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low fidelity Simulation on Sensory Impairments in Older Adults: Undergraduate Nursing Students’ Self-Reported Perceptions on Learning

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Open Journal of Nursing 2021年第3期11卷 89-103页

作者： Ronie Walters Leah Macaden Abbi Tracey Annetta Smith Department of Nursing and Midwifery University of the Highlands and Islands Centre for Health Science Inverness Scotland

Globally, the population is living longer and by 2050, it is predicted to reach 2.1 billion people. Sensory and cognitive impairments are common long-term conditions among older Europeans and have considerable functional, social, emotional and economic impacts on the individual and those caring for them. Nurses have frequent encounters with patients with these impairments and are expected to prioritise people, assess their needs and accommodate practice to meet these needs. In order to develop the requisite knowledge and understanding to support people living with these impairments, student nurses require an immersive and experiential approach to learning as opposed to just information transfer. This study reports on a cross-sectional analysis of a low fidelity simulation on sensory impairments as part of a wider dementia curriculum in semester one of the undergraduate nursing programme at the University of Highlands and Islands. Findings from an online questionnaire-based survey and content analysis of free text responses revealed that students found the simulation activities critical for gaining subject knowledge, understanding and insight. This study concluded that low-fidelity simulation of sensory/cognitive impairments, within the context of a broader curriculum of supportive activities, can be effective at developing relevant knowledge, understanding and gaining insights in this subject area among undergraduate nursing students.

关键词： low fidelity Simulation Sensory/Cognitive Impairments Nurse Education Empathy

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Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

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Computers, Materials & Continua 2022年第9期72卷 4303-4321页

作者： Saman Riaz Ali Arshad Shahab S.Band Amir Mosavi Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Computer Science Institute of Space TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliu64002YunlinTaiwan Faculty of Civil Engineering Technische Universitat DresdenDresden 01069Germany

The way towards generating a website front end involves a designersettling on an idea for what kind of layout they want the website to have, thenproceeding to plan and implement each aspect one by one until they haveconverted what they initially laid out into its Html front end form, this processcan take a considerable time, especially considering the first draft of the designis traditionally never the final one. This process can take up a large amountof resource real estate, and as we have laid out in this paper, by using a Modelconsisting of various Neural Networks trained on a custom dataset. It can beautomated into assisting designers, allowing them to focus on the other morecomplicated parts of the system they are designing by quickly generating whatwould rather be straightforward busywork. Over the past 20 years, the boomin how much the internet is used and the sheer volume of pages on it demands ahigh level of work and time to create them. For the efficiency of the process, weproposed a multi-model-based architecture on image captioning, consisting ofConvolutional neural network (CNN) and Long short-term memory (LSTM)models. Our proposed approach trained on our custom-made database can beautomated into assisting designers, allowing them to focus on the other morecomplicated part of the system. We trained our model in several batches overa custom-made dataset consisting of over 6300 files and were finally able toachieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50hand-drawn images at 87.86%.

关键词： Deep learning wireframes front-end low fidelity high fidelity design process html computer vision dsl

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