The non-stationary and spatially varying associations between hand, foot and mouth disease and multiple environmental factors: A Bayesian spatiotemporal mapping model study.

The transmission and prevalence of Hand, Foot and Mouth Disease (HFMD) are affected by a variety of natural and socio-economic environmental factors. This study aims to quantitatively investigate the non-stationary and spatially varying associations between various environmental factors and HFMD risk. We collected HFMD surveillance cases and a series of relevant environmental data from 2013 to 2021 in Xi'an, Northwest China. By controlling the spatial and temporal mixture effects of HFMD, we constructed a Bayesian spatiotemporal mapping model and characterized the impacts of different driving factors into global linear, non-stationary and spatially varying effects. The results showed that the impact of meteorological conditions on HFMD risk varies in both type and magnitude above certain thresholds (temperature: 30 °C, precipitation: 70 mm, solar radiation: 13000 kJ/m2, pressure: 945 hPa, humidity: 69%). Air pollutants (PM2.5, PM10, NO2) showed an inverted U-shaped relationship with the risk of HFMD, while other air pollutants (O3, SO2) showed nonlinear fluctuations. Moreover, the driving effect of increasing temperature on HFMD was significant in the 3-year period, while the inhibitory effect of increasing precipitation appeared evident in the 5-year period. In addition, the proportion of urban/suburban/rural area had a strong influence on HFMD, indicating that the incidence of HFMD firstly increased and then decreased during the rapid urbanization process. The influence of population density on HFMD was not only limited by spatial location, but also varied between high and low intervals. Higher road density inhibited the risk of HFMD, but higher night light index promoted the occurrence of HFMD. Our findings further demonstrated that both ecological and socioeconomic environmental factors can pose multiple driving effects on increasing the spatiotemporal risk of HFMD, which is of great significance for effectively responding to the changes in HFMD epidemic outbreaks.

Synergistic driving effects of risk factors on human brucellosis in Datong City, China: A dynamic perspective from spatial heterogeneity.

Brucellosis is a highly contagious zoonotic and systemic infectious disease caused by Brucella, which seriously affects public health and socioeconomic development worldwide. Particularly, in China accumulating eco-environmental changes and agricultural intensification have increased the expansion of human brucellosis (HB) infection. As a traditional animal husbandry area adjacent to Inner Mongolia, Datong City in northwestern China is characterized by a high HB incidence, demonstrating obvious variations in the risk pattern of HB infection in recent years. In this study, we built Bayesian spatiotemporal models to detect the transfer of high-risk clusters of HB occurrence in Datong from 2005 to 2020. Geographically and Temporally Weighted Regression and GeoDetector were employed to investigate the synergistic driving effects of multiple potential risk factors. Results confirmed an evident dynamic expansion of HB from the east to the west and south in Datong. The distribution of HB showed a negative correlation with urbanization level, economic development, population density, temperature, precipitation, and wind speed, while a positive correlation with the normalized difference vegetation index, and grassland/cropland cover areas. Especially, the local animal husbandry and related industries imposed a large influence on the spatiotemporal distribution of HB. This work strengthens the understanding of how HB spatial heterogeneity is driven by environmental factors, through which helpful insights can be provided for decision-makers to formulate and implement disease control strategies and policies for preventing the further spread of HB.

QGORE: Quadratic-Time Guaranteed Outlier Removal for Point Cloud Registration.

With the development of 3D matching technology, correspondence-based point cloud registration gains more attention. Unfortunately, 3D keypoint techniques inevitably produce a large number of outliers, i.e., outlier rate is often larger than 95%. Guaranteed outlier removal (GORE) Bustos and Chin has shown very good robustness to extreme outliers. However, the high computational cost (exponential in the worst case) largely limits its usages in practice. In this paper, we propose the first O(N2) time GORE method, called quadratic-time GORE (QGORE), which preserves the globally optimal solution while largely increases the efficiency. QGORE leverages a simple but effective voting idea via geometric consistency for upper bound estimation, which achieves almost the same tightness as the one in GORE. We also present a one-point RANSAC by exploring "rotation correspondence" for lower bound estimation, which largely reduces the number of iterations of traditional 3-point RANSAC. Further, we propose a l p-like adaptive estimator for optimization. Extensive experiments show that QGORE achieves the same robustness and optimality as GORE while being 1  âˆ¼ 2 orders faster. The source code will be made publicly available.

Predicting the Spatial-Temporal Distribution of Human Brucellosis in Europe Based on Convolutional Long Short-Term Memory Network.

Brucellosis is a chronic infectious disease caused by brucellae or other bacteria directly invading human body. Brucellosis presents the aggregation characteristics and periodic law of infectious diseases in temporal and spatial distribution. Taking major European countries as an example, this study established the temporal and spatial distribution sequence of brucellosis, analyzed the temporal and spatial distribution characteristics of brucellosis, and quantitatively predicted its epidemic law by using different traditional or machine learning models. This paper indicates that the epidemic of brucellosis in major European countries has statistical periodic characteristics, and in the same cycle, brucellosis has the characteristics of piecewise trend. Through the comparison of the prediction results of the three models, it is found that the prediction effect of long short-term memory and convolutional long short-term memory models is better than autoregressive integrated moving average model. The first mock exam using Conv layer and data vectorizations predicted that the convolutional long short-term memory model outperformed the traditional long short-term memory model. Compared with the monthly scale, the prediction of the trend stage of brucellosis can achieve better results under the single model prediction. These findings will help understand the development trend and liquidity characteristics of brucellosis, provide corresponding scientific basis and decision support for potential risk assessment and brucellosis epidemic prevention and control, and reduce the loss of life and property.

The impact of anti-COVID-19 nonpharmaceutical interventions on hand, foot, and mouth disease-A spatiotemporal perspective in Xi'an, northwestern China.

Growing evidence has shown that anti-COVID-19 nonpharmaceutical interventions (NPIs) can support prevention and control of various infectious diseases, including intestinal diseases. However, most studies focused on the short-term mitigating impact and neglected the dynamic impact over time. This study is aimed to investigate the dynamic impact of anti-COVID-19 NPIs on hand, foot, and mouth disease (HFMD) over time in Xi'an City, northwestern China. Based on the surveillance data of HFMD, meteorological and web search data, Bayesian Structural Time Series model and interrupted time series analysis were performed to quantitatively measure the impact of NPIs in sequent phases with different intensities and to predict the counterfactual number of HFMD cases. From 2013 to 2021, a total number of 172,898 HFMD cases were reported in Xi'an. In 2020, there appeared a significant decrease in HFMD incidence (-94.52%, 95% CI: -97.54% to -81.95%) in the first half of the year and the peak period shifted from June to October by a small margin of 6.74% compared to the previous years of 2013 to 2019. In 2021, the seasonality of HFMD incidence gradually returned to the bimodal temporal variation pattern with a significant average decline of 61.09%. In particular, the impact of NPIs on HFMD was more evident among young children (0-3 years), and the HFMD incidence reported in industrial areas had an unexpected increase of 51.71% in 2020 autumn and winter. Results suggested that both direct and indirect NPIs should be implemented as effective public health measures to reduce infectious disease and improve surveillance strategies, and HFMD incidence in Xi'an experienced a significant rebound to the previous seasonality after a prominent decline influenced by the anti-COVID-19 NPIs.

Spatiotemporal association of rapid urbanization and water-body distribution on hemorrhagic fever with renal syndrome: A case study in the city of Xi'an, China.

Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis characterized by clinical features of high fever, hemorrhage, and renal damage. China has the largest number of HFRS cases worldwide, accounting for over 90% of the total reported cases. In this paper, we used surveyed HFRS data and satellite imagery to conduct geostatistical analysis for investigating the associations of rapid urbanization, water bodies, and other factors on the spatiotemporal dynamics of HFRS from year 2005 to 2018 in Xi'an City, Northwest China. The results revealed an evident epidemic aggregation in the incidence of HFRS within Xi'an City with a phenomenal fluctuation in periodic time series. Rapid urbanization was found to greatly affect the HFRS incidence in two different time phases. HFRS caused by urbanization influences farmers to a lesser extent than it does to non-farmers. The association of water bodies with the HFRS incidence rate was found to be higher within the radii of 696.15 m and 1575.39 m, which represented significant thresholds. The results also showed that geomatics approaches can be used for spatiotemporally investigating the HFRS dynamic characteristics and supporting effective allocations of resources to formulate strategies for preventing epidemics.

Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau.

Grassland is the vegetation type with the widest coverage on the Qinghai-Tibet Plateau. Under the influence of multiple factors, such as global climate change and human activities, grassland is undergoing temporal and spatially different disturbances and changes, and they have a significant impact on the grassland ecosystem of the Qinghai-Tibet Plateau. Therefore, timely and dynamic monitoring of grassland disturbances and distinguishing the reasons for the changes are essential for ecological understanding and management. The purpose of this research is to propose a knowledge-based strategy to realize grassland dynamic distribution mapping and analysis of grassland disturbance changes in the region that are suitable for the Qinghai-Tibet Plateau. The purpose of this study is to propose an analysis algorithm that uses first annual mapping and then establishes temporal disturbance rules, which is applicable to the integrated exploration of disturbance changes in highland-type grasslands. The characteristic indexes of greenness and disturbance indices in the growing period were constructed and integrated with deep neural network learning to dynamically map the grassland for many years. The overall accuracy of grassland mapping was 94.11% and that of Kappa was 0.845. The results show that the area of grassland increased by 11.18% from 2001 to 2017. Then, the grassland disturbance change analysis method is proposed in monitoring the grassland distribution range, and it is found that the area of grassland with significant disturbance change accounts for 10.86% of the total area of the Qinghai-Tibet Plateau, and the disturbance changes are specifically divided into seven types. Among them, the type of degradation after disturbance mainly occurs in Tibet, whereas the main types of vegetation greenness increase in Qinghai and Gansu. At the same time, the study finds that climate change, altitude, and human grazing activities are the main factors affecting grassland disturbance changes in the Qinghai-Tibet Plateau, and there are spatial differences.

RIFT: Multi-modal Image Matching Based on Radiation-variation Insensitive Feature Transform.

Traditional feature matching methods, such as scale-invariant feature transform (SIFT), usually use image intensity or gradient information to detect and describe feature points; however, both intensity and gradient are sensitive to nonlinear radiation distortions (NRD). To solve this problem, this paper proposes a novel feature matching algorithm that is robust to large NRD. The proposed method is called radiation-variation insensitive feature transform (RIFT). There are three main contributions in RIFT. First, RIFT uses phase congruency (PC) instead of image intensity for feature point detection. RIFT considers both the number and repeatability of feature points and detects both corner points and edge points on the PC map. Second, RIFT originally proposes a maximum index map (MIM) for feature description. The MIM is constructed from the log-Gabor convolution sequence and is much more robust to NRD than traditional gradient map. Thus, RIFT not only largely improves the stability of feature detection but also overcomes the limitation of gradient information for feature description. Third, RIFT analyses the inherent influence of rotations on the values of the MIM and realises rotation invariance. We use six different types of multi-modal image datasets to evaluate RIFT, including optical-optical, infrared-optical, synthetic aperture radar (SAR)-optical, depth-optical, map-optical, and day-night datasets. Experimental results show that RIFT is superior to SIFT and SAR-SIFT on multi-modal images. To the best of our knowledge, RIFT is the first feature matching algorithm that can achieve good performance on all the abovementioned types of multi-modal images. The source code of RIFT and the multi-modal image datasets are publicly available1.

An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment.

Aiming at the problem of how to enable the mobile robot to navigate and traverse efficiently and safely in the unknown indoor environment and map the environment, an eight-direction scanning detection (eDSD) algorithm is proposed as a new pathfinding algorithm. Firstly, we use a laser-based SLAM (Simultaneous Localization and Mapping) algorithm to perform simultaneous localization and mapping to acquire the environment information around the robot. Then, according to the proposed algorithm, the 8 certain areas around the 8 directions which are developed from the robot's center point are analyzed in order to calculate the probabilistic path vector of each area. Considering the requirements of efficient traverse and obstacle avoidance in practical applications, the proposal can find the optimal local path in a short time. In addition to local pathfinding, the global pathfinding is also introduced for unknown environments of large-scale and complex structures to reduce the repeated traverse. The field experiments in three typical indoor environments demonstrate that deviation of the planned path from the ideal path can be kept to a low level in terms of the path length and total time consumption. It is confirmed that the proposed algorithm is highly adaptable and practical in various indoor environments.

Real-Time Geometric Parameter Measurement of High-Speed Railway Fastener Based on Point Cloud from Structured Light Sensors.

With the increase in the number of service years for high-speed railways, the foundation of the rail track suffers from settlement, which causes rail track irregularity. To adjust the position of the track and meet track regularity demands, several components of the fastening system will be replaced by different sized components. It is important to measure the exact geometric parameters for the components of a fastening system before adjusting the track. Currently, the measurement process is conducted manually, which is laborious and error-prone. In this paper, a real-time geometric parameter measurement system for high-speed railway fastener based on 2-D laser profilers is presented. Dense and precise 3-D point clouds of high-speed railway fasteners are obtained from the system. A fastener extraction method is presented to extract fastener point cloud and a region-growing algorithm is used to locate key components of the fastener. Then, the geometric parameter of the fastener is worked out. An experiment was conducted on a high-speed railway near Wuhan, China to verify the accuracy and repeatability of the system. The maximum root-mean-square-error between the manual measurement and the system measurement is 0.3 mm, which demonstrates adequate accuracy. This system can replace manual measurements and greatly improve the efficiency of geometric parameter measurements for fasteners.

Railway Tunnel Clearance Inspection Method Based on 3D Point Cloud from Mobile Laser Scanning.

Railway tunnel clearance is directly related to the safe operation of trains and upgrading of freight capacity. As more and more railway are put into operation and the operation is continuously becoming faster, the railway tunnel clearance inspection should be more precise and efficient. In view of the problems existing in traditional tunnel clearance inspection methods, such as low density, slow speed and a lot of manual operations, this paper proposes a tunnel clearance inspection approach based on 3D point clouds obtained by a mobile laser scanning system (MLS). First, a dynamic coordinate system for railway tunnel clearance inspection has been proposed. A rail line extraction algorithm based on 3D linear fitting is implemented from the segmented point cloud to establish a dynamic clearance coordinate system. Second, a method to seamlessly connect all rail segments based on the railway clearance restrictions, and a seamless rail alignment is formed sequentially from the middle tunnel section to both ends. Finally, based on the rail alignment and the track clearance coordinate system, different types of clearance frames are introduced for intrusion operation with the tunnel section to realize the tunnel clearance inspection. By taking the Shuanghekou Tunnel of the Chengdu-Kunming Railway as an example, when the clearance inspection is carried out by the method mentioned herein, its precision can reach 0.03 m, and difference types of clearances can be effectively calculated. This method has a wide application prospects.

Feature-Based Laser Scan Matching and Its Application for Indoor Mapping.

Scan matching, an approach to recover the relative position and orientation of two laser scans, is a very important technique for indoor positioning and indoor modeling. The iterative closest point (ICP) algorithm and its variants are the most well-known techniques for such a problem. However, ICP algorithms rely highly on the initial guess of the relative transformation, which will reduce its power for practical applications. In this paper, an initial-free 2D laser scan matching method based on point and line features is proposed. We carefully design a framework for the detection of point and line feature correspondences. First, distinct feature points are detected based on an extended 1D SIFT, and line features are extracted via a modified Split-and-Merge algorithm. In this stage, we also give an effective strategy for discarding unreliable features. The point and line features are then described by a distance histogram; the pairs achieving best matching scores are accepted as potential correct correspondences. The histogram cluster technique is adapted to filter outliers and provide an accurate initial value of the rigid transformation. We also proposed a new relative pose estimation method that is robust to outliers. We use the lq-norm (0 < q < 1) metric in this approach, in contrast to classic optimization methods whose cost function is based on the l2-norm of residuals. Extensive experiments on real data demonstrate that the proposed method is almost as accurate as ICPs and is initial free. We also show that our scan matching method can be integrated into a simultaneous localization and mapping (SLAM) system for indoor mapping.

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database.

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m.