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Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robots, which results in low operating efficiency. Moreover, it is time-consuming and labor-intensive, and the pests and diseases cannot be eliminated in time. Based on multi robot navigation technology, multiple robots can work simultaneously, improving work efficiency. One of the main challenges faced by multi robot navigation technology is to develop a safe and robust collision avoidance strategy, so that each robot can safely and efficiently navigate from its starting position to the expected target. In this article, we propose a low-cost multi-robot collision avoidance method to solve the problem that multiple robots are prone to collision when working in field at the same time. This method has achieved good results in simulation. In particular, our collision avoidance method predicts the possibility of collision based on the robot’s position and environmental information, and changes the robot’s path in advance, instead of waiting for the robot to make a collision avoidance decision when it is closer. Finally, we demonstrate that a multi-robot collision avoidance approach provides an excellent solution for safe and effective autonomous navigation of a single robot working in complex sweet potato fields. Our collision avoidance method allows the robot to move forward effectively in the field without getting stuck. More importantly, this method does not require expensive hardware and computing power, nor does it require tedious parameter tuning.

ABSTRACTThe increasing availability of global observational data has sparked a demand for deep learning algorithms on spherical grids to enable intelligent analysis at a global scale. However, a spherical surface cannot be subdivided into completely identical grid cells through recursive division, and its nonuniformity and irregular deformations lead to uncertainties in the spherical convolutional neural network (SCNN). This paper proposes a multimetric evaluation method to assess the impact of the icosahedral diamond grid quality on the performance of the SCNN by introducing the random forest algorithm to establish nonlinear relationships between multiple grid quality metrics and the SCNN performance and using feature importance analysis to assign impact weights to each grid quality metric considering the SCNN performance. The results show an R2 score of 0.80 for the evaluation method, with four indicators having different weights: cell wall midpoint ratio (0.47), distance between grid points and neighbouring points (0.29), zone standardized compactness (0.13), and angle between a grid point and its two neighbours (0.11). The cell wall midpoint ratio indicator has the most significant impact on the SCNN performance among all grid indicators.

ABSTRACTMethods for fine-grained sample collection are essential for detecting land cover changes at large scales. The complexity of wetland types increases the difficulty of obtaining training samples for high-precision wetland changes, while existing methods mainly focus on coarse-grained classification of urban areas, ignoring the physical growth cycle of vegetation. To solve the above problems, we propose a method for phenological knowledge transfer-based fine grained land cover change sample collection (PKT). Taking the Yellow River Delta as an example, the experimental results are shown as follows. (1) The overall accuracy of the results of the PKT method is 77.03%, and k is 0.42, which is better than the results of the other methods. (2) The PKT method is able to obtain the area of wetland change more accurately and can identify the wetland type changes in the area of change. (3) Making full use of multisource data and fine-grained category information can effectively improve the accuracy of change training samples. (4) Changes in coastal wetlands are the result of the interaction between natural factors and human activities. (5) Further restoration and management of wetlands can be carried out in terms of appropriate protective measures and restrictions on construction behavior.

The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning, resource allocation, government decision-making, disaster assessment, ecological protection, and other sustainability research. However, the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series, while GHS-POP only utilizes land use data with limited accuracy. In view of the limited remote sensing images of long time series, it is necessary to combine existing multi-source remote sensing data for population spatialization research. In this research, we developed a nighttime light desaturation index (NTLDI). Through the cross-sensor calibration model based on an autoencoder convolutional neural network, the NTLDI was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data. Then, the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL. Furthermore, the change characteristics and the driving factors of China’s population spatial distribution are analyzed. The large-scale, long-term population spatialization results obtained in this study are of great significance in government planning and decision-making, disaster assessment, resource allocation, and other aspects.

Although atmospheric CO2 concentrations collected by satellites play a crucial role in understanding global greenhouse gases, the sparse geographic distribution greatly affects their widespread application. In this paper, a hybrid CNN and spatiotemporal Kriging (CNN-STK) model is proposed to generate a monthly spatiotemporal continuous XCO2 dataset over China at 0.25° grid-scale from 2015 to 2020, utilizing OCO-2 XCO2 and geographic covariates. The validations against observation samples, CAMS XCO2 and TCCON measurements indicate the CNN-STK model is effective, robust, and reliable with high accuracy (validation set metrics: R2 = 0.936, RMSE = 1.3 ppm, MAE = 0.946 ppm; compared with TCCON: R2 = 0.954, RMSE = 0.898 ppm and MAE = 0.741 ppm). The accuracy of CNN-STK XCO2 exhibits spatial inhomogeneity, with higher accuracy in northern China during spring, autumn, and winter and lower accuracy in northeast China during summer. XCO2 in low-value-clustering areas is notably influenced by biological activities. Moreover, relatively high uncertainties are observed in the Qinghai-Tibet Plateau and Sichuan Basin. This study innovatively integrates deep learning with the geostatistical method, providing a stable and cost-effective approach for other countries and regions to obtain regional scales of atmospheric CO2 concentrations, thereby supporting policy formulation and actions to address climate change.

Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 km2 and 2293 km2, while the maximum nighttime differences were 484 km2 and 265 km2. Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands.

Paraphoma chrysanthemicola, an endophytic fungus isolated from the roots of Codonopsis pilosula, influences salicylic acid (SA) levels. The interaction mechanism between SA and P. chrysanthemicola within C. pilosula remains elusive. To elucidate this, an experiment was conducted with four treatments: sterile water (CK), P. chrysanthemicola (FG), SA, and a combination of P. chrysanthemicola with salicylic acid (FG+SA). Results indicated that P. chrysanthemicola enhanced plant growth and counteracted the growth inhibition caused by exogenous SA. Physiological analysis showed that P. chrysanthemicola reduced carbohydrate content and enzymatic activity in C. pilosula without affecting total chlorophyll concentration and attenuated the increase in these parameters induced by exogenous SA. Secondary metabolite profiling showed a decrease in soluble proteins and lobetyolin levels in the FG group, whereas SA treatment led to an increase. Both P. chrysanthemicola and SA treatments decreased antioxidase-like activity. Notably, the FG group exhibited higher nitric oxide (NO) levels, and the SA group exhibited higher hydrogen peroxide (H2O2) levels in the stems. This study elucidated the intricate context of the symbiotic dynamics between the plant species P. chrysanthemicola and C. pilosula, where an antagonistic interaction involving salicylic acid was prominently observed. This antagonism was observed in the equilibrium between carbohydrate metabolism and secondary metabolism. This equilibrium had the potential to engage reactive oxygen species (ROS) and nitric oxide (NO).

The Voronoi diagram on the Earth’s surface is a significant data model, characterized by natural proximity and dynamic stability, which has emerged as one of the most promising solutions for global spatial dynamic management and analysis. However, traditional algorithms for generating spherical raster Voronoi diagrams find it challenging to dynamically adjust the Voronoi diagram while maintaining precision and efficiency. The efficient and accurate construction of the spherical Voronoi diagram has become one of the bottleneck issues limiting its further large-scale application. To this end, this paper proposes a dynamic construction scheme for the spherical Voronoi diagram based on the QTM (Quaternary Triangular Mesh) system, with the aim of enabling efficient generation, local updates, and multi-scale visualization of the spherical Voronoi diagrams. In this paper, canonical ordering is introduced. Tailored for the properties of the spherical triangular grid, it constructs a unified and standardized sorting strategy for the dilation of the spherical grids. The construction and updating of the spherical Voronoi diagram are achieved through the ordered dilation of sites. Furthermore, the multi-scale visualization of the spherical Voronoi diagram is realized through the hierarchical structure of the QTM. The paper presents our algorithm intuitively through pseudocode, conducts comparative experiments on the feasibility and efficiency, and designs an experiment for the dynamic navigation and management of ocean-going vessels based on the global multi-resolution Voronoi diagram. The experimental results demonstrate that our algorithm effectively controls the error of the generation of the raster Voronoi diagram and has a significant efficiency advantage when processing dynamic environments.

Coal mining is seriously threatened by fault water inrush, showing the disaster characteristics of hysteresis and concealment. Mastering the evolution mechanism and process law of fault water inrush disaster has important theoretical guiding significance for carrying out deep water prevention and control work. By constructing water inrush evolution analysis model , the conduction path of confined water and the evolution characteristics of fault zone are analyzed. Using true triaxial rock test system of coupled stress-seepage , combined with acoustic emission and digital speckle technology, large-size rock-like specimens containing fault fracture zone fillings were designed. The instability and failure characteristics of specimens with different lithologic fillings and fault occurrences during biaxial loading were studied. The acoustic emission events and specimen deformation characteristics were obtained. Finally, the modified and upgraded mining floor water inrush simulation test system and parallel electrical on-line monitoring, stress and water pressure monitoring subsystems were used to reproduce the whole process of mining fault water inrush evolution in the laboratory, and the evolution characteristics of monitoring parameters were obtained. The results show that the response of fault zone to mining disturbance is stronger, and it is easier to destroy and destabilize to provide space for confined water conduction. To a certain extent, the nature of fault fillings determines the difficulty of fault activation, then affects the temporal and spatial evolution process of fault water inrush. The fault tip is obviously affected by fluid-solid coupling. The cracks generated by the fault zone and the aquiclude undergo the initiation-expansion-through stage, the relative position relationship between the fault and the working face determines the time and space position of the fault evolution zone and the floor failure zone. According to the conduction path of confined water, the process of fault water inrush is divided into three stages : fault activation, water conduction, double zone (fault evolution zone and floor failure zone) docking, the stage delineation of fault water inrush evolution process are realized.

Studying driving factors of the urban heat island phenomenon is vital for enhancing urban ecological environments. Urban functional zones (UFZs), key for planning and management, have a substantial impact on the urban thermal environment through their two-dimensional (2D)/three-dimensional (3D) morphology. Despite prior research on land use and landscape patterns, understanding the effects of 2D/3D urban morphology in different UFZs is lacking. This study employs Landsat-8 remote sensing data to retrieve the land surface temperature (LST). A method combining supervised and unsupervised classification is proposed for UFZ mapping, utilizing multi-source geospatial data. Subsequently, parameters defining the 2D/3D urban morphology of UFZs are established. Finally, the Pearson correlation analysis and GeoDetector are used to analyze the driving factors. The results indicate the following: (1) In the Fifth Ring Road area of Beijing, the residential zones exhibit the highest LST, followed by the industrial zones. (2) In 2D urban morphology, the percentage of built-up landscape (built-PLAND) and Shannon’s diversity index (SHDI) are the main factors influencing LST. In 3D urban morphology, building density, the sky view factor (SVF), and the area-weighted mean shape index (shape index) are the main factors influencing LST. Therefore, low-density buildings with simple and dispersed shapes contribute to mitigating LST, while fragmented distributions of trees, grasslands, and water bodies also play important roles in alleviating LST. (3) In the interactive detection results, all UFZs show the highest interaction detection results with the built-PLAND. (4) Spatial variations are observed in the impact of different UFZs on LST. For instance, in the residential zones, industrial zones, green space zones, and public service zones, the SVF is negatively correlated with LST, while in the commercial zones, the SVF exhibits a positive correlation with LST.

Satellite image time-series change detection methods have become an effective means of obtaining information on land cover change. However, the temporal, spectral, and spatial features and their derived features of land cover objects are of great importance for time-series change detection. The existing studies have made insufficient use of these features, which may affect the results of land cover change detection. In order to fully integrate the above features to portray and represent change information, this study proposes a coupled temporal–spectral–spatial multidimensional information change detection framework (TSSF) method. First, the derived index features are calculated to construct intra-annual temporal–spectral information to reduce the underutilization of spectral features. Second, the intra-annual temporal–spectral information is extended to the spatiotemporal domain by the simple noniterative clustering method and the SG filtering method to increase the exploitation of spatial features. Then, the value and shape-based dynamic time warping method and the change vector analysis in the posterior probability space method are employed to obtain change information from the spectral, index, and class probability perspectives. Finally, the change type in the change region is obtained from the class probability information of the change magnitude according to the Bayesian criterion. Tianjin City was used as the study area to explore the land cover change from 1990 to 2020. The results show that the TSSF method is feasible in expressing temporal–spectral–spatial change information compared with the existing methods and is conducive to the efficient acquisition and identification of change areas and change types.

Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets.

Fine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and urban planning. In the past, nighttime light (NTL) data were generally used to map the spatial distribution of the population at a large scale because of their low spatial resolution. The new generation of Luojia1-01 NTL data can be used for fine-scale social and economic analysis with its high spatial resolution and quantitative range. However, due to the geometry and background noise of the data themselves, the accuracy of the original NTL data is still low. Points-of-interest (POI) also can be used to map the population spatialization, but the indicative relationship between the POI and population is not clear, especially in rural and urban areas with different landscape structures. To solve the above-mentioned problems, this study proposes an improved nighttime light (INTL) index to better use the Luojia1-01 NTL data. Meanwhile, a zonal classification model based on INTL and impervious surface area is proposed to distinguish urban and rural areas. Compared with previous research and existing datasets, our result had the highest accuracy (R² = 0.86). This study explains that the INTL index is applicable to population spatialization research with the emergence of high-resolution and multispectral NTL satellite data. Moreover, the zonal classification model in this research can significantly improve the accuracy of population spatialization in rural areas. This study provides a possible way to use NTL and POI data in other social and economic spatialization research.

Paraphoma chrysanthemicola is a newly identified endophytic fungus. The focus of most studies on P. chrysanthemicola has been on its isolation, identification and effects on plants. However, the limited genomic information is a barrier to further research. Therefore, in addition to studying the morphological and physiological characteristics of P. chrysanthemicola, we sequenced its genome and compared it with that of Paraphoma sp. The results showed that sucrose, peptone and calcium phosphate were suitable sources of carbon, nitrogen and phosphorus for this strain. The activities of amylase, cellulase, chitosanase, lipase and alkaline protease were also detected. Sequencing analysis revealed that the genome of P. chrysanthemicola was 44.1 Mb, with a scaffold N50 of 36.1 Mb and 37,077 protein-coding genes. Gene Ontology (GO) annotation showed that mannose-modified glycosylation was predominant in monosaccharide utilisation. The percentage of glycoside hydrolase (GH) modules was the highest in the carbohydrate-active enzymes database (CAZy) analysis. Secondary metabolite-associated gene cluster analysis identified melanin, dimethylcoprogen and phyllostictine A biosynthetic gene clusters (>60% similarity). The results indicated that P. chrysanthemicola had a mannose preference in monosaccharide utilisation and that melanin, dimethylcoprogen and phyllostictine A were important secondary metabolites for P. chrysanthemicola as an endophytic fungus.

The increased climate change is having a huge impact on the world, with the climatic change sensitive and vulnerable regions at significant risk particularly. Effective understanding and integration of climate information are essential. It helps to reduce the risks associated with adverse weather conditions and to better adapt to the impacts of climate variability and change. Using the hindcast data from Japan Meteorological Agency/Meteorological Research Institute (JMA/MRI) coupled prediction system version 2 (JMA/MRI-CPS2), National Centers for Environmental Prediction (NCEP) Climate Forecast System model version 2 (CFSv2), and Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled Climate Model, versions 3 (CanCM3) seasonal prediction model systems, the performance of seasonal prediction for global surface temperature in boreal summer and winter is comprehensively evaluated and compared for 1982–2011 from the perspective of deterministic and probabilistic forecast skills in this study, and a partial regression correction (PRC) method is introduced to correct seasonal predictions. The results show high prediction skills in the tropics, particularly in the equatorial Pacific, while poor skills on land. In general, JMA/MRI-CPS2 has slightly better prediction performance than CFSv2 and CanCM3 in the tropics. CFSv2 is generally superior to JMA/MRI-CPS2 and CanCM3 in the extratropical northern hemisphere and East Asia, especially for the abnormal low winter temperature prediction in East Asia. CanCM3 shows good deterministic forecast skills in extra-tropics but performs slightly worse in probabilistic forecasting. Based on the respective strengths of each seasonal prediction model, an ensemble forecast correction with observational constraint is implemented by partial regression, and the improvement of skills in ensemble predicting has been analyzed. Compared to the simple multi-model ensemble (MME), the correction improved the global-average temporal correlation coefficient and multi-year mean anomaly correlation coefficient by about 0.1 and 0.13, respectively. The validation tests indicate that the corrected ensemble forecast has higher ranked probability skill scores than that of the MME, which is improved by more than 0.06 in the tropics. Meanwhile, when the training period is sufficiently long, it may have the potential for future seasonal temperature predictions from the perspective of stable zonal partial regression coefficients.

Abstract The switched‐capacitor equalizer (SCE) has a broad application prospect because of its small size, easy control, and implementation. However, the conventional SCE by shifting energy between two adjacent cells usually has a lower balancing speed the number of imbalanced battery cells increases. Therefore, an automatic SCE is proposed to improve the balancing speed for a long battery string. The main advantage of the proposed method is taking the highest‐voltage battery cell as the source for balancing. By controlling the MOSFETs alternately with one pair of complementary pulse width modulation signals, the energy can be directly delivered from the highest‐voltage battery cell to other lower‐voltage ones, to avoid the frequent energy inflow or outflow from the same battery cell. A prototype for four lithium battery cells is implemented, and the comparisons between the proposed SCE and other SCEs in simulation and experiment are presented. Results show that the proposed SCE topology exhibits an obvious improvement in balancing speed at different initial voltage mismatch distributions.

Ordos City is an important energy supply city for Chinese provinces and cities, providing a secure energy supply for China while also generating corresponding environmental pollution. Examining the spatiotemporal patterns of net primary productivity (NPP) in Ordos City and its driving factors is relevant to the realization of the carbon emission policy in Inner Mongolia. This study was undertaken to analyze NPP and its driving factors in Ordos City from 2000 to 2019 using NPP data, CO2 spatial grid data, meteorological data and statistical yearbook data accordingly. The NPP in Ordos City increased significantly from 2000 to 2019, mainly showing low values of NPP in the northwest and high values in the southeast. The usable grassland area and annual mean precipitation had a significant positive correlation with NPP, whereas the other factors had a more significant negative correlation. The usable grassland area had the largest influence on NPP, and fixed asset investment had the smallest influence on NPP. The total NPP–anthropogenic factor regression model and the mean NPP–natural factor regression model constructed allow for the prediction of NPP. Anthropogenic carbon emissions, population growth and usable grassland area were the main causes of NPP changes. Planting and protecting green plants and scientific and effective energy extraction plans are measures that enhance the degree of carbon sequestration in Ordos City.

Floor water inrush has always been an important problem affecting coal mine safety. The water inrush from the deep coal seam floor is the final manifestation of the water inrush channel formed by the expansion and penetration of rock mass cracks under the combined action of a high ground temperature, high ground stress, high karst water pressure and strong mining disturbance. The fault is a structurally weak plane developed in the complete rock layer, which can be regarded as a permeable channel with good permeability. In order to explore the location of confined water from the fault to rock stratum caused by the tensile fracture of a fault during floor water inrush, floor water inrush is regarded as the result of the joint action of water pressure and mining stress. The COMSOL Multiphysics numerical simulation software was used to simulate the advancing process of the working face of the coal seam with a fault structure. Different fault dip models were established for comparative analysis. The objective weight CRITIC based on data volatility was used to analyze the water pressure and principal stress near the fault. The results show that the uplift strength of highly confined water increases with the increase in the fault dip angle, and the weight of water pressure near the fault has a certain relationship with the location of the water body entering the rock stratum from the fault.

Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.

The construction of a cut-off wall is a common reinforcement method for earth rock dams. At present, compared with the in-depth study on homogeneous earth dams, more and more attention is being paid to the stability and deformation of earth dams strengthened by a concrete cut-off wall. In this study, aiming at the Wujing project of the earth dam strengthened by cut-off wall, the influence of the water level rise and fall on the stability of the dam slope, the deformation of the dam body, and the crack width on dam crest were analyzed by numerical calculation and in situ measurement. The analysis results show that when the reservoir encounters a sudden drawdown, the safety factor of the dam slope decreases sharply. The faster the sudden drawdown, the faster the safety factor decreases. When the reservoir water level rises, the dam’s horizontal displacement shifts to the upstream direction, and the change of horizontal displacement of the downstream slope is significantly larger than that at the measuring point of the upstream slope. The water level of the reservoir rises, and the surface of the dam body rises, and the fluctuation of settlement deformation shows that the upstream side is larger than the downstream side, especially during the period of abrupt change in the reservoir water level. The longitudinal cracks on the dam crest show a tendency of shrinkage when the reservoir water level rises, and opening decreases with the decrease of deformation gradient increment and increases with the increase of gradient increment.

Based on the newly released China Merged Surface Temperature (China-MST) dataset, we diagnose the leading mode of covariation between surface temperature and the summer precipitation in Eastern China (EC). A possible connection between the surface temperature in East Asia and EC summer precipitation since the last century (1901–2016) is analyzed. Results indicate that there exists a remarkable meridional dipole pattern of surface temperature, defined as the meridional temperature difference index (MTDI). Meanwhile, there is a tripolar pattern of summer precipitation, which coincides with the first principal component of summer precipitation in EC. Further analyses show that when the MTDI is high in summer, an anomalous anticyclonic/cyclonic circulation is in East Asia’s mid/low latitude. The water vapor is down in the Yangtze and Huaihe River basins, and the ascending motion is weak, resulting in reduced precipitation over this region. At the same time, more water vapor and intensified ascending motion in North and South China tend to produce more summer precipitation. The opposite situations are found when the MTDI is low. Moreover, the MTDI closely relates with the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation at interdecadal and multi-decadal scales. These results have been substantiated in the application of information flow analysis, a recently developed method in physics for the inference of causality between dynamical events.

DGGS (Discrete Global Grid System) has many subdivision models and coding methods. Due to the lack of underlying consistency of different DGGS codes, most of them are converted through longitude–latitude, which greatly reduces the interoperability efficiency of different DGGS data and has become one of the bottlenecks in efficient integration of multi-source DGGS data. Therefore, a direct mapping method from one grid code to another (Grid to Grid, GtoG) for multi-type DGGSs is proposed based on three classical DGGSs (triangular, diamond and hexagonal grids) and two commonly used filling curves (Hilbert curve and Z-curve). The mutual conversion rules of different grids expressing spatial point, line and surface data are constructed. Then, the above method is extended to the spherical icosahedral grid framework, and three different region coding mapping rule tables of the basic inside cells, boundary cells and vertex cells are designed. Finally, the experimental results show that, compared with the longitude–latitude conversion method, the average conversion efficiency of spatial point, line and surface data is increased by 2–4 orders of magnitude. This new method greatly improves the interoperability efficiency and provides a feasible solution for the efficient integration of multi-source DGGS data.

In order to study the matrix loss process and skeleton seepage law in the fracture of the fault rock, the three-dimensional model of the skeletal rock sample of the fault rock was obtained by CT scan, and the porous media seepage model was established with different structural types of natural fractures, and the flow rate and pressure distribution law of the seepage in the fracture was obtained by FLUENT software simulation. The results show that: the seepage under different pressure conditions is approximately the same, and the velocity increases continuously with the increase in pressure; The water seepage in different directions of the fracture channels under the same pressure conditions is not exactly the same, which is caused by the different microstructures of the pores. For the pressure distribution, it gradually decreases along the direction of water seepage, and for the speed distribution, it shows the law of changing from large to small and then increasing.

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

The rock is a kind of geological medium with damages of different degrees including fissures, faults, joints, and other structural defects. Many underground rock engineering projects, such as mining and tunnel excavation, can break the three-dimensional stress balance state of rock mass and make it subject to two-dimensional or even one-dimensional stress, thus inducing stress concentration which leads to rapid failure. In order to investigate the failure law of the rock mass with such defects under two-dimensional stress, based on the similarity theory, we first prepared rocklike specimens with fissures featuring actual mechanical properties and then systematically analyzed the fissure-tip crack propagation and specimen failure law and mechanical mechanism under two-dimensional stress in view of the stress field theory. The results demonstrate that with the increase of load, the microcracks developed and propagated gradually, during which a number of branch paths were generated from the fissure tips of the specimens; the upper and lower cracks were connected first due to the main crack propagation, forming a sliding surface which caused the failure of the specimens, and the strengths of the specimens also fluctuated according to the different combinations of the fissure dip angles and rock bridge dip angles. In view of acoustic emission (AE), we calculated and obtained the spatial positions of stress peaks in each direction at the fissure tips; through comparison and analysis, the angle corresponding to the negative angle peak of the maximum circumferential tensile stress and the maximum radial tensile stress is basically the same as the angle of the main crack propagation direction generated from the preexisting fissure; it can be inferred that the tensile stress is the main stress inducing crack initiation and specimen failure, which is consistent with the physical characteristics of rock (resistant to compression but not tension). This may serve as a guidance for judging the direction along which new cracks are generated in a rock mass with double structural planes.

Weak impact occurs during roadway excavation in some extremely thick coal seams in China. Although this hazard is not enough to destroy the roadway, it will cause fracturing and large deformation of the roadway surrounding rock, resulting in the fracturing of bolts and anchor cables and bringing great difficulties to roadway support. In the hope of solving this problem, firstly, the reason for impact occurrence in the roadway of the extremely thick coal seam is analyzed from the perspective of energy. Then, the surrounding rock fracture evolution in such a roadway is explored by means of numerical simulation, microseism, and borehole observation. Furthermore, the “pressure relief and yielding support” joint prevention and control technology is proposed and applied to Yili No. 1 Coal Mine. The field engineering application results show that the joint prevention and control technology can effectively reduce the impact energy and ensure the stability of the roadway surrounding rock in the extremely thick coal seam. The research findings can provide a theoretical foundation for the roadway support of the same type.

Background. This study is aimed at investigating whether albumin-to-fibrinogen ratio (AFR) could independently predict the prognosis in patients with peritonitis-induced sepsis. Methods. A total of 246 eligible patients who were scheduled to undergo surgical treatment for peritonitis-induced sepsis were enrolled in this study. The primary observational endpoint was 28-day hospital mortality. Cox proportional hazards regression analysis with the Wald test was performed to identify prognostic factors for 28-day mortality in septic patients. Receiver operating characteristic (ROC) and Kaplan-Meier curve analyses were carried out to evaluate the association of baseline AFR and prognosis in septic patients. Results. Of all the cohort study participants, there were 59 nonsurvivors with a 28-day mortality of 24.0% (59/246). Baseline AFR (hazard ratio (HR): 0.67, 95% confidence interval (CI): 0.42–0.93, P=0.018) and the presence of septic shock (HR: 2.43, 95% CI: 1.42–3.91, P=0.021) were two independent prognostic factors for 28-day mortality in patients with peritonitis-induced sepsis by multivariate Cox analysis. Baseline AFR was a significant predictor for 28-day mortality with an area under the curve (AUC) of 0.751, a cut-off value of 8.85, a sensitivity of 66.10%, and a specificity of 70.05%, respectively (95% CI: 0.688–0.813, P

The influence of rock seepage must be considered in geotechnical engineering, and understanding the fluid flow in rock fractures is of great concern in the seepage effect investigation. This study is aimed at developing a model for inversion of rock fracture domains based on digital images and further study of non-Darcy flow. The visualization model of single rock fracture domain is realized by digital images, which is further used in flow numerical simulation. We further discuss the influence of fracture domain geometry on non-Darcy flow. The results show that it is feasible to study non-Darcy flow in rock fracture domains by inversion based on digital images. In addition, as the joint roughness coefficient (JRC) increases or the fracture aperture decreases, distortion of the fluid flow path increases, and the pressure gradient loss caused by the inertial force increases. Both coefficients of the Forchheimer equation decrease with increasing fracture aperture and increase with increasing JRC. Meanwhile, the critical Reynolds number tends to decrease when JRC increases or the fracture aperture decreases, indicating that the fluid tends to non-Darcy flow. This work provides a reference for the study of non-Darcy flow through rock fractures.

The standards of the 5th generation communication technology were picking up.Compared with previous generations’communication technologies,5G will facemore complex business requirements,more extreme user experience andmore dense network architecture.Moreover,5G needs to lay a good communication foundation for IoT before the arrival of the era of big data and artificial intelligence.Some hot technologies of 5G were briefly introduced.Firstly,the techni cal vision and demand target of 5G was given,and then the research progress of three hot technologies ofmassive MIMO,ultra dense network and non-orthogonalmulti-access technology was expounded.

The characteristics of a wide variety of scales about objects and complex texture features of high-resolution remote sensing images make deep learning-based change detection methods the mainstream method. However, existing deep learning methods have problems with spatial information loss and insufficient feature representation, resulting in unsatisfactory effects of small objects detection and boundary positioning in high-resolution remote sensing images change detection. To address the problems, a network architecture based on 2-dimensional discrete wavelet transform and adaptive feature weighted fusion is proposed. The proposed network takes Siamese network and Nested U-Net as the backbone; 2-dimensional discrete wavelet transform is used to replace the pooling layer; and the inverse transform is used to replace the upsampling to realize image reconstruction, reduce the loss of spatial information, and fully retain the original image information. In this way, the proposed network can accurately detect changed objects of different scales and reconstruct change maps with clear boundaries. Furthermore, different feature fusion methods of different stages are proposed to fully integrate multi-scale and multi-level features and improve the comprehensive representation ability of features, so as to achieve a more refined change detection effect while reducing pseudo-changes. To verify the effectiveness and advancement of the proposed method, it is compared with seven state-of-the-art methods on two datasets of Lebedev and SenseTime from the three aspects of quantitative analysis, qualitative analysis, and efficiency analysis, and the effectiveness of proposed modules is validated by an ablation study. The results of quantitative analysis and efficiency analysis show that, under the premise of taking into account the operation efficiency, our method can improve the recall while ensuring the detection precision, and realize the improvement of the overall detection performance. Specifically, it shows an average improvement of 37.9% and 12.35% on recall, and 34.76% and 11.88% on F1 with the Lebedev and SenseTime datasets, respectively, compared to other methods. The qualitative analysis shows that our method has better performance on small objects detection and boundary positioning than other methods, and a more refined change map can be obtained.

The improvement of overall uniformity and smoothness of spherical icosahedral grids, the basic framework of atmospheric models, is a key to reducing simulation errors. However, most of the existing grid optimization methods have optimized grid from different aspects and not improved overall uniformity and smoothness of grid at the same time, directly affecting the accuracy and stability of numerical simulation. Although a well-defined grid with more than 12 points cannot be constructed on a sphere, the area uniformity and the interval uniformity of the spherical grid can be traded off to enhance extremely the overall grid uniformity and smoothness. To solve this problem, an overall uniformity and smoothness optimization method of the spherical icosahedral grid is proposed based on the optimal transformation theory. The spherical cell decomposition method has been introduced to iteratively update the grid to minimize the spherical transportation cost, achieving an overall optimization of the spherical icosahedral grid. Experiments on the four optimized grids (the spring dynamics optimized grid, the Heikes and Randall optimized grid, the spherical centroidal Voronoi tessellations optimized grid and XU optimized grid) demonstrate that the grid area uniformity of our method has been raised by 22.60% of SPRG grid, −1.30% of HR grid, 38.30% of SCVT grid and 38.20% of XU grid, and the grid interval uniformity has been improved by 2.50% of SPRG grid, 2.80% of HR grid, 11.10% of SCVT grid and 11.00% of XU grid. Although the grid uniformity of the proposed method is similar with the HR grid, the smoothness of grid deformation has been enhanced by 79.32% of grid area and 24.07% of grid length. To some extent, the proposed method may be viewed as a novel optimization approach of the spherical icosahedral grid which can improve grid overall uniformity and smoothness of grid deformation.

Opportunistic beamforming (OBF) is a potential technique in the fifth generation (5G) and beyond 5G (B5G) that can boost the performance of communication systems and encourage high user quality of service (QoS) through multi-user selection gain. However, the achievable rate tends to be saturated with the increased number of users, when the number of users is large. To further improve the achievable rate, we proposed a multi-antenna opportunistic beamforming-based relay (MOBR) system, which can achieve both multi-user and multi-relay selection gains. Then, an optimization problem is formulated to maximize the achievable rate. Nevertheless, the optimization problem is a non-deterministic polynomial (NP)-hard problem, and it is difficult to obtain an optimal solution. In order to solve the proposed optimization problem, we divide it into two suboptimal issues and apply a joint iterative algorithm to consider both the suboptimal issues. Our simulation results indicate that the proposed system achieved a higher achievable rate than the conventional OBF systems and outperformed other beamforming schemes with low feedback information.

Social distancing protocols have been highly recommended by the World Health Organization (WHO) to curb the spread of COVID-19. However, one major challenge to enforcing social distancing in public areas is how to perceive people in three dimensions. This paper proposes an innovative pedestrian 3D localization method using monocular images combined with terrestrial point clouds. In the proposed approach, camera calibration is achieved based on the correspondences between 2D image points and 3D world points. The vertical coordinates of the ground plane where pedestrians stand are extracted from the point clouds. Then, using the assumption that the pedestrian is always perpendicular to the ground, the 3D coordinates of the pedestrian’s feet and head are calculated iteratively using collinear equations. This allows the three-dimensional localization and height determination of pedestrians using monocular cameras, which are widely distributed in many major cities. The performance of the proposed method was evaluated using two different datasets. Experimental results show that the pedestrian localization error of the proposed approach was less than one meter within tens of meters and performed better than other localization techniques. The proposed approach uses simple and efficient calculations, obtains accurate location, and can be used to implement social distancing rules. Moreover, since the proposed approach also generates accurate height values, exclusionary schemes to social distancing protocols, particularly the parent-child exemption, can be introduced in the framework.

Force transfer in misaligned columns in building structures can be implemented using overlapping column transfer assemblies. Two 1/6 scaled models of overlapping column transfer assemblies were tested to investigate their mechanical behavior under monotonic loadings. Both specimens were constructed using reinforced concrete and had an overlapping ratio of 0.56, one of which was encased with a steel truss with a cross-sectional steel plate ratio of 3.73%. A finite element method-based model was built, verified and used for parametric studies. Results indicated that the load bearing capacities of the steel-encased assembly increased by 35% compared to that of the assembly without steel. The increase in cross-sectional steel plate ratios and decrease in overlapping ratios improved the load bearing capacities of steel-encased assemblies.

Evaluating the deterioration of ecological environment and vegetation of coalfields caused by China’s large-scale coal mining activities is important because of the fragile ecological environment and low temperature in cold and arid areas. This study takes the open coal pits of Haizhou, Gulianhe, and Huolinhe as examples and proposes a method for evaluating their ecological environment using Landsat time series images based on the Normalized Difference Vegetation Index (NDVI) variations of open-pit coalfields in cold and arid zones. The average NDVI value of the mining area each month was calculated using Landsat image data from 2000 to 2015. The vegetation cover area in the coalfields was extracted according to the NDVI threshold, and the scatter plots of the annual maximum NDVI and vegetation cover area were drawn. We fitted the variation trend line of maximum NDVI value and vegetation cover area to reduce the effect of meteorological factors on NDVI values. Results show that after the closure of open pit and reclamation of dump area, the NDVI of open-pit coalfields and vegetation cover area have been increasing rapidly over the last decade, and the ecological environment of these coalfields has obviously improved. The coal mining activities have led to the rapid decline of annual maximum NDVI and vegetation cover area of the coalfields in permafrost zones, and the ecological environment of coalfields continues to deteriorate. Although the quarterly average NDVI remains unchanged in non-permafrost mining coalfields under coal exploitation, the vegetation cover area in the coalfields decreases linearly, indicating that the ecological environment of the coalfields tends to deteriorate. From an ecological environment protection perspective, the results of this study provide a basis for decision making in constructing large-scale open pits in cold and arid zones.

Due to time and cost limitations, multi-stress accelerated life tests (ALTs) have been gradually applied in the fields of experimental design and reliability estimation for highly reliable and long-life products. To develop more efficient and concise test plans, it is necessary to study an equivalent experimental design method to create an equivalent conversion approach for different ALT plans. This paper presents the equivalent optimum design method combined with D-efficiency and asymptotic variance. In this method, the Fisher information matrix determinant is proposed as the equivalent feature variable. D-efficiency is a new equivalent criterion applied in the ALT design method and is measured using the ratio of Fisher information matrix determinants. The objective is to minimize the test time at a given efficiency and under asymptotic variance constraints without loss of test estimation accuracy. Based on the proposed equivalent method, a constant-stress ALT plan was successfully converted into its equivalent step-stress or ramp-stress ALT plan. Finally, an example is presented to demonstrate the equivalent ALT design of an electrical product with two stresses. The constant-stress ALT is equivalently designed for the ramp-stress ALT plan, and the sensitivity of the D-efficiency is analyzed. The results show that the proposed equivalent test scheme can shorten test time while achieving the same estimation accuracy.

Although the mechanism and influence of fault water inrush have been widely studied, there are still few studies on the migration of filling particles and the evolution process of seepage characteristics within faults. In this work, the coupling effects of water flow, particle migration, and permeability evolution are considered synthetically, and the evolution model of seepage characteristics with multifield coupling is established. This model was used to investigate the evolution process of water inrush within faults and the effects of water pressure, initial effective porosity, and initial permeability on water flow rate. The results show that the evolution of seepage characteristics can be divided into three phases: (i) low velocity seepage, (ii) drastic changes with substantial particle migration, and (iii) steady-state water flow. The multifield coupling causes the effective porosity, permeability, flow velocity, and particle concentration to accelerate each other during the dramatic phase. Moreover, the increases in initial water pressure, initial porosity, and initial permeability have different degrees of promotion on the water flow rate. Finally, the simulation results are approximately the same as the data of water inrush in the mining area, which verifies the correctness of the evolution model established in this work. This work provides new approaches to the evolution process and prevention of water inrush in faults.

Although a Discrete Global Grid (DGG) is uniform in its initial subdivision, its geometric deformation increases with the level of subdivisions. The Goodchild Criteria are often used to evaluate the quality of DGGs. However, some indicators in these criteria are mutually incompatible and overlap. If the criteria are used directly, the evaluation of the DGGs is inaccurate or unreliable. In this paper, we calculated and analyzed the correlation between the evaluation indicators of the DGG and reconstructed a quality evaluation system of DGGs with independent indicators. Firstly, we classified the Goodchild Criteria into quantitative and qualitative indicators. Then, we calculated the correlation among the quantitative indicators and extracted the independent evaluation factors and related weights of the observed values by factor analysis. After eliminating or merging the incompatible and overlapping quantitative indicators and performing a logical reasoning of the qualitative indicators, we reconstructed a comprehensive evaluation system with independent indicators. Finally, taking the Quaternary Triangular Mesh (QTM) model as an example, we verified the independence of the indicators and the feasibility of the evaluation system. The new indicator system ensures the reliability of the evaluation of DGGs in many fields.

Comparing with the circular concrete columns confined with fiber reinforced polymer (FRP) wrap or tube, the rectilinear confined columns were reported much less. Due to the non-uniform distribution of confining pressure in the rectilinear confined columns, the FRP confinement effectiveness was significant reduced. This paper presents findings of an experimental program where nine prefabricated rectangular cross-section CFRP tubes with CFRP integrated crossties filled concrete to form concrete-filled FRP tube (CFFT) short columns and three plain concrete control specimens were tested. All specimens were axially loaded until failure. The rest results showed that the stress-strain curves of CFFTs consisted of two distinct branches, an ascending branch before the concrete peak stress was reaches and a second branch that terminated when the tube ruptured, and that the CFFTs with integrated crossties experienced most uniform confinement pressure distribution. Test research also found that the stress-strain curves of CFFTs indicated an increase in ductility. These demonstrate that this confinement system can produce higher lateral confinement stiffness. DOI: http://dx.doi.org/10.5755/j01.ms.20.4.6035

Adjacent geotechnical engineering activities, such as deep excavation, may adversely affect or even damage adjacent tunnels. Ground reinforcement before excavation may be an effective approach to reduce tunnel heave as a result of stress relief. However, there are few quantitative studies on the effect of soil reinforcement on tunnel deformation. Moreover, the reinforcement mechanism of the reinforced soil and the reinforcement depth are not fully understood. In order to investigate the effect of reinforcing the ground on the tunnel response, a finite element analysis was conducted based on a previously reported centrifugal model test with no ground reinforcement. The effect of the Young’s modulus and depth of the reinforced soil on tunnel deformation was analyzed. Soil stresses around the tunnel were also considered to explain the tunnel response. The results revealed that the Young’s modulus of the reinforced soil and the reinforcement depth had a significant impact on tunnel deformation as a result of basement excavation. The tunnel heave in the longitudinal direction decreased by 18% and 27% for modulus of the reinforced soil, five times and ten times higher than that of the non-reinforced soil, respectively. The reinforcement depth was effective with regard to controlling the tunnel heave caused by stress relief. This is because the reinforced soil blocked the stress transfer and thus reduced the tunnel heave caused by excavation unloading. It is expected that this study will be useful with regard to taking effective measures and ensuring the safety and serviceability of existing metro tunnels during adjacent excavation.

Mesenteric panniculitis is a rare, benign disease characterized by a chronic non-specific inflammatory process of mesenteric fat tissue with unknown etiology. Its clinical outcome ranges from complete resolution without any treatment to rapid progression culminating in death. So far, there is no established treatment available for its management. A 53-year-old female was admitted to our hospital because of intermittent abdominal pain, abdominal distension and early satiety for three months. Her abdominal CT scan revealed a solitary, well-defined, heterogeneous mass with a diameter of approximately 3.5cm x 2.8cm in the mesentery of small bowel. Exploratory laparotomy revealed yellowish-grey mass with a gritty consistency and histopathology of the resected specimen confirmed it to be mesenteric panniculitis. An increased awareness may lead to the use of a less invasive diagnostic approach and optimal management of this rare disease entity. Keywords: Computerized Tomography (CT); Mesenteric Panniculitis (MP); treatment.

MODIS (MODerate Resolution Imaging Spectroradiometer) aerosol products are the most widely used satellite retrieved aerosol optic depth (AOD) products, which compensate for the spatial lack of ground-based sun photometer observations. The newly released Collection 6 (C6) aerosol products have some improvements compared to the Collection 5.1 (C5.1) products with optimized algorithms and newly revised upstream products. Additionally, a three-kilometer resolution AOD product was added in the C6 product. In this study, the accuracies and regional applicability of long-term (2001–2015) different MODIS C5.1 and C6 aerosol products in China were evaluated against the 16 AERONET (Aerosol Robotic Network) observations with observations over more than three years. The overall analysis indicates that the C6 DT (Dark Target) 10 km products slightly improved the retrieval accuracies, with about 3% more data falling within the Expected Error (EE) envelope. However, for Deep Blue (DB) products, the C6 algorithm significantly improved the accuracy over all of China, and increased the successful retrieval number by extending retrieval coverages. Regional analysis demonstrated that the C6 DT 10 km product did not perform well in East China, with only 33.5% of retrievals falling within the EE envelope. For the DB product, the C6 algorithm significantly increased the number successfully retrieved, and was more accurate in all four regions in China. The validation of the DT 3 km product suggests large differences existed between the Terra and Aqua results. The accuracy of the Aqua DT 3 km product is obviously higher than that of the Terra DT 3 km product. The results of the study suggest that proper AOD products need to be considered when evaluating aerosol loading situations in different regions in China.

Liposarcoma is one of the most common soft-tissue sarcomas and classified as five groups. Dedifferentiated liposarcoma includes multiple elements such as solid tissue, adipose tissue and calcified tissue, which is different from common liposarcoma in tissue constitue. Dedifferentiation to osteosarcoma is rarely described in the literature. We reported the radiological and pathological findings in a case of dedifferentiated liposarcoma with osteo-sarcomatous dedifferentiation in the retroperitoneum and discussed the characteristic features of this specific tumor. Complete surgical resection of the neoplasm is the only curative means, but establishing a prognosis remains a challenge for long-term evaluation.

A global multi-resolution image data model and a feasible solution for its seamless management and archiving remain a challenging vision. The traditional methods of the raster pixel data structure based on the idea of map projections are effective to support local or small-scale areas. However, if this structure is applied to large-scale or whole global image archiving, some significant drawbacks are unavoidable, such as data discontinuity (or overlapping), geometric distortions, etc. To overcome these deficiencies, in this paper the Quaternary Triangular Mesh (QTM) (Dutton, 1989), as a continuous, hierarchal quadtree data structure with uniform grids on a sphere, is proposed for global-scale seamless image archiving. First, the mapping relation between raster image pixels and QTM pixels is approached based on the QTM subdivision and Quaternary coding scheme (Bartholdi & Goldsman, 2001), and a corresponding algorithm of QTM pixel grey level calculation is also developed. Then, the storage structure of global-scale image archiving based on QTM pixels is presented in detail. In the end, an experiment is described using the 1km resolution NOAA data for China, comparing the differences in pixel grey levels between original image pixels and QTM pixels. The result indicates that the QTM pixel data structure can keep global-scale images seamless, and the accuracy of transformation from the imaging pixel to the QTM pixel is a loss of less than 2 grey levels for 94.5% of all pixels, the loss from 2 to 4 is 1.9%, the loss from 4 to 10 is 2%, and the rest is 1.6%. The results are good and acceptable.