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Abstract Background Periodontitis can be avoided with a healthy lifestyle. However, studies have only looked at one lifestyle, ignoring the connection between lifestyle patterns and periodontitis. The purpose of this study was to look at the association between modifiable lifestyle patterns and periodontitis. Methods Data were obtained from the National Health and Nutrition Examination Survey in 2009–2010 and 2011–2012. Smoke, drink, exercise, sleep duration, oral exams, and self-rated oral health were all lifestyle factors. The CDC/AAP classification/case definition was used to evaluate periodontitis. Drawing upon latent class analysis, distinct patterns of lifestyle were identified, with each participant exclusively affiliated with a single pattern. The association between lifestyle classes and periodontitis was then examined using ordinal logistic regression. Results 4686 (52%) of the total 9034 participants, with a mean age of 54.08, were women. Three lifestyle latent classes were found by fitting 2–10 models: “Class 1” (52%), " Class 2” (13%), and " Class 3” (35%). The “Class 1” displayed a prevalence of oral examination (75%), favorable self-rated oral health (92%), and engagement in physical activity (50%). The ‘Class 2’ exhibited the lowest alcohol consumption (64%) and smoking rates (73%) but the highest prevalence of physical inactivity (98%). The ‘Class 3’ showed a tendency for smoking (72%), alcohol consumption (78%), shorter sleep duration (50%), absence of oral examinations (75%), and suboptimal self-rated oral health (68%). The influencing variables for the latent classes of lifestyle were age, education, and poverty level. Periodontitis risk may rise by 24% for each additional unhealthy lifestyle practiced by participants (OR = 1.24, 95% CI: 1.18–1.31). The ‘Class 3’ (OR = 1.80, 95% CI: 1.52–2.13) had a greater risk of periodontitis compared to the ‘Class 1’. Conclusions Our analysis revealed that unhealthy lifestyle patterns are associated with periodontitis. These different lifestyle patterns need to be taken into account when developing public health interventions and clinical care.

ABSTRACTThe presence of airborne allergenic pollen causes a variety of immune reactions and respiratory diseases, threatening human life in severe cases. Climate change is exacerbating the allergenic pollen-induced health risks and adding a significant economic burden to societies. Despite the pressing threats, vital health-related information is not available to the public to date, and the reshaping of future geographic allergenic pollen patterns remains unknown. To help establish a critical allergenic pollen forecasting capacity, a systematic review was conducted and three promising future directions were identified: (1) resolving heterogeneous urban plant species distribution and phenology using fine-resolution satellite constellations; (2) acquiring ancillary information about allergenic pollen and patient symptoms from emerging geospatial big data, such as social media; (3) deciphering the coupled effect of climate change and urbanization on future geographic patterns and phenology of allergenic species. On this basis, we recommend an optimized workflow that combines real-time pollen monitoring networks with high-resolution vegetation information and weather forecast systems, comprehensively considering the production and diffusion process of pollen to establish advanced prediction models. By focusing on critical knowledge gaps, this review provides much needed insight to propel the allergenic pollen forecasting research and eventually benefit the management of urban public health.

Abstract Background This study aims to evaluate the difference of three-dimensional (3D) reconstructed palatal morphology between subjects with skeletal Class III and skeletal Class I in different vertical patterns using cone beam computed tomography (CBCT). Methods In this study, 89 subjects with skeletal Class III (49 females, 40 males; 25.45 ± 3.81 years) and 85 subjects with skeletal Class I (45 females, 40 males; 23.95 ± 4.45 years) were collected retrospectively and divided into hyperdivergent, normodivergent and hypodivergent groups. Dolphin software was used to reorient the CBCT images of these subjects. After segmenting 3D object of maxilla from the 3D skull by ProPlan software, Geomagic Studio was used to reconstruct 3D palatal morphology and establish an average 3D palatal morphology for each group. The differences of 3D palatal morphology between different groups were compared by deviation patterns on 3D colored map analysis. Results 3D colored map analysis showed the posterior part of male’s palate was higher and wider than that of female’s palate in skeletal Class III subjects. In skeletal Class III subjects, males with hyperdivergent pattern had a higher and narrower palate compared with hypodivergent subjects, while females with hyperdivergent had a higher but not obviously narrower palate compared with hypodivergent subjects. In the similar vertical patterns, skeletal Class III subjects had a flatter but not narrower palate compared with skeletal Class I subjects, along with a smaller palate volume. Conclusions This method allows more intuitive between-group comparisons of the differences of 3D palatal morphology. In skeletal Class III subjects, as the vertical dimension increased, the palate tends to be higher and narrower. Therefore, the influence of vertical patterns on the palatal morphology should be fully considered in the orthodontic and orthognathic treatment of skeletal Class III subjects.

Human activities significantly impact the environment. Understanding the patterns and distribution of these activities is crucial for ecological protection. With location-based technology advancement, big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data. In this study, Qilian Mountain National Park (QMNP) was chosen as the research area, and Tencent location data were used to construct time series data. Time series clustering and decomposition were performed, and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data. The study found two distinct human activity patterns, Pattern A and Pattern B, in QMNP. Compared to Pattern B, Pattern A had a higher volume of location data and clear nighttime peaks. By incorporating land use and trajectory data, we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations, respectively. Moreover, the study identified seasonal variations in human activities, with human activity in summer being approximately two hours longer than in winter. We also conducted an analysis of human activities in different counties within the study area.

The network-constrained flow is defined as the movement between two locations along road networks, such as the residence-workplace flow of city dwellers. Among flow patterns, clustering (i.e. the origins and destinations are aggregated simultaneously) is one of the cities’ most common and vital patterns, which assists in uncovering fundamental mobility trends. The existing methods for detecting the clustering pattern of network-constrained flows do not consider the impact of road network topology complexity on detection results. They may underestimate the flow clustering between networks of simple topology (roads with simpler shapes and fewer links, e.g. straight roads) but with high network intensity (i.e. flow number per network flow space), and determining the actual scale of an observed pattern remains challenging. This study develops a novel method, the length-squared L-function, to identify clustering patterns of network-constrained flows. We first use the L-function and its derivative to examine the clustering scales. Further, we calculate the local L-function to ascertain the locations of the clustering patterns. In synthetic and practical experiments, our method can identify flow clustering patterns of high intensities and reveal the residents’ main travel behavior trends with taxi OD flows, providing more reasonable suggestions for urban planning.

The rapid development of chip manufacturing technology has increased the demand for precise characterization techniques. The characterization technique of the physical field on the surface of a chip is crucial for analyzing chip failures and diagnosing faults. In this review, the latest advancements based on different measurement mechanisms are analyzed and summarized for the electromagnetic field characterization of the chip surfaces. In addition, their advantages and limitations are discussed. Finally, prospects for future development are presented.

Research into metacognitive listening instruction under the Metacognitive Pedagogical Cycle (MPC) has been growing in recent decades, but its effects on L2 listening comprehension, metacognitive awareness, and self-efficacy remain inconclusive. In this mixed-method study, we developed a self-directed online listening practice based on the MPC and investigated its effects on 89 Chinese intermediate EFL learners over 14 weeks. Learners were assigned to either an experimental group, which used the online metacognitive listening practice, or a control group, which used the traditional listening practice without stressing metacognitive awareness. Multiple data sources (listening tests, questionnaires, reflective notes, and interviews) were used to assess learners' listening comprehension, metacognitive awareness, and listening self-efficacy. Results showed that online metacognitive listening practice significantly improved the learners' listening comprehension, but there was little evidence that it increased metacognitive awareness or listening self-efficacy. This study suggests that deploying online listening practice under MPC is a more effective way to improve L2 learners' listening comprehension than traditional listening practice. However, the task-setting of MPC and the task-dependence of self-efficacy may constrain the development of some factors of metacognitive awareness and self-efficacy.

Abstract The mid-depth ocean circulation is critically linked to actual changes in the long-term global climate system. However, in the past few decades, predictions based on ocean circulation models highlight the lack of data, knowledge, and long-term implications in climate change assessment. Here, using 842,421 observations produced by Argo floats from 2001-2020, and Lagrangian simulations, we show that only 3.8% of the mid-depth oceans, including part of the equatorial Pacific Ocean and the Antarctic Circumpolar Current, can be regarded as accurately modelled, while other regions exhibit significant underestimations in mean current velocity. Knowledge of ocean circulation is generally more complete in the low-latitude oceans but is especially poor in high latitude regions. Accordingly, we propose improvements in forecasting, model representation of stochasticity, and enhancement of observations of ocean currents. The study demonstrates that knowledge and model representations of global circulation are substantially compromised by inaccuracies of significant magnitude and direction, with important implications for modelled predictions of currents, temperature, carbon dioxide sequestration, and sea-level rise trends.

Understanding the public’s diverse linguistic expressions about rainfall and flood provides a basis for flood disaster studies and enhances linguistic and cultural awareness. However, existing research tends to overlook linguistic complexity, potentially leading to bias. In this study, we introduce a novel algorithm capturing rainfall and flood-related expressions, considering the relationship between precipitation observations and linguistics expressions. Analyzing 210 million social media microblogs from 2017, we identified 594 keywords, 20 times more than usual manually created bag-of-words. Utilizing Large Language Model, we categorized these keywords into rainfall, flood, and other related terms. Semantic features of these keywords were analyzed from the viewpoint of popularity, credibility, time delay, and part-of-speech, finding rainfall-related terms most common-used, flood-related keywords often more time delayed than precipitation, and notable differences in part-of-speech across categories. We also assessed spatial characteristics from keyword and city-centric perspectives, revealing that 49.5% of the keywords have significant spatial correlation with differing median centers, reflecting regional variations. Large and disaster-impacted cities show the richest expression diversity for rainfall and flood-related terms.

The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a new data source for housing price studies. The SVI contains both objective and subjective information and can be used to extract objective measurements describing the physical environment and subjective measurements depicting human perceptions. Compared to conventional methods, there is consistency between subjective and objective information extracted from SVIs, and the two types of information are acquired from the perspective of the human visual perceptual system. Therefore, using both objective and subjective information extracted from street view images to study their relationship with housing prices has several advantages. In this study, focusing on the city of Suzhou, China, we extracted subjective perception and objective view indices from SVIs and systematically assessed their effects on housing prices. The global ordinary least squares (OLS) regression model and the local geographically weighted regression (GWR) model were used to model the correlations between these measures and housing prices. The OLS reveals that overall objective measures have stronger explanatory power, and built environment factors have a greater impact on housing prices. GWR shows that subjective factors can explain more variance in housing prices on the local scale and that home buyers care more about the subjective perceptions of the neighborhood’s surroundings. The map of the GWR local coefficients demonstrates that the perception indicators have both positive and negative effects on housing prices in different places. In addition, a Monte Carlo test was performed to verify the spatially varying relationships between these measures. Our findings provide important references for urban designers and guide various applications, such as safe neighborhood design and sustainable city planning.

A new, facultatively anaerobic, light-yellow, and rod-shaped bacterium designated as 3B26T isolated from Qi’ao Island’s tidal flat sediment was identified. Strain 3B26T can hydrolyze gelatin, aesculin, and skim milk. The major cellular fatty acids were identified as iso-C15:0, referred to as summed feature 3, and C16:0; the polar lipids consisted of phosphatidylethanolamine, phosphatidylglycerol, and phospholipid; and the quinones contained Q-7, Q-8, MK-7, and MMK7. The genomic size of strain 3B26T was 4,682,650 bp, and its genomic DNA G + C content was 54.8%. While a 16S rRNA gene-based phylogenetic analysis confirmed that strain 3B26T belongs to the genus Shewanella, both phylogenomic inference and genomic comparison revealed that strain 3B26T is distinguishable from its relatives, and digital DNA-DNA hybridization (dDDH) values of 24.4–62.6% and average nucleotide identities (ANIs) of 83.5–95.6% between them were below the 70% dDDH and 96% ANI thresholds for bacterial species delineation. Genomic functional analysis demonstrated that strain 3B26T possesses complete gene clusters of eicosapentaenoic acid biosynthesis and denitrification. Based on the evidence above, strain 3B26T is considered to represent a novel species of the genus Shewanella, and the name Shewanella zhuhaiensis sp. nov. (type strain 3B26T = GDMCC 1.2057T = KCTC 82339T) is proposed.

The centrality of a place reflects its relative importance in serving other places, and quantifying centrality is a prerequisite for assessing the urban spatial structure and facilitates policy-making of sustainable urban development. Two important aspects in quantifying the centrality of a place are the amount of service it provides to other places and the spatial diversity of the places it serves. For example, a central city attracts not only many people but also people from various cities. However, few existing centrality measures can address the two aspects simultaneously. In this study, following the idea of the scholar’s H-index, we propose a novel flow-based locational measure, the X-index, which quantifies the centrality of a place by considering both the volume and directions of flows toward the place in an automated way. We demonstrate the effectiveness and advantages of the X-index over other common centrality measures by quantifying the centrality of Chinese cities and spatial grid cells in Beijing. The results show that: (1) The X-index can successfully detect all the official national central cities and reveal the hierarchy of cities. (2) The X-index is positively correlated with the variety of urban functions in grid cells, which is consistent with central place theory.

Isoxaflutole and atrazine are representative pesticides for weed control in corn fields. Formulations containing these two pesticides have been registered in China, and their residues may threaten food safety and human health. In this study, a method for simultaneous determination of isoxaflutole, atrazine, and their metabolites in fresh corn, corn kernels, and corn straw was established based on modified QuEChERS pre-treatment and high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS). The linearity of seven compounds was good (R2 ≥ 0.9912), and the matrix effect was 48.5–77.1%. At four spiked levels of 0.01, 0.02, 0.05, and 0.5 mg kg−1, all compounds’ average recovery was 76% to 116%, with relative standard deviation (RSD) less than 18.9%. Field experiments were conducted in Liaoning, Heilongjiang, Inner Mongolia, Shanxi, Beijing, and Yunnan provinces to study the terminal residues. The terminal residues of all compounds were below the LOQ (0.01 mg kg−1) in fresh corn and corn kernels, and atrazine residues in corn straw ranged from −1 to 0.17 mg kg−1. Finally, a dietary risk assessment was conducted based on residues from field trials, food consumption, and acceptable daily intake (ADI). For all populations, the chronic dietary risk probability (RQc) of atrazine was between 0.0185% and 0.0739%, while that of isoxaflutole was 0.0074–0.0296%, much lower than 100%. The results may provide scientific guidance for using isoxaflutole and atrazine in corn field ecosystems.

The novel coronavirus disease of 2019 (COVID-19) first appeared in Wuhan and subsequently spread rapidly in cities and provinces across the country and all over the world. In order to effectively control the spread of the epidemic in different areas, zonal management and endemic prevention and control policies should be implemented according to local epidemic situations. This study proposes a time-series clustering method to discover dynamic characteristics of the COVID-19 epidemic by categorizing the epidemic situations in China’s major cities into groups based on daily reported confirmed cases and analysing the driving factors of the city background conditions for each category. Our results show that according to the dynamic patterns of the COVID-19 epidemic there are eight types of epidemic situations, including extreme outbreak areas, large spread areas, potential resurged areas, middle spread areas, controlled outbreak areas, limited growth areas, delayed outbreak areas, and lag report areas. These dynamic patterns are mainly related to the city background conditions, such as population flow, local resident number, government emergency response capability, and medical resource conditions. Based on our results, different endemic prevention and control measures are recommended for containing the COVID-19 epidemic in cities with different types of epidemic situations.

Geographic information science (GIScience) and remote sensing have long provided essential data and methodological support for natural resource challenges and environmental problems research. With increasing advances in information technology, natural resource and environmental science research faces the dual challenges of data and computational intensiveness. Therefore, the role of remote sensing and GIScience in the fields of natural resources and environmental science in this new information era is a key concern of researchers. This study clarifies the definition and frameworks of these two disciplines and discusses their role in natural resource and environmental research. GIScience is the discipline that studies the abstract and formal expressions of the basic concepts and laws of geography, and its research framework mainly consists of geo-modeling, geo-analysis, and geo-computation. Remote sensing is a comprehensive technology that deals with the mechanisms of human effects on the natural ecological environment system by observing the earth surface system. Its main areas include sensors and platforms, information processing and interpretation, and natural resource and environmental applications. GIScience and remote sensing provide data and methodological support for resource and environmental science research. They play essential roles in promoting the development of resource and environmental science and other related technologies. This paper provides forecasts of ten future directions for GIScience and eight future directions for remote sensing, which aim to solve issues related to natural resources and the environment.

ABSTRACT Members of the genus Qipengyuania are heterotrophic bacteria frequently isolated from marine environments with great application potential in areas such as carotenoid production. However, the genomic diversity, metabolic function, and adaption of this genus remain largely unclear. Here, 16 isolates related to the genus Qipengyuania were recovered from coastal samples and their genomes were sequenced. The phylogenetic inference of these isolates and reference type strains of this genus indicated that the 16S rRNA gene was insufficient to distinguish them at the species level; instead, the phylogenomic reconstruction could provide the reliable phylogenetic relationships and confirm 15 new well-supported branches, representing 15 putative novel genospecies corroborated by the digital DNA-DNA hybridization and average nucleotide identity analyses. Comparative genomics revealed that the genus Qipengyuania had an open pangenome and possessed multiple conserved genes and pathways related to metabolic functions and environmental adaptation, despite the presence of divergent genomic features and specific metabolic potential. Genetic analysis and pigment detection showed that the members of this genus were identified as carotenoid producers, while some proved to be potentially aerobic anoxygenic photoheterotrophs. Collectively, the first insight into the genetic diversity and metabolic potentials of the genus Qipengyuania will contribute to better understanding of the speciation and adaptive evolution in natural environments. IMPORTANCE The deciphering of the phylogenetic diversity and metabolic features of the abundant bacterial taxa is critical for exploring their ecological importance and application potential. Qipengyuania is a genus of frequently isolated heterotrophic microorganisms with great industrial application potential. Numerous strains related to the genus Qipengyuania have been isolated from diverse environments, but their genomic diversity and metabolic functions remain unclear. Our study revealed a high degree of genetic diversity, metabolic versatility, and environmental adaptation of the genus Qipengyuania using comparative genomics. Fifteen novel species of this genus have been established using a polyphasic taxonomic approach, expanding the number of described species to almost double. This study provided an overall view of the genus Qipengyuania at the genomic level and will enable us to better uncover its ecological roles and evolutionary history.

Location-aware big data from social media have been widely used to quantitatively characterize natural disasters and disaster-induced losses. It is not clear how human activities collectively respond to a disaster. In this study, we examined the collective human activities in response to Typhoon Hato at multi spatial scales using aggregated location request data. We proposed a Multilevel Abrupt Changes Detection (MACD) methodological framework to detect and characterize the abrupt changes in location requests in response to Typhoon Hato. Results show that, at the grid level, most anomaly grids were located within a radius of 53 km around the typhoon trajectory. At the city level, there are significant spatial difference in terms of the human activity recovery duration (230 h on average). At the subnational level, the absolute magnitude of abrupt location request changes is strongly correlated with the typhoon-induced economic losses and the population affected.

The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people infected and thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multi-source big data, rapid visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, balancing and management of the supply and demand of material resources, and social-emotional guidance and panic elimination, which provided solid spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against the widespread epidemic, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management. At the data level, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

Flow clustering is one of the most important data mining methods for the analysis of origin-destination (OD) flow data, and it may reveal the underlying mechanisms responsible for the spatial distributions and temporal dynamics of geographical phenomena. Existing flow clustering approaches are based mainly on the extension of traditional clustering methods to points by redefining basic concepts or some spatial association indictors of flows and the implementation of classic clustering processes, such as aggregating, collecting or searching. However, current techniques still suffer from two main problems: poor identification accuracy and complicated parameter selection processes. To resolve these problems, a new clustering method is proposed in this study for arbitrarily shaped flow clusters based on the density domain decomposition of flows. Simulation experiments based on our method and existing methods show that our method outperforms the three most commonly used methods in terms of the overall identification rate and almost all F1 measures, and it does not require any manual adjustments during the parameter selection process. Finally, a case study is conducted on taxi trip data from Beijing. Several flow clusters are identified to represent different types of residents' travel behaviors, including daily commuting, return travel, tourism and behaviors on special days.

Consider the problem of clustering objects with temporally changing multivariate variables, for instance, in the classification of cities with several changing socioeconomic indices in geographical research. If the changing multivariate can be recorded simultaneously as a multivariate time series, in which the length of each subseries is equal and the subseries can be correlated, the problem is transformed into a multivariate time series clustering problem. The available methods consider the correlations between distinct time series but overlook the shape of each time series, which causes multivariate time series with similar correlations and opposite shapes to be clustered into the same class. To overcome this problem, this paper proposes a two-phase multivariate time series clustering algorithm that considers both correlation and shape. In Phase I, the discrete wavelet transform is applied to capture the wavelet variances and the correlation coefficients between each pair of variables to realize the initial clustering of multivariate time series, where time series with a similar correlation but opposite shape may be assigned to the same cluster. In Phase II, multivariate time series are clustered based on shape via the symbolic aggregate approximation (SAX) method. In this phase, time series with similar correlations but opposite morphologies are differentiated. The method is evaluated using multivariate time series of incoming and outgoing passenger volumes from Beijing IC card data; these volume data were collected between March 4, 2013 and March 17, 2013. Based on the silhouette coefficient, our approach outperforms two popular multivariate time series clustering methods: a wavelet-based method and the SAX method.

The first wave of the 2019 novel coronavirus (COVID-19) epidemic in China showed there was a lag between the reduction in human mobility and the decline in COVID-19 transmission and this lag was different in cities. A prolonged lag would cause public panic and reflect the inefficiency of control measures. This study aims to quantify this time-lag effect and reveal its influencing socio-demographic and environmental factors, which is helpful to policymaking in controlling COVID-19 and other potential infectious diseases in the future. We combined city-level mobility index and new case time series for 80 most affected cities in China from Jan 17 to Feb 29, 2020. Cross correlation analysis and spatial autoregressive model were used to estimate the lag length and determine influencing factors behind it, respectively. The results show that mobility is strongly correlated with COVID-19 transmission in most cities with lags of 10 days (interquartile range 8 - 11 days) and correlation coefficients of 0.68 ± 0.12. This time-lag is consistent with the incubation period plus time for reporting. Cities with a shorter lag appear to have a shorter epidemic duration. This lag is shorter in cities with larger volume of population flow from Wuhan, higher designated hospitals density and urban road density while economically advantaged cities tend to have longer time lags. These findings suggest that cities with compact urban structure should strictly adhere to human mobility restrictions, while economically prosperous cities should also strengthen other non-pharmaceutical interventions to control the spread of the virus.

The impact on inadequate water quality on water scarcity is unclear. Here the authors quantify China’s present-day water scarcity and show that inadequate water quality exacerbates China’s water scarcity, which is unevenly distributed across the country.

Rapid and accurate strain identification of the most closely related genera Myxococcus, Corallococcus, and Pyxidicoccus can enhance the efficiency of the mining of novel secondary metabolites through dereplication. However, the commonly used 16S rRNA gene sequencing cannot accurately differentiate members of the three genera above, and the whole-genome sequencing is unable to rapidly and inexpensively provide species assignation toward a large number of isolates. To overcome the limitations, the gyrB gene was investigated as a candidate genetic marker for exploring the phylogenetic relationships of bacteria within the three genera and for developing the gyrB-based typing method. Here, the bacterial phylogeny and species affiliations of the three genera were determined based on the phylogenomic reconstruction and the analysis of digital DNA–DNA hybridization values among 90 genomes, further confirming nine novel taxa and assigning over one-third of genomes to defined species. The phylogenetic relationships of these strains based on the gyrB gene sequences were congruent with those based on their genome sequences, allowing the use of the gyrB gene as a molecular marker. The gyrB gene-specific primers for the PCR-amplification and sequencing of bacteria within the three genera were designed and validated for 31 isolates from our group collection. The gyrB-based taxonomic tool proved to be able to differentiate closely related isolates at the species level. Based on the newly proposed 98.6% identity threshold for the 966-bp gyrB gene and the phylogenetic inference, these isolates were assigned into two known species and eight additional putative new species. In summary, this report demonstrated that the gyrB gene is a powerful phylogenetic marker for taxonomy and phylogeny of bacteria within the closely related genera Myxococcus, Corallococcus, and Pyxidicoccus, particularly in the case of hundreds or thousands of isolates in environmental studies.

The present study examined the effects of web-based metacognitive listening practice on L2 learners’ listening comprehension over 14 weeks. Participants (N = 67) came from two intact classes of intermediate EFL university learners in China. The experimental group was involved in the web-based metacognitive listening practice built on the metacognitive listening principles. The control group undertook a traditional web-based listening practice with the same listening materials texts, yet without training on their metacognitive awareness. TOEFL tests and MALQ were used to track the development of listening achievements and metacognitive awareness. ANCOVA was employed to detect the differences between the two groups regarding listening achievements and metacognitive development. The results show that the experimental group made significantly greater gains than the control group in listening achievements. However, the development of metacognition remained inconclusive. The study concludes that metacognitive listening practice under web-based environment could outperform the traditional web-based listening practice in improving listening achievements among Chinese intermediate EFL learners. Besides, some recommendations for further study are discussed.

Interaction in large-scale outdoor space has been extensively studied due to its importance in understanding the human-land relationship. Models such as the gravity have been proven to be able to quantify the interaction between two places. However, interaction in indoor space still remains unclear even though humans spend over 70% of their time indoors. Few studies attempt to construct an indoor interaction model. In this study, we analyze the interaction between stores in shopping malls via customer flow to determine whether indoor mobility interaction follows the gravity law and what are its influencing factors. Based on indoor positioning data, two customer flow measures (connectivity flow, indicating the direct connection between stores, and association flow, indicating the association relationship between stores) and two distance measures (path distance, indicating the minimum travel cost, and store distance, indicating the mean travel cost) are defined to fit the traditional and extended gravity models (considering store floor and type). We find that 1) interaction between stores follows a power law distribution, indicating that only a small fraction of store pairs is closely related; 2) customer mobility is governed by the gravity law, where the distance decay exponent is 1-2 for the connectivity flow and 0-1 for the association flow; and 3) store floor and type are two important factors that affect the interaction between stores. These findings provide insights for modeling indoor interactions and can support indoor settings to optimize their layout, estimate the customer flow and promote sales.

Indoor positioning data reflects human mobility in indoor spaces. Revealing patterns of indoor trajectories may help us understand human indoor mobility. Clustering methods, which are based on the measurement of similarity between trajectories, are important tools for identifying those patterns. However, due to the specific characteristics of indoor trajectory data, it is difficult for clustering methods to measure the similarity between trajectories. These characteristics are manifested in two aspects. The first is that the nodes of trajectories may have clear semantic attributes; for example, in a shopping mall, the node of a trajectory may contain information such as the store type and visit duration time, which may imply a customer’s interest in certain brands. The semantic information can only be obtained when the position precision is sufficiently high so that the relationship between the customer and the store can be determined, which is difficult to realize for outdoor positioning, either using GPS or mobile base station, due to the relatively large positioning error. If the tendencies of customers are to be considered, the similarity of geometrical morphology does not reflect the real similarity between trajectories. The second characteristic is the complex spatial shapes of indoor trajectory caused by indoor environments, which include elements such as closed spaces, multiple obstacles and longitudinal extensions. To deal with these challenges caused by indoor trajectories, in this article we proposed a new method called E-DBSCAN, which extended DBSCAN to trajectory clustering of indoor positioning data. First, the indoor location data were transformed into a sequence of residence points with rich semantic information, such as the type of store customer visited, stay time and spatial location of store. Second, a Weighted Edit Distance algorithm was proposed to measure the similarity of the trajectories. Then, an experiment was conducted to verify the correctness of E-DBSCAN using five days of positioning data in a shopping mall, and five shopping behavior patterns were identified and potential explanations were proposed. In addition, a comparison was conducted among E-DBSCAN, the k-means and DBSCAN algorithms. The experimental results showed that the proposed method can discover customers’ behavioral pattern in indoor environments effectively.

China has entered an era of rapid high-speed railway (HSR) development and the spatial structure of urban agglomerations will evolve in parallel with the development and evolution of the spatial structure of the HSR network. In this study, we explore how the spatial structure of an HSR network evolves at regional and local scales. Existing research into HSR network structures has mostly been carried out at a regional scale, and has therefore failed to reveal the spatial connections within a city. In this work, we progress the science by exploring it at a local scale. To describe the HSR network more accurately, we use the dwell time to simulate the passenger flow between stations and use the simulated passenger flow as the network weight. We use complex network analysis to investigate the evolution of the network’s spatial structure. Our results present the evolution of station locations, of community structure, and of the locations of connections between stations at a regional scale, and also show how HSR network development within core cities has impacted structures and connectivity at a local scale. These results help us to understand the spatial structure of urban agglomerations and cities, and provide evidence that can be used to optimize the structure of the HSR network within regions and cities.

Tourism is playing an important role in the economic development of the Qinghai-Tibet Plateau (QTP). To better develop tourism in this region, the spatial heterogeneity of influencing factors on tourism needs to be studied. Using the spatial distribution of tourism potential from social media data, this paper analyzes the influencing factors of tourism on the QTP from the perspective of spatial heterogeneity. We extract microblogs related to travel topics connected to the QTP in 2017 from Sina Weibo to capture tourism potential. Then, factors considered from six aspects (tourism resources, amenities, transportation, geography, population, and the economy) are selected, and a geographic detector (Geodetector) is employed to detect the explanatory power of these factors for tourism potential. The results indicate different influential tourism factors in Qinghai and Tibet. In Qinghai, the main factors are hotels, tourist attractions, and road network density, and the explanatory power of the factors mainly comes from eastern and western Qinghai. In Tibet, the main factors are road network density, regional GDP (Gross Domestic Product), and urban land. It is suggested that tourism in the central region of Qinghai can be improved by enhancing the publicity and utilization of tourism resources, and Tibet should enhance tourism resource utilization and improve tourism amenities and infrastructure.

Backgrounds and Objectives: The north–south height distinctions in Chinese children suggest that some geographical–climatic factors could determine height variation of short stature. In a national health survey, we aimed to explore the spatial distribution of short stature on city scales, and detect its socio-economic and geographical–climatic factors.Methods: Data on the prevalence of short stature were obtained from a 2014 cross-sectional survey of China (CNSSCH). In total, 213,795 Han Chinese students aged 7–18 years, from 106 cities across 30 provinces, were included. Both China and World Health Organization (WHO) growth references were adopted to define short stature.Results: A spatial clustering was apparent in the distribution of short stature. After multivariable adjustment, altitude and humidity significantly increased the risk of high prevalence in short stature, according to the WHO (odds ratio [OR] = 1.61 and 1.26, 95% confidence interval [CI]: 1.20–2.17 and 1.03–1.54) and China (OR = 1.54 and 1.26; 95% CI: 1.15–2.05 and 1.02–1.55) growth references. Additionally, education significantly decreased the risk of high prevalence in short stature according to the WHO (OR = 0.40; 95% CI: 0.22–0.74) and China (OR = 0.42; 95% CI: 0.22–0.77) growth references. Combining both altitude >400 m and education 70%, received the largest effect-size estimate, and significance retained after multivariable adjustment.Conclusions: Our findings indicate that high altitude and humidity increased the risk of high prevalence in short stature, high education was associated with low prevalence. Additionally, we observed possible interactions between education and altitude/humidity. They may interactively explain spatial discrepancy and predict short stature in Chinese school children. Further validations are necessary.

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

Offline stores are seriously challenged by online shops. To attract more customers to compete with online shops, the patterns of customer flows and their influence factors are important knowledge. To address this issue, we collected indoor positioning data of 534,641 and 59,160 customers in two shopping malls (i.e., Dayuecheng (DYC) in Beijing and Longhu (LH) in Chongqing, China) for one week, respectively. The temporal patterns of the customer flows show that (1) total customer flows are high on weekends and low midweek and (2) peak hourly flow is related to mealtimes for LH and only on weekdays for DYC. The difference in temporal patterns between the two malls may be attributed to the difference in their locations. The customer flows to stores reveal that the customer flows to clothing, food and general stores are the highest; specifically, in DYC, the order is clothing, food and general, while in LH, it is food, clothing and general. To identify the factors influencing customer flow, we applied linear regression to the inflow density of stores (customers per square meter) of two major classes (clothing and food stores), with 10 locational and social factors as independent variables. The results indicate that flow density is significantly influenced by store location, visibility (except for food stores in DYC) and reputation. Besides, the difference between the two store classes is that clothing stores are influenced by more convenience factors, including distance to an elevator and distance to the floor center (only for LH). Overall, the two shopping malls demonstrate similar customer flow patterns and influencing factors with some obvious differences also attributed to their layout, functions and locations.

As a symbol of Chinese culture, Chinese traditional-style architecture defines the unique characteristics of Chinese cities. The visual qualities and spatial distribution of architecture represent the image of a city, which affects the psychological states of the residents and can induce positive or negative social outcomes. Hence, it is important to study the visual perception of Chinese traditional-style buildings in China. Previous works have been restricted by the lack of data sources and techniques, which were not quantitative and comprehensive. In this paper, we proposed a deep learning model for automatically predicting the presence of Chinese traditional-style buildings and developed two view indicators to quantify the pedestrians’ visual perceptions of buildings. Using this model, Chinese traditional-style buildings were automatically segmented in streetscape images within the Fifth Ring Road of Beijing and then the perception of Chinese traditional-style buildings was quantified with two view indictors. This model can also help to automatically predict the perception of Chinese traditional-style buildings for new urban regions in China, and more importantly, the two view indicators provide a new quantitative method for measuring the urban visual perception in street level, which is of great significance for the quantitative research of tourism route and urban planning.

The daily nighttime lights (NTL) and the amount of location-service requests (NLR) data have been widely used as a proxy for measures of disaster-induced power outages and geo-tagged human activity dynamics. However, the association between the two datasets is not well understood. In this study, we investigated how the NTL signals and geo-tagged human activities changed in response to Typhoon Mangkhut. The confusion matrix is constructed to quantify the changes of the NLR in response to Typhoon Mangkhut, as well as the changes of the NTL signals at the grid level. Geographically-weighted regression and quantile regression were used to examine the associations between the changes of the NTL and the NLR at both grid and county levels. The quantile regressions were also used to quantify the relationships between the dimmed NTL signals and the change of the NLR in disaster damage estimates at the county level. Results show that the percent of the grids with anomalous human activities is significantly correlated with the nearby air pressure and wind speed. Geo-tagged human activities varied in response to the evolution of Mangkhut with significant areal differentiation. Over 69.3% of the grids with significant human activity change is also characterized by declined NTL brightness, which is closely associated with abnormal human activities. Significant log-linear and moderate positive correlations were found between the changes of the NTL and NLR at both the grid and county levels, as well as between the county-level changes of NLR/NTL and the damage estimates. This study shows the geo-tagged human activities are closely associated with the changes of the daily NTL signals in response to Typhoon Mangkhut. The two datasets are complimentary in sensing the typhoon-induced losses and damages.

The decomposition of a point process is useful for the analysis of spatial patterns and in the discovery of potential mechanisms of geographic phenomena. However, when a local repulsive cluster is present in a complex heterogeneous point process, the traditional solution, which is based on clustering, may be invalid for decomposition because a repulsive pattern is not subject to a specific probability distribution function and the effects of aggregative and repulsive components may be counterbalanced. To solve this problem, this paper proposes a method of decomposing repulsive clusters in complex point processes with multiple heterogeneous components. A repulsive cluster is defined as a set of repulsive density-connected points that are separated by a certain distance at a small scale and aggregated at a large scale simultaneously. The H-function is used to identify repulsive clusters by determining the repulsive distance and extracting repulsive points for further clustering. Through simulation experiments based on three datasets, the proposed method has been shown to effectively perform repulsive cluster decomposition in heterogeneous point processes. A case study of the point of interest (POI) dataset in Beijing also indicates that the method can identify meaningful repulsive clusters from types of POIs that represent different service characteristics of shops in different local regions.

As urban thermal environments are being caused by global climatic changes and urbanization is not uniform on diurnal, seasonal, or annual scales, the spatiotemporal patterns of surface urban heat islands (SUHI) similarly vary between cities across regions. This research assessed the spatiotemporal variations in SUHI intensities (SUHII), and then revealed their spatiotemporal patterns and relationships that existed within 32 major cities in China using spatialization technologies, such as the self-organizing map (SOM) method and statistical methods. Results showed that the spatial patterns of the SUHII patterns in China were significantly affected by the climatic types, whereas human heat discharge also disturbed the patterns to a certain extent. Specifically, the daytime SUHIIs in China had much higher seasonal variations in North China than in South China. The nighttime SUHIIs were much weaker and more stable than the daytime SUHIIs, and had far more obvious spatial patterns with much higher values in North China than in South China. As for the temporal regimes, the temporal variation in the SUHIIs in one city was more related to the development of the urbanization. To be specific, not all cities were experiencing increasingly worse urban thermal environments with urbanization as reported by previous studies. This research not only proposes a spatiotemporal framework to study the SUHIIs patterns and their relationships, but also provides an in-depth and comprehensive understanding of SUHIIs in China.

Trajectory pattern mining is becoming increasingly popular because of the development of ubiquitous computing technology. Trajectory data contain abundant semantic and geographic information that reflects people’s movement patterns, i.e., who is performing a certain type of activity when and where. However, the variety and complexity of people’s movement activity and the large size of trajectory datasets make it difficult to mine valuable trajectory patterns. Moreover, most existing trajectory similarity measurements only consider a portion of the information contained in trajectory data. The patterns obtained cannot be interpreted well in terms of both semantic meaning and geographic distributions. As a result, these patterns cannot be used accurately for recommendation systems or other applications. This paper introduces a novel concept of the semantic-geographic pattern that considers both semantic and geographic meaning simultaneously. A flexible density-based clustering algorithm with a new trajectory similarity measurement called semantic intensity is used to mine these semantic-geographic patterns. Comparative experiments on check-in data from the Sina Weibo service demonstrate that semantic intensity can effectively measure both semantic and geographic similarities among trajectories. The resulting patterns are more accurate and easy to interpret.

Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

Many patients prefer to use the best hospitals even if there are one or more other hospitals closer to their homes; this behavior is called “hospital bypass behavior”. Because this behavior can be problematic in urban areas, it is important that it be reduced. In this paper, the taxi GPS data of Beijing and Suzhou were used to measure hospital bypass behavior. The “bypass behavior index” (BBI) represents the bypass behavior for each hospital. The results indicated that the mean hospital bypass trip distance value ranges from 5.988 km to 9.754 km in Beijing and from 4.168 km to 10.283 km in Suzhou. In general, the bypass shares of both areas show a gradually increasing trend. The following hospitals exhibited significant patient bypass behavior: the 301 Hospital, Beijing Children’s Hospital, the Second Affiliated Hospital of Soochow University and the Suzhou Hospital of Traditional Chinese Medicine. The hospitals’ reputation, transport accessibility and spatial distribution were found to be the main factors affecting patient bypass behavior. Although the hospital bypass phenomena generally appeared to be more pronounced in Beijing, the bypass trip distances between hospitals were found to be more significant in Suzhou.