为推动夏玉米科学施肥,在河南省砂壤、中壤和黏壤质潮土上采用田间试验研究了Nutrient Expert(NE)和Agro Services International Inc(ASI)法推荐施肥对夏玉米产量和经济效益的影响及基于NE推荐施肥的氮磷钾肥利用效率。结果表明,各推...为推动夏玉米科学施肥,在河南省砂壤、中壤和黏壤质潮土上采用田间试验研究了Nutrient Expert(NE)和Agro Services International Inc(ASI)法推荐施肥对夏玉米产量和经济效益的影响及基于NE推荐施肥的氮磷钾肥利用效率。结果表明,各推荐施肥处理夏玉米产量、纯收益和蛋白质产量均表现为黏壤>中壤>砂壤,NE推荐施肥处理的产投比最高,其次是ASI推荐施肥处理,推荐施肥可显著增加夏玉米植株养分积累量,促进籽粒产量和蛋白质产量的提高。在砂壤、中壤和黏壤上的NE推荐施肥处理比农民习惯施肥处理分别增产7.22%、3.84%和11.32%,ASI推荐施肥分别增产13.44%、10.60%和11.20%。NE推荐施肥处理中施氮对夏玉米的增产效应最大,氮肥农学效率和氮磷钾肥利用率均表现为黏壤>中壤>砂壤,磷、钾肥农学效率均表现为砂壤>黏壤>中壤,3种质地潮土的肥料农学效率均表现为磷肥>钾肥>氮肥。由此得出,NE推荐施肥适宜在黏壤质潮土推行,而ASI法推荐施肥适宜于砂壤和中壤质潮土。展开更多
近年来,伴随着人工智能的发展及法院裁判文书的公开化,"智慧司法"、案例推荐成为热点问题.针对案例推荐中存在的推荐准确性差、传统知识图谱向量化表示精度不高等问题,提出基于知识图谱的案件推荐(Knowledge Graph based Case...近年来,伴随着人工智能的发展及法院裁判文书的公开化,"智慧司法"、案例推荐成为热点问题.针对案例推荐中存在的推荐准确性差、传统知识图谱向量化表示精度不高等问题,提出基于知识图谱的案件推荐(Knowledge Graph based Case Recommendation,KGCR)模型.该模型以知识图谱为辅助信息,利用文本分类和信息抽取技术构建面向刑事案例的知识图谱,针对当事人的陈词供述,利用知识表示学习求解相似的案件,进一步实现法条推荐.针对TransH算法的负采样问题进行改进,提出FU-TransH算法模型.以公开的刑事判决书为数据集进行实验,实验结果表明,与相关的具有代表性的算法相比,该算法的推荐准确率更高.展开更多
With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filt...With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this study.Firstly,a profile labeling system was constructed based on user characteristics.This study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user profiles.TF-IDF algorithm was used to determine the weights of user-item feature labels.Secondly,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these weights.Finally,personalized recommendations were generated using Top-N method.Validation with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).展开更多
文摘为推动夏玉米科学施肥,在河南省砂壤、中壤和黏壤质潮土上采用田间试验研究了Nutrient Expert(NE)和Agro Services International Inc(ASI)法推荐施肥对夏玉米产量和经济效益的影响及基于NE推荐施肥的氮磷钾肥利用效率。结果表明,各推荐施肥处理夏玉米产量、纯收益和蛋白质产量均表现为黏壤>中壤>砂壤,NE推荐施肥处理的产投比最高,其次是ASI推荐施肥处理,推荐施肥可显著增加夏玉米植株养分积累量,促进籽粒产量和蛋白质产量的提高。在砂壤、中壤和黏壤上的NE推荐施肥处理比农民习惯施肥处理分别增产7.22%、3.84%和11.32%,ASI推荐施肥分别增产13.44%、10.60%和11.20%。NE推荐施肥处理中施氮对夏玉米的增产效应最大,氮肥农学效率和氮磷钾肥利用率均表现为黏壤>中壤>砂壤,磷、钾肥农学效率均表现为砂壤>黏壤>中壤,3种质地潮土的肥料农学效率均表现为磷肥>钾肥>氮肥。由此得出,NE推荐施肥适宜在黏壤质潮土推行,而ASI法推荐施肥适宜于砂壤和中壤质潮土。
文摘近年来,伴随着人工智能的发展及法院裁判文书的公开化,"智慧司法"、案例推荐成为热点问题.针对案例推荐中存在的推荐准确性差、传统知识图谱向量化表示精度不高等问题,提出基于知识图谱的案件推荐(Knowledge Graph based Case Recommendation,KGCR)模型.该模型以知识图谱为辅助信息,利用文本分类和信息抽取技术构建面向刑事案例的知识图谱,针对当事人的陈词供述,利用知识表示学习求解相似的案件,进一步实现法条推荐.针对TransH算法的负采样问题进行改进,提出FU-TransH算法模型.以公开的刑事判决书为数据集进行实验,实验结果表明,与相关的具有代表性的算法相比,该算法的推荐准确率更高.
文摘With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this study.Firstly,a profile labeling system was constructed based on user characteristics.This study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user profiles.TF-IDF algorithm was used to determine the weights of user-item feature labels.Secondly,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these weights.Finally,personalized recommendations were generated using Top-N method.Validation with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).
