This study describes the gradient analysis of the freshwater macroinvertebrate assemblages in eight streams of Tenerife and La Gomera (Canary Islands) over a 16-year period. During this period, a total of 75 taxa belo...This study describes the gradient analysis of the freshwater macroinvertebrate assemblages in eight streams of Tenerife and La Gomera (Canary Islands) over a 16-year period. During this period, a total of 75 taxa belonging to 34 taxonomic families were found. Endemism has an important presence in the streams on both islands, especially regarding Trichoptera and Coleoptera. The overall status of freshwater macroinvertebrates is rather uncertain as recent data on these communities are scarce and focused on a limited number of sites. Overexploitation of aquifers and the diversion of natural water flows for irrigation have resulted in the drying up of numerous natural streams, inevitably endangering the fauna that inhabits them. A reduction in number and abundance of endemic and sensitive species was observed in the majority of the sampled streams resulting in a lower ecological rating. Therefore, it is proposed that the protection of streams of high conservation value is essential to conserve freshwater macroinvertebrate fauna native to the Canary Islands.展开更多
With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between busine...With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.展开更多
大数据时代,流数据大量涌现.概念漂移作为流数据挖掘中最典型且困难的问题,受到了越来越广泛的关注.集成学习是处理流数据中概念漂移的常用方法,然而在漂移发生后,学习模型往往无法对流数据的分布变化做出及时响应,且不能有效处理不同...大数据时代,流数据大量涌现.概念漂移作为流数据挖掘中最典型且困难的问题,受到了越来越广泛的关注.集成学习是处理流数据中概念漂移的常用方法,然而在漂移发生后,学习模型往往无法对流数据的分布变化做出及时响应,且不能有效处理不同类型概念漂移,导致模型泛化性能下降.针对这个问题,提出一种面向不同类型概念漂移的两阶段自适应集成学习方法(two-stage adaptive ensemble learning method for different types of concept drift,TAEL).该方法首先通过检测漂移跨度来判断概念漂移类型,然后根据不同漂移类型,提出“过滤-扩充”两阶段样本处理机制动态选择合适的样本处理策略.具体地,在过滤阶段,针对不同漂移类型,创建不同的非关键样本过滤器,提取历史样本块中的关键样本,使历史数据分布更接近最新数据分布,提高基学习器有效性;在扩充阶段,提出一种分块优先抽样方法,针对不同漂移类型设置合适的抽取规模,并根据历史关键样本所属类别在当前样本块上的规模占比设置抽样优先级,再由抽样优先级确定抽样概率,依据抽样概率从历史关键样本块中抽取关键样本子集扩充当前样本块,缓解样本扩充后的类别不平衡现象,解决当前基学习器欠拟合问题的同时增强其稳定性.实验结果表明,所提方法能够对不同类型的概念漂移做出及时响应,加快漂移发生后在线集成模型的收敛速度,提高模型的整体泛化性能.展开更多
网络直播广告作为一种新型营销方式快速发展,优化直播广告运营主体努力水平及定价策略是一项值得深入研究的课题。本文基于广告投放效果的两种定价模式,构建了包含两个广告商和一个主播的网络直播广告定价决策模型,探索广告商与主播的...网络直播广告作为一种新型营销方式快速发展,优化直播广告运营主体努力水平及定价策略是一项值得深入研究的课题。本文基于广告投放效果的两种定价模式,构建了包含两个广告商和一个主播的网络直播广告定价决策模型,探索广告商与主播的最优努力水平选择及广告定价策略。研究发现:CPW(cost per watch)定价模式下,广告商承担了消费者是否购买的不确定性风险,当消费者敏感性系数偏低时,广告商会提交较低的出价,且B/D两类广告商赢得竞拍的概率相等;对比CPW模式,在CPA(cost per action)定价模式下广告商的努力水平更低,且CPA定价模式中B型(品牌型)广告商赢得竞拍的概率更大,但赢得竞拍的广告商边际利润往往较低;与广告商相反,主播在CPA定价模式下的收益大于CPW,且随消费者敏感性系数的增加,两种定价模式下的收益差逐渐增大;CPW定价模式下预期观看直播的用户量和购买率均高于CPA,网络直播市场倾向于从CPW广告定价合同中获得较大收益。展开更多
Stream sediment sampling is a significant tool in geochemical exploration. The stream sediment composition reflects the bedrock geology, overburden cover, and metalliferous mineralization. This research article focuse...Stream sediment sampling is a significant tool in geochemical exploration. The stream sediment composition reflects the bedrock geology, overburden cover, and metalliferous mineralization. This research article focuses on assessing selected trace element concentrations in stream sediments and interpreting their inter-element relationships using multivariate statistical methods. Tagadur Ranganathaswamy Gudda and its surroundings in the Nuggihalli schist belt of southern India have been investigated in the present work. The geology of the study area is complex, with a diverse range of litho units and evidence of strong structural deformation. The area is known for its mineralization potential for chromite, vanadiferous titanomagnetite, and sulfides. The topography of the region is characterized by an undulating terrain with a radial drainage pattern. Most part of the schist belt is soil covered except the Tagadur Ranganathaswamy Gudda area. For this study, a discrete stream sediment sampling method was adopted to collect the samples. Stream sediment samples were collected using a discrete sampling method and analyzed for trace elements using an ICP-AES spectrophotometer: Fe, Cr, Ti, V, Cu, Ni, Zn, Pb, Mn, Cd, and As have been analyzed. The analytical data were statistically treated using the SPSS software, including descriptive statistics, normalization of data using natural log transformation, and factor analysis with varimax rotation. The transformed data showed a log-normal distribution, indicating the presence of geochemical anomalies. The results of the study provide valuable insights into the geochemical processes and mineralization potential of the study area. The statistical analysis helps in understanding the inter-element relationships and identifying element groups and their implications on bedrock potential mineralization. Additionally, spatial analysis using inverse distance weighting interpolation provides information about the distribution of geochemical parameters across the study area. Overall, this research contributes to the understanding of stream sediment geochemistry and its application in mineral exploration. The findings have implications for future exploration efforts and can aid in the identification of potential ore deposits in the Nuggihalli schist belt and similar geological settings.展开更多
数据流分类是数据流挖掘领域一项重要研究任务,目标是从不断变化的海量数据中捕获变化的类结构.目前,几乎没有框架可以同时处理数据流中常见的多类非平衡、概念漂移、异常点和标记样本成本高昂问题.基于此,提出一种非平衡数据流在线主...数据流分类是数据流挖掘领域一项重要研究任务,目标是从不断变化的海量数据中捕获变化的类结构.目前,几乎没有框架可以同时处理数据流中常见的多类非平衡、概念漂移、异常点和标记样本成本高昂问题.基于此,提出一种非平衡数据流在线主动学习方法(Online active learning method for imbalanced data stream,OALM-IDS).AdaBoost是一种将多个弱分类器经过迭代生成强分类器的集成分类方法,AdaBoost.M2引入了弱分类器的置信度,此类方法常用于静态数据.定义了基于非平衡比率和自适应遗忘因子的训练样本重要性度量,从而使AdaBoost.M2方法适用于非平衡数据流,提升了非平衡数据流集成分类器的性能.提出了边际阈值矩阵的自适应调整方法,优化了标签请求策略.将概念漂移程度融入模型构建过程中,定义了基于概念漂移指数的自适应遗忘因子,实现了漂移后的模型重构.在6个人工数据流和4个真实数据流上的对比实验表明,提出的非平衡数据流在线主动学习方法的分类性能优于其他5种非平衡数据流学习方法.展开更多
文摘This study describes the gradient analysis of the freshwater macroinvertebrate assemblages in eight streams of Tenerife and La Gomera (Canary Islands) over a 16-year period. During this period, a total of 75 taxa belonging to 34 taxonomic families were found. Endemism has an important presence in the streams on both islands, especially regarding Trichoptera and Coleoptera. The overall status of freshwater macroinvertebrates is rather uncertain as recent data on these communities are scarce and focused on a limited number of sites. Overexploitation of aquifers and the diversion of natural water flows for irrigation have resulted in the drying up of numerous natural streams, inevitably endangering the fauna that inhabits them. A reduction in number and abundance of endemic and sensitive species was observed in the majority of the sampled streams resulting in a lower ecological rating. Therefore, it is proposed that the protection of streams of high conservation value is essential to conserve freshwater macroinvertebrate fauna native to the Canary Islands.
文摘With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.
文摘大数据时代,流数据大量涌现.概念漂移作为流数据挖掘中最典型且困难的问题,受到了越来越广泛的关注.集成学习是处理流数据中概念漂移的常用方法,然而在漂移发生后,学习模型往往无法对流数据的分布变化做出及时响应,且不能有效处理不同类型概念漂移,导致模型泛化性能下降.针对这个问题,提出一种面向不同类型概念漂移的两阶段自适应集成学习方法(two-stage adaptive ensemble learning method for different types of concept drift,TAEL).该方法首先通过检测漂移跨度来判断概念漂移类型,然后根据不同漂移类型,提出“过滤-扩充”两阶段样本处理机制动态选择合适的样本处理策略.具体地,在过滤阶段,针对不同漂移类型,创建不同的非关键样本过滤器,提取历史样本块中的关键样本,使历史数据分布更接近最新数据分布,提高基学习器有效性;在扩充阶段,提出一种分块优先抽样方法,针对不同漂移类型设置合适的抽取规模,并根据历史关键样本所属类别在当前样本块上的规模占比设置抽样优先级,再由抽样优先级确定抽样概率,依据抽样概率从历史关键样本块中抽取关键样本子集扩充当前样本块,缓解样本扩充后的类别不平衡现象,解决当前基学习器欠拟合问题的同时增强其稳定性.实验结果表明,所提方法能够对不同类型的概念漂移做出及时响应,加快漂移发生后在线集成模型的收敛速度,提高模型的整体泛化性能.
文摘网络直播广告作为一种新型营销方式快速发展,优化直播广告运营主体努力水平及定价策略是一项值得深入研究的课题。本文基于广告投放效果的两种定价模式,构建了包含两个广告商和一个主播的网络直播广告定价决策模型,探索广告商与主播的最优努力水平选择及广告定价策略。研究发现:CPW(cost per watch)定价模式下,广告商承担了消费者是否购买的不确定性风险,当消费者敏感性系数偏低时,广告商会提交较低的出价,且B/D两类广告商赢得竞拍的概率相等;对比CPW模式,在CPA(cost per action)定价模式下广告商的努力水平更低,且CPA定价模式中B型(品牌型)广告商赢得竞拍的概率更大,但赢得竞拍的广告商边际利润往往较低;与广告商相反,主播在CPA定价模式下的收益大于CPW,且随消费者敏感性系数的增加,两种定价模式下的收益差逐渐增大;CPW定价模式下预期观看直播的用户量和购买率均高于CPA,网络直播市场倾向于从CPW广告定价合同中获得较大收益。
文摘Stream sediment sampling is a significant tool in geochemical exploration. The stream sediment composition reflects the bedrock geology, overburden cover, and metalliferous mineralization. This research article focuses on assessing selected trace element concentrations in stream sediments and interpreting their inter-element relationships using multivariate statistical methods. Tagadur Ranganathaswamy Gudda and its surroundings in the Nuggihalli schist belt of southern India have been investigated in the present work. The geology of the study area is complex, with a diverse range of litho units and evidence of strong structural deformation. The area is known for its mineralization potential for chromite, vanadiferous titanomagnetite, and sulfides. The topography of the region is characterized by an undulating terrain with a radial drainage pattern. Most part of the schist belt is soil covered except the Tagadur Ranganathaswamy Gudda area. For this study, a discrete stream sediment sampling method was adopted to collect the samples. Stream sediment samples were collected using a discrete sampling method and analyzed for trace elements using an ICP-AES spectrophotometer: Fe, Cr, Ti, V, Cu, Ni, Zn, Pb, Mn, Cd, and As have been analyzed. The analytical data were statistically treated using the SPSS software, including descriptive statistics, normalization of data using natural log transformation, and factor analysis with varimax rotation. The transformed data showed a log-normal distribution, indicating the presence of geochemical anomalies. The results of the study provide valuable insights into the geochemical processes and mineralization potential of the study area. The statistical analysis helps in understanding the inter-element relationships and identifying element groups and their implications on bedrock potential mineralization. Additionally, spatial analysis using inverse distance weighting interpolation provides information about the distribution of geochemical parameters across the study area. Overall, this research contributes to the understanding of stream sediment geochemistry and its application in mineral exploration. The findings have implications for future exploration efforts and can aid in the identification of potential ore deposits in the Nuggihalli schist belt and similar geological settings.
文摘数据流分类是数据流挖掘领域一项重要研究任务,目标是从不断变化的海量数据中捕获变化的类结构.目前,几乎没有框架可以同时处理数据流中常见的多类非平衡、概念漂移、异常点和标记样本成本高昂问题.基于此,提出一种非平衡数据流在线主动学习方法(Online active learning method for imbalanced data stream,OALM-IDS).AdaBoost是一种将多个弱分类器经过迭代生成强分类器的集成分类方法,AdaBoost.M2引入了弱分类器的置信度,此类方法常用于静态数据.定义了基于非平衡比率和自适应遗忘因子的训练样本重要性度量,从而使AdaBoost.M2方法适用于非平衡数据流,提升了非平衡数据流集成分类器的性能.提出了边际阈值矩阵的自适应调整方法,优化了标签请求策略.将概念漂移程度融入模型构建过程中,定义了基于概念漂移指数的自适应遗忘因子,实现了漂移后的模型重构.在6个人工数据流和4个真实数据流上的对比实验表明,提出的非平衡数据流在线主动学习方法的分类性能优于其他5种非平衡数据流学习方法.
