497 research outputs found
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Facilitating insight into a simulation model using visualization and dynamic model previews
This paper shows how model simplification, by replacing iterative steps with unitary predictive equations, can enable dynamic interaction with a complex simulation process. Model previews extend the techniques of dynamic querying and query previews into the context of ad hoc simulation model exploration. A case study is presented within the domain of counter-current chromatography. The relatively novel method of insight evaluation was applied, given the exploratory nature of the task. The evaluation data show that the trade-off in accuracy is far outweighed by benefits of dynamic interaction. The number of insights gained using the enhanced interactive version of the computer model was more than six times higher than the number of insights gained using the basic version of the model. There was also a trend for dynamic interaction to facilitate insights of greater domain importance
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Advanced modelling and visualisation of liquid-liquid separations of complex sample components, with variable phase distribution and mode of operation
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is about liquid-liquid chromatography modelling. While the main focus was on liquid-liquid chromatography, where the stationary and mobile phases are both liquid, theory of different types of chromatography, including the currently most used techniques, were considered as well. The main goal of this research was to develop a versatile liquid-liquid separation model, able to model all potential operating scenarios and modes of operation. A second goal was to create effective and usable interfaces to such a model, implying primarily information visualisation, and secondarily educative visualisation. The first model developed was a model based on Counter-Current Distribution. Next a new more elemental model was developed, the probabilistic model, which better models continuous liquid-liquid chromatography
techniques. Finally, a more traditional model was developed using transport theory. These models were used and compared to experimental data taken from literature. The models were demonstrated to model all main liquid-liquid chromatography techniques, incorporated the different modes of operation, and were able to accurately model many sample components and complex sample injections. A model interface was developed, permitting functional and effective model configuration, exploration and analysis using visualisation and interactivity. Different versions of the interface were then evaluated using questionnaires, group interviews and Insight Evaluation. The visualisation and interactivity enhancements have proven to contribute understanding and insight of the underlying chromatography process. This also proved the value of the Insight Evaluation method, providing valuable qualitative evaluation results desired for this model interface evaluation. A prototype of a new graphical user interface developed, and showed great potential for combining model parameter input and exploring the liquid-liquid chromatography processes. Additionally, a new visualisation method was developed that can accurately visualise different modes of operation. This was used to create animations, which were also evaluated. The results of this evaluation show the new visualisation helps understanding of the liquid-liquid chromatography process amongst CCC novices. The model software will be a valuable tool for industry for predicting, evaluating and validating experimental
separations and production processes. While effective models already existed, the use of
interactive visualisation permits users to explore the relationship between parameters and performances in a simpler yet more powerful way. It will also be a valuable tool for academia for teaching & training, both staff and students, on how to use the technology. Prior to this work no such tool existed or existing tools were limited in their accessibility and educational value.This study was supported by Brunel University and the Royal Academy of Engineering
The interplay between methodologies, tasks and visualisation formats in the study of visual expertise
The author examines the methodological contributions of the ten unique reviews developed in this special issue on the methodologies for studying visual expertise. Opportunities, research results and lessons, offered by each methodology are analyzed according to four dimensions criteria: (i) The different levels of visual and cognitive processes targeted in the different methods; (ii) The effect of the task gaols and  task design on the outcomes; (iii) The potential effect of the format (dynamic versus static) of the visual material and the interactive features provided in previous studies (iv) The potential modulating effect of learners individual differences. Concluding comments are developed  about the limitations of each method but also about the challenge and promises of designing and using combined and synchronized methods
Metric for attractor overlap
We present the first general metric for attractor overlap (MAO) facilitating
an unsupervised comparison of flow data sets. The starting point is two or more
attractors, i.e., ensembles of states representing different operating
conditions. The proposed metric generalizes the standard Hilbert-space distance
between two snapshots to snapshot ensembles of two attractors. A reduced-order
analysis for big data and many attractors is enabled by coarse-graining the
snapshots into representative clusters with corresponding centroids and
population probabilities. For a large number of attractors, MAO is augmented by
proximity maps for the snapshots, the centroids, and the attractors, giving
scientifically interpretable visual access to the closeness of the states. The
coherent structures belonging to the overlap and disjoint states between these
attractors are distilled by few representative centroids. We employ MAO for two
quite different actuated flow configurations: (1) a two-dimensional wake of the
fluidic pinball with vortices in a narrow frequency range and (2)
three-dimensional wall turbulence with broadband frequency spectrum manipulated
by spanwise traveling transversal surface waves. MAO compares and classifies
these actuated flows in agreement with physical intuition. For instance, the
first feature coordinate of the attractor proximity map correlates with drag
for the fluidic pinball and for the turbulent boundary layer. MAO has a large
spectrum of potential applications ranging from a quantitative comparison
between numerical simulations and experimental particle-image velocimetry data
to the analysis of simulations representing a myriad of different operating
conditions.Comment: 33 pages, 20 figure
sMolBoxes: Dataflow Model for Molecular Dynamics Exploration
We present sMolBoxes, a dataflow representation for the exploration and
analysis of long molecular dynamics (MD) simulations. When MD simulations reach
millions of snapshots, a frame-by-frame observation is not feasible anymore.
Thus, biochemists rely to a large extent only on quantitative analysis of
geometric and physico-chemical properties. However, the usage of abstract
methods to study inherently spatial data hinders the exploration and poses a
considerable workload. sMolBoxes link quantitative analysis of a user-defined
set of properties with interactive 3D visualizations. They enable visual
explanations of molecular behaviors, which lead to an efficient discovery of
biochemically significant parts of the MD simulation. sMolBoxes follow a
node-based model for flexible definition, combination, and immediate evaluation
of properties to be investigated. Progressive analytics enable fluid switching
between multiple properties, which facilitates hypothesis generation. Each
sMolBox provides quick insight to an observed property or function, available
in more detail in the bigBox View. The case study illustrates that even with
relatively few sMolBoxes, it is possible to express complex analyses tasks, and
their use in exploratory analysis is perceived as more efficient than
traditional scripting-based methods.Comment: 10 pages, 9 figures, IEEE VIS, TVC
sMolBoxes: Dataflow Model for Molecular Dynamics Exploration
We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.acceptedVersio
Collaborative geographic visualization
DissertaçaÌo apresentada na Faculdade de CiĂȘncias e Tecnologia da Universidade Nova de
Lisboa para a obtençaÌo do grau de Mestre em Engenharia do Ambiente, perfil GestĂŁo e
Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative
visualization purposes.
Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment
Quadruple Helix Engagement: Reviews on Shariah Fintech Based SMEs Digitalization Readiness
The development of Sharia Financial Technology (Fintech) after Covid-19 has experienced significant fluctuations in affecting the countryâs economy. The importance of the role of Financial Transactions in the digitalization readiness of SMEs makes a considerable contribution to the use of Financial Technology as an effort to maintain the sustainability of SMEs. Identification of the involvement of the quadruple Helix (government, investors, academics, and communities) in maintaining the financial stability of SMEs through Sharia Fintech emerged as new challenges and opportunities in improving the economy after Covid 19 in Indonesia. Therefore, a systematic literature review investigation regarding how to utilize Sharia fintech products that are adjusted to the degree of digitization is required to help SMEsâ sustainability as well as explore the role of the quadruple Helix in pursuing this success. We identified 110 papers published on Sharia Fintech for SMEs, SMEs Digitalization Readiness, and The Role of Quadruple Helix between 2003-2021 with 87 specifications from Scopus journals and 23 from proceedings conferences. The analysis was performed using Atlas.ti 9 Software Packages on the above topic by limiting the discussion to inclusion and exclusion criteria. The literature review found a lack of study about the evaluation of Quadruple Helix Engagement for Sharia Fintech Based SMEs Digitalization Readiness. Thus, it needs an enhancement of a new model of Sharia fintech quadruple helix recommendation focuses on the SMEs digital readiness assessment as an attempt to increase the utilization of proper Sharia fintech products for SMEs
Designing AI Experiences: Boundary Representations, Collaborative Processes, and Data Tools
Artificial Intelligence (AI) has transformed our everyday interactions with technology through automation, intelligence augmentation, and human-machine partnership. Nevertheless, we regularly encounter undesirable and often frustrating experiences due to AI. A fundamental challenge is that existing software practices for coordinating system and experience designs fall short when creating AI for diverse human needs, i.e., ``human-centered AI'' or HAI. ``AI-first'' development workflows allow engineers to first develop the AI components, and then user experience (UX) designers create end-user experiences around the AI's capabilities. Consequently, engineers encounter end-user blindness when making critical decisions about AI training data needs, implementation logic, behavior, and evaluation. In the conventional ``UX-first'' process, UX designers lack the needed technical understanding of AI capabilities (technological blindness) that limits their ability to shape system design from the ground up. Human-AI design guidelines have been offered to help but neither describe nor prescribe ways to bridge the gaps in needed expertise in creating HAI.
In this dissertation, I investigate collaboration approaches between designers and engineers to operationalize the vision for HAI as technology inspired by human intelligence that augments human abilities while addressing societal needs. In a series of studies combining technical HCI research with qualitative studies of AI production in practice, I contribute (1) an approach to software development that blurs rigid design-engineering boundaries, (2) a process model for co-designing AI experiences, and (3) new methods and tools to empower designers by making AI accessible to UX designers. Key findings from interviews with industry practitioners include the need for ``leaky'' abstractions shared between UX and AI designers. Because modular development and separation of concerns fail with HAI design, leaky abstractions afford collaboration across expertise boundaries and support human-centered design solutions through vertical prototyping and constant evaluation. Further, by observing how designers and engineers collaborate on HAI design in an in-lab study, I highlight the role of design `probes' with user data to establish common ground between AI system and UX design specifications, providing a critical tool for shaping HAI design. Finally, I offer two design methods and tool implementations --- Data-Assisted Affinity Diagramming and Model Informed Prototyping --- for incorporating end-user data into HAI design.
HAI is necessarily a multidisciplinary endeavor, and human data (in multiple forms) is the backbone of AI systems. My dissertation contributions inform how stakeholders with differing expertise can collaboratively design AI experiences by reducing friction across expertise boundaries and maintaining agency within team roles. The data-driven methods and tools I created provide direct support for software teams to tackle the novel challenges of designing with data. Finally, this dissertation offers guidance for imagining future design tools for human-centered systems that are accessible to diverse stakeholders.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169917/1/harihars_1.pd
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