How do different warning signs compare with the guideline daily amount and traffic-light system?

Nutritional warnings are rapidly gaining relevance, particularly in the region of the Americas, as a front-of-package nutrition labelling scheme that facilitate the identification of products with high content of nutrients associated with non-communicable diseases (NCDs). Several graphic designs are being developed as countries discuss the implementation of nutritional warnings, which makes it necessary to develop research to evaluate their efficacy. In this context, the aim of the present work was to compare the efficacy of a series of nutritional warnings that are being considered by Brazilian national authorities for the country with two of the most studied schemes: the guidelines daily amounts (GDA) and the traffic light system. Two studies were conducted. In the first one, visual search was conducted with 62 participants to evaluate the time needed by them to identify the schemes on food labels and to identify whether a product had high nutrient content. In the second study, an online survey with 1932 participants was used to evaluate their ability to use FOP nutrition labelling schemes to correctly identify the most healthful product in a set, as well as high nutrient content in a product. In addition, the influence of FOP nutrition labelling schemes on perceived healthfulness was evaluated. Finally, consumers’ perception of the schemes was gathered using an open-ended question. Results confirmed the advantages of nutritional warnings compared to the GDA and the traffic-light system to facilitate the identification of products with high content of nutrients associated with NCDs. In addition, the use of familiar signs frequently used to convey a ‘warning message’ outperformed other unfamiliar signs in terms of their ability to facilitate the interpretation of nutrition information. Regarding colour, black signs tended to required significantly less time to be detected when included on colour food labels compared to red signs.     

关于信息化测绘时代工程测量发展的思考

如今我国信息化技术全面发展，尤其对于工程测量工作领域来讲，不管是在工程建设和管理方面都产生不小的支撑引导效用。由此，笔者具体结合如今工程建设信息化测绘核心任何以及设备布置细节，进行结构整体安全管理周期和周边地理空间信息技术发展能效整理解析，试图将工程测量最新发展机遇和技术挑战问题处理完全。希望能够借此为日后一定时期范围内相关工程测量规划主体提供合理指导性建议内容，最终为我国各类工程事业可持续发展前景绽放奠定深刻适应基础。     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.     

Animations showing Lego manipulative tasks: Three potential moderators of effectiveness

Evidence suggests that transient visual information, such as animations, may be more challenging to learn than static visualizations. However, when a procedural-manipulative task is involved, our evolved embodied cognition seems to reverse this transitory challenge. Hence, for object manipulative tasks, instructional animations may be more suitable than statics. We investigated this argument further by comparing animations with statics using a Lego task shown to university students, by examining three potential moderators of effectiveness: (a) the environment of manipulation (virtual or physical), (b) the quality of visual information (focused or unfocused), and (c) the presence of hands (no hands or with hands). In Experiment 1 we found an advantage of animation over statics, and no differences among the environments. In Experiment 2, we again observed an animation advantage, a small advantage of focused static information compared to unfocused static information, and a positive effect of not showing the hands.     

基于强化学习的电力市场信息披露程度对市场成员交易行为影响研究

基于信息披露的主体框架，分析披露体系中具体的供需信息对于市场成员的引导作用。建立日前市场的双层竞价优化模型，采用强化学习的方法，通过市场中有无关键供求信息披露对于发电商报价预期的影响，推演模拟不同信息披露程度下发电商报价行为，然后从策略演化过程、策略偏离度以及策略收敛速度3个角度进行信息披露程度对市场成员交易行为影响的定量分析，验证市场供需信息的发布程度对于市场成员的引导作用，最后基于推演结果进行总结，并对我国信息披露的建设提出建议与思考。     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

基于开源技术的城市地理信息平台构建方法研究

摘 要：针对城市地理信息平台构建过程中存在的数据获取困难、研发成本较高等问题， 引入开源技术，对基于开源数据、开源开发平台与开源工具的城市地理信息平台构建技术进行 研究。所提出的城市地理信息平台由数据模块与可视化模块 2 部分组成。为构建数据模块，由 开源地理信息项目(OSM)获取了建筑与区划数据，并设计了建筑合并算法以提升数据质量。建 筑数据随之基于行政区划分级存储，通过由 ASP. NET 构建的数据接口提供给具体应用。可视 化模块则基于开源地理信息系统(GIS)项目 Cesium 实现，并设计了完善的用户界面。以北京市 为例，完成了原型系统的开发与测试，证实了该技术体系的可行性，通过开源技术降低平台研 发成本，推进城市地理信息平台的研究与应用。     

高熵氧化物的制备及应用研究进展

高熵氧化物作为近几年发展起来的新型氧化物体系，打破了传统掺杂氧化物的设计理念，由五种及以上氧化物以等摩尔或近等摩尔构成，因其具有简单的结构和优异的性能等受到国内外研究人员的广泛关注。高熵氧化物由于多主元且主元之间混乱排列，易形成岩盐型、氟化钙型、尖晶石型或钙钛矿等固溶体结构，从而表现出优异的性能，尤其在能源存储材料和磁性材料方面有十分广阔的应用前景，但目前对高熵氧化物应用研究较少。本工作介绍了国内外高熵氧化物的制备方法，主要包括固相法、热解法、共沉淀法、水热合成法和液相燃烧合成法等，比较了各方法的优缺点和发展前景；归纳了高熵氧化物作为锂离子电极材料、巨介电材料、磁性材料和催化材料等方面的应用；指出了高熵氧化物目前研究存在的问题，讨论了解决措施，展望了高熵氧化物未来的发展趋势。     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。     

基于密集神经网络的灰度图像着色算法

针对在灰度图像着色领域中，传统算法信息提取率不高、着色效果不理想的问题，提出了基于密集神经网络的灰度图像着色算法，以实现改善着色效果，让人眼更好地观察图片信息的目的。利用密集神经网络的信息提取高效性，构建并训练了一个端到端的深度学习模型，对图像中的各类信息及特征进行提取。训练网络时与原图像进行对比，以逐渐减小网络输出结果的信息、分类等各类型的损失。训练完成后，只需向网络输入一张灰度图片，即可生成一张颜色饱满、鲜明逼真的彩色图片。实验结果表明，引入密集网络后，可有效改善着色过程中的漏色、细节信息损失、对比度低等问题，所提算法着色效果较基于VGG网络及U-Net、双流网络结构、残差网络（ResNet）等性能优异的先进着色算法而言取得了显著的改进。