The memory behavior in liquid crystals(LCs)that is characterized by low cost,large area,high speed,and high-density memory has evolved from a mere scientific curiosity to a technology that is being applied in a variet...The memory behavior in liquid crystals(LCs)that is characterized by low cost,large area,high speed,and high-density memory has evolved from a mere scientific curiosity to a technology that is being applied in a variety of commodities.In this study,we utilized molybdenum disulfide(MoS_(2))nanoflakes as the guest in a homotropic LCs host to modulate the overall memory effect of the hybrid.It was found that the MoS₂nanoflakes within the LCs host formed agglomerates,which in turn resulted in an accelerated response of the hybrids to the external electric field.However,this process also resulted in a slight decrease in the threshold voltage.Additionally,it was observed that MoS₂nanoflakes in a LCs host tend to align homeotropically under an external electric field,thereby accelerating the refreshment of the memory behavior.The incorporation of a mass fraction of 0.1%2μm MoS₂nanoflakes into the LCs host was found to significantly reduce the refreshing memory behavior in the hybrid to 94.0 s under an external voltage of 5 V.These findings illustrate the efficacy of regulating the rate of memory behavior for a variety of potential applications.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
Jerry W.Ward,Jr.(1943-2025)is a renowned African American scholar and poet.This article presents a critique of Dr.Ward by remembering him as a literary scholar and critic,educator,mentor,and cultural and academic amba...Jerry W.Ward,Jr.(1943-2025)is a renowned African American scholar and poet.This article presents a critique of Dr.Ward by remembering him as a literary scholar and critic,educator,mentor,and cultural and academic ambassador to China and the world.It reveals the uniqueness in Dr.Ward’s critical thinking about African American culture,tradition and history,his importance as an internationally known scholar,his originality in his poetic art,and the value of his legacy for African American literature and its scholarship.展开更多
文摘The memory behavior in liquid crystals(LCs)that is characterized by low cost,large area,high speed,and high-density memory has evolved from a mere scientific curiosity to a technology that is being applied in a variety of commodities.In this study,we utilized molybdenum disulfide(MoS_(2))nanoflakes as the guest in a homotropic LCs host to modulate the overall memory effect of the hybrid.It was found that the MoS₂nanoflakes within the LCs host formed agglomerates,which in turn resulted in an accelerated response of the hybrids to the external electric field.However,this process also resulted in a slight decrease in the threshold voltage.Additionally,it was observed that MoS₂nanoflakes in a LCs host tend to align homeotropically under an external electric field,thereby accelerating the refreshment of the memory behavior.The incorporation of a mass fraction of 0.1%2μm MoS₂nanoflakes into the LCs host was found to significantly reduce the refreshing memory behavior in the hybrid to 94.0 s under an external voltage of 5 V.These findings illustrate the efficacy of regulating the rate of memory behavior for a variety of potential applications.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘Jerry W.Ward,Jr.(1943-2025)is a renowned African American scholar and poet.This article presents a critique of Dr.Ward by remembering him as a literary scholar and critic,educator,mentor,and cultural and academic ambassador to China and the world.It reveals the uniqueness in Dr.Ward’s critical thinking about African American culture,tradition and history,his importance as an internationally known scholar,his originality in his poetic art,and the value of his legacy for African American literature and its scholarship.
