Based on investigations of 112 Chinese firms and studies on foreign leading corporations, a theoretical framework of dynamic capabilities based strategy innovation (SI) is put forward. Several large firms in China wi...Based on investigations of 112 Chinese firms and studies on foreign leading corporations, a theoretical framework of dynamic capabilities based strategy innovation (SI) is put forward. Several large firms in China winning through SI were studied empirically. This paper complements previous publications on the theories of innovation and strategy. This work's findings will be useful for managers interested in our approach, which highlights the importance of SI and focuses on and points out the major pitfalls in the innovation processes. Implementing the dynamic capabilities based strategy innovation can effectively cultivate and develop core competences of corporations. It is concluded that implementing SI is the only path for Chinese enterprise growth in the intensified competition in the knowledge economy.展开更多
Atmospheric water harvesting offers a powerful and promising solution to address the problem of global freshwater scarcity.In the past decade,significant progress has been achieved in utilizing hydrolytically stable m...Atmospheric water harvesting offers a powerful and promising solution to address the problem of global freshwater scarcity.In the past decade,significant progress has been achieved in utilizing hydrolytically stable metal-organic frameworks as recyclable water-sorbent materials under low relative humidity,especially in those arid areas.Recently,Yaghi's group has employed a combined crystallographic and theoretical technique to decipher the water filling mechanism in MOF-303,where the polar organic linkers rather than the inorganic units of MOF are demonstrated as the key factor.Hence,the hydrophilic strength of the water-binding pocket in MOFs can be optimized through the approach of multivariate modulations,resulting in enhanced water harvesting properties.展开更多
To enhance the cost-effectiveness of bulk hybrid AC-DC power systems and promote wind consumption,this paper proposes a two-stage risk-based robust reserve scheduling(RRRS)model.Different from traditional robust optim...To enhance the cost-effectiveness of bulk hybrid AC-DC power systems and promote wind consumption,this paper proposes a two-stage risk-based robust reserve scheduling(RRRS)model.Different from traditional robust optimization,the proposed model applies an adjustable uncertainty set rather than a fixed one.Thereby,the operational risk is optimized together with the dispatch schedules,with a reasonable admissible region of wind power obtained correspondingly.In addition,both the operational base point and adjustment capacity of tielines are optimized in the RRRS model,which enables reserve sharing among the connected areas to handle the significant wind uncertainties.Based on the alternating direction method of multipliers(ADMM),a fully distributed framework is presented to solve the RRRS model in a distributed way.A dynamic penalty factor adjustment strategy(DPA)is also developed and applied to enhance its convergence properties.Since only limited information needs to be exchanged during the solution process,the communication burden is reduced and regional information is protected.Case studies on the 2-area 12-bus system and 3-area 354-bus system illustrate the effectiveness of the proposed model and approach.展开更多
针对大数据环境下DCNN(deep convolutional neural network)算法中存在网络冗余参数过多、参数寻优能力不佳和并行效率低的问题,提出了大数据环境下基于特征图和并行计算熵的深度卷积神经网络算法MR-FPDCNN(deep convolutional neural n...针对大数据环境下DCNN(deep convolutional neural network)算法中存在网络冗余参数过多、参数寻优能力不佳和并行效率低的问题,提出了大数据环境下基于特征图和并行计算熵的深度卷积神经网络算法MR-FPDCNN(deep convolutional neural network algorithm based on feature graph and parallel computing entropy using MapReduce)。该算法设计了基于泰勒损失的特征图剪枝策略FMPTL(feature map pruning based on Taylor loss),预训练网络,获得压缩后的DCNN,有效减少了冗余参数,降低了DCNN训练的计算代价。提出了基于信息共享搜索策略ISS(information sharing strategy)的萤火虫优化算法IFAS(improved firefly algorithm based on ISS),根据“IFAS”算法初始化DCNN参数,实现DCNN的并行化训练,提高网络的寻优能力。在Reduce阶段提出了基于并行计算熵的动态负载均衡策略DLBPCE(dynamic load balancing strategy based on parallel computing entropy),获取全局训练结果,实现了数据的快速均匀分组,从而提高了集群的并行效率。实验结果表明,该算法不仅降低了DCNN在大数据环境下训练的计算代价,而且提高了并行系统的并行化性能。展开更多
文摘Based on investigations of 112 Chinese firms and studies on foreign leading corporations, a theoretical framework of dynamic capabilities based strategy innovation (SI) is put forward. Several large firms in China winning through SI were studied empirically. This paper complements previous publications on the theories of innovation and strategy. This work's findings will be useful for managers interested in our approach, which highlights the importance of SI and focuses on and points out the major pitfalls in the innovation processes. Implementing the dynamic capabilities based strategy innovation can effectively cultivate and develop core competences of corporations. It is concluded that implementing SI is the only path for Chinese enterprise growth in the intensified competition in the knowledge economy.
基金supported by the National Natural Science Foundation of China(Grant Nos.21471118,21971199,22025106,51202127,91545205,and 91622103)National Key Research and Development Project of China(2018YFA0704000)+1 种基金Natural Science Foundation of Hubei Province(2016CFB382)Fundamental Research Funds for the Central Universities(2042017kf0227,2042019kf0205)。
文摘Atmospheric water harvesting offers a powerful and promising solution to address the problem of global freshwater scarcity.In the past decade,significant progress has been achieved in utilizing hydrolytically stable metal-organic frameworks as recyclable water-sorbent materials under low relative humidity,especially in those arid areas.Recently,Yaghi's group has employed a combined crystallographic and theoretical technique to decipher the water filling mechanism in MOF-303,where the polar organic linkers rather than the inorganic units of MOF are demonstrated as the key factor.Hence,the hydrophilic strength of the water-binding pocket in MOFs can be optimized through the approach of multivariate modulations,resulting in enhanced water harvesting properties.
基金supported by the National Key Research and Development Program of China (2016YFB0900100)the State Key Program of National Natural Science Foundation of China (51537010)the project of State Grid Corporation of China (52110418000T)。
文摘To enhance the cost-effectiveness of bulk hybrid AC-DC power systems and promote wind consumption,this paper proposes a two-stage risk-based robust reserve scheduling(RRRS)model.Different from traditional robust optimization,the proposed model applies an adjustable uncertainty set rather than a fixed one.Thereby,the operational risk is optimized together with the dispatch schedules,with a reasonable admissible region of wind power obtained correspondingly.In addition,both the operational base point and adjustment capacity of tielines are optimized in the RRRS model,which enables reserve sharing among the connected areas to handle the significant wind uncertainties.Based on the alternating direction method of multipliers(ADMM),a fully distributed framework is presented to solve the RRRS model in a distributed way.A dynamic penalty factor adjustment strategy(DPA)is also developed and applied to enhance its convergence properties.Since only limited information needs to be exchanged during the solution process,the communication burden is reduced and regional information is protected.Case studies on the 2-area 12-bus system and 3-area 354-bus system illustrate the effectiveness of the proposed model and approach.
文摘针对大数据环境下DCNN(deep convolutional neural network)算法中存在网络冗余参数过多、参数寻优能力不佳和并行效率低的问题,提出了大数据环境下基于特征图和并行计算熵的深度卷积神经网络算法MR-FPDCNN(deep convolutional neural network algorithm based on feature graph and parallel computing entropy using MapReduce)。该算法设计了基于泰勒损失的特征图剪枝策略FMPTL(feature map pruning based on Taylor loss),预训练网络,获得压缩后的DCNN,有效减少了冗余参数,降低了DCNN训练的计算代价。提出了基于信息共享搜索策略ISS(information sharing strategy)的萤火虫优化算法IFAS(improved firefly algorithm based on ISS),根据“IFAS”算法初始化DCNN参数,实现DCNN的并行化训练,提高网络的寻优能力。在Reduce阶段提出了基于并行计算熵的动态负载均衡策略DLBPCE(dynamic load balancing strategy based on parallel computing entropy),获取全局训练结果,实现了数据的快速均匀分组,从而提高了集群的并行效率。实验结果表明,该算法不仅降低了DCNN在大数据环境下训练的计算代价,而且提高了并行系统的并行化性能。