目的分析并探讨丁苯酞联合神经节苷脂对老年缺血性脑血管病患者血小板聚集水平(PAL)及神经功能学评分(NDS)的影响。方法选取2014年3月至2015年3月在长江大学附属第一医院接受治疗的老年缺血性脑血管病患者50例,采用随机数字表分为观察...目的分析并探讨丁苯酞联合神经节苷脂对老年缺血性脑血管病患者血小板聚集水平(PAL)及神经功能学评分(NDS)的影响。方法选取2014年3月至2015年3月在长江大学附属第一医院接受治疗的老年缺血性脑血管病患者50例,采用随机数字表分为观察组和对照组,各25例。两组患者均给予常规的支持治疗,包括血糖和血压的控制,常规口服阿司匹林肠溶片,100 mg/次,1次/d。在此基础上,观察组给予丁苯酞联合神经节苷脂治疗,对照组仅给予丁苯酞治疗,丁苯酞口服,0.2 g/次,3次/d。神经节苷脂100 mg加入到250 m L 0.9%Na Cl溶液中,静脉注射,1次/d。连续治疗10 d。观察两组患者治疗后临床效果及血小板聚集水平(PAL)和NDS评分。结果观察组有效率显著高于对照组[84.0%(21/25)比56.0%(14/25)],差异有统计学意义(P<0.05)。治疗后,观察组总胆固醇、三酰甘油以及低密度脂蛋白水平与对照组比较,差异无统计学意义[(5.0±1.0)mmol/L比(5.5±1.1)mmol/L,(1.5±0.3)mmol/L比(1.7±0.4)mmol/L,(2.9±0.5)mmol/L比(3.1±0.6)mmol/L,P>0.05]。观察组PAL、NDS评分显著低于对照组[(52.7±3.2)%比(59.4±3.0)%,(5.2±2.1)分比(7.5±2.5)分],差异有统计学意义(P<0.05)。结论丁苯酞联合神经节苷脂对老年缺血性脑血管病患者临床效果显著,可有效地改善PAL和NDS评分,临床上值得推广。展开更多
Discovering the hierarchical structures of differ- ent classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, be- havior modeling, data preprocessing, patte...Discovering the hierarchical structures of differ- ent classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, be- havior modeling, data preprocessing, pattern recognition and decision making, etc. In this paper, we call this process as associative categorization, which is different from classical clustering, associative classification and associative cluster- ing. Focusing on representing the associations of behaviors and the corresponding uncertainties, we propose the method for constructing a Markov network （MN） from the results of frequent pattern mining, called item-associative Markov net- work （IAMN）, where nodes and edges represent the frequent patterns and their associations respectively. We further dis- cuss the properties of a probabilistic graphical model to guar- antee the IAMN＇s correctness theoretically. Then, we adopt the concept of chordal to reflect the closeness of nodes in the IAMN. Adopting the algorithm for constructing join trees from an MN, we give the algorithm for IAMN-based associa- tive categorization by hierarchical bottom-up aggregations of nodes. Experimental results show the effectiveness, efficiency and correctness of our methods.展开更多
针对先进的长期演进(long term evolution advanced,LTE-A)系统中跨载波聚合技术下的频谱效率问题,考虑到载波衰减特性不同,研究发现,过多的控制开销限制了低频分量载波良好的数据承载能力,为此,提出基于信干噪比(signal to interferenc...针对先进的长期演进(long term evolution advanced,LTE-A)系统中跨载波聚合技术下的频谱效率问题,考虑到载波衰减特性不同,研究发现,过多的控制开销限制了低频分量载波良好的数据承载能力,为此,提出基于信干噪比(signal to interference plus noise ratio,SINR)的分量载波分配方案。该方案根据控制信息能否被正确解调将用户进行分组,然后针对不同分组的用户分配不同的主分量载波,从而减少低频分量载波承载的控制信息开销,提高频谱效率。仿真结果表明,改进的分配方案有效地提高了小区总吞吐量。展开更多
In recent years,federated learning(FL)has played an important role in private data-sensitive scenarios to perform learning tasks collectively without data exchange.However,due to the centralized model aggregation for ...In recent years,federated learning(FL)has played an important role in private data-sensitive scenarios to perform learning tasks collectively without data exchange.However,due to the centralized model aggregation for heterogeneous devices in FL,the last updated model after local training delays the convergence,which increases the economic cost and dampens clients’motivations for participating in FL.In addition,with the rapid development and application of intelligent reflecting surface(IRS)in the next-generation wireless communication,IRS has proven to be one effective way to enhance the communication quality.In this paper,we propose a framework of federated learning with IRS for grouped heterogeneous training(FLIGHT)to reduce the latency caused by the heterogeneous communication and computation of the clients.Specifically,we formulate a cost function and a greedy-based grouping strategy,which divides the clients into several groups to accelerate the convergence of the FL model.The simulation results verify the effectiveness of FLIGHT for accelerating the convergence of FL with heterogeneous clients.Besides the exemplified linear regression(LR)model and convolutional neural network(CNN),FLIGHT is also applicable to other learning models.展开更多
文摘目的分析并探讨丁苯酞联合神经节苷脂对老年缺血性脑血管病患者血小板聚集水平(PAL)及神经功能学评分(NDS)的影响。方法选取2014年3月至2015年3月在长江大学附属第一医院接受治疗的老年缺血性脑血管病患者50例,采用随机数字表分为观察组和对照组,各25例。两组患者均给予常规的支持治疗,包括血糖和血压的控制,常规口服阿司匹林肠溶片,100 mg/次,1次/d。在此基础上,观察组给予丁苯酞联合神经节苷脂治疗,对照组仅给予丁苯酞治疗,丁苯酞口服,0.2 g/次,3次/d。神经节苷脂100 mg加入到250 m L 0.9%Na Cl溶液中,静脉注射,1次/d。连续治疗10 d。观察两组患者治疗后临床效果及血小板聚集水平(PAL)和NDS评分。结果观察组有效率显著高于对照组[84.0%(21/25)比56.0%(14/25)],差异有统计学意义(P<0.05)。治疗后,观察组总胆固醇、三酰甘油以及低密度脂蛋白水平与对照组比较,差异无统计学意义[(5.0±1.0)mmol/L比(5.5±1.1)mmol/L,(1.5±0.3)mmol/L比(1.7±0.4)mmol/L,(2.9±0.5)mmol/L比(3.1±0.6)mmol/L,P>0.05]。观察组PAL、NDS评分显著低于对照组[(52.7±3.2)%比(59.4±3.0)%,(5.2±2.1)分比(7.5±2.5)分],差异有统计学意义(P<0.05)。结论丁苯酞联合神经节苷脂对老年缺血性脑血管病患者临床效果显著,可有效地改善PAL和NDS评分,临床上值得推广。
文摘Discovering the hierarchical structures of differ- ent classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, be- havior modeling, data preprocessing, pattern recognition and decision making, etc. In this paper, we call this process as associative categorization, which is different from classical clustering, associative classification and associative cluster- ing. Focusing on representing the associations of behaviors and the corresponding uncertainties, we propose the method for constructing a Markov network （MN） from the results of frequent pattern mining, called item-associative Markov net- work （IAMN）, where nodes and edges represent the frequent patterns and their associations respectively. We further dis- cuss the properties of a probabilistic graphical model to guar- antee the IAMN＇s correctness theoretically. Then, we adopt the concept of chordal to reflect the closeness of nodes in the IAMN. Adopting the algorithm for constructing join trees from an MN, we give the algorithm for IAMN-based associa- tive categorization by hierarchical bottom-up aggregations of nodes. Experimental results show the effectiveness, efficiency and correctness of our methods.
文摘针对先进的长期演进(long term evolution advanced,LTE-A)系统中跨载波聚合技术下的频谱效率问题,考虑到载波衰减特性不同,研究发现,过多的控制开销限制了低频分量载波良好的数据承载能力,为此,提出基于信干噪比(signal to interference plus noise ratio,SINR)的分量载波分配方案。该方案根据控制信息能否被正确解调将用户进行分组,然后针对不同分组的用户分配不同的主分量载波,从而减少低频分量载波承载的控制信息开销,提高频谱效率。仿真结果表明,改进的分配方案有效地提高了小区总吞吐量。
基金National Natural Sci-ence Foundation of China(NSFC)(62001387)Shang-hai Academy of Spaceflight Technology(SAST)(SAST2020124)+1 种基金NSF(CNS-2107216)NSF(CNS-2128368)。
文摘In recent years,federated learning(FL)has played an important role in private data-sensitive scenarios to perform learning tasks collectively without data exchange.However,due to the centralized model aggregation for heterogeneous devices in FL,the last updated model after local training delays the convergence,which increases the economic cost and dampens clients’motivations for participating in FL.In addition,with the rapid development and application of intelligent reflecting surface(IRS)in the next-generation wireless communication,IRS has proven to be one effective way to enhance the communication quality.In this paper,we propose a framework of federated learning with IRS for grouped heterogeneous training(FLIGHT)to reduce the latency caused by the heterogeneous communication and computation of the clients.Specifically,we formulate a cost function and a greedy-based grouping strategy,which divides the clients into several groups to accelerate the convergence of the FL model.The simulation results verify the effectiveness of FLIGHT for accelerating the convergence of FL with heterogeneous clients.Besides the exemplified linear regression(LR)model and convolutional neural network(CNN),FLIGHT is also applicable to other learning models.
