Geographic Location Information Research Articles

Ruminant microbiome data are skewed and unFAIR, undermining their usefulness for sustainable production improvement

The ruminant microbiome plays a key role in the health, feed utilization and environmental impact of ruminant production systems. Microbiome research provides insights to reduce the environmental footprint and improve meat and milk production from ruminants. However, the microbiome composition depends on the ruminant species, habitat and diet, highlighting the importance of having a good representation of ruminant microbiomes in their local environment to translate research findings into beneficial approaches. This information is currently lacking. In this study, we examined the metadata of farmed ruminant microbiome studies to determine global representativeness and summarized information by ruminant species, geographic location, body site, and host information. We accessed data from the International Nucleotide Sequence Database Collaboration via the National Center for Biotechnology Information database. We retrieved 47,628 sample metadata, with cattle accounting for more than two-thirds of the samples. In contrast, goats, which have a similar global population to cattle, were underrepresented with less than 4% of the total samples. Most samples originated in Western Europe, North America, Australasia and China but countries with large ruminant populations in South America, Africa, Asia, and Eastern Europe were underrepresented. Microbiomes from the gastrointestinal tract were the most frequently studied, comprising about 87% of all samples. Additionally, the number of samples from other body sites such as the respiratory tract, milk, skin, reproductive tract, and fetal tissue, has markedly increased over the past decade. More than 40% of the samples lacked basic information and many were retrieved from generic taxonomic classifications where the ruminant species was manually recovered. The lack of basic information such as age, breed or sex can limit the reusability of the data for further analysis and follow-up studies. This requires correct taxonomic assignment of the ruminant host and basic metadata information using accepted ontologies adapted to host-associated microbiomes. Repositories should require this information as a condition of acceptance. The results of this survey highlight the need to encourage studies of the ruminant microbiome from underrepresented ruminant species and countries worldwide. This shortfall in information poses a challenge for the development of microbiome-based strategies to meet sustainability requirements, particularly in areas with expanding livestock production systems.

Cloud detection for HY-1C/COCTS over the ocean based on spectral-and-textural-information-guided deep neural network

AbstractGiven that clouds can absorb and scatter radiation signals in the visible and infrared bands, cloud detection is a key preprocessing step for ocean color and sea surface temperature retrievals. In this research, a Spectral-and-Textural-Information-Guided deep neural Network (STIGNet) is designed for cloud detection in global ocean data from the Haiyang-1C (HY-1C)/Chinese Ocean Color and Temperature Scanner (COCTS). Considering the spectral and textural properties of clouds, the model incorporates HY-1C/COCTS spectral data, differences in brightness temperature (BT), local statistical characteristics of BT, and geographical location information–all of which are closely related to cloud features. Notably, an edge learning module is implemented to emphasize edge features during the training process. We construct a HY-1C/COCTS cloud detection dataset to train and test the cloud detection model. In the dataset, labels are generated by combining the Bayesian cloud detection method with a manual mask. Analysis of the resulting cloud detection images indicates that STIGNet exhibits accurate performance across various types of clouds while showing minimal overestimated errors in areas such as ocean fronts or sun glints, where they tend to occur frequently. The ablation experiments performed on physical-based input features and edge learning modules show enhancements in cloud detection accuracy. Evaluation results demonstrate an overall accuracy of 96.64%, with a cloud overestimated error of 1.61% and a cloud missed error of 1.76%. These findings highlight the effectiveness of STIGNet in generating precise cloud masks for HY-1C/COCTS data.

Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles

To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.

An Integrated Approach for Ammunition Depot Location Selection and Ammunition Distribution Network Design Based on P-Median and Vehicle Routing Problems

During the war, the safe provision of the required ammunition at the desired place and time can affect the fate of the war and even the country. Determining the number and locations of ammunition depots needed in the war is as important as planning their distribution. Ammunition supply has always been a force multiplier for military units. Although the timely delivery of ammunition to the unit is not sufficient for success on its own, it is an important factor. These two issues are directly related to each other. This study presents a two-stage methodology to solve the problems in question. In the first stage, it was decided which of the 3 different types of depots, whose establishment locations were known with the p-median model, would be opened, and the unit-depot assignments were determined. In the second stage, the distribution network design for the distribution of ammunition from the ammunition depot to the deployed artillery units was realized with the Capacity-Constrained Vehicle Routing Problem (CCVRP) model. This methodology was tested for a fictitious situation where the geographical location and numerical information of the depots and artillery batteries were generated generically. By solving the mathematical model in the General Algebraic Modeling System (GAMS) program, it was determined which depot should distribute how many vehicles to which artillery batteries, and the results were presented visually.

Spatial analysis of the impact of urban built environment on cardiovascular diseases: a case study in Xixiangtang, China

BackgroundThe built environment, as a critical factor influencing residents' cardiovascular health, has a significant potential impact on the incidence of cardiovascular diseases (CVDs).MethodsTaking Xixiangtang District in Nanning City, Guangxi Zhuang Autonomous Region of China as a case study, we utilized the geographic location information of CVD patients, detailed road network data, and urban points of interest (POI) data. Kernel density estimation (KDE) and spatial autocorrelation analysis were specifically employed to identify the spatial distribution patterns, spatial clustering, and spatial correlations of built environment elements and diseases. The GeoDetector method (GDM) was used to assess the impact of environmental factors on diseases, and geographically weighted regression (GWR) analysis was adopted to reveal the spatial heterogeneity effect of environmental factors on CVD risk.ResultsThe results indicate that the built environment elements and CVDs samples exhibit significant clustering characteristics in their spatial distribution, with a positive correlation between the distribution density of environmental elements and the incidence of CVDs (Moran’s I > 0, p < 0.01). Further factor detection revealed that the distribution of healthcare facilities had the most significant impact on CVDs (q = 0.532, p < 0.01), followed by shopping and consumption (q = 0.493, p < 0.01), dining (q = 0.433, p < 0.01), and transportation facilities (q = 0.423, p < 0.01), while the impact of parks and squares (q = 0.174, p < 0.01) and road networks (q = 0.159, p < 0.01) was relatively smaller. Additionally, the interaction between different built environment elements exhibited a bi-factor enhancement effect on CVDs. In the local analysis, the spatial heterogeneity of different built environment elements on CVDs further revealed the regional differences and complexities.ConclusionsThe spatial distribution of built environment elements is significantly correlated with CVDs to varying degrees and impacts differently across regions, underscoring the importance of the built environment on cardiovascular health. When planning and improving urban environments, elements and areas that have a more significant impact on CVDs should be given priority consideration.

SD-GPSR: A Software-Defined Greedy Perimeter Stateless Routing Method Based on Geographic Location Information

To mitigate the control overhead of Software-Defined Mobile Ad Hoc Networks (SD-MANETs), this paper proposes a novel approach, termed Software-Defined Greedy Perimeter Stateless Routing (SD-GPSR), which integrates geographical location information. SD-GPSR optimizes routing functions by decentralizing them within the data plane of SD-MANET, utilizing the geographic location information of nodes to enhance routing efficiency. The controller is primarily responsible for providing location services and facilitating partial centralized decision-making. Within the data plane, nodes employ an enhanced distance and angle-based greedy forwarding algorithm, denoted as GPSR_DA, to efficiently forward data. Additionally, to address the issue of routing voids in the data plane, we employ the A* algorithm to compute an optimal routing path that circumvents such voids. Finally, we conducted a comparative analysis with several state-of-the-art approaches. The evaluation experiments demonstrate that SD-GPSR significantly reduces the control overhead of the network. Simultaneously, there is a notable improvement in both end-to-end latency and packet loss rate across the network.

P-256 AI-enhanced precision: unveiling the future of blastocyst quality assessment

Abstract Study question Can artificial intelligence (AI) effectively assess various regions of embryos to estimate the quality of human embryos captured through an optical microscope? Summary answer Artificial Intelligence excels in analyzing diverse embryonic regions based on morphology, ensuring precise grade predictions, assessing human embryos via optical microscopes. What is known already In recent research, the integration of AI and computer vision has marked significant strides in embryo selection, providing precise predictions of clinical pregnancy based on images of human embryos, particularly at Day 5. This study extends its findings by incorporating techniques that exhibit resilience to variations in camera and microscope types. Notably, the AI algorithms underwent rigorous testing on double-blind datasets, comprising optical microscopic images sourced from Optical microscope systems across diverse regions such as India, Europe, the UK, the US, and Australia. These results signify a promising advancement in the standardization and applicability of AI-assisted embryo assessment methodologies. Study design, size, duration Approximately 11824 static 2D images of Day 5 blastocysts with related pregnancy, preimplantation genetic testing for aneuploidy (PGT-A) outcomes, demographic and clinic geographical location information have been collected. Images were divided into three groups: training, validation and blind test sets. An AI model was trained on 3473 embryo images, validated on 1184 and tested on further blind set of 1253 images from a separate clinics and demographsic. Gardner based grading system was used. Participants/materials, setting, methods Dataset included optical-microscope images from fertility patients across 25 IVF laboratories in six countries was utilized to train, validate, and test the AI. Dataset was graded by 9 embryologists and final grade was decided based on max-voting. The identity of participants was kept secret and did not associate with used data. Assessment encompassed accuracy, distribution, and robustness concerning camera/microscope type. The dataset spanned various embryo development stages, excluding frozen, degenerated, non-blastocyst embryos. Main results and the role of chance The study distinguishes itself by incorporating data from diverse environments, enhancing generalizability. Leveraging transformer-based attention models ensures meticulous focus on regions relevant to the Gardner grading system, ensuring accurate predictions akin to human judgment and minimizing errors prone to subjectivity. AI’s predictive accuracy on blind dataset for expansion is 82.18%, for ICM is 76.53%, and for trophectoderm is 77.53%, surpassing previous studies. The model demonstrates specificity 79.16%, sensitivity of 85.95% and accuracy of 82.44% in estimating embryo quality. Direct application on embryoscope images is hindered by differing morphology, excluding the embryo part. However, employing segmentation and applying models yields comparable performance with approx. 2.87% drop in accuracy. These findings suggest that the proposed method, with AI trained on a globally diverse dataset, establishes a generalizable AI robust to variations in camera type and focal settings. Limitations, reasons for caution Data collected for this solution has been limited to mainly static images. The study’s focus on good-quality Day 5 blastocyst embryos might limit generalizability to other stages. The dataset’s global diversity may not fully capture demographic nuances. Ongoing AI advancements may necessitate continual updates for relevance and accuracy. Wider implications of the findings Broader implications of the findings suggests transformative potential for AI in embryo quality assessment. The methodology’s success in diverse environments and robustness to variations in camera types underscore its applicability on global scale. This could lead to standardized, automated, and precise embryo evaluations, offering significant advancements in assisted reproductive technology. Trial registration number not applicable

FLsM: Fuzzy Localization of Image Scenes Based on Large Models

This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstrates tremendous potential for research. Deep learning has exhibited a strong performance in image processing, particularly in developing visual navigation and localization techniques using large-scale visual models. This paper introduces a sophisticated scene image localization technique based on large models in a vast spatial sample environment. The study involved training convolutional neural networks using millions of geographically labeled images, extracting image position information using large model algorithms, and collecting sample data under various conditions in elastic scene space. Through visual computation, the shooting position of photos was inferred to obtain the approximate position information of users. This method utilizes geographic location information to classify images and combines it with landmarks, natural features, and architectural styles to determine their locations. The experimental results show variations in positioning accuracy among different models, with the most optimal model obtained through training on a large-scale dataset. They also indicate that the positioning error in urban street-based images is relatively small, whereas the positioning effect in outdoor and local scenes, especially in large-scale spatial environments, is limited. This suggests that the location information of users can be effectively determined through the utilization of geographic data, to classify images and incorporate landmarks, natural features, and architectural styles. The study’s experimentation indicates the variation in positioning accuracy among different models, highlighting the significance of training on a large-scale dataset for optimal results. Furthermore, it highlights the contrasting impact on urban street-based images versus outdoor and local scenes in large-scale spatial environments.

Improving multi-site photovoltaic forecasting with relevance amplification: DeepFEDformer-based approach

The present research on photovoltaic (PV) forecasting is devoted to the use of spatial information about PV sites to improve the accuracy of the models, but most of the models have to increase their network complexity to learn the spatial dependence. In this paper, we propose to take advantage of the known geographic location information of PV sites and embed them directly into the input information of Decoder, which makes it easier for the model to focus its attention. We refer to this process as relevance amplification. Based on this, this paper proposes Relevance Amplification based DeepFEDformer (RAD-FEDformer), where DeepFEDformer adds multiple Multi-Layer Perceptron (MLP) layers to FEDformer to improve the perception of deep features. The Relevance Amplification Module (RAM) is designed to receive geographic correlation information as a way to enhance its influence on the Seasonality component of the Decoder input and improve the performance of the attention mechanism in the model. Using the power generation data from PV plants distributed in 11 regions of Belgium as our case study, we evaluated the performance of RAD-FEDformer in predicting PV data at 48/96/192 time steps into the future for each plant. Compared to other Transformer family models, RAD-FEDformer achieved SOTA results by demonstrating significant R2 improvements of 16.70%, 51.45%, and 23.50% over FEDformer, along with an average MSE reduction of 16.9%. We designed Ablation Experiments to validate and compare the performance of MLPs of different sizes based on the ETTm1 dataset and discussed the performance of RAM and its effect on the attention mechanism based on the PV dataset. The results show that our model enhances the performance of the attention mechanism in Multi-Site PV prediction scenarios, and conclude that the optimization of RAM is more effective in longer sequence predictions.

Robust Energy Efficiency Optimization Strategy for Emergency Communication Based on Fixed-Wing UAV

&lt;p&gt;The geographic location information of a user serves as the foundation for post-disaster emergency applications. However, uncertainties in the user positioning may arise due to factors such as building obstruction and damage to ground base stations. To ensure optimal communication quality, unmanned aerial vehicles (UAVs) can be implemented to maintain close proximity to the user with a minimal turning radius. However, a small turning radius can result in increased energy consumption due to propulsion requirements. To achieve an optimal balance between high communication throughput and low energy consumption, a robust energy efficiency optimization strategy is proposed based on a fixed-wing UAV, addressing the energy efficiency of emergency communication in imprecise user locations. First, the air-to-ground channel model is established, taking into account UAV propulsion energy consumption and formulating a multi-constraint problem to maximize emergency energy efficiency. Second, by incorporating the circular region method to address location uncertainty and considering the worst-case scenario, a robust problem is formulated. Finally, the worst-case scenario is addressed by utilizing the first-order Taylor approximation and successive convex approximation (SCA) technique to solve the nonconvex problem. Through simulation experiments, the proposed scheme is compared with three benchmark schemes, demonstrating its superior energy efficiency and robustness.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Global Chert Database: A summary record of global chert samples

AbstractChert is a sedimentary rock abundant and conspicuous throughout the geologic record. It serves as an essential geological archive and is vital for palaeogeographic reconstruction. The Chert Database Working Group is part of the OneSediment Working Groups of the Deep‐time Digital Earth (DDE) Big Science Program. To facilitate the sharing of chert information and promote chert research, we have compiled a summary of literature containing chert information worldwide and established the Global Chert Database (GCDB). The main body of the current database consists of seven data tables, each providing details on references, lithology, depositional age, geographic location, depositional environment and geochemical information for each sample. As of December 2023, the GCDB contains 8417 sample data from 617 pieces of literature, which can be downloaded from the ‘DDE Data Publish &amp; Repository’.

A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval

In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega (τ−w) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m2, respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m2, respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.

A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information

In recent years, the installed capacity of wind power has rapidly increased. And the wind power prediction is the foundation for ensuring large-scale wind power grid connection. The current short-term prediction methods of wind farm cluster (WFC) are difficult to sufficiently extract spatiotemporal features to achieve high-precision prediction. The article proposes a short-term power prediction method for WFC based on deep attention embedded graph clustering-TimesNet (DAEGC-TimesNet). Firstly, the directed power curves of WFC are proposed to analyze wind data. Then, a graph attention network is constructed based on geographic location and numerical weather prediction (NWP) information to guide clustering algorithms to achieve effective cluster partitioning. Finally, the input of model is constructed based on the feature information from various sub-clusters of WFC and the prediction result is obtained through the TimesNet. The method is applied to WFCs in Inner Mongolia, Jilin province and Yunnan province of China. The result shows that the RMSE reduces 0.0155 and the MAE reduces 0.0156 as well as the coefficient of determination and accuracy rate are highest comparing with comparative algorithms averagely based on above three WFCs. The simulation results are superior to the comparison algorithms, which makes greater contributions to ensure regional power supply.

A Literature Review of Exploratory Analysis of Geolocational Data

Geospatial data is a key part of the digital age. It contains geographic coordinates and location information of people, vehicles, objects and natural phenomena. The rapid growth in its use is due to smartphones, GPS, social media and various location-based applications. This has led to a new paradigm shift in how data is used across industries. This article looks at the multifaceted role of geolocation data in today's society and examines how important it is in the development and change of various industries in today's world. Geospatial data plays a key role in the digital age and the birth of the Internet of Things. In addition to smooth navigation, it also helps industries better allocate resources, improve decision-making and increase overall efficiency. This report explores the growing importance of spatial data and its critical role in urban planning, transportation, environmental monitoring and surveillance, marketing, and public safety

Estimating Winter Arctic Sea Ice Motion Based on Random Forest Models

Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images’ spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In this study, we developed a method based on random forest (RF) models to obtain Arctic SIM in winter by incorporating wind field and coastal geographic location information. These random forest models were trained using Synthetic Aperture Radar (SAR) SIM data. Our results show good consistency with SIM data retrieved from satellite imagery and buoy observations. With respect to the SAR data, compared with SIM estimated with RF model training using reanalysis surface wind, the results by additional coastal information input had a lower root mean square error (RMSE) and a higher correlation coefficient by 31% and 14% relative improvement, respectively. The latter SIM result also showed a better performance for magnitude, especially within 100 km of the coastline in the north of the Canadian Arctic Archipelago. In addition, the influence of coastline on SIM is quantified through variable importance calculation, at 22% and 28% importance of all RF variables for east and north SIM components, respectively. These results indicate the great potential of RF models for estimating SIM over the whole Arctic Ocean in winter.

Deep Neural Networks for Location Reference Identification From Bilingual Disaster-Related Tweets

Twitter is increasingly being used during disasters to communicate with authorities, ascertain the ground reality, and coordinate real-time rescue and recovery activities. Geographical location information about users and events is critical in these scenarios. Geotagged tweets are extremely infrequent, and other location fields, such as user location and place name, are unreliable. The extraction of geographical information from tweet text is limited by the fact that individuals frequently publish multilingual tweets that contain numerous grammatical and spelling errors, as well as nonstandard acronyms. As a result, determining the geographical location of the tweet is a challenging problem. This article presents a technique based on deep neural networks for extracting geographical references mentioned in bilingual tweets. Several deep learning-based models, including convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and attention-based Bi-LSTM, are implemented on real-world English and Hindi language tweets to determine their suitability for extracting location references. The proposed CNN, along with a conditional random field at the last layer, is found to perform better than other models, with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_1$</tex-math> </inline-formula> -score of 0.858. The findings of this study can aid in early event detection, pinpointing the area of devastation and victims, real-time traffic management, and a number of other location-based applications. The suggested system’s code and trained model may be obtained at https://github.com/Abhinavkmr/Bi-lingual-location-reference-identification.git.

Deep learning-based glioma grading and feature visualization analysis

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Research on the fast geolocation algorithm based on Bloom filter

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Seismic 3D full-horizon tracking based on a knowledge graph to represent the stratigraphic sequence relationship

Seismic 3D full-horizon tracking is a fundamental and crucial step in sequence analysis and reservoir modeling. Existing automatic full-horizon tracking approaches lack effective methods for representing the stratigraphic sequence relationships in seismic data. However, the inability to represent the stratigraphic sequence relationships fully and accurately makes it challenging to address discontinuous areas affected by faults and unconformities. To address this issue, we develop a knowledge graph representing the stratigraphic sequence relationship, which enables the simultaneous extraction of all horizon surfaces once the stratigraphic distribution of the seismic data is obtained. This method first generates horizon patches and calculates the fault attributes, followed by the construction of an initial knowledge graph that characterizes the overall distribution of horizon patches and faults. The initial knowledge graph comprises nodes and edges. The nodes represent horizon patches, and their attributes cover the geographical location information of the patches and faults. Simultaneously, the edges represent the relationship between the horizon patches, including the stratigraphic sequence relationship, and their attributes illustrate the potential for connecting these patches. Furthermore, we introduce a multilayer knowledge graph based on the point-set topology to fuse the nodes. This allows for the continuous merging of the horizon patches to obtain horizon surfaces across discontinuities with the constraints of fault attributes and stratigraphic sequence relationships in 3D space. Synthetic and field examples demonstrate that our approach can effectively represent stratigraphic sequence relationships and accurately track horizons located in discontinuous areas with faults and unconformities.

Risk perception and resilience assessment of flood disasters based on social media big data

Social media, as an emerging source of data, has become a valuable data source for disaster prevention and management with its vast database. This study takes the “7·20” extreme rainstorm disaster in Zhengzhou as an example, extracts social media information related to the flooding disaster on social media platforms, constructs a dataset of the rainstorm and flooding disaster, and analyzes its temporal evolution and public sentiment change trends. Secondly, the K-means text clustering method based on TF-IDF was used to extract geographic location information related to waterlogging in social media data, and the resilience matrix was used as a framework to construct a resilience assessment model for urban infrastructure systems in combination with the Pressure-State-Response model. The results show that there was a sharp increase in the discussion of microblog topics about flooding information during heavy rainfall, and there was a 4-h lag between the peak time of topic discussion and the peak time of rainfall intensity. The geographical locations affected by waterlogging extracted based on microblogs can cover 87.5 % of the officially announced waterlogging points. In the resilience assessment of infrastructure systems, the transportation system and drainage system of Zhengzhou performed poorly in response to this rainstorm disaster, whereas the power system and communication system had relatively stronger resilience. This study provides an effective solution to help identify disaster events and promote disaster risk management.