Feasibility study of large-scale mass customization 3D printing framework system with a case study on Nanjing Happy Valley East Gate

At present, the development and implementation of digital transformation are the keys to promoting high-quality industry development. The new digital fabrication method of robotic 3D printing is a research area being studied by many to tackle the issue of the declining productivity of traditional construction methods. Although many studies have been done, most of the current 3D printing projects are facing limitations in terms of scale. In order to bridge the gap, this article proposed a mass customization 3D printing framework system for large-scale projects. This article discusses how mass customization is made possible through the joint operation of the FUROBOT software and 3D printing hardware. By taking the east gate of Nanjing Happy Valley Plaza as a case study, the article demonstrates and studies the feasibility of the large-scale mass customization 3D printing framework system.     

面向中小型服装企业的个性化定制订单管理体系构建

在开展个性化定制业务过程中,中小型服装企业易存在市场应变能力不强、信息化投入不足、数据利用能力较弱等问题,为此,构建了一个基于软件即服务(SaaS)模式的订单云管理体系。在分析该管理体系主要功能需求的基础上,优化提出了可充分借助云计算服务的服装个性化定制业务流程,设计了易于计算机处理的物料管理编码和业务数据信息体系。基于三级分层搭建了该订单管理体系的技术架构,设计了主要业务单元的智能化操作平台和核心计算技术。最后采用Java语言进行订单管理体系的开发,实现了订单信息的全流程、实时化、全方位共享,以期有效提升服装个性化定制业务的信息化管理水平。     

Gastric Adenocarcinomas and Signet-Ring Cell Carcinoma: Unraveling Gastric Cancer Complexity through Microbiome Analysis—Deepening Heterogeneity for a Personalized Therapy

Gastric cancer (GC) is the fifth most prevalent cancer worldwide and the third leading cause of global cancer mortality. With the advances of the omic studies, a heterogeneous GC landscape has been revealed, with significant molecular diversity. Given the multifaceted nature of GC, identification of different patient subsets with prognostic and/or predictive outcomes is a key aspect to allow tailoring of specific treatments. Recently, the involvement of the microbiota in gastric carcinogenesis has been described. To deepen this aspect, we compared microbiota composition in signet-ring cell carcinoma (SRCC) and adenocarcinoma (ADC), two distinct GC subtypes. To this purpose, 10 ADC and 10 SRCC and their paired non-tumor (PNT) counterparts were evaluated for microbiota composition through 16S rRNA analysis. Weighted and unweighted UniFrac and Bray–Curtis dissimilarity showed significant community-level separation between ADC and SRCC. Through the LEfSe (linear discriminant analysis coupled with effect size) tool, we identified potential microbial biomarkers associated with GC subtypes. In particular, SRCCs were significantly enriched in the phyla Fusobacteria, Bacteroidetes, Patescibacteria, whereas in the ADC type, Proteobacteria and Acidobacteria phyla were found. Overall, our data add new insights into GC heterogeneity and may contribute to deepening the GC classification.     

融合时间隐语义填充和子群划分的推荐算法

随着个性化推荐技术的发展，推荐系统面临着越来越多的挑战。传统的推荐算法通常存在数据稀疏性和推荐精度低等问题。针对以上问题，提出了一种融合时间隐语义填充和子群划分的推荐算法[K]-TLFM（Time Based Latent Factor Model Integrated with [k]-means）。该算法利用融合时间因素的隐语义模型对原始用户物品评分矩阵缺失项进行填充，避免了用全局平均值或者用户/物品平均值补全矩阵带来的误差，有效缓解了数据稀疏性问题，同时融合时间因素有效地刻画了用户偏好随时间的变化；完成评分矩阵缺失项填充后，基于二分[k]-means聚类算法将偏好、兴趣特征相似的对象划分到同一个子群中，在目标用户所属的子群中基于选定的协同过滤算法为用户产生推荐列表，提高了推荐效率和准确性。在MovieLens和Netflix数据集上对该算法的推荐性能进行了对比实验，结果表明该算法具有更高的推荐精度。     

个性化三维虚拟试衣系统研究

为满足用户对虚拟试衣的个性化需求和提高虚拟试衣体验，提出一种包括自动化蒙皮、基于单张图片人脸三维自动重建、人体参数化变形以及服装模型穿透处理等模块的个性化虚拟试衣系统。设计了自动化蒙皮算法，解决已有虚拟试衣服装模型制作成本高的问题；通过基于单张图片的三维人脸自动重建和人体参数化变形，满足消费者个性化需求;运用基于自动透明贴图计算的服装穿透处理方法，改进系统试衣效果。实验表明，所提出的系统能够以较低的构建与运行成本实现较好的试衣效果，提供良好的个性化虚拟试衣体验。     

服务营销在规模化服装定制企业的应用研究

随着现代信息化技术的发展,传统营销方法已经不能满足规模化服装定制企业发展的需要。文章介绍了规模化服装定制企业发展现状,基于服务营销理论的研究,结合服装定制企业营销管理存在的问题,提出了针对8P理论的服务营销战略框架和服务营销策略。     

Evaluating the commercial airliner cabin environment with different air distribution systems

Ventilation systems for commercial airliner cabins are important in reducing contaminant transport and maintaining thermal comfort. To evaluate the performance of a personalized displacement ventilation system, a conventional displacement ventilation system, and a mixing ventilation system, this study first used the Wells‐Riley equation integrated with CFD to obtain the SARS quanta value based on a specific SARS outbreak on a flight. This investigation then compared the three ventilation systems in a seven‐row section of a fully occupied, economy‐class cabin in Boeing 737 and Boeing 767 airplanes. The SARS quanta generation rate obtained for the index patient could be used in future studies. For all the assumed source locations, the passengers’ infection risk by air in the two planes was the highest with the mixing ventilation system, while the conventional displacement ventilation system produced the lowest risk. The personalized ventilation system performed the best in maintaining cabin thermal comfort and can also reduce the infection risk. This system is recommended for airplane cabins.     

个性化学习路径推荐综述

近年来，伴随着现代信息技术的迅猛发展，以人工智能为代表的新兴技术在教育领域得到了广泛应用，引发了学习理念和方式的深刻变革.在这种大背景下，在线学习超越了时空的限制，为学习者“随时随地”学习提供了更多的可能性，从而得到了蓬勃发展.然而，在线学习中师生时间、空间分离的特征，导致教师无法及时掌握学生的学习状态，一定程度上制约了在线学习中教学质量的提升.面对多元化的学习需求及海量学习资源，如何迅速完成学习目标、降低学习成本、合理分配学习资源等问题成为限制个人和时代发展的重大问题.然而，传统的“一刀切”的教育模式已经不能满足人们获取知识的需求了，需要一个更高效、更科学的个性化教育模式，以帮助学习者以最小的学习成本最大限度地完成学习目标.基于以上背景，如何自动高效识别学习者特征，高效地组织和分配学习资源，为每一位学习者规划个性化路径，成为面向个体的精准化教育资源匹配机制研究中亟待解决的问题.系统地综述并分析了当前个性化学习路径推荐的研究现状，并从多学科领域的角度分析了对于同一问题的不同研究思路，同时也归纳总结了当前研究中最为主流的核心推荐算法.最后，强调当前研究存在的主要不足之处.     

基于数据挖掘的开源电子资源访问行为统计模型

为了解决当前图书馆资源个性化推荐过程中存在推荐的准确率、召回率以及效率较低的问题,采用二维距离模型构建用户社区模型,用于描述访问用户与图书馆开源电子资源之间的关系,并对互联网用户需求和访问行为进行模糊规则推理.依据互联网用户属性和图书馆资源访问需求属性之间的模糊规则,建立图书馆开源电子资源访问行为统计模型,并利用该模型向用户提供个性化推荐服务.仿真结果表明,所建模型的推荐召回率高达98. 4%,推荐准确率为99. 2%,运行时间小于0. 04 s.所建模型能够为互联网用户提供准确、高效地图书馆资源个性化推荐服务.