A Data Model for Exploration of Temporal Virtual Reality Geographic Information Systems

Geographic information systems deal with the exploration, analysis, and presentation of geo-referenced data. Virtual reality is a type of human-computer interface that comes close to the way people perceive information in the real world. Thus, virtual reality environments become the natural paradigm for extending and enhancing the presentational and exploratory capability of GIs applications in both the spatial and temporal domains. The main motivation of this thesis is the lack of a framework that properly supports the exploration of geographic information in a multi-dimensional and multi-sensorial environment (i.e., temporal virtual reality geographic information systems). This thesis introduces a model for virtual exploration of animations. Virtual exploration of animations is a framework composed of abstract data types and a user interface that allow non-expert users to control, manipulate, analyze, and present objects\u27 behaviors in a virtual-reality environment. In the model for virtual exploration of animations, the manipulation of the dynamic environment is accomplished through a set of operations performed over abstractions that represent temporal characteristics of actions. An important feature of the model is that the temporal information is treated as first-class entities and not as a mere attribute of action\u27s representations. Therefore, entities of the temporal model have their own built-in functionality and are able to represent complex temporal structures. In an environment designed for the manipulation of the temporal characteristics of actions, the knowledge of relationships among objects\u27 behaviors plays a significant role in the model. This information comes from the knowledge base of the application domain and is represented in the model through constraints among entities of the temporal model. Such constraints vary from simply relating the end points of two intervals to a complex mechanism that takes into account all relations between sequences of intervals of cyclic behaviors. The fact that the exploration of the information takes place in a virtual reality environment imposes new requirements on the animation model. This thesis introduces a new classification of objects in a VR environment and describes the associated semantics of each element in the taxonomy. These semantics are used to direct the way an object interacts with an observer and with other objects in the environment

Face recognition in Service robotics: Analysis of the padding effect according to people age / Reconhecimento facial em robótica de serviço: análise do efeito de preenchimento de acordo com a idade das pessoas

Service robots usually perform repetitive tasks such as collecting garbage, cleaning the house, among others. This kind of robot needs different skills to perform its daily tasks, being people´s recognition a critical skill. One of the techniques used to improve face recognition is padding. The padding technique increases, by a given scale factor, the bounding box of a detected face. In previous work, we had presented a comparative analysis of the influence of the padding in the algorithm used for face recognition. This paper extends the previous analysis by considering the effect of various padding scale factors among different life stages (i.e., toddler, children, teenager, adult, senior, and golden oldie). The result of this analysis shows that increasing the bounding box of detected faces is less efficient for middle-aged people than for younger and elderly people

Health-related quality of life in patients with type 1 diabetes mellitus in the different geographical regions of Brazil : data from the Brazilian Type 1 Diabetes Study Group

Background: In type 1 diabetes mellitus (T1DM) management, enhancing health-related quality of life (HRQoL) is as important as good metabolic control and prevention of secondary complications. This study aims to evaluate possible regional differences in HRQoL, demographic features and clinical characteristics of patients with T1DM in Brazil, a country of continental proportions, as well as investigate which variables could influence the HRQoL of these individuals and contribute to these regional disparities. Methods: This was a retrospective, cross-sectional, multicenter study performed by the Brazilian Type 1 Diabetes Study Group (BrazDiab1SG), by analyzing EuroQol scores from 3005 participants with T1DM, in 28 public clinics, among all geographical regions of Brazil. Data on demography, economic status, chronic complications, glycemic control and lipid profile were also collected. Results: We have found that the North-Northeast region presents a higher index in the assessment of the overall health status (EQ-VAS) compared to the Southeast (74.6 ± 30 and 70.4 ± 19, respectively; p < 0.05). In addition, North- Northeast presented a lower frequency of self-reported anxiety-depression compared to all regions of the country (North-Northeast: 1.53 ± 0.6; Southeast: 1.65 ± 0.7; South: 1.72 ± 0.7; Midwest: 1.67 ± 0.7; p < 0.05). These findings could not be entirely explained by the HbA1c levels or the other variables examined. Conclusions: Our study points to the existence of additional factors not yet evaluated that could be determinant in the HRQoL of people with T1DM and contribute to these regional disparities

Allergen sensitization linked to climate and age, not to intermittent-persistent rhinitis in a cross-sectional cohort study in the (sub)tropics

Background: Allergen exposure leads to allergen sensitization in susceptible individuals and this might influence allergic rhinitis (AR) phenotype expression. We investigated whether sensitization patterns vary in a country with subtropical and tropical regions and if sensitization patterns relate to AR phenotypes or age. Methods: In a national, cross-sectional study AR patients (2-70 y) seen by allergists underwent blinded skin prick testing with a panel of 18 allergens and completed a validated questionnaire on AR phenotypes. Results: 628 patients were recruited. The major sensitizing allergen was house dust mite (HDM) (56%), followed by Bermuda grass (26%), ash (24%), oak (23%) and mesquite (21%) pollen, cat (22%) and cockroach (21%). Patients living in the tropical region were almost exclusively sensitized to HDM (87%). In the central agricultural zones sensitization is primarily to grass and tree pollen. Nationwide, most study subjects had perennial (82.2%), intermittent (56.5%) and moderate-severe (84.7%) AR. Sensitization was not related to the intermittent-persistent AR classification or to AR severity; seasonal AR was associated with tree (p < 0.05) and grass pollen sensitization (p < 0.01). HDM sensitization was more frequent in children (0-11 y) and adolescents (12-17 y) (subtropical region: p < 0.0005; tropical region p < 0.05), but pollen sensitization becomes more important in the adult patients visiting allergists (Adults vs children + adolescents for tree pollen: p < 0.0001, weeds: p < 0.0005). Conclusions: In a country with (sub)tropical climate zones SPT sensitization patterns varied according to climatological zones; they were different from those found in Europe, HDM sensitization far outweighing pollen allergies and Bermuda grass and Ash pollen being the main grass and tree allergens, respectively. Pollen sensitization was related to SAR, but no relation between sensitization and intermittent-persistent AR or AR severity could be detected. Sensitization patterns vary with age (child HDM, adult pollen). Clinical implications of our findings are dual: only a few allergens –some region specific- cover the majority of sensitizations in (sub)tropical climate zones. This is of major importance for allergen manufacturers and immunotherapy planning. Secondly, patient selection in clinical trials should be based on the intermittent-persistent and severity classifications, rather than on the seasonal-perennial AR subtypes, especially when conducted in (sub)tropical countries

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries