Search

Among the existing platforms, the mobile platform provides most of the augmented reality experiences for users. Augmented reality solutions have been applied in several contexts due to technological advances in this area. However, many of these advances are still out of reach of more massive access by regular users due to access limitations or the cost of these new technologies. After conducting a literature review considering the application of data visualization in augmented reality environments, we have identified an evolution of equipment used in this context with a decrease of low-cost proposals. Hence, the works related to these two areas showed high use of technologies not yet accessible to the vast majority of users. Thus, we propose a low-cost augmented reality application that can be used on mobile devices for data visualization. We developed a hybrid proposal of an augmented reality prototype to interacting with cards through the mobile device to support the process of creating data visualization. The tangible cards have a design that helps the user in the visualization creation process, indicating each card’s role in the interaction process. The proposed application supports a multi-user environment, allowing multiple users to interact simultaneously with data visualizations in the AR environment. The visualizations are generated on a dedicated server for this function, providing a set of seven different types of visualization. To validate the developed prototype we performed two tests, the first being a stress assessment on the server that generates data visualizations to analyze the proposed application’s scalability. The second was a remote usability test with twelve participants, to evaluate the interaction and design of the cards, and the viability of this solution for augmented reality applications.

Decision-making processes and decision support systems (DSS) have been improved by a variety of methods originated from several scientific fields, such as information science and artificial intelligence (AI). Situated visualization (SV) allows presenting visual data representations in context and may support better DSS. Its main characteristic is to display data representations near the data referent. As augmented reality (AR) is becoming more mature, affordable, and widespread, using it as a tool for SV becomes viable in several situations. Moreover, it may provide a positive contribution to more effective and efficient decision-making, as the users have contextual, relevant, and appropriate information that fosters more informed choices. As new challenges and opportunities arise, it is important to understand the relevance of intertwining these fields. Based on literature analysis, this paper introduces the main concepts involved, and, through practical examples, addresses and discusses current areas of application, benefits, challenges, and opportunities of using SV through AR to visualize data in context to support better decision-making processes. In the end, a set of guidelines for the design and implementation of DSS based on situated augmented reality are proposed.

Fluid overload is a common complication in patients with cirrhosis. B-type natriuretic peptide (BNP) is a marker of increased blood volume, commonly used in heart failure, that has been shown to be elevated in patients with liver disease. This study examined if BNP levels can be used to determine prognosis and predict worsening of ascites in patients with cirrhosis without concomitant heart disease. A retrospective study was performed at a large urban hospital in Chicago, Illinois and included 430 patients with cirrhosis who had BNP levels ordered during their hospital stay. Patients with clinical heart failure, arrhythmias or pulmonary hypertension were excluded. The primary outcome was 90-day mortality and the secondary outcome was a requirement for therapeutic paracentesis in the 90 days following BNP results. 53 patients (12%) had BNP levels ≥ 300 pg/mL. They had significantly increased serum levels of creatinine, bilirubin, and International Normalized Ratio (INR) when compared to those with BNP p = 0.037) and were more likely to require therapeutic paracentesis (HR 2.26; p = 0.02) in the next 90 days. A BNP ≥ 300 pg/mL had specificity of 88.2% in predicting 90-day mortality. BNP may serve as a practical and reliable marker of underlying disease severity in patients with cirrhosis, with potential to be included in prognostication tools for assessment of end-stage liver disease.

Understanding the interactions and the solvent effects on the distribution of several species in equilibrium and how it can influence the 1H-NMR properties, spectroscopy (UV-vis absorption), and the acid–base equilibria can be especially challenging.

Data generators are applications that produce synthetic datasets, which are useful for testing data analytics applications, such as machine learning algorithms and information visualization techniques. Each data generator application has a different approach to generate data. Consequently, each one has functionality gaps that make it unsuitable for some tasks (e.g., lack of ways to create outliers and non-random noise). This paper presents a data generator application that aims to fill relevant gaps scattered across other applications, providing a flexible tool to assist researchers in exhaustively testing their techniques in more diverse ways. The proposed system allows users to define and compose known statistical distributions to produce the desired outcome, visualizing the behavior of the data in real-time to analyze if it has the characteristics needed for efficient testing. This paper presents in detail the tool functionalities and how to create datasets, as well as a usage scenario to illustrate the process of data creation.

To support the nuances of collaborative work, many researchers have been exploring the field of Augmented Reality (AR), aiming to assist in co-located or remote scenarios. Solutions using AR allow taking advantage from seamless integration of virtual objects and real-world objects, thus providing collaborators with a shared understanding or common ground environment. However, most of the research efforts, so far, have been devoted to experiment with technology and mature methods to support its design and development. Therefore, it is now time to understand where the field stands and how well can it address collaborative work with AR, to better characterize and evaluate the collaboration process. In this article, we perform an analysis of the different dimensions that should be taken into account when analysing the contributions of AR to the collaborative work effort. Then, we bring these dimensions forward into a conceptual framework and propose an extended human-centered taxonomy for the categorization of the main features of Collaborative AR. Our goal is to foster harmonization of perspectives for the field, which may help create a common ground for systematization and discussion. We hope to influence and improve how research in this field is reported by providing a structured list of the defining characteristics. Finally, some examples of the use of the taxonomy are presented to show how it can serve to gather information for characterizing AR-supported collaborative work, and illustrate its potential as the grounds to elicit further studies.

Even with the growth of digital data sources and support for creating visualizations, much information disseminated in chart format is still static, whether printed or digital. Some common problems of visualization components can be easily corrected on the chart design using a charting tool, but in the real world, mainly in the printed media, it is not a simple task. We investigate a method to reconstruct visualizations in real-time and in a real-world environment using Augmented Reality. We propose a prototype that interacts with extracted data from a bitmap chart in the real world, and we evaluated it with usability experts. The results show a high degree of satisfaction on many factors, mainly filter usage and valuable feedback about interaction.

This work aims to review the academic literature on information visualization techniques used in dashboards applied to strategic business or management decision-making in different application areas. The review used the snowballing method to obtain academic works and applied a filter to focus on papers published in journals or conference proceedings, reaching 44 papers. We propose four research questions and one taxonomy to classify the works, carry out the analyses, and later a research agenda to address the identified gaps. For instance, this review revealed a lack of academic papers that discuss the subject involving state-of-the-art information visualization and machine learning techniques. This paper represents our initial effort to examine academic works that use information visualization techniques and theory applied to dashboards that support decision-making in business or management areas.

Primary sternal osteomyelitis (PSO) is a rare clinical entity, and usually, it is associated with predisposing factors such as intravenous drug use, diabetes mellitus, or human deficiency virus infection. In an otherwise healthy adult, it becomes an even rarer entity. Early diagnosis and treatment minimize associated morbidity, like the need for surgical debridement, longer courses of medication, and length of in-hospital stay. We describe the case of a 54-year-old man without any predisposing risk factors for PSO, who presented with chest pain, erythema, tenderness, and warmth at the right parasternal region. A non-enhanced thoracic tomography showed a 33 mm suspicious pulmonary nodule and no signs of sternum abnormalities. To better evaluate this finding, a positron emission tomography with fluorine-18 fluorodeoxyglucose was performed, showing abnormal uptake of the radionuclide at the sternomanubrial synchondrosis and no abnormal uptake at the lung parenchyma. The presence of Staphylococcus aureus in blood cultures, in conjunction with these results, supported the diagnosis of PSO. The patient completed six weeks of microbiologically oriented antibacterial therapy with complete recovery.

Augmented Reality (AR) has been explored with the objective to assist in scenarios of co-located or remote collaboration. To help understand how well collaborative work can be addressed with AR, it is important to foster harmonization of perspectives and create a common ground for systematization and discussion. In this vein, understand relationships among existing dimensions of collaboration, as well as identify research opportunities, is of paramount importance and thus tools that allow visually exploring information associated with Collaborative AR may be most valuable. In this paper, we present a first effort towards the creation of such an interactive visualization tool for exploration and analysis of collaborative AR research. It allows visualize data of selected papers organized according to a human-centered taxonomy on collaborative AR. In order to get insights into whether the structure was understood and if the representation was clear and efficient to use, we evaluated the proposed tool through a user study with 40 participants. Results suggest the tool has potential towards the creation of a shared understanding and identification of existing patterns, trends and opportunities within the field of collaborative AR.

The causes and management of pyogenic liver abscess (PLA) have undergone multiple changes over the past decades. It is a relatively rare disease in the USA, and its incidence rate in the USA is increasing. The last US community hospital experience of PLA was published in 2005. We performed a retrospective study of patients admitted with PLA to an urban safety net hospital. To ascertain risk factors, management approaches, and outcomes of PLA. Electronic medical record was queried for diagnosis codes related to PLA during the years 2009–2018. Clinical information was compiled in an electronic database which was later analyzed. Main study outcomes were in-hospital mortality, 30-day readmission rate, and intensive care utilization rate. A total of 77 patients with PLA were admitted in the study period. Most common risk factors were diabetes mellitus (23.4%), previous liver surgery (20.7%), and hepatic malignancy (16.9%). 89% of patients were treated with percutaneous drainage or aspiration, and surgical drainage was reserved for other with other indications for laparotomy. In-hospital mortality, 30-day readmission, and intensive care utilization rates were 2.6%, 7% and 22%, respectively. Median length of stay was 11 days (inter-quartile range 7). Rate of antimicrobial resistance in abscess fluid cultures was 40%; 13 cases of Klebsiella pneumoniae liver abscess were noted in our cohort, most of whom were Hispanic or Asian. PLA was principally managed by percutaneous drainage or aspiration with good outcomes. Further studies investigating the racial predilection of K. pneumoniae liver abscesses could reveal clues to its pathogenesis.

Skin metastasis is an infrequent manifestation in oncology, with reported incidence of about 1.3% in cervical cancer. When present, it usually signals advanced disease with very limited, mostly palliative treatment options. Here we present a patient who was diagnosed with cervical cancer at an early stage and later recurred twice with skin lesions of different morphologies., Highlights • Biopsy should be considered for skin lesions in patients with cervical cancer • Skin metastasis may be the presenting symptom in cervical cancer recurrence • No specific morphology is pathognomonic of cutaneous spread of cervical cancer

The cultural heritage of a city is of great importance for the maintenance and enhancement of its history. Users can use innovative technologies such as Computer Vision to emphasize the city’s treasures attractively and playfully. In Belem, Para, azulejo is a meaningful cultural heritage case, which goes back to its foundation. The image recognition can facilitate and speed up the search for an azulejo and its historical information since, although cataloged, its visual appearance identifies it better than a name, which implies that the image-based search is much more natural than a text-based search. In this way, this work presents a prototype that uses Convolutional Neural Networks (CNN) to classify the azulejos from Belem by an image-based search. CNN’s training used two image datasets. The first contains images that show azulejos and other environmental elements (for instance, walls, doors, streets, and people). The second dataset contains images that show only azulejos, and in both datasets, they only have one type of azulejo per image. The trained model consists of twelve different types of azulejos, representing the recognizable classes. Thus, after training, the tflite (Tensorflow Lite) model is generated with azulejos classes to be used in the mobile device image classification task. Finally, we developed an application in which the user takes a photo, and the system sends it to the classification module that contains the trained CNN model. After the image classification process, the module returns the five classes’ values with the best accuracy and historical details about the azulejos.

Artificial Intelligent (AI) techniques, such as ma-chine learning (ML), have been making significant progress over the past decade. Many systems have been applied in sensitive tasks involving critical infrastructures which affect human well-being or health. Before deploying an AI system, it is necessary to validate its behavior and guarantee that it will continue to perform as expected when deployed in a real-world environment. For this reason, it is important to comprehend specific aspects of such systems. For example, understanding how neural networks produce final predictions remains a fundamental challenge. Existing work on interpreting neural network predictions for images via feature visualization often focuses on explaining predictions for neurons of one single convolutional layer. Not presenting a global perspective over the features learned by the model leads the user to miss the bigger picture. In this work we focus on providing a representation based on the structure of deep neural networks. It presents a visualization able to give the user a global perspective over the feature maps of a convolutional neural network (CNN) in a single image, revealing potential problems of the learning representations present in the network feature maps.

With vast amounts of data, comes vast numbers of problems. The process of collecting data is far from perfect, either due to human factors or technological errors, which can lead to inaccuracies and uncertainties in the data. One such issue is missing data: the absence of information. Several methods can deal with missing values, but to choose the correct approach, it is necessary to diagnose the missing data mechanisms, which describe how the distribution of missingness in a given data variable correlates to other variables. This diagnosis can be made with statistical tests or data visualization techniques. However, statistical tests provide an uncertainty estimation that is often misinterpreted, and the visualizations readily available in data analysis packages have some scalability issues, such as cognitive overload and lack of screen space. Thus, this paper proposes a visual-interactive idiom for diagnosing missing data mechanisms. The proposed solution consists of a set of visual encodings and two derived metrics that synthesizes the missing data mechanisms and the uncertainty associated with this synthesis. We present the concepts behind the visual encodings, derived metrics, and interactions of the idiom.

O presente número temático da Revista Tempus Actas de Saúde Coletiva evidencia aevolução da Saúde Bucal Brasileira, quer seja pública ou privada, como sendo o reflexo do processo de mudanças que ocorreram em nossa sociedade. Os artigos selecionados...

Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. In general, a well-constructed data chart leads to an intuitive understanding of its underlying data. In the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Other features in real-world images that can make this task difficult are photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. This paper proposes a classification, detection, and perspective correction process that is suitable for real-world usage, when considering the data used for training a state-of-the-art model for the extraction of a chart in real-world photography. The results showed that, with slight changes, chart recognition methods are now ready for real-world charts, when taking time and accuracy into consideration.

The risk of infectious and noninfectious complications associated with long peripheral catheters (LPCs) is unknown. In this retrospective study of 539 catheters, we found LPCs were often placed for the indications of difficult access and long-term antibiotics. Rates of deep vein thrombosis (1.7%) and catheter-related infection (0.6%) were low. LPCs may represent a novel and safe option for short-term venous access.