目的分析急性脑梗死患者沉默信息调节因子2(silent information regulator 2,SIRT2)与认知功能障碍发生及病情程度的关系。方法回顾性选择2022年5月至2024年5月武汉市第一医院神经内科收治的急性脑梗死合并认知功能障碍患者150例(研究...目的分析急性脑梗死患者沉默信息调节因子2(silent information regulator 2,SIRT2)与认知功能障碍发生及病情程度的关系。方法回顾性选择2022年5月至2024年5月武汉市第一医院神经内科收治的急性脑梗死合并认知功能障碍患者150例(研究组),研究组依据认知功能障碍严重程度分为轻度组45例、中度组66例和重度组39例。另选取同期武汉市第一医院就诊的急性脑梗死未合并认知功能障碍患者125例(对照组)。采用Spearman相关性分析血清SIRT2水平与认知功能障碍及病情程度的相关性,ROC曲线分析血清SIRT2水平对认知功能障碍及病情的评估价值,并计算曲线下面积(area under curve,AUC)。结果研究组血清SIRT2水平明显高于对照组,差异有统计学意义[(20.38±5.19)mg/L vs(14.66±4.49)mg/L,P<0.05]。重度组血清SIRT2水平明显高于中度组和轻度组,中度组血清SIRT2水平明显高于轻度组,差异有统计学意义(P<0.05)。Spearman相关性分析显示,血清SIRT2水平与认知功能障碍发生及严重程度呈正相关(r=0.510,r=0.527,P<0.01)。ROC曲线分析显示,血清SIRT2水平评估急性脑梗死患者认知功能障碍发生的AUC为0.796(95%CI:0.743~0.842),临界值为19.0 mg/L,敏感性为63.33%,特异性为84.00%;血清SIRT2水平评估急性脑梗死患者认知功能障碍严重程度的AUC为0.747(95%CI:0.655~0.824),临界值为17.2 mg/L,敏感性为75.76%,特异性为71.11%(P<0.05)。结论急性脑梗死患者血清SIRT2水平与认知功能障碍发生及病情密切相关。展开更多
基于碳卫星的遥感是一种正在发展的大范围高精度CO_(2)监测方法,但当监测对象为我国长三角区域这种大空间尺度时,碳卫星数据会存在时空稀疏性的问题。本文提出了一种新的模型ST-SAN(space time soft attention network),旨在提高碳卫星...基于碳卫星的遥感是一种正在发展的大范围高精度CO_(2)监测方法,但当监测对象为我国长三角区域这种大空间尺度时,碳卫星数据会存在时空稀疏性的问题。本文提出了一种新的模型ST-SAN(space time soft attention network),旨在提高碳卫星数据的高时空分辨率XCO_(2)(大气CO_(2))浓度估算精度。本文将2016—2020年的多源数据(包括人类活动数据、气象数据和植被数据)与碳卫星数据结合,生成空间分辨率为0.05°的无间隙XCO_(2)日浓度数据集。通过ST-SAN模型对这些数据进行训练和预测。实验结果表明,重建后的XCO_(2)数据集与OCO-2卫星数据和地面站点数据具有高度一致性,验证了本方法在高时空分辨率XCO_(2)浓度估算中的有效性。展开更多
The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-n...The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.展开更多
文摘目的分析急性脑梗死患者沉默信息调节因子2(silent information regulator 2,SIRT2)与认知功能障碍发生及病情程度的关系。方法回顾性选择2022年5月至2024年5月武汉市第一医院神经内科收治的急性脑梗死合并认知功能障碍患者150例(研究组),研究组依据认知功能障碍严重程度分为轻度组45例、中度组66例和重度组39例。另选取同期武汉市第一医院就诊的急性脑梗死未合并认知功能障碍患者125例(对照组)。采用Spearman相关性分析血清SIRT2水平与认知功能障碍及病情程度的相关性,ROC曲线分析血清SIRT2水平对认知功能障碍及病情的评估价值,并计算曲线下面积(area under curve,AUC)。结果研究组血清SIRT2水平明显高于对照组,差异有统计学意义[(20.38±5.19)mg/L vs(14.66±4.49)mg/L,P<0.05]。重度组血清SIRT2水平明显高于中度组和轻度组,中度组血清SIRT2水平明显高于轻度组,差异有统计学意义(P<0.05)。Spearman相关性分析显示,血清SIRT2水平与认知功能障碍发生及严重程度呈正相关(r=0.510,r=0.527,P<0.01)。ROC曲线分析显示,血清SIRT2水平评估急性脑梗死患者认知功能障碍发生的AUC为0.796(95%CI:0.743~0.842),临界值为19.0 mg/L,敏感性为63.33%,特异性为84.00%;血清SIRT2水平评估急性脑梗死患者认知功能障碍严重程度的AUC为0.747(95%CI:0.655~0.824),临界值为17.2 mg/L,敏感性为75.76%,特异性为71.11%(P<0.05)。结论急性脑梗死患者血清SIRT2水平与认知功能障碍发生及病情密切相关。
文摘基于碳卫星的遥感是一种正在发展的大范围高精度CO_(2)监测方法,但当监测对象为我国长三角区域这种大空间尺度时,碳卫星数据会存在时空稀疏性的问题。本文提出了一种新的模型ST-SAN(space time soft attention network),旨在提高碳卫星数据的高时空分辨率XCO_(2)(大气CO_(2))浓度估算精度。本文将2016—2020年的多源数据(包括人类活动数据、气象数据和植被数据)与碳卫星数据结合,生成空间分辨率为0.05°的无间隙XCO_(2)日浓度数据集。通过ST-SAN模型对这些数据进行训练和预测。实验结果表明,重建后的XCO_(2)数据集与OCO-2卫星数据和地面站点数据具有高度一致性,验证了本方法在高时空分辨率XCO_(2)浓度估算中的有效性。
基金国家自然科学基金项目“青藏高原露天煤矿排土场地形-土壤-植被响应机理及地貌重塑研究”(编号:41977415)中国地质调查局项目“全球冰川及荒漠化遥感地质调查”(编号:DD20190515)+1 种基金中欧科技合作“龙计划”五期项目“Integration of multi-source Remote Sensing Data to detect and monitoring large and rapid landslides and use of Artificial Intelligence for Cultural Heritage preservation”(编号:56796)JAXA EO-RA2项目“Application of Radar Remote Sensing Technology in Resource Environment Monitoring”(编号:P3073002)共同资助。
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation 2022M720419 to provide fund for conducting experiments。
文摘The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.
