How to use the Surveillance,Epidemiology,and End Results(SEER)data:research design and methodology

In the United States(US),the Surveillance,Epidemiology,and End Results(SEER)program is the only comprehensive source of population-based information that includes stage of cancer at the time of diagnosis and patient survival data.This program aims to provide a database about cancer incidence and survival for studies of surveillance and the development of analytical and methodological tools in the cancer field.Currently,the SEER program covers approximately half of the total cancer patients in the US.A growing number of clinical studies have applied the SEER database in various aspects.However,the intrinsic features of the SEER database,such as the huge data volume and complexity of data types,have hindered its application.In this review,we provided a systematic overview of the commonly used methodologies and study designs for retrospective epidemiological research in order to illustrate the application of the SEER database.Therefore,the goal of this review is to assist researchers in the selection of appropriate methods and study designs for enhancing the robustness and reliability of clinical studies by mining the SEER database.

Product Specification Analysis for Modular Product Design Using Big Sales Data

Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design

Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.

Big Data Bot with a Special Reference to Bioinformatics

There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential rate.Many scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough undertaking.This data should go through several processes,including mining,data processing,analysis,and classification.This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention.This software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line tools.The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel AMPs.Furthermore,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank.As a result,data collection from the web will become faster and less expensive,with no need for manual data extraction.The software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.

An Ui Design Optimization Strategy for General App in Big Data Environment

Due to the huge amount of increasing data, the requirements of people forelectronic products such as mobile phones, tablets, and notebooks are constantlyimproving. The development and design of various software applications attach greatimportance to users’ experiences. The rationalized UI design should allow a user not onlyenjoy the visual design experience of the new product but also operating it morepleasingly. This process is to enhance the attractiveness and performance of the newproduct and thus to promote the active usage and consuming conduct of users. In thispaper, an UI design optimization strategy for general APP in the big data environment isproposed to get better user experience while effectively obtaining information. Anexperimental example of a library APP is designed to optimize the user experience. Theexperimental results show that the user-centered UI design is the core of optimization,and user portrait based on big data platforms is the key to UI design.

Research on the Applications of Big Data on Artistic Designing and Its Infl uences on the Design Behavior

In this paper, we conduct research on the applications of big data on artistic designing and its infl uences on the design behavior. Modern art design enhance the grade of the product, increase the added value of products, promote the development of the economy, make the product of the aesthetic value and economic value of the perfect unity. In today’s society due to the value of the product are much more than the product itself contains the value of performance, usage, etc. is more of a product in gradually improve the aesthetic value, sometimes even more than the use of the product value and exchange value, therefore the product has become the dominant value. Under this basis, we propose the new idea on the design pattern that will be meaningful.

Design of the visualization of the big data

The arrival of the era of the Internet has brought about the rapid dissemination and spread of a big amount of the information and data. At present, we are surrounded by all kinds of the information, but the rich and diversified information resources also brought about the chaos, so that the query of the messages is no way to start. In fact, the information resources can provide us with more convenience, but we have to spend a lot of energy to organize and filter the information, and the costs and the time of the investment are immeasurable. Usually, the information we want to query is often easy to understand, and the information design uses the more intuitive and vivid computing means to achieve the visualization of the big data, in order to reflect the beauty of the big data.

Engineering the future cereal crops with big biological data:toward intelligence-driven breeding by design

How to feed 10 billion human populations is one of the challenges that need to be addressed in the following decades,especially under an unpredicted climate change.Crop breeding,initiating from the phenotype-based selection by local farmers and developing into current biotechnology-based breeding,has played a critical role in securing the global food supply.However,regarding the changing environment and ever-increasing human population,can we breed outstanding crop varieties fast enough to achieve high productivity,good quality,and widespread adaptability?This review outlines the recent achievements in understanding cereal crop breeding,including the current knowledge about crop agronomic traits,newly developed techniques,crop big biological data research,and the possibility of integrating them for intelligence-driven breeding by design,which ushers in a new era of crop breeding practice and shapes the novel architecture of future crops.This review focuses on the major cereal crops,including rice,maize,and wheat,to explain how intelligence-driven breeding by design is becoming a reality.

“Data Hegemony”: Reflections for the Application and Development Direction of Metaverse Technology in Urban Design based on Digital

This paper is based on the phenomenon of Tittytainment and big data control.It discusses the ways in which working youths live and work in their large suburban communities in Beijing as well as their leisure and entertainment activities.Then develops a problem statement and research questions.In addition,the project explores this reality phenomenon dialectically through the analysis and speculation of the new virtual reality technology“Metaverse.”Based on a critical design approach,the phenomenon is explored through a detailed description and examination of reality,before critically suggesting the possibility of future social control by big data companies through the“metaverse”space.Furthermore,this paper alerts to the neoliberalism that dominates globalization through anti-utopian expressions,waking up young people who are compromised by big data control and addicted to Tittytainment.

Manufacturers’channel selections under the influence of the platform with big data analytics

To obtain the platform s big data analytics support,manufacturers in the traditional retail channel must decide whether to use the direct online channel.A retail supply chain model and a direct online supply chain model are built,in which manufacturers design products alone in the retail channel,while the platform and manufacturer complete the product design in the direct online channel.These two models are analyzed using the game theoretical model and numerical simulation.The findings indicate that if the manufacturers design capabilities are not very high and the commission rate is not very low,the manufacturers will choose the direct online channel if the platform s technical efforts are within an interval.When the platform s technical efforts are exogenous,they positively influence the manufacturers decisions;however,in the endogenous case,the platform s effect on the manufacturers is reflected in the interaction of the commission rate and cost efficiency.The manufacturers and the platform should make synthetic effort decisions based on the manufacturer s development capabilities,the intensity of market competition,and the cost efficiency of the platform.

大数据牵引背景下的服装设计开发模式

在大数据牵引背景下,随着数据量呈爆炸式增长和大数据应用的不断深化,大数据资源以数据要素的形式牵引着传统服装设计模式的改变。通过对服装设计相关的大数据进行分类甄别,探讨大数据在多方面赋能服装设计协同价值创造,多维度比较传统服装设计开发与以大数据牵引的服装设计开发模式,以大数据赋能服装精准设计,构建基于大数据引导下的服装设计框架;将该框架应用于MZ品牌的开发案例中,从商品企划、设计开发和预售下单3个方面论述大数据在服装设计开发中的实践应用。大数据时代设计模式的演变将更加聚焦于设计师与海量数据之间的相互作用,应用大数据牵引服装设计开发模式的迭代,可实现更高精准度和更快反应能力的服装设计开发。

非遗在食品包装设计中的运用研究进展

文章通过发文量、关键词、研究对象、研究视角及思路对非物质文化遗产在食品包装设计中的运用研究进展进行了总结和分析;从设计原则、策略和方法3个方面对非遗在食品包装设计中的运用研究内容进行了分析,并对其未来发展方向进行了展望,指出未来该研究可以用理论研究来指导设计实践,同时采用大数据模型对设计实践进行验证。

智慧灌溉大数据管理平台设计与应用

以粗放型为主的漫灌方式浪费水资源,探索高效节水农业灌溉意义重大。本研究研制了集数据采集、数据管理、数据可视化、分析决策和远程管理为一体的智慧灌溉大数据管理平台,旨在以数字化管理与智能化控制方式提升水资源利用效率。平台利用嵌入式、物联网、互联网、3S(GIS、GPS、RS)等技术,采取“1+1+N”的设计模式,构建作物需水预报模型与灌溉决策模型,基于B/S构架和Java语言,设计了1个灌溉数据中心、1个灌溉数据管理系统、4个应用系统,打造了智慧灌溉大数据管理平台。平台在河北、河南、山东、江苏等地区设有多个示范基地,汇聚了8个科研小组、24个试验基地的有关多种作物的生长、灌溉、气象、土壤等数据,平均采集数据18829条/天,帮助管理人员摸清家底;集成了多个团队的软件系统和62套物联网设备,及时、定量地呈现了农作物生长状况及环境状态,实现了农作区动态监测,助力生产决策;生成农作物需水预报和灌溉决策方案,完成了远程灌溉目标,并且经过实地试验,验证了自动灌溉的有效性,将灌溉水有效利用系数最高提升了31%以上;形成了大田产业、温室中心、数字科研和区域灌溉等不同专题可视化布局,满足多种农业场景应用需求。为农业生产和跨区域管理提供了便捷工具,为农业灌溉数字化系统搭建和应用提供了参考。

模块化教学模式探析--以“Hadoop大数据处理”课程模块化教学设计为例

课程模块化教学是对课程内容的重组以及整合,利用教学平台,引入企业项目案例资源,把传统的教学模式转变为以学生为主体、教师为主导的模式。本文以高等职业院校“Hadoop大数据处理”课程为例,采用模块化教学模式以适应大数据专业的工作岗位,介绍了模块化教学模式的概念、国内外研究现状,进行了“Hadoop大数据处理”课程模块化设计,分别是教学主体转换、内容模块化设计、教师分工协作模块化设计、应用信息化教学平台。以“Hadoop大数据处理”课程中大数据计算分析模块中“微博精准广告营销”为例说明课程模块化教学设计的具体实施步骤和最终的教学效果。

解码混沌:大数据驱动的电商服饰设计模式创新

大数据融入设计活动带来的数智协同创新变革打破单一的电商服饰设计模式,解决了经验设计所导致的不确定性问题。基于大数据成为电商服饰设计的转译工具,本文从设计元素、设计思维和设计决策三个维度梳理大数据重构电商服饰设计模式的表现特征,以电商大数据为锚点,通过剖析大数据驱动的电商女装和跨境服饰设计新模式,讨论设计师在大数据驱动电商服饰设计环境中角色的转变,以推动数智服饰设计的创新发展。

膝骨关节炎全流程数字化医疗健康:创新与前沿技术综述

目的本文旨在系统综述膝骨关节炎(Knee Osteoarthritis)全病程数字化医疗健康领域的最新创新与前沿技术,探讨这些技术如何提升膝骨关节炎患者的康复效果、治疗体验及个性化全病程管理的科学性。方法利用文献调研方法,分析膝骨关节炎全病程中各阶段数字化技术的应用,包括筛查、治疗及康复过程中的数字化工具、载体及其设计方法,并对比这些技术在不同医疗场景中的具体应用形式。结果研究明确了数字化技术在膝骨关节炎病程中不同阶段的应用价值,涵盖从筛查阶段的早期风险评估到治疗中的个性化干预及康复阶段的实时监测与反馈。同时总结了当前数字化医疗健康领域的研究难点及未来发展趋势。结论膝骨关节炎全病程管理中的数字化创新,特别是智能算法的引入、数字化社区康复场景的拓展,以及跨学科合作模式的深化,显著提高了膝骨关节炎患者的康复效率、治疗体验和管理精度。这些前沿技术的发展将持续推动膝骨关节炎治疗与管理的创新与提升。

轨道打磨电动机监测管理系统设计与实现

打磨电动机是铁路轨道打磨作业的重要设备。但其工作环境较为恶劣,故障率较高。文中从实际需求出发,研究并构建1套轨道打磨电动机监测管理系统,以实现对电动机实时状态进行监测和相关数据的采集。系统针对海量数据的高效采集存储、故障诊断方面进行了深入研究和设计,同时构建友好的用户交互界面。该系统对于推动铁路轨道打磨的实际应用和设备的智能化有一定的促进作用。

颗粒度动态控制的负载均衡算法的大数据分析

本文提出了一种创新的动态颗粒度控制算法,此算法融合了延迟进程生成与负载内联的特点,通过构建数学模型、优化算法,解决了传统负载均衡方法在动态环境下处理并行任务的难题。旨在最大化并行处理效率并削减不必要的开销。为大数据分析和并行处理提供了一种高效、灵活的负载均衡方案,对提升系统性能与降低成本有一定的参考价值。

互联网与大数据背景下学习通教学设计——以《Web前端开发》课程为例

学习通属于支持自主线上学习的重要软件程序,采取学习通的移动大数据以及互联网平台能够全面支持学生自主完成各个学科的学习实践探索。具体针对于《Web前端开发》的课程教学总体实施方案在进行合理的优化设计中,教师应当重视构建学习通的信息化教学支撑平台,鼓励学生完成线上收集学习资源以及独立探索课程实践任务等过程。文章主要探讨《Web前端开发》的学习通课程教学基本设计思路,结合大数据以及互联网的创新技术形式优化课程教学成效。

基于大数据及云计算技术的警务战术训练仿真系统设计

为了提升警务战术训练仿真系统的建模精度和响应效率,提高警员的战术技能水平,提出基于大数据及云计算技术的警务战术训练仿真系统设计方法;利用云计算技术设计系统的总体架构,构建警务战术训练仿真系统的分层模型,确定系统各层在运行过程中的主要职责;根据系统总体构架设计信息管理模块、主控模块、判定模块、信息采集模块和情景控制模块,完成系统的硬件设计;在软件设计中创新地引入全卷积孪生网络和大数据技术,重构相似块组,优化图像质量;应用Sketchup软件处理基本矢量图数据,构建训练场景矢量图,对警务战术训练场景进行三维建模,以此实现警务战术的仿真训练;实验结果表明,应用所提方法处理后的图像峰值信噪比为65.9 dB,结构相似度为0.923,具有较高的图像采集质量、三维建模精度和系统响应效率;可为警务战术训练场景的三维建模提供可靠依据,有效优化警员的战术训练效果。