目的探讨中性粒细胞/淋巴细胞比值及空腹血糖在脑出血患者短期死亡评估中的作用。方法选取2016-06-01—2018-05-31在郑州大学第一附属医院神经内科住院诊疗的189例急性脑出血患者为研究对象,选取同期健康体检者189例为参照组,单因素分析...目的探讨中性粒细胞/淋巴细胞比值及空腹血糖在脑出血患者短期死亡评估中的作用。方法选取2016-06-01—2018-05-31在郑州大学第一附属医院神经内科住院诊疗的189例急性脑出血患者为研究对象,选取同期健康体检者189例为参照组,单因素分析2组临床及实验室资料;发病30 d时统计生存与死亡人数,分为生存组与死亡组,单因素分析2组临床、影像学及实验室资料,采用多因素Logistic回归分析统计影响急性脑出血短期死亡的危险因素,采用受试者工作特征(receiver operating characteristic,ROC)曲线评价NLR和FBG对短期死亡的预测价值。结果急性脑出血组与对照组比较,NLR(5.67±1.53 vs 4.01±1.53)和FBG(7.07±2.47 vs 5.59±1.89)差异有统计学意义(P<0.05);死亡组与存活组比较,NLR(6.82±1.04 vs 5.52±1.46)和FBG(10.06±2.51 vs 6.69±2.21)差异有统计学意义(P<0.05);多因素Logistic回归分析显示,WBC、FBG、NLR、GCS与急性脑出血发病30 d时的死亡独立相关(P<0.05);ROC曲线分析表明,NLR和FBG的ROC曲线下面积(AUC)分别为0.777(95%CI0.667~0.877,P<0.01)、0.864(95%CI0.792~0.937,P<0.01)。结论NLR和FBG是预测急性脑出血患者短期死亡的独立危险因素。展开更多
四维弹簧模型(Four-Dimensional Lattice Spring Model,4D-LSM)是一种考虑额外维相互作用的新型离散数值计算方法。该方法用于岩石破坏分析需要消耗大量计算资源,不适合在普通个人电脑上运行。基于多核并行技术,在阿里云和多核工作站等...四维弹簧模型(Four-Dimensional Lattice Spring Model,4D-LSM)是一种考虑额外维相互作用的新型离散数值计算方法。该方法用于岩石破坏分析需要消耗大量计算资源,不适合在普通个人电脑上运行。基于多核并行技术,在阿里云和多核工作站等多种硬件环境下对4D-LSM的计算极限性能及瓶颈进行详细分析,主要研究了求解规模、求解类型、线程数、硬件配置等对4D-LSM求解效能的影响。研究发现,内存容量决定可计算的模型规模,弹性问题的计算时间与模型规模成正比,并行计算效率受CPU性能和内存带宽的共同影响。在不考虑经济因素的情况下,云计算在多核匹配和内存分配方面的灵活性特别适合于四维弹簧模型的并行计算分析。结果表明:基于阿里云的4D-LSM最大运算规模可以达到十亿单元,由于目前的瓶颈在于前后处理,4D-LSM目前的可分析规模仍然限制在两千万单元。最后,展示了采用极限规模的并行四维弹簧模型求解三维币形裂纹扩展的实际应用案例。展开更多
In this paper, a low complexity direction of arrival(DOA) estimation method for massive uniform circular array(UCA) with single snapshot is proposed.Firstly, the coarse DOAs are estimated by finding the peaks from the...In this paper, a low complexity direction of arrival(DOA) estimation method for massive uniform circular array(UCA) with single snapshot is proposed.Firstly, the coarse DOAs are estimated by finding the peaks from the circular convolution between a fixed coefficient vector and the received data vector.Thereafter, in order to refine coarse DOA estimates, we reconstruct the direction matrix based on the coarse DOA estimations and take the first order Taylor expansion with DOA estimation offsets into account.Finally, the refined estimations are obtained by compensating the offsets, which are obtained via least squares(LS) without any complex searches.In addition, the refinement can be iteratively implemented to enhance the estimation results.Compared to the offset search method, the proposed method achieves a better estimation performance while requiring lower complexity.Numerical simulations are presented to demonstrate the effectiveness of the proposed method.展开更多
文摘目的探讨中性粒细胞/淋巴细胞比值及空腹血糖在脑出血患者短期死亡评估中的作用。方法选取2016-06-01—2018-05-31在郑州大学第一附属医院神经内科住院诊疗的189例急性脑出血患者为研究对象,选取同期健康体检者189例为参照组,单因素分析2组临床及实验室资料;发病30 d时统计生存与死亡人数,分为生存组与死亡组,单因素分析2组临床、影像学及实验室资料,采用多因素Logistic回归分析统计影响急性脑出血短期死亡的危险因素,采用受试者工作特征(receiver operating characteristic,ROC)曲线评价NLR和FBG对短期死亡的预测价值。结果急性脑出血组与对照组比较,NLR(5.67±1.53 vs 4.01±1.53)和FBG(7.07±2.47 vs 5.59±1.89)差异有统计学意义(P<0.05);死亡组与存活组比较,NLR(6.82±1.04 vs 5.52±1.46)和FBG(10.06±2.51 vs 6.69±2.21)差异有统计学意义(P<0.05);多因素Logistic回归分析显示,WBC、FBG、NLR、GCS与急性脑出血发病30 d时的死亡独立相关(P<0.05);ROC曲线分析表明,NLR和FBG的ROC曲线下面积(AUC)分别为0.777(95%CI0.667~0.877,P<0.01)、0.864(95%CI0.792~0.937,P<0.01)。结论NLR和FBG是预测急性脑出血患者短期死亡的独立危险因素。
文摘四维弹簧模型(Four-Dimensional Lattice Spring Model,4D-LSM)是一种考虑额外维相互作用的新型离散数值计算方法。该方法用于岩石破坏分析需要消耗大量计算资源,不适合在普通个人电脑上运行。基于多核并行技术,在阿里云和多核工作站等多种硬件环境下对4D-LSM的计算极限性能及瓶颈进行详细分析,主要研究了求解规模、求解类型、线程数、硬件配置等对4D-LSM求解效能的影响。研究发现,内存容量决定可计算的模型规模,弹性问题的计算时间与模型规模成正比,并行计算效率受CPU性能和内存带宽的共同影响。在不考虑经济因素的情况下,云计算在多核匹配和内存分配方面的灵活性特别适合于四维弹簧模型的并行计算分析。结果表明:基于阿里云的4D-LSM最大运算规模可以达到十亿单元,由于目前的瓶颈在于前后处理,4D-LSM目前的可分析规模仍然限制在两千万单元。最后,展示了采用极限规模的并行四维弹簧模型求解三维币形裂纹扩展的实际应用案例。
基金supported by the National Natural Science Foundation of China (61971217, 61601167)Jiangsu Planned Project for Postdoctoral Research Funds (2020Z013)+2 种基金China Postdoctoral Science Foundation (2020M681585)the fund of State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE 2021Z0101B)the fund of State Key Laboratory of Marine Resource Utilization in South China Sea (Hainan University)(MRUKF2021033)。
文摘In this paper, a low complexity direction of arrival(DOA) estimation method for massive uniform circular array(UCA) with single snapshot is proposed.Firstly, the coarse DOAs are estimated by finding the peaks from the circular convolution between a fixed coefficient vector and the received data vector.Thereafter, in order to refine coarse DOA estimates, we reconstruct the direction matrix based on the coarse DOA estimations and take the first order Taylor expansion with DOA estimation offsets into account.Finally, the refined estimations are obtained by compensating the offsets, which are obtained via least squares(LS) without any complex searches.In addition, the refinement can be iteratively implemented to enhance the estimation results.Compared to the offset search method, the proposed method achieves a better estimation performance while requiring lower complexity.Numerical simulations are presented to demonstrate the effectiveness of the proposed method.
