10,370 research outputs found

    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    1st Design Factory Global Network Research Conference ‘Designing the Future’ 5-6 October 2022

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    DFGN.R 2022 -Designing the Future - is the first research conference organised by the Design Factory Global Network. The open event offers the opportunity for all like-minded educators, designers and researchers to share their insights and inspire others on education, methods, practices and ecosystems of co-creation and innovation. The DFGN.R conference is a two-day event hosted on-site in Leeuwarden, the Netherlands. The conference is organized alongside International Design Factory Week 2022, the annual gathering of DFGN members. This year's conference is organized in collaboration with Aalto University from Helsinki Finland and hosted by the NHL Stenden University of Applied Sciences

    TeamSTEPPS and Organizational Culture

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    Patient safety issues remain despite several strategies developed for their deterrence. While many safety initiatives bring about improvement, they are repeatedly unsustainable and short-lived. The index hospital’s goal was to build an organizational culture within a groundwork that improves teamwork and continuing healthcare team engagement. Teamwork influences the efficiency of patient care, patient safety, and clinical outcomes, as it has been identified as an approach for enhancing collaboration, decreasing medical errors, and building a culture of safety in healthcare. The facility implemented Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS), an evidence-based framework which was used for team training to produce valuable and needed changes, facilitating modification of organizational culture, increasing patient safety compliance, or solving particular issues. This study aimed to identify the correlation between TeamSTEPPS enactment and improved organizational culture in the ambulatory care nursing department of a New York City public hospital

    Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images

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    Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression. For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired. In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database

    Machine learning and mixed reality for smart aviation: applications and challenges

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    The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency

    Soundscape in Urban Forests

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    This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests

    Methods for eliciting and measuring behavioral and physiological consequences of stress and uncertainty in virtual reality

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    Military operations are characterized by high levels of stress and uncertainty, and these states can influence cognitive and physical performance outcomes. These states, however, can be difficult to reliably induce in laboratory contexts, making it challenging to quantify and model their influences on perceptual and cognitive processes underlying performance on applied tasks. Herein we describe the development and validation of a novel scenario-based virtual reality methodology, the decision making under uncertainty and stress (DeMUS) scenario, that accomplishes four primary goals. First, it induces physiological and biochemical stress responses through a threat of shock manipulation. Second, it induces transient states of uncertainty by manipulating stimulus clarity in a perceptual decision-making task. Third, it generates several performance metrics regarding recognition memory, spatial orienting, threat classification, and marksmanship decision making. Finally, the task combines behavioral, physiological, and biochemical measures to provide a more comprehensive understanding of how stress and uncertainty influence applied task performance. To provide an initial validation of the scenario and its associated tasks and measures, we conducted a pilot study (n = 18) involving stress induction and cognitive performance assessment. Analyses revealed that: 1) the DeMUS scenario elicited tonic and phasic biochemical (salivary alpha amylase and cortisol) and physiological (heart rate, pupil diameter) stress responses, 2) the scenario elicited variable sympathetic autonomic nervous system and hypothalamic-pituitary adrenal (HPA) axis responses, and 3) stress influenced some measures of memory and decision-making in both negative and positive directions. Continuing research will assess individual- and group-level predictors of performance on these virtual reality tasks, and emerging performance enhancement techniques that can help military personnel sustain performance during stressful operations

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port
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