Viologen-based flexible electrochromic devices

Electrochromic technology has gained significant attention in various fields such as displays,smart windows,biomedical monitoring,military camouflage,human-machine interaction,and electronic skin due to its ability to provide reversible and fast color changes under applied voltage.With the rapid development and increasing demand for flexible electronics,flexible electrochromic devices(FECDs)that offer smarter and more controllable light modulation hold great promise for practical applications.The electrochromic material(ECM)undergoing color changes during the electrochemical reactions is one of the key components in electrochromic devices.Among the ECMs,viologens,a family of organic small molecules with 1,1'-disubstituted-4,4'-dipyridinium salts,have garnered extensive research interest,due to their well-reversible redox reactions,excellent electron acceptance ability,and the ability to produce multiple colors.Notably,viologen-based FECDs demonstrate color changes in the liquid or semisolid electrolyte layer,eliminating the need for two solid electrodes and thus simplifying the device structure.Consequently,viologens offer significant potential for the development of FECDs with high optical contrast,fast response speed,and excellent stability.This review aims to provide a comprehensive overview of the progress and perspectives of viologen-based FECDs.It begins by summarizing the typical structure and recent exciting developments in viologen-based FECDs,along with their advantages and disadvantages.Furthermore,the review discusses recent advancements in FECDs with additional functionalities such as sensing,photochromism,and energy storage.Finally,the remaining challenges and potential research directions for the future of viologen-based FECDs are addressed.

MART(Splitting-Merging Assisted Reliable)Independent Component Analysis for Extracting Accurate Brain Functional Networks

Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN estimation.Here,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders.We extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI data.Based on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters.Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age increases.Furthermore,both small-scale and large-scale brain FN templates are provided as benchmarks for future studies.Taken together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.

基于多源异构数据融合的移动群智感知任务推荐算法

现有的移动群智感知任务推荐方法主要包括基于时空信息和社交关系两大类,分别受到时空信息时变性和数据稀疏性问题的限制。为了突破上述限制并综合多源工人行为数据对任务推荐准确度的影响,本文提出了一种基于多源异构数据融合的移动群智感知任务推荐算法。该算法综合考虑了工人地理位置、时间可用性以及社交网络关系等多方面的因素,利用隐语义模型、双向长短时记忆网络模型分别生成空间、时间维度的工人–任务匹配度矩阵实现对工人偏好时空变化的挖掘,并利用Node2Vec图嵌入模型生成社交维度的工人–任务匹配度矩阵,缓解数据稀疏性问题。最后利用BP神经网络将上述三个矩阵进行融合以实现工人行为的多源异构数据融合,并最终进行任务推荐。实验结果表明,本文方法比单一数据源的任务推荐方法具有更好的推荐准确度。

基于外部知识辅助的人群健康数据预测方法

随着深度学习技术的发展和引入,现有人群健康数据预测方法的性能不断提高,但仍然受到数据质量问题的限制。为此,本文提出了一种基于外部知识辅助的人群健康数据预测方法。首先,该方法以与冠心病患病率相关性较强的高血压患病率数据和选区老年人口比例数据作为外部知识辅助填补冠心病患病率数据稀疏部分,对上述数据进行预处理后,构建CNN模型对高血压患病率数据和选区老年人口比例数据提取特征矩阵,并和随机噪声、部分完整的冠心病患病率数据作为CGAN模型的输入,以生成用来填补原冠心病患病率数据中稀疏部分的人工样本;然后,该方法将填补后的完整数据集通过ARIMA模型拟合得到模型特征,并输入GRU模型进行预测分析。实验结果表明,本文方法在MAE和RMSE上和KNN模型和RNN模型相差不多,但MPAE大大降低。

WT-PE:Prime editing with nuclease wild-type Cas9 enables versatile large-scale genome editing

Large scale genomic aberrations including duplication,deletion,translocation,and other structural changes are the cause of a subtype of hereditary genetic disorders and contribute to onset or progress of cancer.The current prime editor,PE2,consisting of Cas9-nickase and reverse transcriptase enables efficient editing of genomic deletion and insertion,however,at small scale.Here,we designed a novel prime editor by fusing reverse transcriptase(RT)to nuclease wild-type Cas9(WT-PE)to edit large genomic fragment.