Quantifying co-creative writing experiences

Collaboration with artificial intelligence (AI) is a growing trend even in the field of creativity. This paper examines which quantitative metrics can be used to comparatively analyse human-computer co-creativity with children. To study this question, 24 schoolchildren of age 10–11 wrote a poem with three co-creative poetry writing processes: a human-computer, a human-human, and a human-human-computer process. The computational participant in the processes was an AI-based application called the Poetry Machine. The children were asked to evaluate their user experience with a 5-point Likert-type questionnaire after each writing process and a comparative questionnaire after finishing all processes. The metrics used in the evaluation were immediate fun, long-term enjoyment, creativity, self-expression, outcome satisfaction, ease of starting and finishing writing, quality of ideas and support from others, and ownership.

Significant differences were found in fun, long-term enjoyment, quality of ideas, support, and ownership. The high number of statistically relevant results was enabled by exposing all participants to all writing processes, and the comparative questionnaire. The human-human-computer process was evaluated the best in long-term enjoyment and the human-computer process the weakest in support and idea quality. Creativity and ease of finishing writing turned out to be outlining metrics for the co-creative processes.     

基于模糊多元联系度模型的尾矿库综合安全评价

为准确评价尾矿库的安全等级，以黄金洞尾矿库为例，分析影响尾矿库安全的各因素之间的关系，综合运用模糊多元联系度理论与层次分析法（AHP），建立了包含5个影响因素和26个影响因子的尾矿库综合安全评价模型。由AHP得出各项主、客观指标的权重系数，引入模糊多元联系度理论，综合考虑确定性与不确定性因素，处理综合集成问题，构造隶属函数量化指标的实测值。结果表明：黄金洞尾矿库安全等级为Ⅱ级，尾矿库处于较稳定状态。经比较不确定性因素大小，得出尾矿排放（B₃）>尾矿坝（B₂）>安全管理（B₁）>尾矿输送与回水（B₅）>排洪系统（B₄），说明尾矿排放指标的不确定性因素最大，需对尾矿排放加强管理。该模型得到的评价结果与实际结果相吻合，为尾矿库安全性评价提供了一种可行的方法。     

基于介形类生态特征评价湖南某铀矿山地表水放射性环境

湖南某铀矿为我国一重要铀资源生产基地，长年的矿冶活动使该铀矿冶地域生态环境不可避免地遭到了一定程度破坏。本文以该铀矿冶地域介形类为研究对象，分析其分布与生态特征，并利用该生物评价本矿冶地域地表水放射性污染状况。结果表明，Cypridopsis vidua在溪流与河流中均有分布，Helerocypris incongruens仅发现于河流，两者丰度均不高。利用该两种介形类丰度及总丰度变化可指示水体铀浓度的变化，丰度越小，铀含量越大，反之亦然。Shannon-Wiener物种多样性指数与水体铀浓度呈明显负相关，表明也可利用介形类生物指数指示水体铀浓度。该生物指数明显偏低，说明该铀矿冶地域地表水环境质量需要改善，放射性污染应引起足够重视。     

红外热成像在乳腺疾病检测的应用研究

乳腺疾病已严重危害女性身心健康,其中乳腺癌更位居全球范围内女性癌症发病率和死亡率首位,因此乳腺癌的早期发现意义重大。传统结构影像学早期检测疾病具有一定局限性,而红外热成像作为功能成像技术可为乳腺癌的早期筛查提供有效线索。因此本文主要就红外热成像在乳腺疾病的早期检测及预后评估的应用价值进行综述。     

提升生活排水系统卫生安全性能的关键问题

新冠肺炎确诊患者粪便病毒检测呈阳性,意味着新冠病毒可能会通过气溶胶的方式进行传播,使得生活排水系统的卫生安全问题时隔17年后再次摆在人们面前。以保障人的健康为出发点,从基础研究、排水机理、系统设计、施工监管、维护管理等多方面提出了提升生活排水系统卫生安全性能的关键问题,以期从全生命周期内切断排水系统可能传播病毒的隐形途径,切实保障居民居家健康。     

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.     

水资源质量分析评价系统构建与效益浅析

水资源质量分析评价系统以大幅度提升日常工作效率为出发点,以原始监测数据分析为基础,动态生成多种分析报表、统计报表,同时增加关注信息展示,大量减轻日常统计工作,基本实现水质分析统计业务的自动化。系统具有良好的扩展性,有较长的生命周期。     

Sensory characteristics and storage quality indicators of surimi franks nutritionally enhanced with omega‐3 rich flaxseed oil and salmon oil

Surimi franks were fortified with flaxseed or salmon oil at 2 g/100 g sample, franks without added oil served as a control. Thiobarbituric acid reactive substances values were highest (P < 0.001) in salmon oil franks; however, these values did not change over the 21‐day storage period regardless of frank type (P > 0.05). Frank pH decreased over time (P = 0.011) for all frank types. There were differences in textural properties between frank types (P < 0.05), with the flaxseed franks being softer and less gummy, cohesive and chewy than the control franks. Participants (n = 79; age 18–35) evaluated visual appeal, colour, aroma, texture, flavour and acceptability on a hedonic scale; there were no differences (P > 0.05) between franks. Fifty‐four panellists reported consuming sausage on a weekly to monthly basis, and most (50/79) indicated interest in purchasing this type product. The surimi franks were accepted by young adult consumers, which may indicate market potential of these types of products.     

Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.