Purpose： This research aims to identify product search tasks in online shopplng ana analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach： The experimental dataset contai...Purpose： This research aims to identify product search tasks in online shopplng ana analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach： The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks. Findings： （1） Users issued a similar number of queries （1.43 to 1.47） with similar lengths （7.3-7.6 characters） per task in mono-tasking and multi-tasking sessions, and （2） Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session. Research limitations： The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior.Practical implications： These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction. Originality/value： The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.展开更多
The degradation of ethinylestradiol (EE, an orally bio-active estrogen) in an aqueous-methanolic solution using a Ti/TiO2 thin-film electrode and UV radiation (a photoelectrocatalytic system) was evaluated. Hence, HPL...The degradation of ethinylestradiol (EE, an orally bio-active estrogen) in an aqueous-methanolic solution using a Ti/TiO2 thin-film electrode and UV radiation (a photoelectrocatalytic system) was evaluated. Hence, HPLC/UV analysis shows that EE (at 0.34 mmol) is totally consumed after 30 minutes of exposure to the photoelectrocatalytic system in the presence of Na2SO4 (0.1 mol·L-1) and with an applied bias potential of +1.0 V versus the Ag/AgCl reference electrode. Moreover, monitoring by direct infusion electrospray ionization mass spectrometry (ESI-MS) and SPME-GC/ MS (solid phase microextraction coupled with gas chromatography-mass spectrometry) reveals that apparently no degradation products are formed under these conditions. Hence, this study demonstrates that the photoelectrocatalytic system can be efficiently used to promote the complete degradation (and likely mineralization) of this hormone under these conditions.展开更多
为解决传统A^(*)寻路算法在搜索过程中会产生大量冗余节点,导致算法整体搜索效率低,运算内存消耗大等问题,从A^(*)算法的两个重要决策点出发,改进算法的代价评估函数与邻节点搜索策略,提出一种改进融合算法。首先,采用向量叉积与尺度平...为解决传统A^(*)寻路算法在搜索过程中会产生大量冗余节点,导致算法整体搜索效率低,运算内存消耗大等问题,从A^(*)算法的两个重要决策点出发,改进算法的代价评估函数与邻节点搜索策略,提出一种改进融合算法。首先,采用向量叉积与尺度平衡因子相结合的方法优化传统A^(*)算法的启发函数,减少A^(*)算法寻路过程中在最优路径周围产生的具有相同代价值的冗余节点,减少了对称路径的搜索;其次,融合跳点搜索(Jump point search, JPS)策略,通过逻辑判断实现路径的变步长跳跃搜索,避免了A^(*)算法逐层搜索效率低的弊端。在不同尺寸的栅格地图中进行仿真分析,发现改进融合算法相比于传统A^(*)算法,在路径长度基本相等的情况下,节点搜索数量约减少95%,且与传统JPS寻路算法相比,有效过滤了路径周围复杂形状障碍物产生的大量冗余跳点。最后,将改进融合算法应用于ROS移动机器人并进行对比实验以验证算法的可行性。实验结果表明:改进融合算法在获得高效安全的路径基础上,搜索效率相比于A^(*)算法可提高约94%。展开更多
This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage ti...This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.展开更多
基金supported by the National Science Foundation of China(NSFC)Grant(No.71373015)
文摘Purpose： This research aims to identify product search tasks in online shopplng ana analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach： The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks. Findings： （1） Users issued a similar number of queries （1.43 to 1.47） with similar lengths （7.3-7.6 characters） per task in mono-tasking and multi-tasking sessions, and （2） Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session. Research limitations： The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior.Practical implications： These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction. Originality/value： The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.
文摘The degradation of ethinylestradiol (EE, an orally bio-active estrogen) in an aqueous-methanolic solution using a Ti/TiO2 thin-film electrode and UV radiation (a photoelectrocatalytic system) was evaluated. Hence, HPLC/UV analysis shows that EE (at 0.34 mmol) is totally consumed after 30 minutes of exposure to the photoelectrocatalytic system in the presence of Na2SO4 (0.1 mol·L-1) and with an applied bias potential of +1.0 V versus the Ag/AgCl reference electrode. Moreover, monitoring by direct infusion electrospray ionization mass spectrometry (ESI-MS) and SPME-GC/ MS (solid phase microextraction coupled with gas chromatography-mass spectrometry) reveals that apparently no degradation products are formed under these conditions. Hence, this study demonstrates that the photoelectrocatalytic system can be efficiently used to promote the complete degradation (and likely mineralization) of this hormone under these conditions.
文摘为解决传统A^(*)寻路算法在搜索过程中会产生大量冗余节点,导致算法整体搜索效率低,运算内存消耗大等问题,从A^(*)算法的两个重要决策点出发,改进算法的代价评估函数与邻节点搜索策略,提出一种改进融合算法。首先,采用向量叉积与尺度平衡因子相结合的方法优化传统A^(*)算法的启发函数,减少A^(*)算法寻路过程中在最优路径周围产生的具有相同代价值的冗余节点,减少了对称路径的搜索;其次,融合跳点搜索(Jump point search, JPS)策略,通过逻辑判断实现路径的变步长跳跃搜索,避免了A^(*)算法逐层搜索效率低的弊端。在不同尺寸的栅格地图中进行仿真分析,发现改进融合算法相比于传统A^(*)算法,在路径长度基本相等的情况下,节点搜索数量约减少95%,且与传统JPS寻路算法相比,有效过滤了路径周围复杂形状障碍物产生的大量冗余跳点。最后,将改进融合算法应用于ROS移动机器人并进行对比实验以验证算法的可行性。实验结果表明:改进融合算法在获得高效安全的路径基础上,搜索效率相比于A^(*)算法可提高约94%。
基金Thailand Research Fund (Grant #MRG5480176)National Research University Project of Thailand Office of Higher Education Commission
文摘This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.
