目的:本研究旨在构建适合癌症相关性疼痛(简称癌痛)病人的疼痛评估量表,并探索评估量表用于癌痛病人的信度和效度,以期为临床癌痛病人的管理提供更加综合全面且简便易行的评估工具。方法:基于国内癌痛领域的指导性规范、临床诊疗指南、...目的:本研究旨在构建适合癌症相关性疼痛(简称癌痛)病人的疼痛评估量表,并探索评估量表用于癌痛病人的信度和效度,以期为临床癌痛病人的管理提供更加综合全面且简便易行的评估工具。方法:基于国内癌痛领域的指导性规范、临床诊疗指南、专家共识等,本研究构建了癌痛病人评估量表初始条目池,采取德尔菲法选取我国癌痛领域的二十余名专家进行两轮咨询,统计咨询结果,确认纳入的条目,形成最终的多维疼痛评估量表(multi-dimensional brief test scale for pain,BTS)。经过两轮德尔菲法专家咨询,第一轮纳入21位专家,第二轮纳入10位专家,第一轮纳入19个评估维度,选取重要性评分≥4.5分且变异系数≤20%的7个评估维度,进行第二轮专家咨询。这7个评估维度和条目的重要性结果与第一轮一致,由此最终构建的BTS共包含7个评估维度,15个条目。选取2022年7月至2023年2月中国人民解放军北部战区总医院收治的癌痛病人,使用构建的BTS评估量表进行信效度检验。结果:共纳入166例病人,经过数据清洗,最终纳入分析的病人例数为138例。BTS的信度检测Cronbachα系数为0.78,效度检测的KMO值为0.80,具有较好的信度和效度。结论:构建的BTS条目共纳入7个评估维度,15个条目,具有较好的信度和效度,可在临床实践中推广应用于癌痛病人的疼痛评估。展开更多
Noise reduction analysis of signals is essential for modern underwater acoustic detection systems.The traditional noise reduction techniques gradually lose efficacy because the target signal is masked by biological an...Noise reduction analysis of signals is essential for modern underwater acoustic detection systems.The traditional noise reduction techniques gradually lose efficacy because the target signal is masked by biological and natural noise in the marine environ-ment.The feature extraction method combining time-frequency spectrograms and deep learning can effectively achieve the separation of noise and target signals.A fully convolutional encoder-decoder neural network(FCEDN)is proposed to address the issue of noise reduc-tion in underwater acoustic signals.The time-domain waveform map of underwater acoustic signals is converted into a wavelet low-frequency analysis recording spectrogram during the denoising process to preserve as many underwater acoustic signal characteristics as possible.The FCEDN is built to learn the spectrogram mapping between noise and target signals that can be learned at each time level.The transposed convolution transforms are introduced,which can transform the spectrogram features of the signals into listenable audio files.After evaluating the systems on the ShipsEar Dataset,the proposed method can increase SNR and SI-SNR by 10.02 and 9.5dB,re-spectively.展开更多
In some practical applications modeled by discrete-event systems(DES),the observations of events may be no longer deterministic due to sensor faults/failures,packet loss,and/or measurement uncertainties.In this contex...In some practical applications modeled by discrete-event systems(DES),the observations of events may be no longer deterministic due to sensor faults/failures,packet loss,and/or measurement uncertainties.In this context,it is interesting to reconsider the infinite-step opacity(∞-SO)and K-step opacity(K-SO)of a DES under abnormal conditions as mentioned.In this paper,the authors extend the notions of∞-SO and K-SO defined in the standard setting to the framework of nondeterministic observations(i.e.,the event-observation mechanism is state-dependent and nondeterministic).Obviously,the extended notions of∞-SO and K-SO are more general than the previous standard ones.To effectively verify them,a matrix-based current state estimator in the context of this advanced framework is constructed using the Boolean semi-tensor product(BSTP)technique.Accordingly,the necessary and sufficient conditions for verifying these two extended versions of opacity are provided as well as their complexity analysis.Finally,several examples are given to illustrate the obtained theoretical results.展开更多
文摘目的:本研究旨在构建适合癌症相关性疼痛(简称癌痛)病人的疼痛评估量表,并探索评估量表用于癌痛病人的信度和效度,以期为临床癌痛病人的管理提供更加综合全面且简便易行的评估工具。方法:基于国内癌痛领域的指导性规范、临床诊疗指南、专家共识等,本研究构建了癌痛病人评估量表初始条目池,采取德尔菲法选取我国癌痛领域的二十余名专家进行两轮咨询,统计咨询结果,确认纳入的条目,形成最终的多维疼痛评估量表(multi-dimensional brief test scale for pain,BTS)。经过两轮德尔菲法专家咨询,第一轮纳入21位专家,第二轮纳入10位专家,第一轮纳入19个评估维度,选取重要性评分≥4.5分且变异系数≤20%的7个评估维度,进行第二轮专家咨询。这7个评估维度和条目的重要性结果与第一轮一致,由此最终构建的BTS共包含7个评估维度,15个条目。选取2022年7月至2023年2月中国人民解放军北部战区总医院收治的癌痛病人,使用构建的BTS评估量表进行信效度检验。结果:共纳入166例病人,经过数据清洗,最终纳入分析的病人例数为138例。BTS的信度检测Cronbachα系数为0.78,效度检测的KMO值为0.80,具有较好的信度和效度。结论:构建的BTS条目共纳入7个评估维度,15个条目,具有较好的信度和效度,可在临床实践中推广应用于癌痛病人的疼痛评估。
基金supported by the National Natural Science Foundation of China(No.41906169)the PLA Academy of Military Sciences.
文摘Noise reduction analysis of signals is essential for modern underwater acoustic detection systems.The traditional noise reduction techniques gradually lose efficacy because the target signal is masked by biological and natural noise in the marine environ-ment.The feature extraction method combining time-frequency spectrograms and deep learning can effectively achieve the separation of noise and target signals.A fully convolutional encoder-decoder neural network(FCEDN)is proposed to address the issue of noise reduc-tion in underwater acoustic signals.The time-domain waveform map of underwater acoustic signals is converted into a wavelet low-frequency analysis recording spectrogram during the denoising process to preserve as many underwater acoustic signal characteristics as possible.The FCEDN is built to learn the spectrogram mapping between noise and target signals that can be learned at each time level.The transposed convolution transforms are introduced,which can transform the spectrogram features of the signals into listenable audio files.After evaluating the systems on the ShipsEar Dataset,the proposed method can increase SNR and SI-SNR by 10.02 and 9.5dB,re-spectively.
基金supported by the National Natural Science Foundation of China under Grant Nos.61903274,61873342,61973175the Tianjin Natural Science Foundation of China under Grant No.18JCQNJC74000。
文摘In some practical applications modeled by discrete-event systems(DES),the observations of events may be no longer deterministic due to sensor faults/failures,packet loss,and/or measurement uncertainties.In this context,it is interesting to reconsider the infinite-step opacity(∞-SO)and K-step opacity(K-SO)of a DES under abnormal conditions as mentioned.In this paper,the authors extend the notions of∞-SO and K-SO defined in the standard setting to the framework of nondeterministic observations(i.e.,the event-observation mechanism is state-dependent and nondeterministic).Obviously,the extended notions of∞-SO and K-SO are more general than the previous standard ones.To effectively verify them,a matrix-based current state estimator in the context of this advanced framework is constructed using the Boolean semi-tensor product(BSTP)technique.Accordingly,the necessary and sufficient conditions for verifying these two extended versions of opacity are provided as well as their complexity analysis.Finally,several examples are given to illustrate the obtained theoretical results.
