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Speech and language therapists are health professionals working with people with communication and swallowing-related problems. The scope of practice includes competences that go far beyond clinical and technical abilities. As in other fields, inter and intrapersonal competences are crucial to develop and establish quality health services and a well-succeeded career. Reflective practices are used to achieve a professional profile aligned with 21st century competences, with strong soft skills, and capacity to develop new competences during professional lifetime. This chapter presents the experience of a Portuguese health school where several tools and strategies have systematically been used for the last 15 years in a Speech and Language Therapy degree, as tutorials, portfolio, reflective reports, self- and peer assessments, simulations, and clinical practicum. The outcomes are relevant and may be inspiring for other allied health professions' education, including students, teachers, and clinical educators.

Contains fulltext : 246352.pdf (Publisher’s version ) (Open Access) The European Union (EU) Commission's whitepaper on Artificial Intelligence (AI) proposes shaping the emerging AI market so that it better reflects common European values. It is a master plan that builds upon the EU AI High-Level Expert Group guidelines. This article reviews the masterplan, from a culture cycle perspective, to reflect on its potential clashes with current societal, technical, and methodological constraints. We identify two main obstacles in the implementation of this plan: (i) the lack of a coherent EU vision to drive future decision-making processes at state and local levels and (ii) the lack of methods to support a sustainable diffusion of AI in our society. The lack of a coherent vision stems from not considering societal differences across the EU member states. We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU. Moreover, the Commission aims at changing the AI development culture proposing a human-centred and safety-first perspective that is not supported by methodological advancements, thus taking the risks of unforeseen social and societal impacts of AI. We discuss potential societal, technical, and methodological gaps that should be filled to avoid the risks of developing AI systems at the expense of society. Our analysis results in the recommendation that the EU regulators and policymakers consider how to complement the EC programme with rules and compensatory mechanisms to avoid market fragmentation due to local and global ambitions. Moreover, regulators should go beyond the human-centred approach establishing a research agenda seeking answers to the technical and methodological open questions regarding the development and assessment of human-AI co-action aiming for a sustainable AI diffusion in the society. 22 januari 2022 20 p.

Vascular surgery (VS) has a higher perioperative mortality than other types of surgery. We compared different scores for predicting mortality in patients admitted to the intensive care unit (ICU) after open VS.Patients admitted to the ICU after open VS from 2006 to 2013 were included. We calculated the Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Preoperative Score to Predict Postoperative Mortality (POSPOM). We performed multivariate logistic regression to assess independent factors with the calculation of odds ratios (ORs) and 95% confidence intervals (CIs). We tested the predictive ability of the scores using the area under the receiver operating characteristics curve (AUROC).A total of 833 consecutive patients were included. Hospital mortality was 5.1% (1.3% after intermediate-risk and 8.4% after high-risk surgery). In the multivariate analysis, the age (OR 1.04, 95% CI 1.01-1.08, p = 0.013), smoking status (OR 2.46, 95% CI 1.16-5.21, p = 0.019), surgery risk (OR 2.92, 95% CI 1.05-8.08, p = 0.040), serum sodium level (OR 1.17, 95% CI 1.10-1.26, p 0.001), urea (OR 1.01, 95% CI 1.01-1.02, p = 0.001) and leukocyte count (OR 1.05, 95% CI 1.01-1.10, p = 0.009) at admission were considered independent predictors. Hematocrit (0.86, 95% CI 0.80-0.93, p 0.001) was considered an independent protective factor. The AUROC of our model was 0.860, compared to SAPS (0.752), APACHE (0.774), POSPOM (0.798) and POSSUM (0.829).The observed mortality was within the predicted range (1-5% after intermediate-risk and 5% after high-risk surgery). POSSUM and POSPOM had slightly better predictive capacity than SAPS or APACHE.

An approach to analyzing Streaming Big Data as it comes in while maintaining the proper context of past events is to employ contiguous visualizations with an increasingly aggressive aggregation degree. This allows for the most recent data to be displayed in detail, while older data is shown in an aggregated form according to how long ago it was received. However, the transitions applied between visualizations with different aggregations must not compromise the understandability of the data flow. Particularly, new data should be perceived considering the context established by older data, and the visualizations should not be perceived as independent or un-connected. In this paper, we present the first study on transitions between two contiguous visualizations, focusing on time series data. We developed several animated transitions between a scatter plot, where all data points are individually represented as they arrive, and other visualizations where data is displayed in an aggregated form. We then conducted a user evaluation to assess the most appealing and effective transitions that allow for the best comprehension of the displayed data for each visualization pair.

In InfoVis design, visualizations make use of pre-attentive features to highlight visual artifacts and guide users' perception into relevant information during primitive visual tasks. These are supported by visual marks such as dots, lines, and areas. However, research assumes our pre-attentive processing only allows us to detect specific features in charts. We argue that a visualization can be completely perceived pre-attentively and still convey relevant information. In this work, by combining cognitive perception and psychophysics, we executed a user study with six primitive visual tasks to verify if they could be performed pre-attentively. The tasks were to find: horizontal and vertical positions, length and slope of lines, size of areas, and color luminance intensity. Users were presented with very simple visualizations, with one encoded value at a time, allowing us to assess the accuracy and response time. Our results showed that horizontal position identification is the most accurate and fastest task to do, and the color luminance intensity identification task is the worst. We believe our study is the first step into a fresh field called Incidental Visualizations, where visualizations are meant to be seen at-a-glance, and with little effort.

A forward-looking 36 GHz radar for aircrafts and helicopters is presented. It uses the rotating synthetic aperture for obtaining the azimuth resolution and independent radars with different antenna elevation angles for obtaining the elevation resolution. Main radar functionalities are the guidance for landing phase including the three-dimensional view, strike avoidance, moving target indication and detection of stationary air and ground obstacles. This article describes the radar principle, its modeling and an implementation example for a given operational requirement.

Introduction. Patients proposed to vascular noncardiac surgery (VS) have several comorbidities associated with major adverse cardiac events (MACE). We evaluated incidence, predictors, and outcomes, and compared different scores to predict MACE after VS. Methods. We included all patients admitted from 2006 to 2013. Perioperative MACE included cardiac arrhythmias, myocardial infarction (MI), cardiogenic pulmonary edema (CPE), acute heart failure (AHF), and cardiac arrest (CA). Lee Revised Cardiac Risk Index (RCRI), Vascular Quality Initiative (VQI-CRI), Vascular Study Group of New England (VSG-CRI), and South African Vascular Surgical (SAVS-CRI) Cardiac Risk Indexes were calculated and analyzed. We performed multivariate logistic regression to assess independent predictors with calculation of odds ratio (OR) and 95% confidence interval (CI). To reduce overfitting, we used leave-one-out cross-validation approach. The Predictive ability of scores was tested using area under receiver operating characteristic curve (AUROC). Results. A total of 928 patients were included. We observed 81 MACE (28 MI, 22 arrhythmias, 10 CPE, 9 AHF, 12 CA) in 60 patients (6.5%): 3.3% in intermediate-risk surgery and 9.8% in high-risk surgery. Previous history of coronary artery disease (OR = 3.2, CI = 1.8-5.7), atrial fibrillation (OR = 5.1, CI = 2.4-11.0), insulin-treated diabetes mellitus (OR = 3.26, CI = 1.51-7.06), mechanical ventilation (OR = 2.75, CI = 1.41-4.63), and heart rate (OR = 1.02, CI = 1.01-1.03) at admission were considered independent risk factors in multivariate analysis. The AUROC of our model was 0.79, compared with RCRI (0.66), VSG-CRI (0.69), VQI-CRI (0.71), and SAVS-CRI (0.73). Conclusions. Observed MACE were within predicted range (1% to 5% after intermediate-risk surgery and >5% after high-risk surgery). SAVS-CRI and VQI-CRI had slightly better predictive capacity than VSG-CRI or RCRI.

Companies produce data in large volumes and in multiple varieties. This trend is intensifying with the deployment of Internet of Things (IoT) devices. Companies need to process data more efficiently and at the edge of the network if they are to remain capable of making timely business decisions based on data. Apache Hadoop and Hive are two widely used data processing systems. However, they rely on complex software stacks that cannot run in a typical IoT gateway device, i.e., a computer with low hardware specifications. An approach to solve this problem is to replace the software with a leaner system with the same functionality. This approach is the value proposition of Unicage, a data processing system based on Unix shell scripting, that promises better performance by using the operating system directly for execution and inter-process communication. In this paper, we benchmark data processing systems with workloads that compare Unicage with Hadoop and with Hive. The results show that the complexity of the software stack is indeed a significant bottleneck in data processing.

A real-time monitoring system based on ground-based synthetic aperture radar interferometry (GB-InSAR) were developed and deployed with the objective to gain understanding of landslide dynamics in an area in Xerem (district of Duque de Caxias - RJ, Brazil). The area is subjected to landslide risk and the deployed system aims to provide subsidies to the authorities to prepare actions related to prevention and evacuation, and serve as the basis for a future early-warning system in the framework of the Civil Defense of Duque de Caxias. The system consists of a compact and rugged X-band GB-InSAR newly developed by Bradar, Brazil. The processing and monitoring methodology are proposed. Two campaigns, totaling six days, were carried out. The acquisitions of the first campaign occurred without rain. In the second campaign, rain events, varying from light to torrential rainfall, occurred for a period of time, in subsequent days, during the radar acquisitions. New insights on landslide dynamics are revealed. The monitoring data show that, approximately 10 h after heavy rainfall, the terrain movement on a slope reached 4 mm/h during ca. 5 to 8 h, resulting in velocities of 20 to 32 mm/day.

Noncardiac vascular surgery (VS) patients have comorbidities that increase the risk of death after surgery. Assessing that risk is important to allocate the necessary resources and improve quality of care. We aimed to evaluate the incidence and predictors of 30-day post-operative mortality (POM) after VS and compare the performance of existing risk scores.Prospective cohort study including consecutive patients submitted to elective VS at a tertiary university hospital. We collected patients' demographics/perioperative data and calculated Surgical Apgar, age-adjusted Charlson Comorbidity Index (CCI), Vascular-Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (V-POSSUM) and Preoperative Score to Predict Postoperative Mortality (POSPOM). We performed multivariate logistic regression to assess independent factors with Odds Ratio (OR) and 95% confidence interval (CI) calculation and Cox-regression for time-to-event analysis. We tested the predictive ability of the scores using the area under ROC curve (AUROC).POM was 6.2% (n = 19/306), not different from expected by V-POSSUM (6.5%) or POSPOM (5.6%). Post-operative myocardial infarction (MI) and acute kidney injury (AKI) were associated with higher POM (OR 4.8, p = 0.011 and OR 5.4, p = 0.001, respectively). On multivariate analysis, Chronic kidney disease (CKD) (OR 4.0, p = 0.021), Age (OR 1.1, p = 0.002), Peripheral arterial disease (PAD) (OR 8.0, p = 0.006), intra-operative red blood cells (RBC) Transfusion (OR 1.9, p 0.001) and Atrial fibrillation (OR 8.4, p = 0.002) were considered independent predictors of POM (CAPTA score). The AUROC of our model was 0.882, better V-POSSUM (0.858), POSPOM (0.784), CCI (0.732) or Surgical Apgar (0.649).Observed POM was similar to predicted by V-POSSUM or POSPOM. Age, PAD, CKD, atrial fibrillation and intraoperative RBC transfusion were independent risk factors for POM. Score V-POSSUM performed better than POSPOM, CCI or Surgical Apgar.

Remote sensing has been widely employed for monitoring land cover and usage by change detection techniques. In this paper, we cope with the early detection of the first signs of deforestation, which is the gateway for illegal activities, such as unauthorized urban sprawl and grazing use. In recent years, object-based approaches have emerged as a more suitable alternative than pixel-based methods for change detection in remote sensing images. Even though several classifiers have been tested, there was little effort in selecting appropriated features for the classification of detected changes. After a deep analysis of the existing segmentation, feature extraction, and classification approaches, we propose an object-based methodology that consists of: 1) segmenting multitemporal Xband high-resolution synthetic aperture radar (SAR) images into superpixels employing the simple linear iterative clustering algorithm; 2) extracting features using the object correlation images framework and with the gray-level cooccurrence matrix; and 3) classifying areas into unchanged, deforestation, and other changes by means of a multilayer perceptron supervised learning technique. Experiments were performed using high-resolution SAR images obtained by the airborne sensor OrbiSAR-2 from BRADAR in challenging scenarios of the Brazilian Atlantic Forest, including a wide variety of vegetation, rivers, sea coasts, urban, harvest and open areas, and humidity changes. We perform an extensive experimental analysis of the results, comparing the proposed method with a state-of-the-art approach. The results demonstrate that our method yields an improvement of over 10% in the accuracy while detecting changes and classifying deforested areas.