基于相对资源承载力模型的研究

首先分析了相对资源承载力模型存在的不足,即未考虑各类资源间的匹配以及权重取值过于主观,并对其进行了改进。然后应用改进后的模型对新疆1978—2008年可持续发展情况进行了实证分析,研究表明:①1978—2008年,新疆相对耕地资源承载力最大,其次是耕地和经济资源均衡承载力,经济资源承载力最弱;②新疆的相对综合承载力和过剩人口1978—2000年呈递增趋势,而2001—2008年处于逐渐恢复和扩张的态势;③新疆人口目前仍处于过剩状态。同时比较分析了模型改进前后的实证结果,并对新疆人口的可持续发展提出了一些建议。     

氧化锌的制备及表征

采用直接沉淀法和均匀沉淀法制备氧化锌,考察了反应物配比、反应温度、反应时间对产率的影响,同时考察了煅烧温度、煅烧时间对产品外观的影响,通过测定堆积密度、红外光谱对产品进行了表征,从而确定了最佳的制备工艺条件。研究结果表明,最佳的制备方法为直接沉淀法,最佳的制备工艺条件:碳酸铵与氯化锌的物质的量比为1.2∶1,反应温度为60℃,反应时间为2.0 h,煅烧温度为450℃,煅烧时间为1.5 h。     

用蛋白酶对羊毛改性处理综述

为解决毛纺行业产生的环境污染问题,提出一种羊毛的生态改性方法,即蛋白酶改性技术。首先,介绍了蛋白酶的化学性质和对羊毛改性的机理。其次,详细叙述了羊毛或织物经蛋白酶处理后各项性能的变化,其中包括防毡缩性、抗起毛起球性、柔软性、染色性、仿山羊绒改性等。最后提出羊毛的蛋白酶改性技术将为企业带来巨大的经济效益。     

改性海泡石对丙酮的吸附特性研究

为了解决制药行业典型VOCs物质——丙酮的易挥发、难降解问题,对改性海泡石吸附丙酮的性能和表征进行了研究。研究结果表明:1)质量分数为13%的盐酸改性海泡石12h后对丙酮的吸附效果最好,吸附量达54.41 mg/g;2)水热温度170℃的海泡石对丙酮的吸附量也可达到23.20mg/g;3)对海泡石采用酸和水热改性的方式进行处理后,改性海泡石的实际丙酮平衡吸附过程均符合Lagergren二级吸附速率方程;4)表征结果表明,酸改性可使海泡石比表面积增大,杂质降低,孔道更顺畅,利于丙酮的吸附。该研究为丙酮污染的治理提出了新路线,在工业催化领域拥有广阔的应用前景。     

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。     

绿色水处理剂聚天冬氨酸的改性研究进展

聚天冬氨酸(PASP)具有无毒、生物可降解性、一剂多效等特点,是绿色环保型水处理剂之一。着重介绍了通过共聚改性和开环改性提高聚天冬氨酸的缓蚀阻垢性能的研究进展,并对聚天冬氨酸改性的发展趋势进行了分析和展望。     

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.