改性苯丙乳液的研究进展

综述了几种新型改性苯丙乳液聚合的方法,这些不同的改性苯丙乳液聚合的方法均比未改性的苯丙乳液在性能上有所提高,介绍了改性苯丙乳液的发展近况。     

Heterogeneous Consumer Preferences for Nanotechnology and Genetic‐modification Technology in Food Products

This study investigates heterogeneous consumer preferences for nanofood and genetically‐modified (GM) food and the associated benefits using the results of choice experiments with 1,117 US consumers. We employ a latent class logit model to capture the heterogeneity in consumer preferences by identifying consumer segments. Our results show that nano‐food evokes fewer negative reactions compared with GM food. We identify four consumer groups: ‘Price Oriented/Technology Adopters’, ‘Technology Averse’, ‘Benefit Oriented’, and ‘New Technology Rejecters’. Each consumer group has a distinctive demographic background, which generates deeper insights into the diversified public acceptance of nano‐food and GM food. Our results have policy implications for the adoption of new food technologies.     

聚苯硫醚的生产及改性技术进展

介绍了聚苯硫醚的生产方法以及改性技术,提出了我国今后的发展建议.     

前药原理与新药设计

探索前药原理在新药设计中的规律,推动新药研究工作的开展,通过文献检索,综合、归纳、分析、概括前药原理在新药设计方面的典型事例。前药原理在新药设计中广泛应用,不仅可对经典的含羧基、羟基、氨基药物进行结构修饰制成前药,还可制成偶氮型前药、曼尼希碱型前药、一氧化氮型前药及开环、闭环等新型结构的前药,既保持或增强了原药的药效,又克服了原药的某些缺点。利用前药原理设计新药投资少、风险小、成功率高,适合我国国情,是值得推广的新药研究途径。     

采用铜导电油墨在改性柔性基材上实现喷墨印刷

本文采用铜导电油墨在聚酰亚胺（PI）薄膜上进行喷墨印刷形成铜导电层,并研究基于铜络合物的铜导电油墨与柔性基材的相关性。本研究首先采用氧等离子体对PI薄膜表面进行改性,以测试接触角来表征改性前后薄膜表面性能的变化。通过优化等离子体反应参数使接触角降低。再利用喷墨印刷在改性前后PI薄膜上沉积铜导线,在200℃氢气环境下进行热处理,印制铜线发生烧结而收缩。采用光学显微镜（OM）、场发射扫描电子显微镜（FESEM）、X射线衍射（XRD）、非接触式三维轮廓仪和四点探针分析铜导线形状、微观结构、晶体结构及电导率。结果表明,经氧等离子体改性的PI基材上可成功沉积具有纯铜相的连续铜导线且具有良好的烧结微观结构,同时确定了铜导电油墨与基材表面性能的相关性。     

基于证据源和组合规则双修正的证据合成

针对冲突证据合成时传统证据合成和单一修正改进方法存在的问题，提出了新的证据源预处理和组合规则双修正的证据合成方法。利用夹角余弦理论，计算证据向量距离后转化得到权重系数，依据权重系数对证据进行重新概率分配，完成证据源修正；之后用修正的动态合成规则，进行修正证据的合成;同时提出了算法分析模型。实例结果验证了算法在证据合成时具有最优结果、较强通用性和稳定性，可应用于信息融合、不确定信息决策等领域。     

Order effect of multi-touchpoints on Generation Z consumers

Abundant user-generated content has increasingly driven consumers to form or modify their purchasing decisions based on interactions with other consumers, although the extant research overlooks the customer-to-customer (C2C) interaction touchpoints. Based on the customer journey characteristics of cross-channel and multi-frequency interactions, this study explores the impact of interaction valence and interaction channel sequence on Generation Z consumers' purchase attitudes from the perspective of attitude modification process. Using a scenario-based experimental design in a restaurant setting, we sample 616 Generation Z students. Findings suggest that C2C interactions that have the same valence at each touchpoint strengthen Generation Z consumers' initial purchase attitude, whereas inconsistent valences modify purchase attitudes. Our results reveal that a negative-to-positive ordering is more conducive to the formation of a positive purchase attitude than a positive-to-negative ordering. Moreover, the cross-channel sequence of a customer journey moderates the inconsistent valence order such that the positive-to-negative order improves purchase attitude more than the negative-to-positive order. This study explains theoretically the difference of ambivalent experiences on Generation Z consumer attitudes. We provide practical suggestions for multi-channel restaurant enterprises to successfully employ customer journey touchpoint management.     

How to “improve” prediction using behavior modification

Many internet platforms that collect behavioral big data use it to predict user behavior for internal purposes and for their business customers (e.g., advertisers, insurers, security forces, governments, political consulting firms) who utilize the predictions for personalization, targeting, and other decision-making. Improving predictive accuracy is therefore extremely valuable. Data science researchers design algorithms, models, and approaches to improve prediction. Prediction is also improved with larger and richer data. Beyond improving algorithms and data, platforms can stealthily achieve better prediction accuracy by pushing users’ behaviors towards their predicted values, using behavior modification techniques, thereby demonstrating more certain predictions. Such apparent “improved” prediction can result from employing reinforcement learning algorithms that combine prediction and behavior modification. This strategy is absent from the machine learning and statistics literature. Investigating its properties requires integrating causal with predictive notation. To this end, we incorporate Pearl’s causal $do(.)$ operator into the predictive vocabulary. We then decompose the expected prediction error given behavior modification and identify the components impacting predictive power. Our derivation elucidates implications of such behavior modification to data scientists, platforms, their customers, and the humans whose behavior is manipulated. Behavior modification can make users’ behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when business customers use predictions in practice. Outcomes pushed towards their predictions can be at odds with customers’ intentions, and harmful to manipulated users.