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The ancient Silk Road served as the main connection between East and West Eurasia for several centuries. At any rate, the genetic exchange between populations along the ancient Silk Road was likely to leave traces on the contemporary gene pool of local people in Northwest China, which was the passage of the Northern Silk Road. However, genetic sources from northwestern China are under-represented in the current population-scale genomic database. To characterize the genetic architecture and adaptative history of the Northern Silk Road ethnic populations, we performed whole-genome sequencing on 126 individuals from six ethnolinguistic groups (Tibeto-Burman (TB)-speaking Tibetan, Mongolic (MG)-speaking Dongxiang/Tu/eastern Yugur, and Turkic (TK)-speaking Salar/western Yugur) living in Gansu and Qinghai in the 10K Chinese people Genomic Diversity Project (10K_CPGDP). We observed ethnicity-related differentiated population structures among these geographically close Northwest Chinese populations, that is, Salar and Tu people showed a close affinity with southwestern TB groups, and other studied populations shared more alleles with MG and Tungusic groups. Overall, the patterns of genetic clustering were not consistent with linguistic classifications. We estimated that Dongxiang, Tibetan, and Yugur people inherited more than 10% West Eurasian ancestry, much higher than that of Salar and Tu people (PRIM2, PDE4DIP, NOTCH2, DDAH1, GALNT2, andMLIP) and developmental and neurogenetic diseases (e.g.,NBPFs 8/9/20/25P, etc.). Moreover, theEPAS1gene, a transcription factor regulating hypoxia response, showed relatively high PBS values in our studied groups. The sex-biased admixture history, in which the West Eurasian ancestry was introduced primarily by males, was identified in Dongxiang, Tibetan, and Yugur populations. We determined that the eastern-western admixture occurred ∼783–1131 years ago, coinciding with the intensive economic and cultural exchanges during the historic Trans-Eurasian cultural exchange era.

Fine-scale genetic background characterization of ethnolinguistically diverse populations can gain new insights into the population admixture processes, which is essential for evolutionary and medical genomic research. However, the genetic diversity and population history of southern Chinese indigenous people and their interaction with historic incomers keep unknown. Here, we genotyped 700K genome-wide SNPs from four Guizhou populations belonging to Tai-Kadai (TK), Hmong-Mien (HM), and Tibeto-Burman language families and merged them with publicly available data from 218 modern and ancient East Asian groups to perform one comprehensive demographical and evolutionary history reconstruction. We found that Guizhou populations harbored a strong genetic substructure correlated with language categories. We comprehensively characterized the genetic signatures of geographically diverse HM people and identified the unique HM genetic lineage in Southwest China and Southeast Asia as their shared ancestry component in the demographical history reconstruction. Our identified admixture signatures and times further supported the hypothesis that the HM people originated from Yungui Plateau and then southward migration during the historical period. Admixture models focused on Sino-Tibetan and TK people supported their intense interaction and harbored the most extensive gene flows consistent with their shared linguistic and cultural characteristics and lifestyles. Estimates of identity-by-descent sharing and effective population size showed the extensive population stratification and gene flow events in different time scales. This work focuses on the genetic features of high-density SNP markers, presents one complete landscape of the evolutionary history of ethnolinguistically different southern Chinese populations, and fills the gap of missing diversity in South China.

BackgroundThe underrepresentation of Hmong-Mien (HM) people in Asian genomic studies has hindered our comprehensive understanding of population history and human health. South China is an ethnolinguistically diverse region and indigenously settled by ethnolinguistically diverse HM, Austroasiatic (AA), Tai-Kadai (TK), Austronesian (AN), and Sino-Tibetan (ST) people, which is regarded as East Asia’s initial cradle of biodiversity. However, previous fragmented genetic studies have only presented a fraction of the landscape of genetic diversity in this region, especially the lack of haplotype-based genomic resources. The deep characterization of demographic history and natural-selection-relevant architecture in HM people was necessary.ResultsWe comprehensively reported the population-specific genomic resources and explored the fine-scale genetic structure and adaptative features inferred from the high-density SNP data in 440 individuals from 34 ethnolinguistic populations, including previously unreported She. We identified solid genetic differentiation between inland (Miao/Yao) and coastal (She) southern Chinese HM people, and the latter obtained more gene flow from northern East Asians. Multiple admixture models further confirmed that extensive gene flow from surrounding ST, TK, and AN people entangled in forming the gene pool of coastal southeastern East Asian HM people. Population genetic findings of isolated shared unique ancestral components based on the sharing alleles and haplotypes deconstructed that HM people from Yungui Plateau carried the breadth of genomic diversity and previously unknown genetic features. We identified a direct and recent genetic connection between Chinese and Southeast Asian HM people as they shared the most extended IBD fragments, supporting the long-distance migration hypothesis. Uniparental phylogenetic topology and Network relationship reconstruction found ancient uniparental lineages in southwestern HM people. Finally, the population-specific biological adaptation study identified the shared and differentiated natural-selection signatures among inland and coastal HM people associated with physical features and immune function. The allele frequency spectrum (AFS) of clinical cancer susceptibility alleles and pharmacogenomic genes showed significant differences between HM and northern Chinese people.ConclusionsOur extensive genetic evidence combined with the historic documents supported the view that ancient HM people originated in Yungui regions associated with ancient ‘Three-Miao tribes’ descended from the ancient Daxi-Qujialing-Shijiahe people. And then, some recently rapidly migrated to Southeast Asia, and some culturally dispersed eastward and mixed respectively with Southeast Asian indigenes, coastal Liangzhu-related ancient populations, and incoming southward Sino-Tibetan people. Generally, complex population migration, admixture, and adaptation history contributed to their specific patterns of non-coding or disease-related genetic variations.

In recent years, the problem of matrix completion based on rank minimization has received widespread attention in machine learning. The tightest convex relaxation of this problem is the linearly constrained nuclear norm minimization. Fixed point continuation (FPC), as a representative nuclear norm relaxation matrix completion algorithm, has been proven to perform well in theories and experiments. However, the traditional FPC algorithm initializes the matrix to be completed by zero, which does not make full use of the shrinkage characteristics of the singular value shrinkage operator on the matrix elements and the known field data information, then will lead to slow convergence and poor accuracy. Aiming at this problem, this paper analyzes the shrinkage properties of matrix elements in the iterative process of the FPC algorithm. Combined with the known rating information in the recommendation systems, a new initialization method of overestimation based on FPC is proposed, and it is applied to the rating prediction in the recommendation systems. The experimental results show that the initialization method proposed in this paper greatly improves the algorithm efficiency and prediction accuracy.

Non-recombining regions of the Y-chromosome are inherited male-specifically and recorded the evolutionary traces of male human populations. Recent whole Y-chromosome sequencing studies have identified previously unrecognized population divergence, expansion and admixture processes, which promotes a better understanding and application of the observed patterns of Y-chromosome genetic diversity. Here, we developed one highest-resolution Y-SNP panel for forensic pedigree search and paternal biogeographical ancestry inference, which included 639 phylogenetically informative SNPs (Y-SNPs). We genotyped these loci in 1033 Chinese male individuals from 33 ethnolinguistically diverse populations and identified 257 terminal Y-chromosomal lineages with frequency ranging from 0.010 (singleton) to 0.0687. We identified six dominant common founding lineages associated with different ethnolinguistic backgrounds, which included O2a2b1a1a1a1a1a1a1-M6539, O2a1b1a1a1a1a1a1-F17, O2a2b1a1a1a1a1b1a1b-MF15397, O2a2b2a1b1-A16609, O1b1a1a1a1b2a1a1-F2517 and O2a2b1a1a1a1a1a1-F155. The AMOVA and nucleotide diversity estimates revealed considerable differences and high genetic diversity among ethnolinguistically different populations. We constructed one representative phylogenetic tree among 33 studied populations based on the haplogroup frequency spectrum and sequence variations. Clustering patterns in principal component analysis and multidimensional scaling results showed a genetic differentiation between Tai-Kadai-speaking Li, Mongolic-speaking Mongolian and other Sinitic-speaking Han Chinese populations. Phylogenetic topology inferred from the BEAST and Network relationships reconstructed from the popART further showed the founding lineages from culturally/linguistically diverse populations, such as C2a/C2b was dominant in Mongolian people and O1a/O1b was dominant in island Li people. We also identified many lineages shared by more than two ethnolinguistically different populations with a high proportion, suggesting their extensive admixture and migration history. Our findings indicated that our developed high-resolution Y-SNP panel included major dominant Y-lineages of Chinese populations from different ethnic groups and geographical regions, which can be used as the primary and powerful tool for forensic practice. We should emphasize the necessity and importance of whole-sequencing of more ethnolinguistically different populations, which can help identify more unrecognized population-specific variations for the final promotion of Y-chromosome-based forensic applications.

In recent years, low-rank tensor completion has been widely used in color image recovery. Tensor Train (TT), as a balanced tensor rank minimization method, has achieved good results in actual image recovery because of its ability to capture the hidden information of images. When processing the third-order tensor data of a color image, TT transforms it into a higher-order tensor through kat augmentation to exploit the local feature of the tensor effectively. However, this method actually destroys the original structure of the tensor, which leads to insufficient access to the global structure information. Therefore, the algorithm performs poorly when processing images with a significant amount of missing information. Aiming at this problem, a new tensor completion model is proposed, which combines Tucker rank and Tensor Train rank. Among them, Tucker is used to obtain the global structure information, and Tensor Train is used to capture the local hidden information. To tackle the proposed model, we develop an efficient alternating direction method based algorithm. The numerical experiments using various tensor data show the effectiveness of the model.

Fine-scale genetic backgrounds based on the sharing of alleles and haplotypes can gain new insights into the population admixture processes of ethnolinguistically diverse populations, which is essential for evolutionary and medical phenotypes. However, the genetic diversity and population history of southern Chinese indigenous people and their interaction with Han Chinese populations keep unknown. Here, we genotyped 700K genome-wide SNPs from four Guizhou populations and merged them with publicly available data from 18 Guizhou populations and SNP data from 200 modern and ancient East Asian groups to perform one comprehensive population genetic analysis. We found that Guizhou populations harbored a strong genetic substructure correlated with the ethnolinguistically diverse Tai-Kadai (TK), Hmong-Mien (HM), and Tibeto-Burman (TB) categories. We comprehensively characterized the genetic signatures of geographically diverse HM people. We identified the unique HM genetic lineages in Southwestern China and Southeast Asia as their shared ancestry component in the admixture and demographical history reconstruction. Our identified admixture signatures and times further supported the hypothesis that the HM people originated in the Yungui Plateau and then southward migration during the historical period. Admixture models focused on ST and TK people supported their intense interaction and harbored the most extensive gene flow levels consistent with their shared linguistic and cultural characteristics and lifestyles. Estimates of shared IBD and effective population size showed the extensive population intrafraction and gene flow events in different time scales. This work focuses on the genetic features of high-density SNP markers, presents one complete landscape of the evolutionary genetic history of ethnolinguistically different southern China, and fills the gap of missing diversity in South China.

Background Fine-scale genetic structure of ethnolinguistically diverse Chinese populations can fill the gap in the missing diversity and evolutionary landscape of East Asians, especially anthropologically informed Chinese minorities. Hmong-Mien (HM) people were one of the most important indigenous populations from South China and Southeast Asia, which were suggested to be the descendants of the Yangtze rice farmers based on linguistic and archeological evidence. Guizhou Miao, possessing a specific genetic structure, harbored a close genetic relationship with geographically close Guizhou populations and Guangxi historical people. We genotyped genome-wide SNP data in Guizhou HM-speaking populations and merged it with modern and ancient reference populations to explore the evolutionary and adaptive characteristics of Miao people via a comprehensive population genetic analysis. Results The overall genetic admixture landscape of Guizhou Miao showed genetic differentiation between them and other linguistically diverse Guizhou populations. Admixture models further confirmed that Miao people derived their primary ancestry from geographically close Guangxi Gaohuahua people. The estimated identity by descent (IBD) and effective population size further confirmed a plausible population bottleneck, contributing to their unique genetic diversity and structure pattern. We finally identified several natural selection candidate genes associated with several biological pathways. Conclusions We found Guizhou Miao with a specific genetic structure harbored a close genetic relationship with geographically close Guizhou populations and Guangxi historic people. Miao people derived their major ancestry from geographically close Guangxi Gaohuahua people and a plausible population bottleneck which contributed to the unique pattern of their genetic diversity and structure. Future ancient DNA from Shijiahe and Qujialing would further promote new insights into the origin of the Miao people.

In recent years, with the development of Internet technology, recommender systems have been widely used by virtue of their ability to meet the personalized needs of users. In order to make full use of users’ interactive behaviors, session-based recommender systems have attracted growing research interest. In previous session-based recommender systems, users' historical interactive behavior is utilized to train and update users' preferences, but users' responses to the current recommendation results (immediate feedback) are not effectively exploited to optimize the recommendation strategy. This leads to the decrease of subsequent recommendation accuracy. Aiming at this problem, based on the Recurrent Neural Network (RNN), this paper combines Reinforcement Learning (RL) and Generative Adversarial Networks (GANs). We fully exploit the users’ immediate feedback with RL, and simultaneously take advantage of GAN to satisfy the requirement of training data brought by RL. Furthermore, we optimize the negative sampling method and propose Deep Generative Adversarial Networks-based Collaborative Filtering (DCFGAN). The experimental results show that this algorithm can effectively improve the recommendation accuracy in session-based recommender systems.

Context-aware processing is a research hotspot in the recommendation area, which achieves better recommendation accuracy by considering more context information such as time, location and etc. besides the information of the users, items and ratings. Tensor factorization is an effective algorithm in context-aware recommendation and current approaches show that adding bias to the tensor factorization model can improve the accuracy. However, users’ rating preferences fluctuate greatly over time, which makes bias fluctuate with time too. Current context-aware recommendation algorithms ignore this problem, and usually use the same bias for a user or an item in different time. Aiming at this problem, this paper first considers the time-varying effect on user bias and item bias in context-aware recommendation, and proposes a time-varying bias tensor factorization recommendation algorithm based on the bias tensor factorization model (BiasTF). We experiment on two real datasets, and the experimental results show that the proposed algorithms get better accuracy than other algorithms.

The recommendation algorithm is attracting increasing attention in analyzing big data. Matrix factorization (MF) is one of the recommendation methods and Singular Value Decomposition (SVD) is the most popular matrix factorization method. However, the existing SVD methods usually initialize user and item feature randomly, not fully utilize the information of the data, so require plenty of experiments to determine feature matrix dimension, with low convergence efficiency and low accuracy. This paper presents a hybrid initialization method based on attribute mapping and autoencoder neural network to solve these problems, which consists of three parts: (1) use the number of item attribute types to determine feature matrix dimension in order to avoid multiple experiments to select the optimal dimension value; (2) use items’ attributes to initialize the item feature matrix in SVD＋＋, and use an attribute mapping mechanism to get an item feature vector by fitting the rating matrix to accelerate the convergence; (3) adopt the autoencoder neural network to reduce feature dimension and obtain item latent features for initializing SVD＋＋. The experimental results show that our methods achieve better performance than SVD＋＋ random initialization and also be adopted to other matrix factorization methods.

Wireless local area network (WLAN) fingerprint-based localization has become the most attractive and popular approach for indoor localization. However, the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled fingerprints. The Semi-supervised extreme learning machine (SELM) performs well in reducing calibration effort. Traditional SELM methods only use Received signal strength (RSS) information to construct the neighbor graph and ignores location information, which helps recognizing prior information for manifold alignments. We propose Composite SELM (CSELM) method by using both RSS signals and location information to construct composite graph. Besides, the issue of unlabeled RSS data quality has not been solved. We propose a novel approach called Composite semisupervised extreme learning machine with unlabeled RSS Quality estimation (CSELM-QE) that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph, which considers location information in the semi-supervised extreme learning machine. Experimental results show that the CSELM-QE could construct a precise localization model, reduce the calibration effort for radio map construction and improve localization accuracy. Our quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.

Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. The geographically weighted regression (GWR) model is used to describe the influence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some differences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. This paper provides some references for understanding the internal urban environmental factors generating from the taxi travel demand, and provides insights for reducing the taxi vacancy rate, forecasting taxi temporal-spatial demand and urban public transportation system planning.

Fingerprint-based indoor localisation suffers from influences such as fingerprint pre-collection, environment changes and expending a lot of manpower and time to update the radio map. To solve the problem, we propose an efficient radio map updating algorithm based on K-Means and Gaussian Process Regression (KMGPR). The algorithm builds a Gaussian Process Regression (GPR) predictive model based on a Gaussian mean function and realises the update of the radio map using K-Means. We have conducted experiments to evaluate the performance of the proposed algorithm and results show that GPR using the Gaussian mean function improves localisation accuracy by about 13·76% compared with other functions and KMGPR can reduce the computational complexity by about 7% to 20% with no obvious effects on accuracy.

In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Traditional tensor-based models in context-aware recommendation scenario only consider user-item-context interactions. In this paper, we argue that rating can't be totally explained by the interactions and the rating also influenced by the combined impact of overall mean, user bias, item bias and context bias. Based on this hypothesis, we propose a novel context-aware recommendation model named Bias Tensor Factorization, which take all this factors into account. Additionally, traditional context-aware recommenders with tensor factorization still have three main drawbacks: (1) the model complexity of those models increase exponentially with the number of context features, (2) those models can only handle context features with categorical values and (3) the models fail to select effective features from available context features. To address those problems, we propose a context features auto-encoding algorithm based on regression tree which can both handle numerical features and select effective features. Then we integrate this algorithm with Bias Tensor Factorization. Experiments on a real world contextual dataset and Movielens show that our proposed algorithms outperform the state-of-art context-aware recommendation algorithms, namely tensor factorization and factorization machine.

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

The purpose of this study is to classify and analyze the traffic performance characteristics of urban trunk roads. It can help travelers effectively avoid peak hours and crowded routes, and traffic managers formulate some refined management and control on roads. This paper extracts the operating characteristics of the trunk roads on macro level by analyzing the Traffic Performance Index (TPI) data. The trunk roads are divided into three types using the agglomerative hierarchical clustering algorithm. According to the research of the spatial–temporal principles and differences of various types in traffic performance, the links with frequent traffic jams are identified, and the mechanism of the different traffic conditions is illustrated. It can be concluded that the trunk road is good conditions in most cases. On weekdays, there are two obvious peak times every day. On weekends, TPI data shows that there is no obvious peak time, and the overall road traffic conditions on weekends are better than those on weekdays. In addition, there are significant differences in the change trajectory of the TPI data and spatial distribution of various types of trunk roads. The roads with frequent traffic jams mainly come from the first and third type.

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is a parasitic plant pathogenic nematode that severely affects the growth and development of soybean. Breeding SCN-resistant varieties is one of the safest and most effective ways to manage SCN. ‘Dongnong L-10’ is a well-known SCN-resistant variety in Northeast China, but the molecular mechanisms governing this resistance have not been determined. Hence, in this study, we transcriptome sequenced the roots of ‘Dongnong L-10’ (Resistant varieties of SCN race 4) and ‘Heinong 37’ (Susceptible varieties of SCN race 4) under the stress of HG Type 1.2.3.5.7 (SCN race 4). We obtained a total of 76.75 GB of the raw reads. A total of 4400 and 5724 differentially expressed genes (DEGs) were related to the SCN resistance of ‘Dongnong L-10’ and ‘Heinong 37’. The analysis of DEGs showed that transcription factors (AP2/EREBP, WRKY, MYB, NAC, bHLH and C2H2) and plant hormone signal transduction pathways (the jasmonic acid and salicylic acid pathways) may be involved in the response of soybean to HG Type 1.2.3.5.7. RT-qPCR analysis showed that the relative expression levels of PR protein family gene GmRSCN4-1 (Glyma.05G204800) and cytochrome P450 family gene GmRSCN4-2 (Glyma.16G195600) in ‘Dongnong L-10’ were significantly higher than those in ‘Heinong 37’. Overexpression of GmRSCN4-1 and GmRSCN4-2 in soybean hairy roots resulted in increased HG Type 1.2.3.5.7 resistance in soybean hairy roots. Our results not only provide a theoretical basis for analyzing the resistance mechanism of ‘Dongnong L-10’ to HG Type 1.2.3.5.7, but also provide some genes resources for soybean molecular breeding for resistance to cyst nematode.

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is a common disease of soybean (Glycine max (L.) Merr.) worldwide, seriously affecting yield. Kangxian 2 is a soybean variety with a high level of resistance to H. glycines (HG) Type 0 (SCN race 3) and a yellow seed coat. However, we know little about the mechanism of resistance to HG Type 0 in Kangxian 2. In this study, we used the Illumina HiSeq high-throughput sequencing platform to analyse the transcriptome of Kangxian 2 and obtained 65.74 Gb clean data. Transcriptional changes in Kangxian 2 caused by HG Type 0 stress after 0–10 days are described. Kangxian 2 showed different levels of gene expression after inoculation, and under HG Type 0 stress after different times. Overall, 6854 HG Type 0-induced genes and 5328 HG Type 0-repressed genes were found to be differentially regulated. The greatest number of differential genes annotated to cellular process, metabolic process, single-organism process, binding, catalytic activity and other pathways. In addition to findings of differentially expressed genes similar to other published work, such as the regulation of biosynthesis of many secondary metabolites, carbon sequestration of photosynthetic organisms, other types of O-polysaccharide biosynthesis, phenylpropane biosynthesis, pyruvate metabolism and other pathways, this study also revealed the differential regulation of genes related to the diarylheptanoid and gingerol biosynthesis pathway and found some metabolic pathways that were specifically expressed in the syncytial initiation and establishment stages. Gene expression analyses using real-time fluorescence quantitative PCR showed that the expression of GmMADS and GmTUB changed strongly after 7 days and 10 days of HG Type 0 stress compared with the control. We conclude that GmMADS and GmTUB transcription factor genes may play an important role in the resistance of Kangxian 2 to HG Type 0 stress.

In recent years, context information has been widely used in recommender systems. Tensor factorization is an effective method to process high-dimensional information. However, data sparsity is more serious in tensor factorization, and it is difficult to build a more accurate recommender system only based on user–item–context interaction information. Making full use of user’s social information and implicit feedback can alleviate this problem. In this paper, we propose a new tensor factorization model named TrustTF, which mainly works as follows: (1) Using user’s social trust information and implicit feedback to extend the bias tensor factorization (BiasTF), effectively alleviate data sparsity problem and improve the recommendation accuracy; (2) Dividing user’s trust relationship into unilateral trust and mutual trust, which makes better use of user’s social information. To our knowledge, this is the first work to consider the effects of both user trust and implicit feedback on the basis of the BiasTF model. The experimental results in two real-world data sets demonstrate that the TrustTF proposed in this paper can achieve higher accuracy than BiasTF and other social recommendation methods.

A research method to establish the matching degree of supply and demand of bus stations by using Moran’s I, which describes the spatial agglomerating feature, was proposed based on IC card and GPS data mining. The bus boarding station information was deduced by the time matching method and the passenger demand of the station was represented by the passenger flow in the station, the vehicle data from GPS data were selected as the bus service supply, the matching situation of bus supply and demand in different time periods in the whole city was researched, the degree of station imbalance was identified, to assist the bus system to optimize supply. Take Qingdao city as an example, the empirical research indicated that: It was concluded that the demand and supply of the bus service in the research area matched well on the whole, and the supply of buses at the bus stations basically met the travel demand of residents. According to the LISA diagram of station clustering distribution, the stations with a mismatch of supply and demand in the area were identified through spatio-temporal difference analysis, and the mismatch was relatively more obvious in regions where a small number of stations were located.

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is the most important disease affecting yield of soybean (Glycine max (L.) Merr.). In China, among the main physiological races of SCN causing serious economic loss of soybean, HG Type 0 is the most widely distributed. Breeding SCN-resistant varieties is one of the safest and most effective ways to manage SCN. Soybean cv. Dongnong L-204 has a green seed coat and is a resource for resistance to SCN; however, the transcriptional variation and main molecular mechanism of resistance of Dongnong L-204 are not clear. We obtained 66.42 Gb of raw reads by using Illumina HiSeq high-throughput sequencing of Dongnong L-204. Transcriptional changes at Days 3, 7 and 10 after HG Type 0 stress are described. In total, 11279 differentially expressed genes were responsive to HG Type 0 stress, including 6407 SCN-induced and 4872 SCN-suppressed genes. Gene Ontology analysis emphasised the important roles of the terms metabolic process, single-organism process, catalytic activity and binding under HG Type 0 stress. Among them, the terms related to extracellular matrix, extracellular region part, membrane-enclosed lumen, protein-binding transcription factor activity, and symplast and cell junction existed only in pairwise comparisons Day 7 vs Day 0 and Day 10 vs Day 0. KEGG enrichment analysis showed that metabolic pathway played an important role in the stress response to HG Type 0. Cellular processes, metabolism and organismal systems and other pathway-related genes increased significantly after 7 days. Transcription factor analysis showed that transcription factors such as MYB, AP2-EREBP, bHLH, WRKY and NAC may be involved in the anti-HG Type 0 response of Dongnong L-204. At the same time, few transient inducible transcription factors were found and more transient inhibited transcription factors in Dongnong L-204 in the early stage of HG Type 0 syncytial establishment. RT-qPCR was used to analyse expression of 10 genes closely related to the HG Type 0 stress response. The expression of GmWRKYX1 was >60 times higher at Day 10 after HG Type 0 stress than at Day 0. The WRKY transcription-factor family may play an important role in the resistance of Dongnong L-204 to HG Type 0 stress.

CeO2 is one of the most promising materials applied in chemical mechanical polishing industry. However, due to the high hardness of CeO2, a further surface modification is usually required, especially when polishing a soft substrate, such as fused quartz and optical disk. In this work, we reported a novel process for the synthesis of CeO2/CeF3 composite powders which showed improved suspension stability and CMP properties. Ce2(CO3)3 and HF were employed as the precursor materials for Ce and F, respectively, which were simply milled and annealed at different temperatures to get the composite powders. The results showed that the morphology of the powders changed from microrods to grape-like shapes which had a smaller particle size. And the XRD results confirmed the formation of CeO2/CeF3 composite phases after the HF addition. The suspension stability and CMP property measurements indicated that the change of particle morphologies and the formation of composite structure benefited the polishing process for fused quartz substrate, and a more planar surface was obtained. All these results indicated that the current processing was of great potentials for the synthesis and practical applications of ceria-based CMP slurries.

This study aims to investigate the congestion of the urban business circle based on traffic performance index (TPI) data. A hierarchical clustering algorithm is adopted for the data analysis. A dataset of nearly 64,260 pieces of TPI data from July to October 2017 in Qingdao is collected, and its features, such as interval characteristics, mean time distribution and spatiotemporal correlation, are analyzed. The results show that the southern coastal commercial circle of Qingdao is more congested than the other circles; there exists a morning and afternoon peak, with two peaks on workdays; otherwise, weekend and vacation periods do not show congestion. The congestion level toward the end of the vacation week (October 4th–8th) is lower than that during the beginning (October 1st–3rd). Considering the temporal and spatial dimensions, the causes of the two different congestion states during the holidays are speculated upon The characteristics of traffic congestion in Qingdao business circle and its possible causes are proposed, and a new design and overall plan for Qingdao is promoted.

SUMMARY Space–earth integrated network (SEIN) consists of the space infrastructure that is composed of multiplex functions and various satellite systems and the ground infrastructure that is composed of many heterogeneous networks connected to each other. Because of the high complexity, heterogeneity, and integrity of SEIN, the traditional ground network management and the current satellite network management architecture are both unsuitable for SEIN. A new SEIN network management protocol is given in this paper, the logical correctness of the protocol is verified using Petri net, and five management primitives are designed in the protocol. This protocol can also be compatible to traditional network protocols with its gateway. The simulation of the protocol shows the merit of a short-length PDU (Protocol Data Unit) and speedy response time, which is good to the high delay and limited bandwidth of the SEIN. Copyright © 2012 John Wiley & Sons, Ltd.

A hierarchical wireless sensor network is proposed which separates the sensing ability and routing ability at different layer respectively. It cannot only simplify the hardware design but also can reduce wireless node cost. Also, a mathematical model of data fusion based on hierarchical architecture and Voronoi diagram is given to make sure the connectivity of stochastic sensor nodes and can be more energy efficient. The simulation shows, network communication traffic are cut down sharply, which can reduce nodes energy cost and prolong the lifetime of the sensor network.