value of Visuals Used in Southern California's Shakeout Public Earthquake Public Earthquake Drill

The value of visuals used in public earthquake education is a theme essentially unexplored.This Los Angeles-based study is a preliminary investigation into this topic,focusing on the pioneering 2008 ShakeOut Southern California public earthquake drill: the largest earthquake drill in US history.The ShakeOut scenario is based on a magnitude 7.8 earthquake occurring on the San Andreas Fault,and

Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related articles.Despite the present digital era of big data,there is a lack of visual representations of the knowledge present in scholarly articles,and a time-saving approach for a literature search and visual navigation is warranted.The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation,storage,and dynamic visualization.To address this limitation,the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories.Contemporary topics of research and practice,including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia,warrant deeper insight into the evidence-based knowledge available in the literature.The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles.This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge,such as dementia risk factors.The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles.It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles.Further,it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations.In this study,the implementation of the proposed model in the Neo4j graph data repository,along with the results achieved,is presented as a proof of concept.Using scholarly research articles on dementia risk factors as a case study,automatic knowledge extraction,storage,intelligent search,and visual navigation are illustrated.The implementation of contextual knowledge and its relationship for a visual exploration by researchers show promising results in the knowledge discovery of dementia risk factors.Overall,this study demonstrates the significance of a semantic visualization with the effective use of knowledge maps and paves the way for extending visual modeling capabilities in the future.

A method for visualizing sound propagation in solids and liquids

A new method for visualizing sound propagation in solids and liquids is described in this paper. The method can show the sound propagation process dynamically in two dimensions. Compared with Schlieren method and dynamic photo-elastic method, this method cannot only show the sound field distribution in liquid and solid at different time moments, but also can be applied to non-transparent solid. In addition, it does not strictly require small residual stress of the sample. The sample can, therefore, be easily made. Because the acoustic field is obtained by indirect measurements, the recording can be affected by the after-shock of the receiving sensor and is prone to the influence of the direct wave of the liquid. Putting an aluminum plate into a liquid, we recorded the compression wave, shear wave and surface wave in the aluminum and, in the liquid we also recorded the direct wave and three head waves, which are directly coupled with the compression wave, shear wave and surface wave respectively. The recording clearly depicts the coupling relationship of the sound waves through the interface between the aluminum and the liquid. Putting a plexiglass into a liquid, we also recorded the sound waves in the plexiglass and the coupling relationship between the sound waves in the two mediums.

A Visualizing Software Developing Environment Based Object Model

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Visualizing veins from color skin images using convolutional neural networks

Intravenous cannulation is the most important phase in medical practices.Currently,limited literature is available about visibility of veins and the characteristics of patients associated with difficult intravenous access.In modern medical treatment,a major challenge is locating veins for patients who have difficult venous access.Presently,some products of vein locators are available in the market to improve vein access,but they need auxiliary equipment such as near infrared(NIR)illumination and camera,which add weight and cost to the devices,and cause inconveniences to daily medical care.In this paper,a vein visualization algorithm based on the deep learning method was proposed.Based on a group of synchronous RGB/NIR arm images,a convolutional neural network(CNN)model was designed to implement the mapping from RGB to NIR images,where veins can be detected from skin.The model has a simple structure and less optimization parameters.A color transfer scheme was also proposed to make the network adaptive to the images taken by smartphone in daily medical treatments.Comprehensive experiments were conducted on three datasets to evaluate the proposed method.Subjective and objective evaluations showed the effec-tiveness of the proposed method.These results indicated that the deep learning-based method can be used for visualizing veins in medical care applications.

Visualizing Clashes and Alliances in Social Networks of Political Discussions

Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this context, it is important to know how allies and opponents are positioned, in order to understand the discussion dynamics and plan adequate actions. This paper suggests the use of social network visualizations to explicit oppositions and alliances in order to support the understanding and following of political discussions. A system which supports these visualizations was built. An experiment performed to test the proposed visualizations showed to which extent they can be more efficient in identifying information about clashes and alliances than an online discussion system can.

Visualizing Need: Using Data Visualization as a Decision Support Tool in the Formulation of Social Policy

International social work is facing an exponentially growing number of critical challenges in developing nations in meeting essential social needs as well as the distribution of material goods, social and economic resources and the provision, and effective services delivery to populations of need. There is a need to respond rapidly to populations in need and adapt to quickly changing socio-economic conditions. However, the resources needed to evaluate data and make effective decisions to provide support are often limited by the availability of trained data analysts who can interpret statistical findings and make relevant service delivery decisions for such communities of need. Modern computing technology shows promise of providing a graphical evaluation approach to enriching understanding of the available information. Thereby, the need to make critical decisions is augmented for those decision-makers that are working most closely with marginalized groups. A recent research effort undertaken by the Adventist Development and Relief Agency (ADRA) sought to provide administrators and policy-makers with information regarding the associations between social capital, economic development, and food security in the mountain region of Perú. Survey data collection and descriptive statistical analysis were used to provide information on the relationship between socio-economic development services and the emergence of social capital needed to develop effective human self-support systems. This paper demonstrates how to provide a graphical interface between areas of economic need and development initiatives. A visual exploration of data patterns would serve to inform human service decision-makers as they develop policies and programs. Visualization of data helps to enrich meaning and understanding of core areas of population need. This enhanced information format can identify needs for social policy development and improve the delivery of critical services and resources. This paper will provide a conceptual argument for considering data visualization methods in the evaluation of significant needs for, at risk, international populations. The paper will also present a number of examples for improving data evaluation and the understanding of need through data visualization methods through widely available computing technology. Procedures for graphically representing data will be demonstrated using the Perú survey, and applications for social policy formulation will be discussed.

Geometric phase-encoded stimuli-responsive cholesteric liquid crystals for visualizing real-time remote monitoring:humidity sensing as a proof of concept

Liquid crystals are a vital component of modern photonics,and recent studies have demonstrated the exceptional sensing properties of stimuli-responsive cholesteric liquid crystals.However,existing cholesteric liquid crystal-based sensors often rely on the naked eye perceptibility of structural color or the measurement of wavelength changes by spectrometric tools,which limits their practical applications.Therefore,developing a platform that produces recognizable sensing signals is critical.In this study,we present a visual sensing platform based on geometric phase encoding of stimuli-responsive cholesteric liquid crystal polymers that generates real-time visual patterns,rather than frequency changes.To demonstrate this platform’s effectiveness,we used a humidity-responsive cholesteric liquid crystal polymer film encoded with a q-plate pattern,which revealed that humidity causes a shape change in the vortex beam reflected from the encoded cholesteric liquid crystal polymers.Moreover,we developed a prototype platform towards remote humidity monitoring benefiting from the high directionality and long-range transmission properties of laser beams carrying orbital angular momentum.Our approach provides a novel sensing platform for cholesteric liquid crystals-based sensors that offers promising practical applications.The ability to generate recognizable sensing signals through visual patterns offers a new level of practicality in the sensing field with stimuli-responsive cholesteric liquid crystals.This platform might have significant implications for a broad readership and will be of interest to researchers working in the field of photonics and sensing technology.

Enhanced optical imaging and fluorescent labeling for visualizing drug molecules within living organisms

The visualization of drugs in living systems has become key techniques in modern therapeutics.Recent advancements in optical imaging technologies and molecular design strategies have revolutionized drug visualization.At the subcellular level,super-resolution microscopy has allowed exploration of the molecular landscape within individual cells and the cellular response to drugs.Moving beyond subcellular imaging,researchers have integrated multiple modes,like optical near-infrared II imaging,to study the complex spatiotemporal interactions between drugs and their surroundings.By combining these visualization approaches,researchers gain supplementary information on physiological parameters,metabolic activity,and tissue composition,leading to a comprehensive understanding of drug behavior.This review focuses on cutting-edge technologies in drug visualization,particularly fluorescence imaging,and the main types of fluorescent molecules used.Additionally,we discuss current challenges and prospects in targeted drug research,emphasizing the importance of multidisciplinary cooperation in advancing drug visualization.With the integration of advanced imaging technology and molecular design,drug visualization has the potential to redefine our understanding of pharmacology,enabling the analysis of drug micro-dynamics in subcellular environments from new perspectives and deepening pharmacological research to the levels of the cell and organelles.

simplifyEnrichment:A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results

Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest.However,it may produce a long list of significant terms with highly redundant information that is difficult to summarize.Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters.We propose a new method named binary cut for clustering similarity matrices of functional terms.Through comprehensive benchmarks on both simulated and real-world datasets,we demonstrated that binary cut could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups.We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut,while similarity matrices based on gene overlap showed less consistent patterns.We implemented the binary cut algorithm in the R package simplifyEnrichment,which additionally provides functionalities for visualizing,summarizing,and comparing the clustering.The simplifyEnrichment package and the documentation are available at https://bioconductor.org/packages/simplifyEnrichment/.