A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

Due to the popularity of group activities in social media, group recommendation becomes increasingly significant. It aims to pursue a list of preferred items for a target group. Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users. However, these methods may suffer from data sparsity problem. Except for the interaction between groups and users, there also exist other interactions that may enrich group representation, such as the interaction between groups and items. Such interactions, which take place in the range of a group, form a local view of a certain group. In addition to local information, groups with common interests may also show similar tastes on items. Therefore, group representation can be conducted according to the similarity among groups, which forms a global view of a certain group. In this paper, we propose a novel global and local information fusion neural network (GLIF) model for group recommendation. In GLIF, an attentive neural network (ANN) activates rich interactions among groups, users and items with respect to forming a group′s local representation. Moreover, our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups. Then, it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation. Finally, group recommendation is conducted under neural collaborative filtering (NCF) framework. Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

     

改进模糊关联规则及其在电站锅炉运行优化中的应用

提出了一种改进的模糊关联规则挖掘算法对电厂运行优化目标值进行确定,首先利用竞争凝聚算法决定分类数、软化划分边界并构造优化的模糊数据集,再结合某300 MW机组的历史运行数据,以供电煤耗率作为优化目标,利用频繁模式树生成算法得到的频繁项集进行关联规则挖掘,最终得到运行参数最优值,实验结果和理论分析表明挖掘结果能够正确反映...     

除虫脲在棉叶和土壤中消解动态分析及棉子中残留量膳食摄入评估

目的评价除虫脲在棉叶、土壤中的消解趋势和土壤及棉子中的残留水平。方法棉叶、土壤样品经乙腈提取、氯化钠盐析、弗罗里硅土固相萃取柱净化后,采用高效液相色谱-光电二极管阵列检测器检测;同时对2014年和2015年除虫脲在河南和北京两地棉叶及土壤中的残留消解动态进行分析并对棉子中的最终残留量进行膳食摄入评估。结果除虫脲在棉子、棉叶和土壤空白样品添加的平均回收率在79%~103%之间,相对标准偏差在3%~15%之间,最低检测浓度均为0.1 mg/kg。棉叶中除虫脲的残留消解半衰期在5.0~8.6 d之间,土壤中残留消解半衰期在3.8~14 d之间。按本试验方式进行施药及采样后,棉子中除虫脲的最终残留量低于0.1 mg/kg,土壤中的残留量低于0.19 mg/kg。结论除虫脲普通人群的国家估计每日摄入量是0.873229mg/kg·bw,占日允许摄入量的69.3%左右,认为对一般人群健康不会产生不可接受的风险。     

基于转录组的氯吡脲对草莓果实成熟的调控机制研究

目的 通过对对照组和白熟期外源施用氯吡脲处理组的成熟草莓果实进行转录组学分析,探讨外源喷施氯吡脲影响草莓果实成熟的调控机制。方法 利用高通量测序技术RNA-Seq,结合生物信息学方法,进行差异基因的筛选和其通路的功能富集分析。结果 草莓白熟期喷施氯吡脲,成熟果实中有1176个基因表达上调,2031个基因表达下调。差异基因的富集分析发现,植物-病原体相互作用、植物激素信号传导、淀粉和蔗糖代谢、内质网蛋白加工以及次级代谢物的生物合成等通路为差异基因主要富集的代谢途径。结论 植物激素信号转导、植物-病原体相互作用和次级代谢物的生物合成等通路受氯吡脲影响最为显著,其中植物激素信号转导通路是通过不同激素的共同调控来影响草莓的生长成熟。研究结果为氯吡脲影响草莓果实成熟调控基因的挖掘和深入研究奠定了基础。。     

6种不同种类直支链淀粉相互混合对其回生的影响

目的 研究6种不同种类直支链淀粉相互混合对其回生的影响。方法 将玉米淀粉、甘薯淀粉、木薯淀粉、马铃薯淀粉、糯米淀粉、小麦淀粉等6种不同种类直支链淀粉分离出来, 然后两两混合, 研究不同直支链混合对其回生率的影响。 结果 马铃薯支链淀粉与甘薯支链淀粉以2:8(m:m)混合回生率最低, 为60.0%, 玉米支链淀粉与木薯支链淀粉以8:2(m:m)混合回生率最低为52.6%, 小麦支链淀粉与糯米支链淀粉以8:2(m:m)混合回生率最低为51.2%, 甘薯支链淀粉与小麦支链淀粉以1:1(m:m)混合回生率最低为53.7%。木薯支链淀粉与小麦直链淀粉以1:1(m:m)混合时所得淀粉回生率最大, 达到了92.0%, 混合淀粉回生后X射线晶型为B型。结论 不同种类直支链淀粉混合对其回生率影响很大, 食品加工中尽量不要混合使用木薯支链淀粉与小麦直链淀粉。