Innovative Visualization Techniques Research Articles

Differential Colonization and Mucus Ultrastructure Visualization in Bovine Ileal and Rectal Organoid-Derived Monolayers Exposed to Enterohemorrhagic Escherichia coli.

Enterohemorrhagic Escherichia coli (EHEC) is a critical public health concern due to its role in severe gastrointestinal illnesses in humans, including hemorrhagic colitis and the life-threatening hemolytic uremic syndrome. While highly pathogenic to humans, cattle, the main reservoir for EHEC, often remain asymptomatic carriers, complicating efforts to control its spread. Our study introduces a novel method to investigate EHEC using organoid-derived monolayers from adult bovine ileum and rectum. These polarized epithelial monolayers were exposed to EHEC for four hours, allowing us to perform comparative analyses between the ileal and rectal tissues. Our findings mirrored in vivo observations, showing a higher colonization rate in the rectum compared with the ileum (44.0% vs. 16.5%, p < 0.05). Both tissues exhibited an inflammatory response with increased expression levels of TNF-a (p < 0.05) and a more pronounced increase of IL-8 in the rectum (p < 0.01). Additionally, the impact of EHEC on the mucus barrier varied across these gastrointestinal regions. Innovative visualization techniques helped us study the ultrastructure of mucus, revealing a net-like mucin glycoprotein organization. While further cellular differentiation could enhance model accuracy, our research significantly deepens understanding of EHEC pathogenesis in cattle and informs strategies for the preventative measures and therapeutic interventions.

An integrated complex T-spherical fuzzy set and soft set model for quantum computing and energy resource planning

This study introduces the Complex T-Spherical Fuzzy Soft Set (CTSFSS), a novel and practical model for representing two-dimensional ambiguous information. This is achieved by integrating the Complex T-Spherical Fuzzy Set (CTSFS) with the Soft Set (SS). The development of the CTSFSS theory is motivated by the absence of neutral membership in complex q-rung orthopair fuzzy soft sets and the limitations of Traditional Spherical Fuzzy Soft Sets (TSFSS) in capturing two-dimensional data. Data storage and processing in the modern technological landscape necessitate sophisticated algorithmic computations and robust representation of information. To this end, computational systems predominantly rely on a binary system structured on two fundamental states: 0 (off) and 1 (on). Moreover, the significance of energy resources and computer-aided tools is underscored in this domain, especially considering the potential impact of quantum computing and advances in energy resources on the evolution of digital technology. In addressing these complexities, we propose Complex T-Spherical Fuzzy Soft Relations (CTSFSRs), formulated through the Cartesian product of two CTSFSSs. These relations are adept at handling complex and uncertain data in real-world scenarios. We define and categorize various types of CTSFSRs, followed by an in-depth analysis of their outcomes. Additionally, this paper introduces innovative visualization techniques based on CTSFSRs, tailored for decision-making processes involving energy resources and quantum computing applications. The efficacy and applicability of these proposed methodologies are substantiated through comparative analysis with existing models.

Is Social Tourism a Vector for Destination Resilience to External Shocks? Evidence From Spain

Unpredictable external shocks exacerbate the negative effects usually attributed to cyclical seasonality. Two such recent shocks, the Global Financial Crisis and the COVID-19 pandemic, shifted debates from crisis management toward concerns for greater resilience in visitor economies. Social tourism is an effective stimulus to destination economies and ameliorates some negative effects of seasonality, but there is little evidence on its contributions to greater destination resilience. Furthermore, resilience, a relatively new concept, is poorly defined. We establish a conceptual link between seasonality and resilience through a holistic multivariate analysis of supply, demand and employment patterns. An empirical study examines resilience longitudinally using municipal-level data, applying multivariate analysis and innovative visualization techniques, and assesses how, where and to what degree the Imserso social tourism program has contributed to the greater resilience of Spanish destinations, demonstrating its effectiveness in contributing to resilience in those which are more vulnerable due to high seasonal imbalances.

Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances

This study investigates how age, period, and birth cohorts are related to altering travel distances. We analyze a repeated cross-sectional survey of German pleasure travels for the period 1971–2018 using a holistic age–period–cohort (APC) analysis framework. Changes in travel distances are attributed to the life cycle (age effect), macro-level developments (period effect), and generational membership (cohort effect). We introduce ridgeline matrices and partial APC plots as innovative visualization techniques facilitating the intuitive interpretation of complex temporal structures. Generalized additive models are used to circumvent the identification problem by fitting a bivariate tensor product spline between age and period. The results indicate that participation in short-haul trips is mainly associated with age, while participation in long-distance travel predominantly changed over the period. Generational membership shows less association with destination choice concerning travel distance. The presented APC approach is promising to address further questions of interest in tourism research.

Welcome to AI matters 5(4)

Welcome to the fourth issue of the fifth volume of the AI Matters Newsletter. This issue is full of great new articles and stories! In our regular articles, we open with an event report by Louise Dennis on recent conferences relevant to SIGAI organized around the world. Louise Dennis then provides a summary of upcoming AI events. In the policy column, Larry Medsker covers recent policies on national strategy for AI laid down by the U.S. government, implications of AI on workplace transitions, President Trump's executive order on re-establishing the President's Council of Advisors on Science and Technology (PCAST), and more. Our final regular column is our AI crosswords from Adi Botea. Enjoy! We have three contributed articles in our current issue. In the first article, Sharad Sinha and Clint George provide a summary of the events and activities of a recent workshop conducted in India on AI for high school students. In the second article, Trevor Watkins describes the Cosmology of Artificial Intelligence Project (CAIP) that uses innovative visualization techniques to present the evolution of the field of AI. In the third article, Virginia Dignum discusses how AI is progressively trending to become a multi-disciplinary field of reseach and the implications of this trend on the future of AI.

Implementing Guideline-Based, Experience-Based, and Case-Based Approaches to Enrich Decision Support for the Management of Breast Cancer Patients in the DESIREE Project.

DESIREE is a European-funded project to improve the management of primary breast cancer. We have developed three decision support systems (DSSs), a guideline-based, an experience-based, and a case-based DSSs, resp. GL-DSS, EXP-DSS, and CB-DSS, that operate simultaneously to offer an enriched multi-modal decision support to clinicians. A breast cancer knowledge model has been built to describe within a common ontology the data model and the termino-ontological knowledge used for representing breast cancer patient cases. It allows for rule-based and subsumption-based reasoning in the GL-DSS to provide best patient-centered reconciled care plans. It also allows for using semantic similarity in the retrieval algorithm implemented in the CB-DSS. Rainbow boxes are used to display patient cases similar to a given query patient. This innovative visualization technique translates the question of deciding the most appropriate treatment into a question of deciding the colour dominance among boxes.

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap.

Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.

Diagram based assessment strategy for first-order analysis of masonry arches

Efficiency in structural analysis is economically paramount. First-order assessment strategies play a vital role in establishing this efficiency. Structural masonry has the potential to be an advantageous structural system, but its analysis must be efficient. Utilizing the traditional assumptions of masonry, an equilibrium approach to the kinematic method of limit analyses, and an innovative visualization technique, collapse load diagrams (CLDs) are developed. First the limit analysis method is presented, and its ability to interact with AutoCAD® is discussed. The iterative algorithm for the CLD is developed and applied to a semi-circular arch subjected to an asymmetric point load and constant horizontal acceleration loading conditions. Lastly, a geometric data extraction technique is utilized to create CLDs for two double curvature AutoCAD® drawn arches and used to compare their first-order assessments and establish equivalent capacities.

Virtual reality in advanced medical immersive imaging: a workflow for introducing virtual reality as a supporting tool in medical imaging

Radiologic evaluation of images from computed tomography (CT) or magnetic resonance imaging for diagnostic purposes is based on the analysis of single slices, occasionally supplementing this information with 3D reconstructions as well as surface or volume rendered images. However, due to the complexity of anatomical or pathological structures in biomedical imaging, innovative visualization techniques are required to display morphological characteristics three dimensionally. Virtual reality is a modern tool of representing visual data, The observer has the impression of being “inside” a virtual surrounding, which is referred to as immersive imaging. Such techniques are currently being used in technical applications, e.g. in the automobile industry. Our aim is to introduce a workflow realized within one simple program which processes common image stacks from CT, produces 3D volume and surface reconstruction and rendering, and finally includes the data into a virtual reality device equipped with a motion head tracking cave automatic virtual environment system. Such techniques have the potential to augment the possibilities in non-invasive medical imaging, e.g. for surgical planning or educational purposes to add another dimension for advanced understanding of complex anatomical and pathological structures. To this end, the reconstructions are based on advanced mathematical techniques and the corresponding grids which we can export are intended to form the basis for simulations of mathematical models of the pathogenesis of different diseases.

Visualizing Matrix Multiplication

Efficient visualizations of computational algorithms are important tools for students, educators, and researchers. In this article, we point out an innovative visualization technique for matrix multiplication. This method differs from the standard, formal approach by using block matrices to make computations more visual. We find this method a helpful alternative for introducing matrix multiplication.

The Path-Dependency of Low-Income Neighbourhood Trajectories: An Approach for Analysing Neighbourhood Change

The gap between wealthy and disadvantaged neighbourhoods seems to be increasing in many contemporary Western cities. Most studies of neighbourhood change focus on specific case-studies of neighbourhood downgrading or gentrification. Studies investigating socio-spatial polarisation in larger urban areas often compare neighbourhoods at two points in time, neglecting the underlying dynamic character of neighbourhoods. In the current literature, the question if neighbourhoods with similar characteristics experience similar changes over time remains unanswered. As a result, it is unclear why some neighbourhoods appear to be more prone to change than others. In this paper, we propose a dual approach for analysing neighbourhood change. We argue that researchers should both adopt a long-term perspective (20–40 years), because significant changes are only visible after longer periods of time, and focus on more detailed neighbourhood trajectories to understand how neighbourhood change is interrelated with context. Focussing on Dutch neighbourhoods over the period 1971–2013, we analyse the role of physical characteristics on low-income neighbourhood trajectories using an innovative visualisation technique. A tree-structured discrepancy analysis allows for the visualisation of complete neighbourhood pathways, enabling the analysis of complex, contextualised patterns of change. We find that the original quality of neighbourhoods and dwellings seems to be an important predictor for future neighbourhood trajectories, indicating a high level of path-dependency.

A method to visualize the evolution of multiple interacting spatial systems

Integrated modeling approaches are being increasingly used to simulate the behavior of, and the interaction between, several interdependent systems. They are becoming more and more important in many fields, including, but not being limited to, civil engineering, hydrology and climate impact research. It is beneficial when using these approaches to be able to visualize both, the intermediary and final results of scenario-based analyses that are conducted in both, space and time. This requires appropriate visualization techniques that enable to efficiently navigate between multiple such scenarios. In recent years, several innovative visualization techniques have been developed that allow for such navigation purposes. These techniques, however, are limited to the representation of one system at a time. Improvements are possible with respect to the ability to visualize the results related to multiple scenarios for multiple interdependent spatio-temporal systems. To address this issue, existing multi-scenario navigation techniques based on small multiples and line graphs are extended by multiple system representations and inter-system impact representations. This not only allows to understand the evolution of the systems under consideration but also eases identifying events where one system influences another system significantly. In addition, the concept of selective branching is described that allows to remove otherwise redundant information from the visualization by considering the logical and temporal dependencies between these systems. This visualization technique is applied to a risk assessment methodology that allows to determine how different environmental systems (i.e. precipitation, flooding, and landslides) influence each other as well as how their impact on civil infrastructure affects society. The results of this work are concepts for improved visualization techniques for multiple interacting spatial systems. The successful validation with domain experts of the enhanced small multiples technique proved its usefulness in a use case scenario based on a risk assessment methodology.

Co-Design of a Computer-Assisted Medical Decision Support System to Manage Antibiotic Prescription in an ICU Ward.

About 37 thousand people die per year in Europe due to infections by resistant bacteria. Fighting antimicrobial resistances (AR) is a top priority to save lives and reduce costs. AR is triggered mostly by uncritical antibiotic prescription. This paper presents HAITool, a decision-making information system to support antibiotic prescription. The system was co-developed together with health professionals using Design Science Research Methodology, empowered with innovative data visualization techniques to improve AR management. HAITool includes integrated visualizations of patient, microbiology, and pharmacy data, facilitating clinical decision support, antibiotic prescriptions quality and antibiotic-resistant bacteria monitoring. It also includes an alert module that monitors conformance of antibiotic prescriptions with norms and guidelines. HAITool is evaluated using both the Österle principles and interviews with physicians and infection control team from three participant hospitals.

“Whoa! We’re going deep in the trees!”: Patterns of collaboration around an interactive information visualization exhibit

In this paper we present a qualitative analysis of natural history museum visitor interaction around a multi-touch tabletop exhibit called DeepTree that we designed around concepts of evolution and common descent. DeepTree combines several large scientific datasets and an innovative visualization technique to display a phylogenetic tree of life consisting of over 70,000 species. After describing our design, we present a study involving pairs of children interacting with DeepTree in two natural history museums. Our analysis focuses on two questions. First, how do dyads negotiate their moment-to-moment exploration of the exhibit? Second, how do dyads develop and negotiate their understanding of evolutionary concepts? In order to address these questions we present an analytical framework that describes dyads’ exploration along two dimensions: coordination and target of action. This framework reveals four distinct patterns of interaction, which, we argue, are relevant for similar interactive designs. We conclude with a discussion of the role of design in helping visitors make sense of interactive experiences involving the visualization of large scientific datasets.

Managing a Big Data project: The case of Ramco Cements Limited

Currently many organizations are in the process of implementing Big Data related projects in order to extract meaningful insights from their data for better decision making. Though there are various frameworks postulating the best practices which should be adopted while implementing analytics projects, they do not cater to the complexities associated with a Big Data project. In this paper our goal is two-fold: to develop a new framework that can provide organizations a holistic roadmap in conceptualizing, planning and successfully implementing Big Data projects and to validate this framework through our observation of a descriptive case study of an organization that has implemented such a project. Although the manufacturing sector has been slow in incorporating analytics in their strategic decision making, the situation is changing with increasing use of analytics for product development, operations and logistics. We explore the Big Data project at a manufacturing company, Ramco Cements Limited, India, describe the system developed by them and highlight the benefits accrued from it. We investigate the entire process by which the project is implemented using the lens of our proposed framework. Our results reveal that a clear understanding of the business problem, a detailed and well planned step-by-step project map, a cross functional project team, adoption of innovative visualization techniques, patronage and active involvement of top management and a culture of data driven decision making are essential for the success of a Big Data project.

Innovative visualization technique for energy flow analysis: Waste heat recovery and energy savings opportunities

Improving the performance of existing industrial plants through new technologies and innovative eco-energy solutions is raising a lot of challenges for researchers and practitioners. In the search for new sustainable designs, understanding the current energy distribution within a plant in regards to its energy requirements is essential for the decision-making process. The technique of Composite Curves is commonly employed to evaluate the scope for energy savings. However, it is seldom self-explanatory. In the present work, an alternative to the energy Composite Curves representation is proposed to complement the visualization of energy integration opportunities. A novel energy management assessment based on mapping of the overall process energy is introduced as a practical illustration to support the thermal energy integration.

Single- and Multi-Constituent Condensation of Fluids and Mixtures with Varying Properties in Micro-Channels

The relative magnitudes of the forces governing the transfer of heat, mass, and momentum during microscale condensation are fundamentally different from those in macroscale geometries, primarily due to the increasing importance of surface tension. A systematic series of experiments on condensation flow regimes, pressure drop, and heat transfer was conducted using innovative visualization and measurement techniques for condensation of synthetic and natural refrigerants and their azeotropic and zeotropic mixtures through micro-channels with a wide range of diameters (0.1 < DH < 5 mm), shapes, and operating conditions. These experiments resulted in flow-regime-based heat transfer and pressure drop models with very good predictive capabilities for such micro-channel geometries.

Spatio-temporal location modeling in a 3D indoor environment: the case of AEDs as emergency medical devices

This research innovatively extends optimal emergency facility location models to the interior space of multistory buildings with an integrated spatial-temporal framework. We present the case of deployed emergency medical devices known as automated external defibrillators (AEDs), which serve to treat sudden cardiac arrest on-site within the first few critical minutes of the event. AEDs have become a critical element of basic life support services in many public buildings. The proposed framework is based on the concept of discrete time windows to capture the time-dependence of potential demand and stochastically model the detection time component of impedance as a function of space–time distribution of demand. Different optimization objectives minimizing sudden cardiac arrest outcome consequences (e.g., brain damage or death) as a function of suffering time are formulated and solved. The first model is the multiple-time-window maximal covering location problem model which optimizes the placement of AEDs by maximizing the covered demand over all time periods. The second is the multiple-time-window p-Median model which places AEDs to maximize the expected value of prevented death or permanent brain impairment in case of defibrillation treatment over multiple time windows. Both models are implemented through tight coupling of commercial geographic information system software and a linear programming solver. The models are novel in two primary respects, namely location modeling in 3D microscale spaces and the integration of spatial and temporal considerations. Innovative visualization techniques for AED indoor location and coverage are also presented.

“At the water's edge”: community voices on climate change

The photovoice research project, “At the water's edge”, which gives voice to communities exploring the threats and impact of climate change and rising sea levels on coastal communities, was conducted during an environmental art symposium in the Noosa Biosphere Reserve, Australia in June 2009. Photos by participants illustrated both local and global threats and reflected psychological tension between global and local action. This research partnership with the local people demonstrated an innovative visualisation technique which can be used to collaboratively build capacity and consensus about adaptation to climate change. It also contributed to the Biosphere Reserve's educational and cultural aims in relation to climate change.

Visualizing television archives

Visualizing television archives