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An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network

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Computers, Materials & Continua 2021年第4期67卷 951-964页

作者： Bin Yang Lingyun Xiang Xianyi Chen wenjing jia School of Design Jiangnan UniversityWuxi214122China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical and Data Engineering University of Technology SydneySydney2000Australia

Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in that process.One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,online.This paper presents a novel chronic disease prediction system based on an incremental deep neural network.The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner.With time,the system can predict diabetes more and more accurately by processing the feedback information.Many diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input attributes.In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was created.Users’data collected by different sensors were used to train the network model.We evaluated our system using a real-world diabetes dataset to confirm its effectiveness.The experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.

关键词： Deep learning incremental learning network architecture design chronic disease prediction

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数字技术背景下展陈空间的媒介组合研究

设计艺术研究 2024年第1期14卷 19-24页

作者：贾雯景澳门科技大学中国澳门999078

信息在当下以各种不可预见的方式不断流通,当它们被围合在一定范围的空间中时,会经由各种类型的媒介被最大程度的释义与传递。媒介的多样化与常态化使得具备信息传递功能的展陈空间捕捉到了一种更加全面的陈列组合形式。围绕展陈空间的... 详细信息

信息在当下以各种不可预见的方式不断流通,当它们被围合在一定范围的空间中时,会经由各种类型的媒介被最大程度的释义与传递。媒介的多样化与常态化使得具备信息传递功能的展陈空间捕捉到了一种更加全面的陈列组合形式。围绕展陈空间的静态媒介与动态媒介,以及两者之间的关系对“技术与艺术”进行拓展延伸,探索两大类主体媒介在空间中编排的合理性,并结合在不同类型的展陈空间中静态与动态的媒介组合表现,思考科学技术在具体艺术表现过程中的运用。

关键词：数字技术展陈空间媒介组合技术与艺术

Generating Cartoon Images from Face Photos with Cycle-Consistent Adversarial Networks

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Computers, Materials & Continua 2021年第11期69卷 2733-2747页

作者： Tao Zhang Zhanjie Zhang wenjing jia Xiangjian He Jie Yang School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214000China Key Laboratory of Artificial Intelligence Jiangsu214000China The Global Big Data Technologies Centre University of Technology SydneyUltimoNSW2007Australia The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai201100China

The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style transfer.Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image.CYCLE-GAN is a classic GAN model,which has a wide range of scenarios in style transfer.Considering its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output image.However,it is difficult for CYCLE-GAN to converge and generate high-quality images.In order to solve this problem,spectral normalization is introduced into each convolutional kernel of the discriminator.Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed model.Besides,we use pretrained model(VGG16)to control the loss of image content in the position of l1 regularization.To avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss function.In terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative features.Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.

关键词： Generative adversarial network spectral normalization Lipschitz stability constraint VGG16 l1 regularization term l2 regularization term Frechet inception distance

FACLSTM:ConvLSTM with focused attention for scene text recognition

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Science China(Information Sciences) 2020年第2期63卷 35-48页

作者： Qingqing WANG Ye HUANG wenjing jia Xiangjian HE Michael BLUMENSTEIN Shujing LYU Yue LU Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Faculty of Engineering and Information Technology University of Technology Sydney Shanghai Institute of Intelligent Science and Technology Tongji University

Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM(FC-LSTM) has played a critical role. Owing to the limitation of FC-LSTM, existing methods have to convert 2-D feature maps into 1-D sequential feature vectors, resulting in severe damages of the valuable spatial and structural information of text images. In this paper, we argue that scene text recognition is essentially a spatiotemporal prediction problem for its2-D image inputs, and propose a convolution LSTM(Conv LSTM)-based scene text recognizer, namely,FACLSTM, i.e., focused attention Conv LSTM, where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM. Particularly, the attention mechanism is properly incorporated into an efficient Conv LSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas. The experimental results on benchmark datasets IIIT5 K, SVT and CUTE demonstrate that our proposed FACLSTM performs competitively on the regular,low-resolution and noisy text images, and outperforms the state-of-the-art approaches on the curved text images with large margins.

关键词： scene text recognition convolutional LSTM focused attention spatial correlation sequential prediction

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海洋碳汇渔业低碳绩效测度与时空差异性分析

海洋环境科学 2024年第1期43卷 57-63,73页

作者：郑鹏赵丽男刘永权贾文静大连海洋大学经济管理学院辽宁大连116023

将海洋渔业固碳量与碳排放分别视为期望与非期望产出引入碳汇渔业生产绩效分析框架,以2007-2021年中国沿海九个省份海洋渔业面板数据为样本,构建DEA-Malmquist模型评估我国沿海九个省份碳汇渔业低碳绩效指数,并分析其时空差异。研究结... 详细信息

将海洋渔业固碳量与碳排放分别视为期望与非期望产出引入碳汇渔业生产绩效分析框架,以2007-2021年中国沿海九个省份海洋渔业面板数据为样本,构建DEA-Malmquist模型评估我国沿海九个省份碳汇渔业低碳绩效指数,并分析其时空差异。研究结果表明:2008-2021年沿海九个省份碳汇渔业低碳绩效指数呈现出明显差异,碳汇渔业低碳绩效指数的提高,主要是技术进步效率指数推动的;在研究时段内,沿海九个省份碳汇渔业低碳绩效指数得到持续改善,河北省和江苏省的低碳绩效指数相对较低;碳汇渔业低碳绩效水平有一定的波动性,其增长动力比较单一,需要进一步改善技术效率,实现技术效率和技术进步的双重驱动。

关键词：海洋碳汇渔业低碳绩效评价 DEA-Malmquist模型

碳中和目标下中国海洋渔业碳汇能力及其空间效应研究

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生态经济 2024年第5期40卷 118-123页

作者：郑鹏赵丽男贾文静王韵霖大连海洋大学经济管理学院辽宁大连116023

全球变暖问题日益引起国际社会的广泛关注,而实现碳中和目标的重要路径之一就是海洋渔业的固碳增汇能力,因此如何科学准确地测算海洋渔业碳汇能力并研究其空间效应对缓解全球气候变化尤为重要。论文根据2007—2021年中国9个沿海省份贝... 详细信息

全球变暖问题日益引起国际社会的广泛关注,而实现碳中和目标的重要路径之一就是海洋渔业的固碳增汇能力,因此如何科学准确地测算海洋渔业碳汇能力并研究其空间效应对缓解全球气候变化尤为重要。论文根据2007—2021年中国9个沿海省份贝藻养殖面板数据,利用“可移出碳汇”模型对中国海洋渔业碳汇能力进行测算,并运用空间模型对其空间效应进行分析。研究结果显示:(1)中国海洋渔业碳汇能力时间上呈现整体上升态势、空间上表现分布不均的特征。(2)莫兰指数表明海洋渔业碳汇能力具有空间正相关性,但空间聚集程度具有不稳定性。(3)通过空间杜宾模型分解后得出海洋渔业养殖产量、科技投入、科技推广情况和渔业灾情直接效应为正,劳动力投入直接效应为负。从间接效应来看,海洋渔业发展水平、养殖产量、科技投入和科技推广情况间接效应为正,劳动力投入间接效应为负。因此,应综合考虑海洋渔业碳汇能力的空间关联性,为今后海洋渔业的发展制定科学合理的空间规划。

关键词：碳中和海洋渔业碳汇能力空间效应

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植物甾醇的酶法修饰与生物转化

中国生物工程杂志 2024年第4期44卷 112-121页

作者：毛文刚郭子妍贾文敬黄建忠舒正玉福建师范大学生命科学学院工业微生物发酵技术国家地方联合工程研究中心福州350108

植物甾醇是一类以环戊烷多氢菲为骨架的天然化合物,广泛存在于玉米油等植物性食品中。作为一种新型的食品添加剂,植物甾醇已被广泛应用于各种食品中,如低脂饮料、奶制品、面包等。植物甾醇还具有降低人体血清胆固醇、消炎、抗氧化等作... 详细信息

植物甾醇是一类以环戊烷多氢菲为骨架的天然化合物,广泛存在于玉米油等植物性食品中。作为一种新型的食品添加剂,植物甾醇已被广泛应用于各种食品中,如低脂饮料、奶制品、面包等。植物甾醇还具有降低人体血清胆固醇、消炎、抗氧化等作用。植物甾醇自身的理化性质,如油溶性低、熔点高、生物利用度低等,极大地降低了其应用效果。利用各类生物催化剂对甾醇骨架或侧链进行修饰改性,可显著提高其应用效果;利用各种微生物全细胞转化植物甾醇,将显著增加其附加值。重点介绍了当前酶法改善甾醇的油溶性、水溶性和抗氧化性等性能,综述利用微生物全细胞转化植物甾醇、合成甾醇侧链被氧化或母核羟基化的各种甾醇衍生物(甾醇药物中间体)的研究进展及其存在的问题,展望植物甾醇(酯)未来的发展趋势,为植物甾醇的深入研究提供参考。

关键词：植物甾醇植物甾醇酯酶法合成工艺生物转化

玉米Dof转录因子的全基因组鉴定和表达模式分析

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分子植物育种 2024年第8期22卷 2437-2447页

作者：闵远昌叶钰澜胡欢贾文静南文华西北农林科技大学农学院杨凌712100 山西农业大学农学院太谷030800

为了研究玉米中Dof转录因子家族功能,本研究利用生物信息方法,在全基因组水平对玉米Dof转录因子进行鉴定,并对其理化性质、系统进化、保守结构域、共线性和表达模式进行了系统分析。结果表明,共有46个玉米Dof转录因子被鉴定到,它们不均... 详细信息

为了研究玉米中Dof转录因子家族功能,本研究利用生物信息方法,在全基因组水平对玉米Dof转录因子进行鉴定,并对其理化性质、系统进化、保守结构域、共线性和表达模式进行了系统分析。结果表明,共有46个玉米Dof转录因子被鉴定到,它们不均等分布在9条染色体上。多序列比对发现所有的ZmDof均具有1个保守的C2-C2单锌指结构域。系统进化分析将Dof分为4个亚组,其中亚组I和III是最大的亚组,均包含有30个水稻、拟南芥、玉米Dof转录因子。共线性分析发现ZmDof与二穗短柄草、水稻、高粱Dof基因分别组成了22、37、37对共线性基因对,说明它们具有很好的基因共线性关系。表达模式分析发现所有的ZmDof基因表现特异性表达,说明它们在植物生长发育阶段发挥着重要作用。本研究结果为进一步分析玉米Dof转录因子家族的功能提供了重要依据。

关键词：玉米 Dof转录因子全基因组鉴定表达模式

Impact of Media Information on Social Response in Disasters:A Case Study of the Freezing‑Rain and Snowstorm Disasters in Southern China in 2008

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International Journal of Disaster Risk Science 2024年第1期15卷 73-87页

作者： jia He wenjing Duan Yuxuan Zhou Yun Su College of Geographic Science and Tourism Xinjiang Normal UniversityUrumqi 830054China Xinjiang Laboratory of Lake Environment and Resources in Arid Zone Urumqi 830054China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Key Laboratory of Environmental Change and Natural Disaster Ministry of EducationBeijing Normal UniversityBeijing 100875China

Disaster information content is an objective mapping of disaster situations,social response,and public opinions.Social response to emergency is an important mechanism for implementing and guaranteeing emergency management of major natural hazard-related disasters.Understanding how disaster information content affects social response to emergencies is helpful for managing risk communication and efficient disaster response.Based on the 2008 freezing-rain and snowstorm disasters in southern China,this study used Python to extract 7,857 case-related media reports and applied natural language processing for text analysis.It used three typical cases to identify and analyze disaster media report content and the relationship between these reports and the social response to the emergency.Eight categories of disaster response-such as prewarning and forecasting,announcements by the authorities,and social mobilization-appeared in the disaster information in the media,along with disaster impact information,that is,real-time disaster status.Disaster response information and an appropriate amount of disaster impact information played important roles in prewarning,disaster relief,public opinion guidance,and social stability maintenance and can serve important functions in communicating with all stakeholders of emergency management,assisting or influencing emergency departments or individuals in decision making,and eliminating "information islands." Empathy caused the general public to become "disaster responders" through receiving information.Rumors and an excess of negative information may have a perverse amplification effect on public opinion and increase the unpredictability of the disaster situation and the risk of social crisis.

关键词： Emergency management Freezing-rain and snowstorm disasters Media reports on disasters Social response to emergencies China