一种面向自然语言表达的不确定时态数据的建模方法

在医疗信息系统中,含自然语言表达式的时态信息起着关键性作用。如何处理基于自然语言的不确定时态信息问题一直没有得到很好地解决。针对医疗信息系统的需求,在此对时态数据模型的研究现状以及一些经典的不确定时态医疗信息数据模型进行了研究。对各种传统的时态原语类型进行不确定性扩展。提出了基于自然语言表达式的不确定时态数据模型NLTM。     

黄土高原极端降水变化及其对植被覆盖度影响

全球变暖背景下,黄土高原极端降水事件频发,对社会经济及植被生态产生了重要影响,研究黄土高原极端降水变化特征及其对植被覆盖度(fractional vegetation cover, FVC)影响,可为生态环境保护和区域可持续发展提供科学支撑。基于2000—2020年黄土高原64个气象站点逐日降水数据和MODIS NDVI数据,采用趋势分析、相关分析等方法,分析了黄土高原地区极端降水和FVC时空变化特征及极端降水对FVC的影响。结果表明：2000—2020年黄土高原地区总降水量P_RCPTOT、中雨日数R_10mm、大雨日数R_20mm、强降水日数R_25mm和降水强度S_DII均呈显著上升趋势(P<0.05),区域西部和北部强降水总体增加,但区域南部呈干旱化。2000—2020年黄土高原FVC总体呈显著上升趋势(P<0.05),集中分布在区域中东部,占区域总面积的32.00%;显著下降趋势主要分布在南部少部分地区,仅占区域总面积的4.91%。2000—2020年黄土高原地区F...     

太平洋内区经向热输送的季节变化分析

为刻画整个太平洋内区经向热输送的基本特征及其季节变化,并初步探讨变化的原因,基于Argo温盐资料,利用P矢量法反演了太平洋的绝对地转流(AGC),并结合Ekman流,分析了年平均和季节平均的经向热输送。结果显示,在13°S~13°N,经向热输送主要由Ekman经向热输送控制,季节变化显著,且南北半球变化相反,北半球1-3月有北向输送最大值,8-10月当经向热输送转为南向时,Ekman经向热输送达到最小;南北太平洋经向热输送的季节变化存在显著的不对称特征,北太平洋的明显强于南太平洋,低纬度海域的明显大于高纬度海域。分析表明,经向热输送的季节变化与纬向风应力的变化直接相关,由于未能有效包含西边界流及赤道区域,对比前人的研究结果后推测,西边界流和赤道流在维持南北太平洋经向热输送的平衡中扮演了重要角色。     

河南省畜牧业竞争力时空差异分析

畜牧业竞争力是一个地区畜牧业发展水平的综合体现,以河南省为例,运用主成分分析和聚类分析的方法,选取综合反映畜牧业综合竞争力水平的38个指标,通过分类对比对河南省18个省辖市的畜牧业竞争力进行了分析.从18省辖市畜牧业竞争力得分排名,可以看出从1999年到2008年18个省辖市畜牧业竞争力的动态变化特征,并可分为几种区域...     

南海北部浮游植物的时空异质性研究

 为研究南海北部浮游植物时空变化规律及其与海洋环境响应关系,采用1997-2002年开展的“南海专属经济区和大陆架渔业资源及其栖息环境调查”所取得的浮游植物调查数据,利用GIS空间局部插值法构建景观分析模型,对南海北部浮游植物时空异质性进行了分析。结果表明,南海北部海区浮游植物密度变化范围为0.03(4 179×10⁴ 个/m³,总平均为87.2×10⁴ 个/m³,夏冬春秋四季浮游植物平均密度由高至低。南海北部浮游植物总体近海高于远海,且在近海海域呈斑块状分布格局,斑块一般出现在北部湾中北部上升流区、粤东遮浪上升流区、台湾浅滩上升流区和粤西沿岸电白-阳江上升流区等南海北部几个重要上升流区内。浮游植物密度最高值斑块一般随着季风和沿岸流的季节转换而交替向东或向西方向进行有规律的漂移。     

Variability in the noise-induced modes of climate dynamics

New features of noise-induced climate variability are revealed on the basis of the three-dimensional model derived by Saltzman and Maasch. It is shown that the climate system can be highly noise excitable and it possesses the large-amplitude fluctuations even in those regions where its akin deterministic model does not contain any self-sustained oscillations. Intermittency in small- and large amplitude climate fluctuations between different basins of attraction of a limit cycle and stable equilibria substantially influencing the climate state (from warm to cold and vice versa) are found at various noise intensities. Suddenly occurring jumps between the basins of attraction of two stable equilibria corresponding to the warm and cold climate states are statistically confirmed under a certain diapason of noise intensities. The climate system undergoes transitions between its equilibria in the presence of noise in its prognostic variables. In addition, such transitions become more likely with increasing the noise intensity.     

Sample Entropy as a Tool to Assess Lumbo-Pelvic Movements in a Clinical Test for Low-Back-Pain Patients

Low back pain (LBP) obviously reduces the quality of life but is also the world’s leading cause of years lived with disability. Alterations in motor response and changes in movement patterns are expected in LBP patients when compared to healthy people. Such changes in dynamics may be assessed by the nonlinear analysis of kinematical time series recorded from one patient’s motion. Since sample entropy (SampEn) has emerged as a relevant index measuring the complexity of a given time series, we propose the development of a clinical test based on SampEn of a time series recorded by a wearable inertial measurement unit for repeated bending and returns (b and r) of the trunk. Twenty-three healthy participants were asked to perform, in random order, 50 repetitions of this movement by touching a stool and another 50 repetitions by touching a box on the floor. The angular amplitude of the b and r movement and the sample entropy of the three components of the angular velocity and acceleration were computed. We showed that the repetitive b and r “touch the stool” test could indeed be the basis of a clinical test for the evaluation of low-back-pain patients, with an optimal duration of 70 s, acceptable in daily clinical practice.     

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.     

人工智能应用中基于区间代数的时态推理

时态表示和推理是人工智能领域的重要研究内容之一,它的应用范围分布很广,从逻辑基础研究到知识系统的应用.区间代数是一种独立的与领域无关的时态理论.用区间代数能表示不确定的时态关系,可以很方便地用于时态推理,表达能力强;时态关系的区间表示比较直观,可理解性强;同时区间代数可以进一步扩展到二维空间领域,即将区间代数拓展为矩阵代数,实现二维空间推理.在一维时态推理中,将时态的区间表示和矩阵表示相结合,在提高计算效率的同时,保持了形象直观的时态表示.