Open AccessArticle
Multiple-Criteria Decision Support for a Sustainable Supply Chain: Applications to the Fashion Industry
Informatics 2017, 4(4), 36; doi:10.3390/informatics4040036 -
Abstract
With increasing globalization and international cooperation, the importance of sustainability management across supply chains has received much attention by companies across various industries. Companies therefore strive to implement effective and integrated sustainable supply chain management initiatives to improve their operational and economic performance
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With increasing globalization and international cooperation, the importance of sustainability management across supply chains has received much attention by companies across various industries. Companies therefore strive to implement effective and integrated sustainable supply chain management initiatives to improve their operational and economic performance while also minimizing unnecessary damage to the environment and maintaining their social reputation and images. The paper presents an easy-to-use decision-support approach based on multiple-criteria decision-making (MCDM) methodologies that aim to help companies develop effective models for timely decision-making involving sustainable supply chain management strategies. The proposed approach can be used by practitioners to ultimately build a comprehensive Analytic Network Process model that will adequately capture and reveal all the interrelationships and interdependency among the elements in the problem, which is often a very difficult task. To facilitate and simplify this complex process, we propose that hierarchical thinking be used first to structure the essences of the problem capturing only the major issues, and an Analytic Hierarchy Process (AHP) model be built. Users can learn from the modeling process and gain much insight into the problem. The AHP can then be extended to an Analytic Network Process (ANP) model so as to capture the relationships and interdependencies among the elements. Our approach can reduce the sustainable expertise, effort and information that are often needed to build an ANP model from scratch. We apply our approach to the evaluation of sustainable supply chain management strategies for the fashion industry. Three main dimensions of sustainability—environmental, economic and social—are considered. Based on the literature, we identified four alternative supply chain management strategies. It was found that the Reverse Logistics alternative appears to be the recommended solution by the AHP model. However, the Socially Leagile Supply Chain is recommended by the ANP model, thereby demonstrating the necessity and importance of considering interdependencies in the model. Full article
Open AccessArticle
Assessing the Cost Impact of Multiple Transportation Modes to Enhance Sustainability in an Integrated, Two Stage, Automotive Supply Chain
Informatics 2017, 4(4), 34; doi:10.3390/informatics4040034 -
Abstract
As the automotive industry has been striving to enhance its efficiency, competitiveness, and sustainability, great focus is often placed on opportunities for improving its supply chain operations. We study the effect of introducing multiple modes of transportation in an industry-motivated production and transportation
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As the automotive industry has been striving to enhance its efficiency, competitiveness, and sustainability, great focus is often placed on opportunities for improving its supply chain operations. We study the effect of introducing multiple modes of transportation in an industry-motivated production and transportation problem involving short-term automotive supply chain planning. We consider multiple, heterogeneous modes of transportation that offer a cost vs. delivery time option to the manufacturer. Having multiple modes of transportation in the system promotes supply chain sustainability. We present an integer linear programming model that captures the availability of multiple transportation modes. We then provide a solution approach based on a hybrid simulated annealing algorithm that we use to analyze the problem. Experimental results demonstrate the impact of additional transportation mode lead times compared to costs in the integrated supply chain. Full article
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Open AccessEditorial
Advancing Social Media and Mobile Technologies in Healthcare Education
Informatics 2017, 4(4), 35; doi:10.3390/informatics4040035 -
Abstract
Social media and mobile technologies are important new tools in healthcare education, both to assist healthcare professionals learn and maintain their craft, and for the education of patients and families [...]
Full article
Open AccessArticle
Health Literacy for the General Public: Making a Case for Non-Trivial Visualizations
Informatics 2017, 4(4), 33; doi:10.3390/informatics4040033 -
Abstract
Health literacy is concerned with the degree to which individuals can access and understand information to make health decisions. The multifaceted nature of health data presents challenges for individuals seeking to improve their understanding of health. To aid health literacy efforts, we have
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Health literacy is concerned with the degree to which individuals can access and understand information to make health decisions. The multifaceted nature of health data presents challenges for individuals seeking to improve their understanding of health. To aid health literacy efforts, we have developed HealthConfection, a visualization tool that uses elaborate and non-typical interactive visualizations to represent health data. In this paper, we report on two studies we conducted with HealthConfection. In the first study, we investigate whether individuals can learn to use non-typical visualizations, and the impact that short, minimalist video tutorials will have on participants’ understanding of the visualizations. The findings from this study suggest that individuals can learn to use non-typical visualizations and that participants who used the tutorials achieved higher scores than those without tutorials. This work indicates that non-typical visualizations are a viable option for conveying complex datasets. Based on this foundation, we conducted a second study to investigate if non-typical visualizations can improve health literacy for the general public. Results show that participants who used HealthConfection achieved higher scores than those who did not interact with the tool. Our work suggests that non-typical visualizations can be used to improve health literacy. Full article
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Open AccessArticle
How The Arts Can Help Tangible Interaction Design: A Critical Re-Orientation
Informatics 2017, 4(3), 31; doi:10.3390/informatics4030031 -
Abstract
There is a long history of creative encounters between tangible interface design and the Arts. However, in comparison with media art, tangible interaction seems to be quite anchored into many of the traditional methodologies imported from human–computer interaction (HCI). How can the Arts
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There is a long history of creative encounters between tangible interface design and the Arts. However, in comparison with media art, tangible interaction seems to be quite anchored into many of the traditional methodologies imported from human–computer interaction (HCI). How can the Arts help tangible interaction design? Building on Søren Pold’s Interface Aesthetics, a re-orientation of the role of the artist towards a critical examination of our research medium—tangible interaction—is proposed. In this essay, the benefits of incorporating artistic research and its methodologies into our field are described. With these methodologies it is possible to better assess experiential aspects of interaction—a relevant attribute which traditional HCI approaches cannot afford. In order to inform our community, three examples of critical artworks are comparatively studied and discussed. Full article
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Open AccessArticle
Evaluation Tools to Appraise Social Media and Mobile Applications
Informatics 2017, 4(3), 32; doi:10.3390/informatics4030032 -
Abstract
In a connected care environment, more citizens are engaging in their health care through mobile apps and social media tools. Given this growing health care engagement, it is important for health care professionals to have the knowledge and skills to evaluate and recommend
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In a connected care environment, more citizens are engaging in their health care through mobile apps and social media tools. Given this growing health care engagement, it is important for health care professionals to have the knowledge and skills to evaluate and recommend appropriate digital tools. The purpose of this article is to identify and review criteria or instruments that can be used to evaluate mobile apps and social media. The analysis will review current literature as well as literature designed by professional health care organizations. This review will facilitate health care professionals’ assessment of mobile apps and social media tools that may be pertinent to their patient population. The review will also highlight strategies which a health care system can use to provide guidance in recommending mobile apps and social media tools for their patients, families, and caregivers. Full article
Open AccessArticle
digiMe: An Online Portal to Support Connectivity through E-Learning in Medical Education
Informatics 2017, 4(3), 30; doi:10.3390/informatics4030030 -
Abstract
Connectivity is intrinsic to all aspects of our life today, be it political, economic, technological, scientific, or personal. Higher education is also transcending the previous paradigm of technology enabled content delivery and e-learning, with a new emphasis on connectivity, enabling participants to exchange
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Connectivity is intrinsic to all aspects of our life today, be it political, economic, technological, scientific, or personal. Higher education is also transcending the previous paradigm of technology enabled content delivery and e-learning, with a new emphasis on connectivity, enabling participants to exchange knowledge and collaborate to meet educational goals. In this study, a social media technology supported website—digiMe—was developed and evaluated at the School of Medicine of one Australian university. Connectivity to other medical learners and health professionals is intrinsic to digiMe. This paper reports the functionalities of this website, results of a post-intervention evaluative survey, and statistics of website usage generated from Google Analytics. The results revealed more active adoptions and a more positive attitude towards digiMe from Year 4 students compared to Year 5 students. The participants showed a desire for access to a recommended collection of apps, such as those offered through digiMe. However, many participants did not use digiMe beyond initial introduction to it. digiMe demonstrated its potential in raising awareness of web and mobile apps useful for enhancing connectivity, although it needs to be introduced to students in earlier years of their medical education to achieve a higher impact on their learning. Full article
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Open AccessArticle
Scalable Interactive Visualization for Connectomics
Informatics 2017, 4(3), 29; doi:10.3390/informatics4030029 -
Abstract
Connectomics has recently begun to image brain tissue at nanometer resolution, which produces petabytes of data. This data must be aligned, labeled, proofread, and formed into graphs, and each step of this process requires visualization for human verification. As such, we present the
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Connectomics has recently begun to image brain tissue at nanometer resolution, which produces petabytes of data. This data must be aligned, labeled, proofread, and formed into graphs, and each step of this process requires visualization for human verification. As such, we present the BUTTERFLY middleware, a scalable platform that can handle massive data for interactive visualization in connectomics. Our platform outputs image and geometry data suitable for hardware-accelerated rendering, and abstracts low-level data wrangling to enable faster development of new visualizations. We demonstrate scalability and extendability with a series of open source Web-based applications for every step of the typical connectomics workflow: data management and storage, informative queries, 2D and 3D visualizations, interactive editing, and graph-based analysis. We report design choices for all developed applications and describe typical scenarios of isolated and combined use in everyday connectomics research. In addition, we measure and optimize rendering throughput—from storage to display—in quantitative experiments. Finally, we share insights, experiences, and recommendations for creating an open source data management and interactive visualization platform for connectomics. Full article
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Open AccessArticle
Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks
Informatics 2017, 4(3), 27; doi:10.3390/informatics4030027 -
Abstract
Increasingly fast computing systems for simulations and high-accuracy measurement techniques drive the generation of time-dependent volumetric data sets with high resolution in both time and space. To gain insights from this spatio-temporal data, the computation and direct visualization of pairwise distances between time
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Increasingly fast computing systems for simulations and high-accuracy measurement techniques drive the generation of time-dependent volumetric data sets with high resolution in both time and space. To gain insights from this spatio-temporal data, the computation and direct visualization of pairwise distances between time steps not only supports interactive user exploration, but also drives automatic analysis techniques like the generation of a meaningful static overview visualization, the identification of rare events, or the visual analysis of recurrent processes. However, the computation of pairwise differences between all time steps is prohibitively expensive for large-scale data not only due to the significant cost of computing expressive distance between high-resolution spatial data, but in particular owing to the large number of distance computations (O(|T|2)), with |T| being the number of time steps). Addressing this issue, we present and evaluate different strategies for the progressive computation of similarity information in a time series, as well as an approach for estimating distance information that has not been determined so far. In particular, we investigate and analyze the utility of using neural networks for estimating pairwise distances. On this basis, our approach automatically determines the sampling strategy yielding the best result in combination with trained networks for estimation. We evaluate our approach with a variety of time-dependent 2D and 3D data from simulations and measurements as well as artificially generated data, and compare it against an alternative technique. Finally, we discuss prospects and limitations, and discuss different directions for improvement in future work. Full article
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Open AccessReview
Web Apps Come of Age for Molecular Sciences
Informatics 2017, 4(3), 28; doi:10.3390/informatics4030028 -
Abstract
Whereas server-side programs are essential to maintain databases and run data analysis pipelines and simulations, client-side web-based computing tools are also important as they allow users to access, visualize and analyze the content delivered to their devices on-the-fly and interactively. This article reviews
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Whereas server-side programs are essential to maintain databases and run data analysis pipelines and simulations, client-side web-based computing tools are also important as they allow users to access, visualize and analyze the content delivered to their devices on-the-fly and interactively. This article reviews the best-established tools for in-browser plugin-less programming, including JavaScript as used in HTML5 as well as related web technologies. Through examples based on JavaScript libraries, web applets, and even full web apps, either alone or coupled to each other, the article puts on the spotlight the potential of these technologies for carrying out numerical calculations, text processing and mining, retrieval and analysis of data through queries to online databases and web services, effective visualization of data including 3D visualization and even virtual and augmented reality; all of them in the browser at relatively low programming effort, with applications in cheminformatics, structural biology, biophysics, and genomics, among other molecular sciences. Full article
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Open AccessArticle
Multidimensional Data Exploration by Explicitly Controlled Animation
Informatics 2017, 4(3), 26; doi:10.3390/informatics4030026 -
Abstract
Understanding large multidimensional datasets is one of the most challenging problems in visual data exploration. One key challenge that increases the size of the exploration space is the number of views that one can generate from a single dataset, based on the use
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Understanding large multidimensional datasets is one of the most challenging problems in visual data exploration. One key challenge that increases the size of the exploration space is the number of views that one can generate from a single dataset, based on the use of multiple parameter values and exploration paths. Often, no such single view contains all needed insights. The question thus arises of how we can efficiently combine insights from multiple views of a dataset. We propose a set of techniques that considerably reduce the exploration effort for such situations, based on the explicit depiction of the view space, using a small multiple metaphor. We leverage this view space by offering interactive techniques that enable users to explicitly create, visualize, and follow their exploration path. This way, partial insights obtained from each view can be efficiently and effectively combined. We demonstrate our approach by applications using real-world datasets from air traffic control, software maintenance, and machine learning. Full article
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Open AccessArticle
Visual Analysis of Stochastic Trajectory Ensembles in Organic Solar Cell Design
Informatics 2017, 4(3), 25; doi:10.3390/informatics4030025 -
Abstract
We present a visualization system for analyzing stochastic particle trajectory ensembles, resulting from Kinetic Monte-Carlo simulations on charge transport in organic solar cells. The system supports the analysis of such trajectories in relation to complex material morphologies. It supports the inspection of individual
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We present a visualization system for analyzing stochastic particle trajectory ensembles, resulting from Kinetic Monte-Carlo simulations on charge transport in organic solar cells. The system supports the analysis of such trajectories in relation to complex material morphologies. It supports the inspection of individual trajectories or the entire ensemble on different levels of abstraction. Characteristic measures quantify the efficiency of the charge transport. Hence, our system led to better understanding of ensemble trajectories by: (i) Capturing individual trajectory behavior and providing an ensemble overview; (ii) Enabling exploration through linked interaction between 3D representations and plots of characteristics measures; (iii) Discovering potential traps in the material morphology; (iv) Studying preferential paths. The visualization system became a central part of the research process. As such, it continuously develops further along with the development of new hypothesis and questions from the application. Findings derived from the first visualizations, e.g., new efficiency measures, became new features of the system. Most of these features arose from discussions combining the data-perspective view from visualization with the physical background knowledge of the underlying processes. While our system has been built for a specific application, the concepts translate to data sets for other stochastic particle simulations. Full article
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Open AccessArticle
Big Data Management with Incremental K-Means Trees–GPU-Accelerated Construction and Visualization
Informatics 2017, 4(3), 24; doi:10.3390/informatics4030024 -
Abstract
While big data is revolutionizing scientific research, the tasks of data management and analytics are becoming more challenging than ever. One way to remit the difficulty is to obtain the multilevel hierarchy embedded in the data. Knowing the hierarchy enables not only the
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While big data is revolutionizing scientific research, the tasks of data management and analytics are becoming more challenging than ever. One way to remit the difficulty is to obtain the multilevel hierarchy embedded in the data. Knowing the hierarchy enables not only the revelation of the nature of the data, it is also often the first step in big data analytics. However, current algorithms for learning the hierarchy are typically not scalable to large volumes of data with high dimensionality. To tackle this challenge, in this paper, we propose a new scalable approach for constructing the tree structure from data. Our method builds the tree in a bottom-up manner, with adapted incremental k-means. By referencing the distribution of point distances, one can flexibly control the height of the tree and the branching of each node. Dimension reduction is also conducted as a pre-process, to further boost the computing efficiency. The algorithm takes a parallel design and is implemented with CUDA (Compute Unified Device Architecture), so that it can be efficiently applied to big data. We test the algorithm with two real-world datasets, and the results are visualized with extended circular dendrograms and other visualization techniques. Full article
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Open AccessArticle
Constructing Interactive Visual Classification, Clustering and Dimension Reduction Models for n-D Data
Informatics 2017, 4(3), 23; doi:10.3390/informatics4030023 -
Abstract
Abstract: The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be
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Abstract: The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent n-D points losslessly, i.e., allowing the restoration of n-D data from the graphs. The projections of graphs are used for classification. The method is illustrated by solving machine-learning classification and dimension-reduction tasks from the domains of image processing, computer-aided medical diagnostics, and finance. Experiments conducted on several datasets show that this visual interactive method can compete in accuracy with analytical machine learning algorithms. Full article
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Open AccessArticle
PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs
Informatics 2017, 4(3), 22; doi:10.3390/informatics4030022 -
Abstract
Graphs emerge naturally in many domains, such as social science, neuroscience, transportation engineering, and more. In many cases, such graphs have millions or billions of nodes and edges, and their sizes increase daily at a fast pace. How can researchers from various domains
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Graphs emerge naturally in many domains, such as social science, neuroscience, transportation engineering, and more. In many cases, such graphs have millions or billions of nodes and edges, and their sizes increase daily at a fast pace. How can researchers from various domains explore large graphs interactively and efficiently to find out what is ‘important’? How can multiple researchers explore a new graph dataset collectively and “help” each other with their findings? In this article, we present Perseus-Hub, a large-scale graph mining tool that computes a set of graph properties in a distributed manner, performs ensemble, multi-view anomaly detection to highlight regions that are worth investigating, and provides users with uncluttered visualization and easy interaction with complex graph statistics. Perseus-Hub uses a Spark cluster to calculate various statistics of large-scale graphs efficiently, and aggregates the results in a summary on the master node to support interactive user exploration. In Perseus-Hub, the visualized distributions of graph statistics provide preliminary analysis to understand a graph. To perform a deeper analysis, users with little prior knowledge can leverage patterns (e.g., spikes in the power-law degree distribution) marked by other users or experts. Moreover, Perseus-Hub guides users to regions of interest by highlighting anomalous nodes and helps users establish a more comprehensive understanding about the graph at hand. We demonstrate our system through the case study on real, large-scale networks. Full article
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Open AccessArticle
Modeling the Construct of an Expert Evidence-Adaptive Knowledge Base for a Pressure Injury Clinical Decision Support System
Informatics 2017, 4(3), 20; doi:10.3390/informatics4030020 -
Abstract
The selection of appropriate wound products for the treatment of pressure injuries is paramount in promoting wound healing. However, nurses find it difficult to decide on the most optimal wound product(s) due to limited live experiences in managing pressure injuries resulting from successfully
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The selection of appropriate wound products for the treatment of pressure injuries is paramount in promoting wound healing. However, nurses find it difficult to decide on the most optimal wound product(s) due to limited live experiences in managing pressure injuries resulting from successfully implemented pressure injury prevention programs. The challenges of effective decision-making in wound treatments by nurses at the point of care are compounded by the yearly release of wide arrays of newly researched wound products into the consumer market. A clinical decision support system for pressure injury (PI-CDSS) was built to facilitate effective decision-making and selection of optimal wound treatments. This paper describes the development of PI-CDSS with an expert knowledge base using an interactive development environment, Blaze Advisor. A conceptual framework using decision-making and decision theory, knowledge representation, and process modelling guided the construct of the PI-CDSS. This expert system has incorporated the practical and relevant decision knowledge of wound experts in assessment and wound treatments in its algorithm. The construct of the PI-CDSS is adaptive, with scalable capabilities for expansion to include other CDSSs and interoperability to interface with other existing clinical and administrative systems. The algorithm was formatively evaluated and tested for usability. The treatment modalities generated after using patient-specific assessment data were found to be consistent with the treatment plan(s) proposed by the wound experts. The overall agreement exceeded 90% between the wound experts and the generated treatment modalities for the choice of wound products, instructions, and alerts. The PI-CDSS serves as a just-in-time wound treatment protocol with suggested clinical actions for nurses, based on the best evidence available. Full article
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Open AccessArticle
Visual Exploration of Large Multidimensional Data Using Parallel Coordinates on Big Data Infrastructure
Informatics 2017, 4(3), 21; doi:10.3390/informatics4030021 -
Abstract
The increase of data collection in various domains calls for an adaptation of methods of visualization to tackle magnitudes exceeding the number of available pixels on screens and challenging interactivity. This growth of datasets size has been supported by the advent of accessible
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The increase of data collection in various domains calls for an adaptation of methods of visualization to tackle magnitudes exceeding the number of available pixels on screens and challenging interactivity. This growth of datasets size has been supported by the advent of accessible and scalable storage and computing infrastructure. Similarly, visualization systems need perceptual and interactive scalability. We present a complete system, complying with the constraints of aforesaid environment, for visual exploration of large multidimensional data with parallel coordinates. Perceptual scalability is addressed with data abstraction while interactions rely on server-side data-intensive computation and hardware-accelerated rendering on the client-side. The system employs a hybrid computing method to accommodate pre-computing time or space constraints and achieves responsiveness for main parallel coordinates plot interaction tools on billions of records. Full article
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Open AccessReview
Ambient Assisted Living and Health-Related Outcomes—A Systematic Literature Review
Informatics 2017, 4(3), 19; doi:10.3390/informatics4030019 -
Abstract
The active ageing paradigm aims to contribute to the expectation of a long, autonomous, independent and healthy life. Ambient Assisted Living (AAL) promotes the development of technological solutions that might have a key role in not only the optimization of support services for
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The active ageing paradigm aims to contribute to the expectation of a long, autonomous, independent and healthy life. Ambient Assisted Living (AAL) promotes the development of technological solutions that might have a key role in not only the optimization of support services for older adults but also in the mitigation of their disabilities. This article presents a systematic literature review of how the impact of AAL technologies, products and services is being assessed in terms of its health-related outcomes. The main objective of this article is to contribute to the understanding of how state-of-the-art AAL solutions might influence the health conditions of older adults. The method used to conduct this systematic literature review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The results show that the reviewed articles report not only the use of technological assessment instruments but also instruments to measure health-related outcomes such as quality of life. Full article
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Open AccessArticle
TOPCAT: Desktop Exploration of Tabular Data for Astronomy and Beyond
Informatics 2017, 4(3), 18; doi:10.3390/informatics4030018 -
Abstract
TOPCAT, the Tool for OPerations on Catalogues And Tables, is an interactive desktop application for retrieval, analysis and manipulation of tabular data, offering a powerful and flexible range of interactive visualization options amongst other features. Its visualization capabilities focus on enabling interactive exploration
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TOPCAT, the Tool for OPerations on Catalogues And Tables, is an interactive desktop application for retrieval, analysis and manipulation of tabular data, offering a powerful and flexible range of interactive visualization options amongst other features. Its visualization capabilities focus on enabling interactive exploration of large static local tables—millions of rows and hundreds of columns can easily be handled on a standard desktop or laptop machine, and various options are provided for meaningful graphical representation of such large datasets. TOPCAT has been developed in the context of astronomy, but many of its features are equally applicable to other domains. The software, which is free and open source, is written in Java, and the underlying high-performance visualisation library is suitable for re-use in other applications. Full article
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Open AccessArticle
Web-Scale Multidimensional Visualization of Big Spatial Data to Support Earth Sciences—A Case Study with Visualizing Climate Simulation Data
Informatics 2017, 4(3), 17; doi:10.3390/informatics4030017 -
Abstract
The world is undergoing rapid changes in its climate, environment, and ecosystems due to increasing population growth, urbanization, and industrialization. Numerical simulation is becoming an important vehicle to enhance the understanding of these changes and their impacts, with regional and global simulation models
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The world is undergoing rapid changes in its climate, environment, and ecosystems due to increasing population growth, urbanization, and industrialization. Numerical simulation is becoming an important vehicle to enhance the understanding of these changes and their impacts, with regional and global simulation models producing vast amounts of data. Comprehending these multidimensional data and fostering collaborative scientific discovery requires the development of new visualization techniques. In this paper, we present a cyberinfrastructure solution—PolarGlobe—that enables comprehensive analysis and collaboration. PolarGlobe is implemented upon an emerging web graphics library, WebGL, and an open source virtual globe system Cesium, which has the ability to map spatial data onto a virtual Earth. We have also integrated volume rendering techniques, value and spatial filters, and vertical profile visualization to improve rendered images and support a comprehensive exploration of multi-dimensional spatial data. In this study, the climate simulation dataset produced by the extended polar version of the well-known Weather Research and Forecasting Model (WRF) is used to test the proposed techniques. PolarGlobe is also easily extendable to enable data visualization for other Earth Science domains, such as oceanography, weather, or geology. Full article
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