Objective: To investigate association of parity and short pregnancy with obesity and weight change in Aggarwal Baniya women. Method: A cross-sectional analysis was carried out on a representative sample of 307 adult A...Objective: To investigate association of parity and short pregnancy with obesity and weight change in Aggarwal Baniya women. Method: A cross-sectional analysis was carried out on a representative sample of 307 adult Aggarwal Baniya women aged 30 - 50 years (mean age: 38.7 ± 4.87) using multistage cluster sampling method. Weight, height, various skinfold thicknesses, waist and hip circumference were measured using standardized protocol. Various indices of obesity (BMI, WHR, WHtR, GMT) were calculated subsequently. Comparison groups were defined by the number of births (parity), short pregnancies and total pregnancies. Mean change in weight and other obesity markers were examined for each group separately. Correlation analysis was applied to see the association of childbearing on obesity. Linear regression was applied as an effective measure. Results: There was a gain in weight (3.16 kg) and increase in other obesity markers (BMI: 1.29 kg/m2;WC: 2.38 cm;HC: 3.83 cm) with each increase in each parity. Significant and positive correlation (p 2). Conclusion: Among other risk factors, high parity number may be associated with obesity in women. Therefore, interventions to prevent obesity should be targeted at women prior to initiation of childbearing. However, the impact of reproductive wastage in the form of short pregnancies on women’s obesity needs further exploration.展开更多
为了更全面地对睡眠脑电进行特征提取,提出一种基于多视图与注意力机制的睡眠脑电分期方法。首先针对原始睡眠脑电信号构造时域和时频域两类视图数据;然后设计融合注意力机制的混合神经网络对多视图数据进行表征学习;接着通过双向长短...为了更全面地对睡眠脑电进行特征提取,提出一种基于多视图与注意力机制的睡眠脑电分期方法。首先针对原始睡眠脑电信号构造时域和时频域两类视图数据;然后设计融合注意力机制的混合神经网络对多视图数据进行表征学习;接着通过双向长短时记忆(bi-directional long short-term memory,BiLSTM)网络进一步学习睡眠阶段之间的转换规则;最后使用Softmax函数进行睡眠分期,并利用类别加权损失函数解决睡眠数据类别不均衡的问题。实验使用Sleep-EDF数据库中前20名受试者的单通道脑电信号并采用20折交叉验证对模型进行性能评估,睡眠分期准确率达到83.7%,宏平均F_(1)值达到79.0%,Cohen′s Kappa系数达到0.78。与现有方法相比,算法性能提升明显,证明了所提方法的有效性。展开更多
Short text, based on the platform of web2.0, gained rapid development in a relatively short time. Recommendation systems analyzing user’s interest by short texts becomes more and more important. Collaborative filteri...Short text, based on the platform of web2.0, gained rapid development in a relatively short time. Recommendation systems analyzing user’s interest by short texts becomes more and more important. Collaborative filtering is one of the most promising recommendation technologies. However, the existing collaborative filtering methods don’t consider the drifting of user’s interest. This often leads to a big difference between the result of recommendation and user’s real demands. In this paper, according to the traditional collaborative filtering algorithm, a new personalized recommendation algorithm is proposed. It traced user’s interest by using Ebbinghaus Forgetting Curve. Some experiments have been done. The results demonstrated that the new algorithm could indeed make a contribution to getting rid of user’s overdue interests and discovering their real-time interests for more accurate recommendation.展开更多
文摘Objective: To investigate association of parity and short pregnancy with obesity and weight change in Aggarwal Baniya women. Method: A cross-sectional analysis was carried out on a representative sample of 307 adult Aggarwal Baniya women aged 30 - 50 years (mean age: 38.7 ± 4.87) using multistage cluster sampling method. Weight, height, various skinfold thicknesses, waist and hip circumference were measured using standardized protocol. Various indices of obesity (BMI, WHR, WHtR, GMT) were calculated subsequently. Comparison groups were defined by the number of births (parity), short pregnancies and total pregnancies. Mean change in weight and other obesity markers were examined for each group separately. Correlation analysis was applied to see the association of childbearing on obesity. Linear regression was applied as an effective measure. Results: There was a gain in weight (3.16 kg) and increase in other obesity markers (BMI: 1.29 kg/m2;WC: 2.38 cm;HC: 3.83 cm) with each increase in each parity. Significant and positive correlation (p 2). Conclusion: Among other risk factors, high parity number may be associated with obesity in women. Therefore, interventions to prevent obesity should be targeted at women prior to initiation of childbearing. However, the impact of reproductive wastage in the form of short pregnancies on women’s obesity needs further exploration.
文摘为了更全面地对睡眠脑电进行特征提取,提出一种基于多视图与注意力机制的睡眠脑电分期方法。首先针对原始睡眠脑电信号构造时域和时频域两类视图数据;然后设计融合注意力机制的混合神经网络对多视图数据进行表征学习;接着通过双向长短时记忆(bi-directional long short-term memory,BiLSTM)网络进一步学习睡眠阶段之间的转换规则;最后使用Softmax函数进行睡眠分期,并利用类别加权损失函数解决睡眠数据类别不均衡的问题。实验使用Sleep-EDF数据库中前20名受试者的单通道脑电信号并采用20折交叉验证对模型进行性能评估,睡眠分期准确率达到83.7%,宏平均F_(1)值达到79.0%,Cohen′s Kappa系数达到0.78。与现有方法相比,算法性能提升明显,证明了所提方法的有效性。
文摘Short text, based on the platform of web2.0, gained rapid development in a relatively short time. Recommendation systems analyzing user’s interest by short texts becomes more and more important. Collaborative filtering is one of the most promising recommendation technologies. However, the existing collaborative filtering methods don’t consider the drifting of user’s interest. This often leads to a big difference between the result of recommendation and user’s real demands. In this paper, according to the traditional collaborative filtering algorithm, a new personalized recommendation algorithm is proposed. It traced user’s interest by using Ebbinghaus Forgetting Curve. Some experiments have been done. The results demonstrated that the new algorithm could indeed make a contribution to getting rid of user’s overdue interests and discovering their real-time interests for more accurate recommendation.
