Exploitation of sustainable energy sources requires the use of unique conversion and storage systems,such as solar panels,batteries,fuel cells,and electronic equipment.Thermal load management of these energy conversio...Exploitation of sustainable energy sources requires the use of unique conversion and storage systems,such as solar panels,batteries,fuel cells,and electronic equipment.Thermal load management of these energy conversion and storage systems is one of their challenges and concerns.In this article,the thermal management of these systems using thermoelectric modules is reviewed.The results show that by choosing the right option to remove heat from the hot side of the thermoelectric modules,it will be a suitable local cooling,and the thermoelectric modules increase the power and lifespan of the system by reducing the spot temperature.Thermoelectric modules were effective in reducing panel temperature.They increase the time to reach a temperature above 50℃ in batteries by 3 to 4 times.Also,in their integration with fuel cells,they increase the power density of the fuel cell.展开更多
Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling cap...Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.展开更多
Highly efficient organic solar cells(OSCs)are normally produced using the halogenated solvents chloroform or chlorobenzene,which present challenges for scalable manufacturing due to their toxicity,narrow processing wi...Highly efficient organic solar cells(OSCs)are normally produced using the halogenated solvents chloroform or chlorobenzene,which present challenges for scalable manufacturing due to their toxicity,narrow processing window and low boiling point.Herein,we develop a novel high-speed doctor-blading technique that significantly reduces the required concentration,facilitating the use of eco-friendly,non-halogenated solvents as alternatives to chloroform or chlorobenzene.By utilizing two widely used high-boiling,non-halogenated green solvents-o-xylene(o-XY)and toluene(Tol)-in the fabrication of PM 6:L 8-BO,we achieve power conversion efficiencies(PCEs)of 18.20%and 17.36%,respectively.Additionally,a module fabricated with o-XY demonstrates a notable PCE of 16.07%.In-situ testing and morphological analysis reveal that the o-XY coating process extends the liquid-to-solid transition stage to 6 s,significantly longer than the 1.7 s observed with Tol processing.This prolonged transition phase is crucial for improving the crystallinity of the thin film,reducing defect-mediated recombination,and enhancing carrier mobility,which collectively contribute to superior PCEs.展开更多
针对现有信道估计方案导致正交时频空间(Orthogonal Time Frequency Space,OTFS)调制系统峰均功率比(Peak-to-Average Power Ratio,PAPR)高或频谱效率(Spectral Efficiency,SE)低的问题,提出一种多叠加导频的低PAPR、高SE信道估计方法...针对现有信道估计方案导致正交时频空间(Orthogonal Time Frequency Space,OTFS)调制系统峰均功率比(Peak-to-Average Power Ratio,PAPR)高或频谱效率(Spectral Efficiency,SE)低的问题,提出一种多叠加导频的低PAPR、高SE信道估计方法。发送端利用时域正交性和离散傅里叶域相位的随机性,在时延多普勒域中嵌入与数据相叠加的5导频符号的导频图案实现低PAPR,提高SE。接收端以数据符号与噪声之和的能量均值为基准,实现导频信号检测,同时根据每个导频的不同位置信息恢复出存在相位旋转的数据信号。基于能量准则,利用多个独立的接收信号进行联合信道估计,以降低数据符号的干扰,并采用消息传递算法进行数据恢复。仿真结果表明,该方法比单叠加导频信道估计的PAPR低,同时较嵌入式导频信道估计的SE提高约14.4%。展开更多
为提升地震预测方法评价的标准化和应用的规范化,依托国家重点研发计划尝试把CSEP(Collaboratory for the Study of Earthquake Predictability)移植到中国,建立中国CSEP检验中心。自主研发了加卸载响应比(LURR)、地壳振动、态矢量和地...为提升地震预测方法评价的标准化和应用的规范化,依托国家重点研发计划尝试把CSEP(Collaboratory for the Study of Earthquake Predictability)移植到中国,建立中国CSEP检验中心。自主研发了加卸载响应比(LURR)、地壳振动、态矢量和地震综合概率预测模块;引进了国外的图像信息(PI)、相对强度(RI)、传染型余震序列(ETAS)预测模型并完成模块研发;遴选出Molchan检验、R值评分、N值检验和ROC检验等国际认可的地震预报效能评价方法,以集成方式搭建运行平台。作为开放性检验中心,通过不断纳入新的算法,着力提升地震预测能力、推进地震预测实践,将地震预报业务中常用的地震发生率指数、小震调制比、b值等预测方法纳入到中心运行。中心的软件系统既能够完成回顾性预测检验,又能够实现前瞻性预测分析,可为现有预测方法提供运行环境和技术支持。展开更多
文摘Exploitation of sustainable energy sources requires the use of unique conversion and storage systems,such as solar panels,batteries,fuel cells,and electronic equipment.Thermal load management of these energy conversion and storage systems is one of their challenges and concerns.In this article,the thermal management of these systems using thermoelectric modules is reviewed.The results show that by choosing the right option to remove heat from the hot side of the thermoelectric modules,it will be a suitable local cooling,and the thermoelectric modules increase the power and lifespan of the system by reducing the spot temperature.Thermoelectric modules were effective in reducing panel temperature.They increase the time to reach a temperature above 50℃ in batteries by 3 to 4 times.Also,in their integration with fuel cells,they increase the power density of the fuel cell.
文摘Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.
基金Project(2022YFB3803300)supported by the National Key Research and Development Program of ChinaProjects(U23A20138,52173192)supported by the National Natural Science Foundation of China+1 种基金Project(GZC20233148)supported by the Postdoctoral Fellowship Program of CPSF,ChinaProject(140050043)supported by the Central South University Postdoctoral Research Funding,China。
文摘Highly efficient organic solar cells(OSCs)are normally produced using the halogenated solvents chloroform or chlorobenzene,which present challenges for scalable manufacturing due to their toxicity,narrow processing window and low boiling point.Herein,we develop a novel high-speed doctor-blading technique that significantly reduces the required concentration,facilitating the use of eco-friendly,non-halogenated solvents as alternatives to chloroform or chlorobenzene.By utilizing two widely used high-boiling,non-halogenated green solvents-o-xylene(o-XY)and toluene(Tol)-in the fabrication of PM 6:L 8-BO,we achieve power conversion efficiencies(PCEs)of 18.20%and 17.36%,respectively.Additionally,a module fabricated with o-XY demonstrates a notable PCE of 16.07%.In-situ testing and morphological analysis reveal that the o-XY coating process extends the liquid-to-solid transition stage to 6 s,significantly longer than the 1.7 s observed with Tol processing.This prolonged transition phase is crucial for improving the crystallinity of the thin film,reducing defect-mediated recombination,and enhancing carrier mobility,which collectively contribute to superior PCEs.
文摘针对现有信道估计方案导致正交时频空间(Orthogonal Time Frequency Space,OTFS)调制系统峰均功率比(Peak-to-Average Power Ratio,PAPR)高或频谱效率(Spectral Efficiency,SE)低的问题,提出一种多叠加导频的低PAPR、高SE信道估计方法。发送端利用时域正交性和离散傅里叶域相位的随机性,在时延多普勒域中嵌入与数据相叠加的5导频符号的导频图案实现低PAPR,提高SE。接收端以数据符号与噪声之和的能量均值为基准,实现导频信号检测,同时根据每个导频的不同位置信息恢复出存在相位旋转的数据信号。基于能量准则,利用多个独立的接收信号进行联合信道估计,以降低数据符号的干扰,并采用消息传递算法进行数据恢复。仿真结果表明,该方法比单叠加导频信道估计的PAPR低,同时较嵌入式导频信道估计的SE提高约14.4%。
文摘为提升地震预测方法评价的标准化和应用的规范化,依托国家重点研发计划尝试把CSEP(Collaboratory for the Study of Earthquake Predictability)移植到中国,建立中国CSEP检验中心。自主研发了加卸载响应比(LURR)、地壳振动、态矢量和地震综合概率预测模块;引进了国外的图像信息(PI)、相对强度(RI)、传染型余震序列(ETAS)预测模型并完成模块研发;遴选出Molchan检验、R值评分、N值检验和ROC检验等国际认可的地震预报效能评价方法,以集成方式搭建运行平台。作为开放性检验中心,通过不断纳入新的算法,着力提升地震预测能力、推进地震预测实践,将地震预报业务中常用的地震发生率指数、小震调制比、b值等预测方法纳入到中心运行。中心的软件系统既能够完成回顾性预测检验,又能够实现前瞻性预测分析,可为现有预测方法提供运行环境和技术支持。