Effective Visualization Tool Research Articles

The Opportunities and Barriers in Considering Cumulative Effects for Landscape Assessments

Cumulative effects are defined as the joint and aggregated effects of many factors and processes. Their consideration in landscape and environmental assessments are integral at both the project and strategic level, and they can help to bridge the different spatial and temporal scales. The primary challenge of conducting cumulative effect assessments (CEAs) is the difficulty in understanding the complicated nature of cumulative effects. We used three criteria for a systematic understanding of the barriers to addressing cumulative effects that are critical for improving knowledge systems for sustainable development and environmental assessment: salience, credibility, and legitimacy, and analyzed three cases through a variety of studies and resources: the Middle Humber in the U.K., the Transboundary Crown of the Continent in the U.S. and Canada, and the Great Sandhills in Canada, to understand how CEAs have been applied and obstructed in terms of the three criteria. In addition, a series of focus group interviews with experts and practitioners were performed to illuminate the critical barriers based on the criteria for addressing CEA in the context of South Korea. Based on the lessons, we suggest several key strategies such as securing a cooperative consulting process, and active and transparent partnerships; using a strategic environmental assessment as a framework; understanding and incorporating stakeholder knowledge; using advanced computer modeling and simulation techniques including effective visualization tools; and preparing a simple model design and understandable scientific information.

Wideband compressive beamforming tomography for drive-by large-scale acoustic source mapping.

Noise-mapping is an effective sound visualization tool for the identification of urban noise hotspots, which is crucial to taking targeted measures to tackle environmental noise pollution. This paper develops a high-resolution wideband acoustic source mapping methodology using a portable microphone array, where the joint localization and power spectrum estimation of individual sources sparsely distributed over a large region are achieved by tomographic imaging with the multi-frequency delay-and-sum beamforming power outputs from multiple array positions. Exploiting the fact that a wideband source has a common spatial signal-support across the frequency spectrum, two-dimensional tomographic maps are produced by applying compressive sensing techniques including group least absolute shrinkage selection operator formulation and sparse Bayesian learning to promote group sparsity over multiple frequency bands. The high-resolution mapping is demonstrated with experimental data recorded with a microphone array mounted atop an electric vehicle driven along a road while playing audio clips from a loudspeaker positioned within the adjacent open field.

Coherent anti-Stokes Raman scattering as an effective tool for visualization of single-wall carbon nanotubes.

Strong vibrational coherent anti-Stokes Raman scattering (CARS) signal was observed in single-wall carbon nanotubes (SWCNTs) having three different average diameters (about 0.8 nm, 1.1 nm and 1.3 nm) under optical excitation close to electronic resonance energies of nanotubes. By varying the excitation power from 1 up to 200 µW an optimal regime for non-destructive investigation of nonlinear properties of SWCNTs was determined. The possibility to detect a strong coherent nonlinear signal from small SWCNT-bundles together with CARS advantages over Raman scattering, such as high imaging rate, open new opportunities for fast three-dimensional visualisation of SWCNTs in a polymer matrix.

Metaviz: interactive statistical and visual analysis of metagenomic data.

Large studies profiling microbial communities and their association with healthy or disease phenotypes are now commonplace. Processed data from many of these studies are publicly available but significant effort is required for users to effectively organize, explore and integrate it, limiting the utility of these rich data resources. Effective integrative and interactive visual and statistical tools to analyze many metagenomic samples can greatly increase the value of these data for researchers. We present Metaviz, a tool for interactive exploratory data analysis of annotated microbiome taxonomic community profiles derived from marker gene or whole metagenome shotgun sequencing. Metaviz is uniquely designed to address the challenge of browsing the hierarchical structure of metagenomic data features while rendering visualizations of data values that are dynamically updated in response to user navigation. We use Metaviz to provide the UMD Metagenome Browser web service, allowing users to browse and explore data for more than 7000 microbiomes from published studies. Users can also deploy Metaviz as a web service, or use it to analyze data through the metavizr package to interoperate with state-of-the-art analysis tools available through Bioconductor. Metaviz is free and open source with the code, documentation and tutorials publicly accessible.

Conceptual Design and Non-Linear Analysis of Triphibian Drone

Drones are increasingly used to replicate human presence and are extremely capable whether it is in terms of delivering aid to war torn areas or carrying reconnaissance missions beyond borders. Due to their increasing demand in all forms, an arsenal of drones has to be maintained and there is a need to make existing drones capable of maneuvering wherever the need be. Design of drone with land, water and air affinity is proposed along with non-linear analysis of drone structure. Physical contacts in the drone structure and gravity is accounted for and simulated. Weight carrying capacity of the drone is measured by increasing the weight of the components in each simulation run till the point of failure. Landing capability, structural integrity and stress is studied to ensure the structure doesn't collapse. Effective visualization tool for variable thrust, rpm and forward speed of the drone is developed, which is able to generate real time data regarding the drone's flight by plotting a three dimensional surface plot of varying thrust, rpm and forward flight speed. Model of the drone is used to study the flight plan of the drone with gravity considerations in non-linear analysis.

Explorative visual analytics on interval-based genomic data and their metadata

BackgroundWith the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless “sense-making” of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines.ResultsThis paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps.ConclusionsGeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/, and its source code is available at https://github.com/Genometric/GeMSE under GPLv3 open-source license.

Toward an Alternative to the Virtual Museum Concept: Collective Memory and the Search for an Inclusive Approach to Digital Triathlon History Making

Abstract Modern triathlon originated in San Diego in 1974. It transitioned from multisport novelty to global sporting movement after an event colloquially known as the Hawaiian Ironman gained worldwide attention in the early 1980s. The ultradistance triathlon consolidated into a commercial entity, while another arm of the sport embraced Olympism. Both the Ironman and Olympic arms of the sport promoted a collective memory of triathlon that suited their respective claims to authenticity. In the process, they rendered the individual and collective memory of early everyday proponents of the sport irrelevant. When I set out to write an inclusive history of triathlon in Australia, I discovered a deeper and more complex triathlon memory in the form of oral traditions and dispersed historical records held by what one nostalgic Facebook group referred to as “The Old Hands.” In this paper, I reflect on the critical triathlon memory presented by the “Old Hands” and look for ways to escape acts of memory that center on these competing claims to authenticity. As part of my search for a suitable model for an inclusive digital triathlon archive that might facilitate the emergence of alternate narratives, I examine the notion of the fragment, as it relates to individual memory, historical records, and digital content, and the potential that participatory digital mechanisms hold for inducing diverse acts of social memory; and explore the possibility that a geospatial mechanism may provide an effective visual and organizational tool.

Glyph‐Based Comparative Stress Tensor Visualization in Cerebral Aneurysms

AbstractWe present the first visualization tool that enables a comparative depiction of structural stress tensor data for vessel walls of cerebral aneurysms. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. Medical researchers emphasize the importance of analyzing the interaction of morphological and hemodynamic information for the patient‐specific rupture risk evaluation and treatment analysis. Tensor data such as the stress inside the aneurysm walls characterizes the interplay between the morphology and blood flow and seems to be an important rupture‐prone criterion. We use different glyph‐based techniques to depict local stress tensors simultaneously and compare their applicability to cerebral aneurysms in a user study. We thus offer medical researchers an effective visual exploration tool to assess the aneurysm rupture risk. We developed a GPU‐based implementation of our techniques with a flexible interactive data exploration mechanism. Our depictions are designed in collaboration with domain experts, and we provide details about the evaluation.

Cloud services as an effective visualization tool

The paper studied the "cloud" technologies as the current vector in the imaging and the most important aspect in the preparation of modern skilled workers. The study is theoretical justification of cloud services as a tool for creating an effective visualization of educational material. The objective of the study is to analyze the main tools for creating high-quality visualizations based cloud services, which use allows to intensify the learning process, increases the effectiveness of training time and costs shows higher learning outcomes. Object is a learning process in secondary school; the subject of theoretical research is the application of imaging technology in the learning process. The study uses general scientific methods of research, study and analysis of psycho-pedagogical, special and methodological literature of examining the problems of generalization of domestic and foreign pedagogical experience. The result of the study is to highlight the usefulness of didactic "cloud" services for information visualization in the educational system schools. A promising area of future research is the study of theoretical and methodological foundations of future teacher to the effective use of imaging technology in the learning process.

Interactive tool SMART kapp in modern education

One of the effective ways in organizing education with informational technologies and media education is a productive use of SMART Education and cloud technologies. The aim of this research is to disclosure the essence of modern educational SMART kapp tool, which is built on principle includes cloud technology. The objectives of the research are to analyze the principle of SMART kapp; to identify opportunities to use it in the classroom. The object of research is the modern educational process. The subject of research is modern tools used for teaching in higher education. The author analyzed the native and foreign experience. It is established that due SMART kapp training participants can take part in training, brainstorming, or presentations regardless of location. Using the tool SMART kapp, which is built on a cloudy principle in modern educational process is an effective tool for collaboration and information visualization. The results of the research will be used to train future teachers of Informatics to use modern technology in professional activities.

Localization and Tracking of Implantable Biomedical Sensors.

Implantable sensor systems are effective tools for biomedical diagnosis, visualization and treatment of various health conditions, attracting the interest of researchers, as well as healthcare practitioners. These systems efficiently and conveniently provide essential data of the body part being diagnosed, such as gastrointestinal (temperature, pH, pressure) parameter values, blood glucose and pressure levels and electrocardiogram data. Such data are first transmitted from the implantable sensor units to an external receiver node or network and then to a central monitoring and control (computer) unit for analysis, diagnosis and/or treatment. Implantable sensor units are typically in the form of mobile microrobotic capsules or implanted stationary (body-fixed) units. In particular, capsule-based systems have attracted significant research interest recently, with a variety of applications, including endoscopy, microsurgery, drug delivery and biopsy. In such implantable sensor systems, one of the most challenging problems is the accurate localization and tracking of the microrobotic sensor unit (e.g., robotic capsule) inside the human body. This article presents a literature review of the existing localization and tracking techniques for robotic implantable sensor systems with their merits and limitations and possible solutions of the proposed localization methods. The article also provides a brief discussion on the connection and cooperation of such techniques with wearable biomedical sensor systems.

Estimating a Covariance Matrix for Market Risk Management and the Case of Credit Default Swaps

We analyze correlation matrix estimation from the perspective of market risk management, where the goal is to obtain accurate estimates of portfolio risk across essentially all portfolios — even those with small standard deviations. We use the portfolio perspective to determine estimators, loss functions and regularizers particularly suitable for market risk management. We propose several specialized loss functions, and a simple but effective visualization tool to assess estimators. Proper regularization of the correlation matrix significantly improves dynamic covariance models. These methods are applied to credit default swaps (CDS), for which correlation matrices are used to set portfolio margin requirements for central clearing. Among the methods we test, the graphical lasso estimator performs particularly well. The graphical lasso and a hierarchical clustering estimator also yield economically meaningful representations of market structure through a graphical model and a hierarchy, respectively. We find that credit default swap log-differences are driven by a strong market factor. The additional effect of natural candidates for other observable market factors is small, but there are latent factors and direct pairwise dependencies at play. We also examine the relationship between credit default swap correlations and implied correlations extracted from equity prices through distance-to-default measures. The difference between actual and implied log-differences is driven by a common factor that may reflect a premium for risk and possibly liquidity.

Statistical data presentation.

Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed, and analyzed. However, no matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers. In this article, the techniques of data and information presentation in textual, tabular, and graphical forms are introduced. Text is the principal method for explaining findings, outlining trends, and providing contextual information. A table is best suited for representing individual information and represents both quantitative and qualitative information. A graph is a very effective visual tool as it displays data at a glance, facilitates comparison, and can reveal trends and relationships within the data such as changes over time, frequency distribution, and correlation or relative share of a whole. Text, tables, and graphs for data and information presentation are very powerful communication tools. They can make an article easy to understand, attract and sustain the interest of readers, and efficiently present large amounts of complex information. Moreover, as journal editors and reviewers glance at these presentations before reading the whole article, their importance cannot be ignored.

Combined Visualization of Vessel Deformation and Hemodynamics in Cerebral Aneurysms.

We present the first visualization tool that combines patient-specific hemodynamics with information about the vessel wall deformation and wall thickness in cerebral aneurysms. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. For the patient-specific rupture risk evaluation and treatment analysis, both morphological and hemodynamic data have to be investigated. Medical researchers emphasize the importance of analyzing correlations between wall properties such as the wall deformation and thickness, and hemodynamic attributes like the Wall Shear Stress and near-wall flow. Our method uses a linked 2.5D and 3D depiction of the aneurysm together with blood flow information that enables the simultaneous exploration of wall characteristics and hemodynamic attributes during the cardiac cycle. We thus offer medical researchers an effective visual exploration tool for aneurysm treatment risk assessment. The 2.5D view serves as an overview that comprises a projection of the vessel surface to a 2D map, providing an occlusion-free surface visualization combined with a glyph-based depiction of the local wall thickness. The 3D view represents the focus upon which the data exploration takes place. To support the time-dependent parameter exploration and expert collaboration, a camera path is calculated automatically, where the user can place landmarks for further exploration of the properties. We developed a GPU-based implementation of our visualizations with a flexible interactive data exploration mechanism. We designed our techniques in collaboration with domain experts, and provide details about the evaluation.

Digital holographic microscopy of phase separation in multicomponent lipid membranes.

Lateral in-homogeneities in lipid compositions cause microdomains formation and change in the physical properties of biological membranes. With the presence of cholesterol and mixed species of lipids, phospholipid membranes segregate into lateral domains of liquid-ordered and liquid-disordered phases. Coupling of two-dimensional intralayer phase separations and interlayer liquid-crystalline ordering in multicomponent membranes has been previously demonstrated. By the use of digital holographic microscopy (DHMicroscopy), we quantitatively analyzed the volumetric dynamical behavior of such membranes. The specimens are lipid mixtures composed of sphingomyelin, cholesterol, and unsaturated phospholipid, 1,2-dioleoyl-sn-glycero-3-phosphocholine. DHMicroscopy in a transmission mode is an effective tool for quantitative visualization of phase objects. By deriving the associated phase changes, three-dimensional information on the morphology variation of lipid stacks at arbitrary time scales is obtained. Moreover, the thickness distribution of the object at demanded axial planes can be obtained by numerical focusing. Our results show that the volume evolution of lipid domains follows approximately the same universal growth law of previously reported area evolution. However, the thickness of the domains does not alter significantly by time; therefore, the volume evolution is mostly attributed to the changes in area dynamics. These results might be useful in the field of membrane-based functional materials.

Geostatistical modeling of fluoride enrichment and nitrate contamination in the groundwater of Lower Bhavani Basin in Tamil Nadu, India

Contamination of groundwater is posing serious threat to the household and drinking water supply schemes all over the world. Increase in concentration of Fluoride (F−) and Nitrate (NO3 −) in the groundwater is frequently reported from Indian subcontinent. In this paper, we have studied the F− and NO3 − concentration in the Lower Bhavani Basin located in Tamil Nadu state in south India. This is a hard rock dominated semi-arid climatic region, where groundwater is the potential source of drinking and agricultural purposes. Among the total 36 groundwater samples, 22% showed a higher concentration than the permissible of Indian Standard 1.2 mg/L for fluoride. The influence of geochemical factors such as pH, conductivity and Na/Ca ratios were identified. On the other hand, 16% of the samples exceeded the permissible limit of nitrate (45 mg/L). The source of nitrate is identified preliminarily as fertilizer usage in the agricultural fields and secondarily from the sewages. Kriging based Geostatistical model were used the spatial variation mapping of these ions. For fluoride, spatial variation maps showed definite correlation with the Geological formations, i.e., the high fluoride samples were observed in the NE regions of the basin. In case of Nitrate, higher concentration was observed in the Southern part of the basin. Mapping of water quality parameters spatially is proved to be an effective tool for better visualization and management of the groundwater resources in the study area.

Quantitative Multi-Parametric Magnetic Resonance Imaging of Tumor Response to Photodynamic Therapy.

ObjectiveThe aim of this study was to characterize response to photodynamic therapy (PDT) in a mouse cancer model using a multi-parametric quantitative MRI protocol and to identify MR parameters as potential biomarkers for early assessment of treatment outcome.MethodsCT26.WT colon carcinoma tumors were grown subcutaneously in the hind limb of BALB/c mice. Therapy consisted of intravenous injection of the photosensitizer Bremachlorin, followed by 10 min laser illumination (200 mW/cm2) of the tumor 6 h post injection. MRI at 7 T was performed at baseline, directly after PDT, as well as at 24 h, and 72 h. Tumor relaxation time constants (T1 and T2) and apparent diffusion coefficient (ADC) were quantified at each time point. Additionally, Gd-DOTA dynamic contrast-enhanced (DCE) MRI was performed to estimate transfer constants (Ktrans) and volume fractions of the extravascular extracellular space (ve) using standard Tofts-Kermode tracer kinetic modeling. At the end of the experiment, tumor viability was characterized by histology using NADH-diaphorase staining.ResultsThe therapy induced extensive cell death in the tumor and resulted in significant reduction in tumor growth, as compared to untreated controls. Tumor T1 and T2 relaxation times remained unchanged up to 24 h, but decreased at 72 h after treatment. Tumor ADC values significantly increased at 24 h and 72 h. DCE-MRI derived tracer kinetic parameters displayed an early response to the treatment. Directly after PDT complete vascular shutdown was observed in large parts of the tumors and reduced uptake (decreased Ktrans) in remaining tumor tissue. At 24 h, contrast uptake in most tumors was essentially absent. Out of 5 animals that were monitored for 2 weeks after treatment, 3 had tumor recurrence, in locations that showed strong contrast uptake at 72 h.ConclusionDCE-MRI is an effective tool for visualization of vascular effects directly after PDT. Endogenous contrast parameters T1, T2, and ADC, measured at 24 to 72 h after PDT, are also potential biomarkers for evaluation of therapy outcome.

Knowledge Construction in Computer Science and Engineering when Learning Through Making

This paper focuses on a design based research study about STEM (Science, Technology, Engineering and Maths) learning by making through collaboration and production. This study examines learning by making by students to explore STEM using a constructionist approach with a particular focus on computer science and engineering. The use of IoT as a technology enhanced learning (TEL) tool created the learning conditions to be studied: (a) collaborative: no one person had the knowledge to complete the project alone, (b) problem-based: no off the shelf solution was used, and (c) multidisciplinary: the learning context pushed the boundaries across the subjects. The study investigated the learning conditions and indicators of collaboration and production taking place when learning about STEM. The results were used to inform the design of effective data analytics and visualization tools for the PELARS project to advance practice-based learning activities in STEM teaching. However, more specifically, the findings provide insight into the knowledge construction process when learning through making in complex environments. These insights illustrate the combined pedagogical value of collaboration and production supporting the multidisciplinary learning opportunities. The importance of community knowledge construction and its relationship to the pedagogical approach is examined. The significance of these findings in the context of IoT TEL tools in education is explored.

3D augmented reality for improving social acceptance and public participation in wind farms planning

Wind energy is one of the most important source of renewable energy characterized by a significant growth in the last decades and giving a more and more relevant contribution to the energy supply. One of the main disadvantages of a faster integration of wind energy into the energy mix is related to the visual impact of wind turbines on the landscape. In addition, the siting of new massive infrastructures has the potential to threaten a community's well-being if new projects are perceived being unfair. The public perception of the impact of wind turbines on the landscape is also crucial for their acceptance. The implementation of wind energy projects is hampered often because of a lack of planning or communication tools enabling a more transparent and efficient interaction between all stakeholders involved in the projects (i.e. developers, local communities and administrations, NGOs, etc.). Concerning the visual assessment of wind farms, a critical gap lies in effective visualization tools to improve the public perception of alternative wind turbines layouts. In this paper, we describe the advantages of a 3D dynamical and interactive visualization platform for an augmented reality to support wind energy planners in order to enhance the social acceptance of new wind energy projects.

SambVca 2. A Web Tool for Analyzing Catalytic Pockets with Topographic Steric Maps

Developing more efficient catalysts remains one of the primary targets of organometallic chemists. To accelerate reaching this goal, effective molecular descriptors and visualization tools can represent a remarkable aid. Here, we present a Web application for analyzing the catalytic pocket of metal complexes using topographic steric maps as a general and unbiased descriptor that is suitable for every class of catalysts. To show the broad applicability of our approach, we first compared the steric map of a series of transition metal complexes presenting popular mono-, di-, and tetracoordinated ligands and three classic zirconocenes. This comparative analysis highlighted similarities and differences between totally unrelated ligands. Then, we focused on a recently developed Fe(II) catalyst that is active in the asymmetric transfer hydrogenation of ketones and imines. Finally, we expand the scope of these tools to rationalize the inversion of enantioselectivity in enzymatic catalysis, achieved by point mutat...