[Interpretation of the "International consensus on early rehabilitation and nutritional management for infants at high risk of neurological impairment"]. [PDF]
Zhongguo Dang Dai Er Ke Za ZhiWang J +5 more
europepmc +1 more source
[Application of P1 response threshold of cortical auditory evoked potential in rehabilitation evaluation of young children with cochlear implant]. [PDF]
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za ZhiJi H +6 more
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[Development and validation of a risk prediction model for cognitive impairment in rural elderly Chinese populations: evidence from the CHARLS study]. [PDF]
Nan Fang Yi Ke Da Xue Xue BaoWang F, Li W, Shang X, Li F.
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基于PLO-SVMD与CNN-BiGRU-ATT的直流微电网故障诊断方法
Gaoya dianqi针对因噪声干扰而导致直流微电网故障识别精度下降的问题，提出一种基于PLO-SVMD与CNN-BiGRU-ATT的故障诊断方法。首先，以最大互信息系数（MIC）为适应度函数，采用极光优化算法（PLO）优化逐次变分模态分解（SVMD）中的惩罚因子，提升信号分解质量与稳定性。其次，依据特征指标筛选出与故障特性高度相关的模态分量，抑制噪声干扰并完成信号重构。最后，构建CNN-BiGRU-ATT分类模型，融合卷积神经网络（CNN）的局部提取能力，双向门控循环单元（BiGRU）的时序建模能力以及注意力机制（ATT ...
毕鑫印, 何山
doaj
[Advances in clinical features, diagnosis, and treatment of pre-acute-on-chronic liver failure]. [PDF]
Zhonghua Gan Zang Bing Za ZhiNie YH, Cai DC.
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基于小样本随机森林模型的低气压下室内设备空气放电故障识别方法研究
Gaoya dianqi空气绝缘的室内电力设备发生放电故障时，利用半导体气体传感器检测空气放电分解物的成分和含量，能够判断放电故障的严重程度，因此研发智能、精准的机器嗅觉算法对监测室内电力设备的正常运行有着重要意义。本文搭建了空气放电实验平台，模拟了低气压高海拔条件下的火花和局部放电故障，通过基于半导体传感器阵列的空气放电分解物检测装置对不同放电故障的气体分解物进行特征提取，然后利用基于主成分分析的小样本扩充增强方法对数据集进行丰富和精简，最后采用随机森林算法对放电分解故障进行分类。结果表明随机森林算法平均识别准确度97.22%
李天辉 +7 more
doaj
[Analyzing the factors influencing speech recognition ability in patients with age-related hearing loss]. [PDF]
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za ZhiZhang W, Yu S.
europepmc +1 more source
[Best evidence summary for management of sleep disorders in children with attention deficit hyperactivity disorder]. [PDF]
Zhongguo Dang Dai Er Ke Za ZhiLin YT, Luo LH, Peng TQ, Tan CW, Lei H.
europepmc +1 more source
[Medical treatment standards for emergency trauma wounded (2025 edition)]. [PDF]
Zhongguo Xiu Fu Chong Jian Wai Ke Za ZhiNational Health Commission Of The People's Republic Of China.
europepmc +1 more source
Gaoya dianqi为了更加有效地对变压器绕组松动故障进行诊断，针对变压器有载运行时的振动信号，提出了一种基于改进多尺度排列熵（MPE）和K-medoids的变压器绕组松动故障诊断方法。首先采用粒子群优化（PSO）的MPE算法对绕组不同状态下的变压器振动信号进行特征提取，以减少MPE算法中参数设置对故障类型识别精度的影响，然后通过K-medoids聚类算法诊断变压器绕组松动故障，以完成故障的分类识别。对某10 kV变压器的绕组松动故障模拟实验结果表明，绕组不同状态下变压器振动信号的MPE值经PSO参数优化后存在明显差异 ...
马宏忠 +4 more
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