A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep learning.The algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the fields of weather forecasting,stock forecasting,action recognition,etc.because of its excellent performance in processing Spatio-temporal sequence data.Among them,algorithms based on LSTM and GRU have developed most rapidly because of their good design.This paper reviews the RNN-based Spatio-temporal sequence prediction algorithm,introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction,and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms.At the same time,it also compares the advantages and disadvantages,and innovations of each algorithm.The purpose of this article is to give readers a clear understanding of solutions to such problems.Finally,it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm.

Spatio-temporal epidemic type aftershock sequence model for Tangshan aftershock sequence

Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.

Landscape of cell heterogeneity and evolutionary trajectory in ulcerative colitis-associated colon cancer revealed by single-cell RNA sequencing

Objective:The goal of this study was to get preliminary insight on the intra-tumor heterogeneity in colitisassociated cancer(CAC)and to reveal a potential evolutionary trajectory from ulcerative colitis(UC)to CAC at the single-cell level.Methods:Fresh samples of tumor tissues and adjacent UC tissues from a CAC patient with pT3N1M0 stage cancer were examined by single-cell RNA sequencing(scRNA-seq).Data from The Cancer Genome Atlas(TCGA)and The Human Protein Atlas were used to confirm the different expression levels in normal and tumor tissues and to determine their relationships with patient prognosis.Results:Ultimately,4,777 single-cell transcriptomes(1,220 genes per cell)were examined,of which 2,250(47%)and 2,527(53%)originated from tumor and adjacent UC tissues,respectively.We defined the composition of cancer-associated stromal cells and identified six cell clusters,including myeloid,T and B cells,fibroblasts,endothelial and epithelial cells.Notable pathways and transcription factors involved in these cell clusters were analyzed and described.Moreover,the precise cellular composition and developmental trajectory from UC to UCassociated colon cancer were graphed,and it was predicted that CD74,CLCA1,and DPEP1 played a potential role in disease progression.Conclusions:scRNA-seq technology revealed intra-tumor cell heterogeneity in UC-associated colon cancer,and might provide a promising direction to identify novel potential therapeutic targets in the evolution from UC to CAC.

Multi-scale regionalization based mining of spatio-temporal teleconnection patterns between anomalous sea and land climate events

Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.

Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy

The advent of single-cell RNA sequencing(scRNA-seq)has provided insight into the tumour immune microenvironment(TIME).This review focuses on the application of scRNA-seq in investigation of the TIME.Over time,scRNA-seq methods have evolved,and components of the TIME have been deciphered with high resolution.In this review,we first introduced the principle of scRNA-seq and compared different sequencing approaches.Novel cell types in the TIME,a continuous transitional state,and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer.Thus,we concluded novel cell clusters of cancerassociated fibroblasts(CAFs),T cells,tumour-associated macrophages(TAMs)and dendritic cells(DCs)discovered after the application of scRNA-seq in TIME.We also proposed the development of TAMs and exhausted T cells,as well as the possible targets to interrupt the process.In addition,the therapeutic interventions based on cellular interactions in TIME were also summarized.For decades,quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer.Summarizing the current findings,we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy,which can subsequently be implemented in the clinic.Finally,we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.

Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences

In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.

Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning methods.However,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive users.To better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal context.First,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window.Subsequently,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active users.We further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,respectively.Extensive experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation metrics.More specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.

地磁序列辅助修正的PSO-PF室内行人定位方法

室内行人定位是位置服务的重要基础,地磁信号具有被随时感知的特点,基于地磁信号的定位方法一直是室内行人定位研究的一个热点。针对当前基于粒子滤波融合定位存在累计误差大、定位精度较低的问题,本文提出变长地磁序列辅助PSO-PF的室内行人定位方法。首先,在传统粒子滤波算法的基础上,融合粒子群算法进行最佳位置寻优提升实时定位的准确性;然后,建立了DTW-A^(*)算法获取变长地磁序列对一段时间累计误差进行修正,以解决基于粒子滤波定位方法的累计误差问题;最后,通过试验将本文方法与现有主流定位方法进行比较。结果表明,本文方法在室内行人定位方面平均误差为0.90 m,比PDR、MaLoc和Magicol方法分别降低了73.1%、68.0%和63.8%。其中,本文方法1.43 m定位精度达90%,比PDR、MaLoc和Magicol方法分别提升了75.1%、68.4%和67.7%。此外,在不同型号手机上进行的试验结果表明,本文方法不仅适用且表现稳定,有望为不同设备的室内定位提供支持。

基于频繁序列挖掘的出租车轨迹特性分析

为进一步厘清不同出租车路径选择行为的差异性,采用频繁序列挖掘方法提取了同一个OD对间的频繁路径,构建路径选择集,分别从静态和动态两个角度分析路径集的相似特性。以西安市出租车的轨迹数据为研究对象,通过栅格划分与路网匹配,获得了不同OD对之间的路径集合。重新定义了频繁路径,采用PrefixSpan演变算法,在得到频繁子序列的基础上引入动态阈值和频繁度指标挖掘频繁路径,提取了最短路径和其他路径,完成了3类有效路径集的构建,并分析了路径集的一般属性。其后,将路径上二维时间序列(轨迹)间的相似度表示为动态相似度,将一维有向序列(路段)间的相似度表示为静态相似度,基于改进的最长公共子序列和动态时间规整算法对3类路径进行了相似性分析。结果表明:频繁路径与最短路径的相似度较高,意味着大多数出租车仍然选择具有最低出行时间的路段,但不一定会选择最短路径;时间和距离仍是出行者选择路径时主要考虑的因素,但出行者并不完全追求时间最短或距离最短;试验得到的动态相似度计算结果显著高于静态相似度计算结果,说明路径上的二维时序相似度高于一维形状相似度;两种方法下频繁路径和最短路径的相似度均最高,最短路径和其他路径的相似度均最低,比较结果的一致性说明可以用动态轨迹的相似度来大致度量静态路径的相似度。文中的频繁路径挖掘算法具有一定的可靠性,可为城市交通管理者进行路径推荐、道路规划等提供支持。

基于Seq2Seq-Att的船舶轨迹预测算法

随着海上船舶日益增多,海情急剧复杂化,及时准确地预测船舶的下一步动向成为海事监管的迫切需求。针对现有船舶轨迹预测算法提取轨迹特征能力较差、预测精度不高的问题,提出了添加Attention注意力机制的序列到序列船舶轨迹预测算法(sequence-to-sequence with attention,Seq2Seq-Att)。通过改进Seq2Seq的编码器结构和添加Attention机制,提高模型对轨迹特征的记忆能力,从而提升算法的预测精度。以东海海域的AIS数据为样本训练模型,预测船舶未来一段时间的经度、纬度、航速和航向。实验结果表明,相较于传统算法,该算法的预测精度更高,且均方根误差明显降低,可以为海事监管和智能航行提供依据。

基于分组地图匹配的异常轨迹检测

为提高轨迹数据的质量,提出一种轨迹点的多尺度综合映射算法。采用分组匹配的思想,依据每组特性剪枝无效的轨迹点,加快映射轨迹点的连接速度,快速得到路网轨迹。在此基础上,为有效进行异常轨迹的检测,提出一种基于分组地图匹配的异常轨迹检测算法,研究字符串相似性搜索,构建相似轨迹序列寻找代表轨迹,基于交叉点行驶状况对轨迹点进行约减,减少轨迹间的计算量,计算轨迹的异常阈值,判定异常轨迹。在真实的出租车数据集上进行实验,验证了提出方法的执行效率和准确性。

启闭机提落闸门作业路径规划与轨迹生成

为解决水电站启闭机提落闸门作业频繁、工作强度大、作业效率低等问题,提出了一种适用于无人自动化作业的启闭机路径规划和轨迹生成方法。针对启闭机提落闸门作业顺序和路径规划问题,定义了启闭机工作空间与起升、小车和大车机构的运动方向,建立了提落闸门作业分析模型、闸门初始位置和卸载位置数据库;根据闸门的装卸顺序,考虑作业环境因素,进行了提落闸门作业路径规划;考虑闸门摆角约束,基于相平面分析闸门摆角和小车加速度之间欠驱动耦合关系,确定运动参数并生成了小车期望轨迹;对闸门入槽干涉和槽内卡阻等现象,提出了闸门下落过程阶梯式减速规划方法。仿真和试验结果表明,启闭机可按照预期规划完成提落闸门作业,且闸门摆角在合理范围内摆动。该方法适用于水电站启闭机无人自动化作业环境,为实现启闭机智能化奠定了基础。

A new monitoring index for ecological vulnerability and its application in the Yellow River Basin,China from 2000 to 2022

The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.

Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer

Advances in chimeric antigen receptor(CAR)-T cell therapy have significantly improved clinical outcomes of patients with relapsed or refractory hematologic malignancies.However,progress is still hindered as clinical benefit is only available for a fraction of patients.A lack of understanding of CAR-T cell behaviors in vivo at the single-cell level impedes their more extensive application in clinical practice.Mounting evidence suggests that single-cell sequencing techniques can help perfect the receptor design,guide gene-based T cell modification,and optimize the CAR-T manufacturing conditions,and all of them are essential for long-term immunosurveillance and more favorable clinical outcomes.The information generated by employing these methods also potentially informs our understanding of the numerous complex factors that dictate therapeutic efficacy and toxicities.In this review,we discuss the reasons why CAR-T immunotherapy fails in clinical practice and what this field has learned since the milestone of single-cell sequencing technologies.We further outline recent advances in the application of single-cell analyses in CAR-T immunotherapy.Specifically,we provide an overview of single-cell studies focusing on target antigens,CAR-transgene integration,and preclinical research and clinical applications,and then discuss how it will affect the future of CAR-T cell therapy.

产后抑郁变化轨迹与阴道菌群变化的关联性研究

目的探讨产妇产后抑郁(PPD)变化轨迹与阴道菌群变化之间的关系。方法于2021年9月至2022年10月在本院选取102例产妇作为研究对象,在产后2~3天、产后6周、产后3个月、产后6个月进行随访并通过爱丁堡产后抑郁量表(EPDS)测量PPD,且于产后6个月时收集产妇阴道菌群生物信息数据。分析产后6个月内产妇PPD变化轨迹及其与阴道菌群之间的关系。结果最终确定两类模型作为代表产后抑郁变化轨迹的最佳模型:第一类(n=37,36.3%)标记为递增型组,第二类(n=65,63.7%)标记为低稳定组。递增型组表现出比低稳定组更高的物种数(393.7±48.1 vs.368.2±65.8,t=4.36,P=0.013)、丰富度(Chao1:429.7±55.0 vs.403.0±72.0,t=3.87,P=0.020)和生物多样性(香农指数:5.25±0.46 vs.5.02±0.59,t=3.95,P=0.018)。与低稳定组相比,递增型组产妇的拟杆菌、粪大肠杆菌、帕拉普氏菌、副普雷沃菌显著较低(16.85%vs.22.69%,7.87%vs.10.20%,0.10%vs.0.31%,0.01%vs.0.11%,P<0.05);但未鉴定的毛螺旋菌、未鉴定的梭菌、毛螺菌、未鉴定的丹毒丝菌、柯林斯氏菌和拉克氏梭状芽胞杆菌属显著较高(6.89%vs.4.58%,1.50%vs.0.77%,1.27%vs.0.76%,1.04%vs.0.66%,0.97%vs.0.36%,0.80%vs.0.54%,P<0.05)。两组产妇阴道菌群相对丰度比较具有显著差异(LDA值>4,P<0.05)。结论递增型组女性的阴道微生物群数目、丰富度和多样性较低稳定组更高,提示与PPD的发生发展相关。

Optimizing segmented trajectory data storage with HBase for improved spatio-temporal query efficiency

The surging accumulation of trajectory data has yielded invaluable insights into urban systems,but it has also presented challenges for data storage and management systems.In response,specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed approaches.However,these systems often utilize storage by point or storage by trajectory methods,both of which have drawbacks.In this study,we evaluate the effectiveness of segmented trajectory data storage with HBase optimizations for spatio-temporal queries.We develop a prototype system that includes trajectory segmentation,serialization,and spatio-temporal indexing and apply it to taxi trajectory data in Beijing.Ourfindings indicate that the segmented system provides enhanced query speed and reduced memory usage compared to the Geomesa system.

Applications of single cell RNA sequencing to research of stem cells

Stem cells(SCs)with their self-renewal and pluripotent differentiation potential,show great promise for therapeutic applications to some refractory diseases such as stroke,Parkinsonism,myocardial infarction,and diabetes.Furthermore,as seed cells in tissue engineering,SCs have been applied widely to tissue and organ regeneration.However,previous studies have shown that SCs are heterogeneous and consist of many cell subpopulations.Owing to this heterogeneity of cell states,gene expression is highly diverse between cells even within a single tissue,making precise identification and analysis of biological properties difficult,which hinders their further research and applications.Therefore,a defined understanding of the heterogeneity is a key to research of SCs.Traditional ensemble-based sequencing approaches,such as microarrays,reflect an average of expression levels across a large population,which overlook unique biological behaviors of individual cells,conceal cell-to-cell variations,and cannot understand the heterogeneity of SCs radically.The development of high throughput single cell RNA sequencing(scRNA-seq)has provided a new research tool in biology,ranging from identification of novel cell types and exploration of cell markers to the analysis of gene expression and predicating developmental trajectories.scRNA-seq has profoundly changed our understanding of a series of biological phenomena.Currently,it has been used in research of SCs in many fields,particularly for the research of heterogeneity and cell subpopulations in early embryonic development.In this review,we focus on the scRNA-seq technique and its applications to research of SCs.

Trajectory Design and Optimization for LEO Satellites in Formation to Observe GEO Satellites’ Beams

This paper presents the methods and results for the trajectory design and optimization for the low earth orbit (LEO) satellites in formation to observe the geostationary orbit (GEO) satellites’ beams. The background of the trajectory design mission is the 9th China Trajectory Optimization Competition (CTOC9). The formation is designed according to the observation demands. The flying sequence is determined by a reference satellite using a proposed improved ephemeris matching method (IEMM). The formation is changed, maintained and transferred following the reference satellite employing a multi-impulse control method (MICM). Then the total observation value is computed by propagating the orbits of the satellites according to the sequence and transfer strategies. Based on the above methods, we have obtained a fourth prize in the CTOC9. The proposed methods are not only fit for this competition, but can also be used to fulfill the trajectory design missions for similar multi-object explorations.

场景目标的稀疏序列融合三维扫描重建

针对场景扫描深度图数据量大、匹配误差累积导致重建结果漂移以及耗时高的问题,提出一种场景级目标的稀疏序列融合三维扫描重建方法.首先,对深度图序列采样以筛选支撑深度图;其次,在支撑深度图子集上划分扫描片段,各扫描片段内执行深度图匹配融合生成表面片段;再次,利用表面片段几何特征执行局部多片段间的连续迭代配准,优化各扫描片段的相机位姿;最后,融合支撑深度图序列生成场景目标三维表面.在消费级深度相机采集的深度图序列和SceneNN与Stanford 3D Scene这2个公开数据集上进行测试,将稀疏序列融合与稠密序列融合方法进行比较.实验结果表明,该方法可将配准过程的均方根误差降低16%~28%,使用8%~54%的数据量即可完成稀疏序列融合,运行时间平均缩短56%;同时,增强了扫描过程的有效性和鲁棒性,显著地提高了扫描场景的重建质量.