Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed ba...Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed based on Classical Test Theory(CTT).Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests.Reliability was measured using Cronbach's alpha and test-retest reliability.Structural validity was explored using principal component analysis.Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test(THAT),the Attentional Control Scale(ACS),and the Attention Network Test(ANT).Results The MARS comprises 12 items spanning six distinct dimensions of attention:focused attention,sustained attention,shifting attention,selective attention,divided attention,and response inhibition.As assessed by six experts,the content validation index(CVI)was 0.95,the Cronbach's alpha for the MARS was 0.78,and the test-retest reliability was 0.81.Four factors were identified(cumulative variance contribution rate 68.79%).The total score of MARS was correlated positively with THAT(r=0.60,P<0.01)and ACS(r=0.78,P<0.01)and negatively with ANT's reaction time for alerting(r=−0.31,P=0.049).Conclusion The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.展开更多
篇章要素识别(discourse element identification)的主要任务是识别篇章要素单元并进行分类.针对篇章要素识别对上下文依赖性理解不足的问题,提出一种基于BiLSTM-Attention的识别篇章要素模型,提高议论文篇章要素识别的准确率.该模型利...篇章要素识别(discourse element identification)的主要任务是识别篇章要素单元并进行分类.针对篇章要素识别对上下文依赖性理解不足的问题,提出一种基于BiLSTM-Attention的识别篇章要素模型,提高议论文篇章要素识别的准确率.该模型利用句子结构和位置编码来识别句子的成分关系,通过双向长短期记忆网络(bidirectional long short-term memory,BiLSTM)进一步获得深层次上下文相关联的信息;引入注意力机制(attention mechanism)优化模型特征向量,提高文本分类的准确度;最终用句间多头自注意力(multi-head self-attention)获取句子在内容和结构上的关系,弥补距离较远的句子依赖问题.相比于HBiLSTM、BERT等基线模型,在相同参数、相同实验条件下,中文数据集和英文数据集上准确率分别提升1.3%、3.6%,验证了该模型在篇章要素识别任务中的有效性.展开更多
交通运输业减排是实现全局减排目标的关键。研究基于改进的随机性环境影响评估(Stochastic Impacts by Regression on Population,Affluence,and Technology,STIRPAT)模型分析影响交通运输业碳排放的主要因素,设置低碳、基准和高碳3种...交通运输业减排是实现全局减排目标的关键。研究基于改进的随机性环境影响评估(Stochastic Impacts by Regression on Population,Affluence,and Technology,STIRPAT)模型分析影响交通运输业碳排放的主要因素,设置低碳、基准和高碳3种情景方案,利用卷积神经网络-长短期记忆网络-注意力机制(Convolutional Neural Networks-Long short-Term Memory-Attention Mec.hanism,CNN-LSTM-Attention)交通运输业碳排放预测模型对中国30个省、自治区、直辖市2022—2035年交通运输业碳排放进行预测。结果显示:人口情况、经济水平和交通运输等3个维度的影响因素对交通运输业碳排放具有正向驱动作用,能源技术维度的影响因素则起负向驱动作用;CNN-LSTM-Attention交通运输业碳排放预测模型提升了模型在小样本数据集的预测能力,预测效果较好;低碳、基准和高碳3种情景下中国交通运输业的碳排放峰值将晚于2030年的总排放峰值目标实现;各省在碳排放峰值和达峰时间上存在异质性,应采取差异化、精准化的政策策略,局部上分区域、分梯次达峰,以整体上实现碳达峰目标。展开更多
文摘Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed based on Classical Test Theory(CTT).Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests.Reliability was measured using Cronbach's alpha and test-retest reliability.Structural validity was explored using principal component analysis.Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test(THAT),the Attentional Control Scale(ACS),and the Attention Network Test(ANT).Results The MARS comprises 12 items spanning six distinct dimensions of attention:focused attention,sustained attention,shifting attention,selective attention,divided attention,and response inhibition.As assessed by six experts,the content validation index(CVI)was 0.95,the Cronbach's alpha for the MARS was 0.78,and the test-retest reliability was 0.81.Four factors were identified(cumulative variance contribution rate 68.79%).The total score of MARS was correlated positively with THAT(r=0.60,P<0.01)and ACS(r=0.78,P<0.01)and negatively with ANT's reaction time for alerting(r=−0.31,P=0.049).Conclusion The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.
文摘篇章要素识别(discourse element identification)的主要任务是识别篇章要素单元并进行分类.针对篇章要素识别对上下文依赖性理解不足的问题,提出一种基于BiLSTM-Attention的识别篇章要素模型,提高议论文篇章要素识别的准确率.该模型利用句子结构和位置编码来识别句子的成分关系,通过双向长短期记忆网络(bidirectional long short-term memory,BiLSTM)进一步获得深层次上下文相关联的信息;引入注意力机制(attention mechanism)优化模型特征向量,提高文本分类的准确度;最终用句间多头自注意力(multi-head self-attention)获取句子在内容和结构上的关系,弥补距离较远的句子依赖问题.相比于HBiLSTM、BERT等基线模型,在相同参数、相同实验条件下,中文数据集和英文数据集上准确率分别提升1.3%、3.6%,验证了该模型在篇章要素识别任务中的有效性.
