基于章水泉竹艺非物质文化遗产的APP设计与研究

目的 针对章水泉竹艺非物质文化遗产在目前所遭遇的传承困境，利用数字媒体技术与互联网平台，打造推动其产品创新与文化传承的APP，助力其文化与产品的传播与推广，进而为湖北地区的非遗文化发展提供参考。方法 从章水泉竹器的本源出发，对其主要产品门类、工艺特点与艺术形式进行整理与分类，分析制约其传承与发展的主要原因，进而提出设计策略，并通过数字化传承、合作创新、成果推广3个策略模块针对性地进行APP设计。结论 章水泉竹艺APP设计通过利用数字媒体技术与互联网平台优势，将其历史与工艺资料进行数字化保存与展示，实现了文化保护功能；将其传统技艺与新锐设计思维通过线上平台进行融合与创新，实现了设计创新功能；将其制作流程与文化精髓通过直播与精准推送展现于公众视野，实现了产品推广功能。最终，为章水泉竹艺的传承与推广提供了新的路径。     

就诊等待服务APP适老化设计研究

目的 将数字化服务融入老年患者就诊流程，建立适老化就诊等待服务系统，在APP开发设计研究中实现软件流程优化。方法 依据ERG理论对老年患者在就诊等待过程中的需求点进行分类整理和层次划分。调研目标用户将分析结果融入服务设计理论，为指导软件开发所涉及的医院就诊流程、用户需求痛点，提出系统性的解决策略。基于交互设计原则展开APP界面设计。结论 构建了以老年患者为中心的就诊等待服务系统，帮助提升其等待过程中的自我效能。完善了基于产品使用方式层级的适老化就诊等待服务APP设计策略，为适老化、数字化产品研发提供了新思路;最终产出APP设计实例，提高了老年群体社会参与度，鼓励老年患者自主就诊，帮助其更加轻松地享受信息化时代带来的红利。     

信息技术在医院病案统计中的应用

伴随着我国综合实力的不断提升,信息技术获得了突飞猛进的发展,如今,医疗改革如火如荼地进行,医院的信息管理工作同以往相比较也实现了极大的发展。病案统计工作是指相关工作人员对医院中患者的信息进行全面统计,若是这项工作完成得足够出色,会给医院后续的信息查询工作带来诸多方便和快捷,也能够在某种程度上使医院管理效率和质量实现一定幅度的提升。因此,将信息技术和医院的病案工管理工作进行密切结合,就成为病案管理人员应当重点思考的问题。病案统计工作是医院管理工作中的一个重点环节,病案统计结果能够为医院整体绩效带来十分深远的影响。因此,投入更多时间和精力来提高对这项工作的重视,显得十分关键和重要。文章对信息技术在医院病案统计中的应用展开了分析。     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

Effect of resiliency and age on musculoskeletal injuries and lost workdays in emergency medical service personnel

Emergency medical service (EMS) personnel are highly skilled health care professionals who often provide lifesaving clinical care to patients. Paradoxically, they may be repeatedly exposed to a unique set of occupational hazards that could endanger their own health. This cross-sectional study sought to examine the relation between resiliency and musculoskeletal injuries (MSIs) and between resiliency and lost workdays due to MSIs, and explore whether age modifies these associations. Multivariable Poisson main effects regression models showed that resiliency had a protective effect against MSIs, but not lost workdays. In the unadjusted regression model to evaluate the relation between resiliency and age, results suggested that no differences in distributions existed between younger and older EMS personnel and resiliency. However, given the same unit increase in resiliency, findings from multivariable Poisson interaction regression models indicated that older workers had a higher prevalence of MSIs and lost workdays than younger workers. Results from main effects models may reflect diverging routes on a pathophysiological pathway, in which resiliency acts as a prognostic factor for MSIs but not lost workdays. Findings might also indicate the association between resiliency, and MSIs and lost workdays varies by age.Relevance to industryThe largest growth of labor in the US is expected to occur in the oldest segments of the population. While older workers may offer more experience and show similar resiliency to younger workers, they might be more vulnerable to individual risk factors and occupational exposures. If management wants to retain older workers as assets, they should design the work environment to match the capabilities of all workers.     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

APP软件性能效率研究

性能效率是APP软件的重要质量属性，但目前缺乏APP软件性能效率的通用模型。分析了APP软件的性能特征，基于ISO/IEC 25010标准提出了APP软件的性能效率模型，定义了APP软件性能效率的子特性和度量指标。基于提出的APP软件性能效率模型，通过实验对APP软件的性能效率进行了度量及相关分析。     

新冠肺炎及呼吸系统病患所需全营养配方食品的开发

新冠肺炎的爆发和全球范围大流行震惊了世界,激发了消费者对食物结构和消费方式的新思考;卫生营养健康的科学理念和美好生活的诉求引起了社会广泛关注,这势必将对食品产业产生深远的影响,特别是刚刚兴起的特医产业。面对尚无特效药的新冠肺炎疫情集中聚集爆发,营养支持治疗是一线抗疫的重要干预手段,而适合于新冠临床患者的特殊医学用途配方食品尚属于空白。基于呼吸系统疾病人群病症特点和营养需求,根据合理配比宏量营养素供能比例,强化微量元素形成适用于呼吸系统疾病诊疗需求的特殊医学用途配方食品开发理念,通过原料配方筛选以及工艺研究和优化,最终形成针对呼吸系统疾病的特定全营养粉剂、乳剂和功能性脂肪乳,为疫情后的营养治疗与生命生存质量提供有力技术支持。     

高温、高湿和光照对特殊医用配方食品流变特性的影响

为确定特殊医用配方食品在高温、高湿、光照条件下的流变性变化,以豌豆肽为原料进行影响因素(高温、高湿、光照)试验、加速试验、长期试验,揭示环境因素对特殊医用配方食品黏性、弹性与流变性质的影响。特殊医用配方食品在高温下5 d之后食用安全性降低;在高湿条件下放置10 d左右的医用食品,其食用安全性相对于放置5 d左右的医用食品安全性要高;在光照条件下5 d之后食用安全性降低。由此可见,温度、湿度、光照对特殊医用配方的食用有很大影响。在加速试验及长期试验中,医用食品最佳保质期时间应控制在2~5个月。文章为具有广阔发展前景的特殊医用食品的开发、食用条件及储存条件提供理论数据和指导依据。     

医用敷料用柔性金属电极的制备及其细胞毒性分析

为研究用于医用敷料的柔性金属电极的制备可行性和安全性,以涤纶非织造布为基布,通过丝网印刷技术,制备以锌为负电极、银为正电极的柔性微电流生物医用敷料。探讨了电极制备过程中的分散剂种类、分散剂含量及黏合剂含量对金属电极电学性能的影响,并分析了金属电极可能引起的细胞毒性问题。实验结果表明:丝网印刷技术能在基布表面均匀地负载锌、银微纳米颗粒;1%的羧甲基纤维素钠和0.5%的吐温-80分别作为锌、银颗粒的分散剂,能使金属颗粒具有良好的分散性;5%和15%的水性丙烯酸乳液分别作为锌、银金属电极的黏合剂,可使电极具有优良的电性能;当金属质量分数为0.5%~1.0%时,金属电极表现出良好的细胞相容性,可作为生物医用敷料为伤口提供外源性微电流。