Introduction: Stroke is the leading cause of mortality and physical disability in sub-Saharan Africa. Objective: Determining medium-term and long-term mortality for stroke and identifying associated factors. Method: I...Introduction: Stroke is the leading cause of mortality and physical disability in sub-Saharan Africa. Objective: Determining medium-term and long-term mortality for stroke and identifying associated factors. Method: It consists in a cross-sectional, prospective, descriptive and analytical study that was conducted from April 1 to August 31, 2013 in the Neurology Department of CNHU-HKM in Cotonou. It involved patients who have known stroke for at least 6 months, and were all admitted and discharged later on. The disease survivors were re-contacted and examined again at home or at hospital. Then, the number of deceased was systematically recorded with precision of death time-limit. Results: The overall mortality rate was 29%. Mortality was higher with patients over 70 years with a frequency of 57.1%. The medium-term mortality rate was 25% against 4% for long-term. The average time-limit for death occurrence after the vascular incident was 7 months ± 6.4 months. Prognostic factors of mortality were: the age of the patient (IC95% = 7.73 [1.49 - 39.99], p = 0.015 ), marital status (IC95% = 0.27 [0.08 to 0.94], p = 0.039 ) and the presence of aphasia (IC95% = 5.52 [1.45 to 20.94 ], p = 0.012). Conclusion: Stroke mortality still remains significant, even after the patients have been discharged from hospital. A good psychological family support and efficient aphasia coverage are essential for its reduction.展开更多
Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becomi...Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.展开更多
针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(sin...针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。展开更多
文摘Introduction: Stroke is the leading cause of mortality and physical disability in sub-Saharan Africa. Objective: Determining medium-term and long-term mortality for stroke and identifying associated factors. Method: It consists in a cross-sectional, prospective, descriptive and analytical study that was conducted from April 1 to August 31, 2013 in the Neurology Department of CNHU-HKM in Cotonou. It involved patients who have known stroke for at least 6 months, and were all admitted and discharged later on. The disease survivors were re-contacted and examined again at home or at hospital. Then, the number of deceased was systematically recorded with precision of death time-limit. Results: The overall mortality rate was 29%. Mortality was higher with patients over 70 years with a frequency of 57.1%. The medium-term mortality rate was 25% against 4% for long-term. The average time-limit for death occurrence after the vascular incident was 7 months ± 6.4 months. Prognostic factors of mortality were: the age of the patient (IC95% = 7.73 [1.49 - 39.99], p = 0.015 ), marital status (IC95% = 0.27 [0.08 to 0.94], p = 0.039 ) and the presence of aphasia (IC95% = 5.52 [1.45 to 20.94 ], p = 0.012). Conclusion: Stroke mortality still remains significant, even after the patients have been discharged from hospital. A good psychological family support and efficient aphasia coverage are essential for its reduction.
文摘Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.
文摘针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。
