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Abstract Historical figures have important significance worldwide. This paper introduces the concept of historical Geographical Information System (HGIS) using the lives of two personalities from Chinese history as case studies: Su Shi, a literary figure of the Northern Song Dynasty, and Zuo Zongtang, a military strategist from the late Qing era. By studying the geographical points marking their life activities, their migratory patterns were explored, along with the dynamics of their social networks within time and space, examining the distinctive phases and characteristics of their individual journeys. Kernel density estimation, standard deviation ellipse, location-based distance analysis, and social network graphs were employed in this work. Our findings reveal that Su Shi’s migrations were largely dictated by official appointments, ultimately rooted in imperial successions and factional strife, with his intellectual pursuits significantly influenced by the humanistic milieu. Conversely, Zuo Zongtang’s migrations were predominantly shaped by war, reflecting the challenges of the late Qing era in relation to governance, sovereignty, and diplomacy. The military encampments selected by Zuo Zongtang tended to be situated near water sources, illustrating how his migratory trajectory was influenced by natural geographic conditions. The social circles of these figures are tied to their social and political stature. Through the socio-historic context, the characteristics of these historical figures’ behavioral trajectories relate to the unfolding process of societal and historical development. These insights offer a glimpse into the life journey behind historical figures, implicitly highlighting their perseverance and resilience in adversity, thereby describing innovative approaches to inheriting and disseminating the cultural legacy.

Vegetation serves as a crucial indicator of ecological environment and plays a vital role in preserving ecosystem stability. However, as urbanization escalates rapidly, natural vegetation landscapes are undergoing continuous transformation. Paradoxically, vegetation is pivotal in mitigating the ecological and environmental challenges posed by urban sprawl. The middle and lower Yangtze River Basin (MLYRB) in China, particularly its economically thriving lower reaches, has witnessed a surge in urbanization. Consequently, this study explored the spatiotemporal variations of normalized difference vegetation index (NDVI) in the MLYRB, with an emphasis on elucidating the impact of climate change and urbanization on vegetation dynamics. The results indicate that a significant increasing trend in NDVI across the MLYRB from 2000 to 2020, a pattern that is expected to persist. An improvement in vegetation was observed in 94.12% of the prefecture-level cities in the study area, predominantly in the western and southern regions. Temperature and wind speed stand out as dominant contributors to this improvement. Nevertheless, significant vegetation degradation was detected in some highly urbanized cities in the central and eastern parts of the study area, mainly attributed to the negative effects of escalating urbanization. Interestingly, a positive correlation between NDVI and the urbanization rate was observed, which may be largely related to proactive ecological preservation policies. Additionally, global climatic oscillations were identified as a key force driving periodic NDVI variations. These findings hold significant importance in promoting harmonious urbanization and ecological preservation, thereby providing invaluable insights for future urban ecological planning efforts.

The relationship between commercial vehicle travel patterns and urban functional areas reveals potential connections between urban form and human geographic flows, which provides critical information for optimizing urban transportation systems. Benefiting from the large-scale trajectory datasets, it would be possible to investigate deeper research by modeling the implied urban travel patterns. This study designs a framework to reveal the collective movement patterns of commercial vehicle trajectories inside the urban environment, focusing on their spatiotemporal variations within functional areas. Stopping behaviors of trajectories were identified to construct spatiotemporal origin-destination (OD) matrices, representing time-varying human geographic flows. The singular value decomposition (SVD) method was employed to quantify spatio-temporal OD matrice to obtain time and space travel features. Travel patterns’ dynamics and spatial interactions within functional areas were then analyzed. The experimental results obtained with real-life datasets from Changsha, China, uncovered three typical travel patterns depicting commercial vehicle activities in urban environment shifts from work-related locations on weekdays to leisure destinations on weekends, with central areas experiencing more short and medium-range trips. The findings provide scientific references for optimizing spatio-temporal travel patterns and functional distribution to meet the demands of urban development and traffic management strategies.

Neuroinflammation plays a key role in the progression of secondary brain injury after ischemic stroke, and exosomes have been increasingly recognized to eliminate inflammatory responses through various mechanisms. This study aimed to explore the effect and possible mechanism of human umbilical vein endothelial cells derived exosomes (H-EXOs) on neuroinflammation. We established a transient middle cerebral artery occlusion/reperfusion (tMCAO/R) in male rats and oxygen-glucose-deprivation/reoxygenation (OGD/R) model in cultured neurons to mimic secondary brain injury after ischemic stroke in vivo. H-EXOs were administered at the same time of reperfusion. Results showed that the production of pro-inflammatory cytokines TNF-α, IL-1β, and IL-6, and the transcription factor Krüppel-like factor 14 (KLF14) were significantly increased both in rat brain tissue and cultured neural cells after ischemic-reperfusion (I/R) injury. H-EXOs treatment significantly improved the cultured cell viability, reduced infarct sizes, mitigated neurobehavioral defects, and alleviated the expression of pro-inflammatory cytokines compared with the control group, indicating that H-EXOs exerted anti-inflammatory effect against I/R injury. Further studies revealed that the anti-inflammatory effect of H-EXOs could be weakened by small-interfering RNA (siKLF4) transfection. KLF14 was a protective factor produced during cerebral ischemia-reperfusion injury. In conclusion, H-EXOs protect neurons from inflammation after I/R injury by enhancing KLF14 expression.

Abstract Vegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year−1 (p

Challenges have been observed in the traditional circumferential-scanning LiDAR underwater to balance between the detection range and the sealing performance. To tackle these challenges, a new scanning unit is presented in this paper, employing a pyramidal-shaped reflector for enhanced performance. Furthermore, an innovative magneto–electric detection module comprising Hall switches and magnetic rings is introduced. It can facilitate the accurate identification of the reflector’s edge, thereby enhancing the precision of the target-orientation detection. A rapid target orientation coding method based on split-frequency clocks is proposed on FPGAs. It can output the target’s initial and termination orientation codes immediately after capturing it, exhibiting a significantly low output delay of 20 ns and a high detection resolution of 15°. Finally, a prototype is fabricated to validate the design in this paper. The experimental results demonstrate that the scanning unit enables reliable scanning and orientation recognition of the target. In addition, it is trustworthy in receiving echo signals when the laser passes through glass and then an aqueous medium.

Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic $c$-means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. First, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Second, objects at the coarsest scale are classified into changed, unchanged, and uncertain categories by the proposed MRFPCM. Third, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged, and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three datasets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach.

At present, the structural changes of extruded materials under thermal-mechanical stress during high-moisture extrusion are still unclear. In this study, the transglutaminase (TG) treatments on the structure of pea protein isolate (PPI) under conditions relevant to high-moisture extrusion processing (50 wt% PPI at 30°C, 120°C and heated to 120°C and subsequently cooled to 30°C) was studied by using a closed cavity rheometer. Strain and frequency sweeping were carried out under various temperature conditions, and the information obtained was drawn into a texture map. Lissajous curves combined with energy dissipation ratio were introduced to characterize the nonlinear response of the samples. The results showed that the storage modulus of PPI increased with the increase of TG concentration during heat treatment. After cooling to 30°C, PPI with 0.25–1%TG could enhance the elasticity, but treating by 2% TG could inhibit the formation of disulfide bonds, the uniform development of the protein network, thus showing the “tough” character. These findings can help to better understand the relationships of material-structure during the extrusion process, and also provide help for further optimization of the quality of meat substitutes.

As a key role in hindering the large-scale application of fuel cells, oxygen reduction reaction has always been a hot issue and nodus. Aiming to explore state-of-art electrocatalysts, this paper reviews the latest development of nonmetallic catalysts in oxygen reduction reactions, including single atoms doped with carbon materials such as N, B, P or S and multi-doped carbon materials. Afterward, the remaining challenges and research directions of carbon-based nonmetallic catalysts are prospected.

Park green space is an important part of the green infrastructure in a city, and urban park space accessibility is an important indicator for measuring the city’s comprehensive strength, living environment, and resident happiness, which greatly affects the sustainable development and construction of the city. The spatial syntax method can discuss the accessibility of urban parks from the perspective of topology, which inherits the objective stability of the topological relationship. There are few research studies on the accessibility of urban parks. Therefore, with the support of space syntax theory and the spatial statistics method, this paper combined the data of park green space and road network to complete the accessibility evaluation of urban parks in the Changsha City urban area. This paper evaluates the accessibility of urban parks in the study area from four aspects, namely global accessibility, perceptual accessibility, local accessibility, and psychological accessibility, by using five quantitative indexes of space syntax, namely connectivity, depth, integration, selection, and synergy. The conclusions are as follows: The spatial layout area of the park is roughly consistent with the areas that have good global and local accessibility of the road network, and the global accessibility and local accessibility of the park in the study area are relatively high. The global spatial structure can be better perceived by the local space at a radius scale greater than or equal to 3000 m, and the perceptual accessibility of the park is high. Most of the urban parks in the Changsha urban area are located in sections with high space efficiency. The park space penetration is good, and nearly 70% of the parks have high psychological accessibility. Based on the overall spatial layout of urban parks, the space syntax method quantitatively and comprehensively evaluates the accessibility of urban parks from the perspective of topology. The conclusion is reliable and has important application value in the evaluation of park accessibility. It can be used as an important supplement to improve the evaluation model of accessibility.

With the significant updates of location-acquisition technologies, there are more spatial-temporal trajectory data available. Spatial-temporal stream plays a crucial role in the research and applications of intelligent transportation. However, there are still some problems in the study of multi-target hotness diffusion in spatial-temporal stream scenarios: such as forecast of POIs' (Points of Interest) visitors or short-term traffic. Passenger and traffic flow can be called spatial-temporal hotness, the law of hotness diffusion in spatial-temporal of multi-hot-spots can be detected by studying their trajectory. Spatial-temporal trajectory data often presents multi-core (or multi-target) characteristics around events or behaviors, it is still a challenge to carry out multi-target modeling at the spatial-temporal level. Here, we provide a compelling method for dealing with multi-target hotness diffusion. To excavate the hotness in the movement and diffusion laws in spatial-temporal, we treat movements as a long network infrastructure flood from its source. Through modeling and analysis of OD (Origin-Destination) stream, the hotness prediction is finally achieved. Finally, two groups of experiments were used to demonstrate our method from the perspectives of passenger flow and traffic flow respectively and the experiments based on real-world data show that the effectiveness of our method in predicting the spatial diffusion state of multi-target hotness in different spatial scales. Based on the R-square, MAE (Mean Absolute Error), MSE (Mean Squared Error), and other evaluation indexes compared to the traditional prediction method and ARIMA (Autoregressive Integrated Moving Average) model. Thus, these findings suggest that our method shows more advantages than others.

Exploring the heterogeneous characteristics of the urban expansion process is essential for understanding the dynamics of the urban spatial structure. Many studies focused on depicting the spatio-temporal characteristics based on urban expansion patches. However, measuring heterogeneous characteristics of urban expansion from agglomeration areas comprising the expanded urban construction land patches have not been adequately explored. This study presents a novel approach and two improved indices for characterizing the heterogeneity of urban spatial agglomeration areas during urban expansion. Firstly, we proposed a Gaussian mixture model considering multiple constrains and density-based spatial clustering of applications with noise (DBSCAN) integration method to identify and extract the urban agglomeration areas automatically. Secondly, the gradient analysis and the compact index using the inverse “S” function are introduced to explore the spatio-temporal characteristics from a macrocosmic perspective. Finally, the compactness index (NCI) and normalized dispersion index (NDIS) are improved based on agglomeration area data. The microcosmic heterogeneous characteristics are measured by these two improved indices and the positional offset characteristics indices (POCIS). The method was implemented in the urban area of Changsha, Hunan Province, China in 2005, 2010, and 2015. The results show that (1) compared to that in the Changsha City Master Plan (2003–2020), the recognition rate was higher in the agglomeration areas than others. (2) The overall expansion trend in Changsha transitioned toward decentralization, making Changsha a polycentric city. (3) The agglomeration of urban expansion in the east-west direction became compact; that in the north-south direction became looser; most clusters expanded to the west and a new sub-center would appear. The proposed method can effectively characterize their heterogeneity, which can provide valuable references for urban planning and policymaking.

GPS trajectory and remote sensing data are crucial for updating urban road networks because they contain critical spatial and temporal information. Existing road network updating methods, whether trajectory-based (TB) or image-based (IB), do not integrate the characteristics of both types of data. This paper proposed and implemented an incremental update method for rapid road network checking and updating. A composite update framework for road networks is established, which integrates trajectory data and UAV remote sensing imagery. The research proposed utilizing connectivity between adjacent matched points to solve the problem of updating problematic road segments in networks based on the features of the Hidden Markov Model (HMM) map-matching method in identifying new road segments. Deep learning is used to update the local road network in conjunction with the flexible and high-precision characteristics of UAV remote sensing. Additionally, the proposed method is evaluated against two baseline methods through extensive experiments based on real-world trajectories and UAV remote sensing imagery. The results show that our method has higher extraction accuracy than the TB method and faster updates than the IB method.

Roughness significantly affects the shear behavior of rock joints, which are widely encountered in geotechnical engineering. Since the existing calculation methods on the joint roughness coefficient (JRC) fail to obtain a sufficiently accurate value of JRC, a new determination method was proposed in this study, where the 3D laser scanning technique and self-compiled Python code, as well as the statistical parameter methods, were applied. Then, the shear strength of jointed rock was evaluated via Barton's model, and therefore, a comprehensive comparison between the calculating results and experimental results was executed. Ultimately, the influencing factors of roughness profile extraction on the accuracy of JRC value, such as the measuring point interval, profile number, and measuring direction, were investigated. The results show that (1) equipped with the 3D laser scanning technique, the roughness profiles can be accurately extracted via the self-compiled Python code, (2) an excellent consistency of shear strength could be observed between the calculating value and experimental results, verifying the validity and accuracy of the proposed method, and (3) a smaller measuring point interval can produce a more accurate digital profile and more accurate JRC value. To a certain extent, the more the sample numbers of profiles, the smaller the value of JRC.

The interface problem exists widely in building. Joints are interfaces of rock mass structures. To further study the influence of morphological characteristics on the shear mechanical properties of sawtooth joints, this paper prepared rock-like materials based on the similarity principle and carried out direct shear tests of sawtooth joints. The results showed that: (1) the peak shear displacement of joints first increases and then decreases with increasing normal stress, but the normal trend of stress during turning is different under different sawtooth angles. When the sawtooth angle of the joints is small, the decrease in shear stress between shear strength and residual shear strength is not obvious, and the rate of decrease is also small. (2) The shear strength of joints is positively correlated with normal stress. Using the Mohr–Coulomb criterion to analyze the shear strength of joints, it was found that the cohesion c and internal friction angle α of joints increased nonlinearly with increasing sawtooth angle, but their increasing trends were different. By introducing the function relation between cohesion, internal friction angle, and sawtooth angle into the classical shear strength equation, an empirical equation for the shear strength of joints was established in consideration of sawtooth angle. (3) There are two modes of shear failure for serrated joints: the “saw-toothed sliding gnawing failure mechanism” (SSG) and the “tensile fracture mechanism” (TFM). In the SSG, the shear failure mode of joints evolves in a slipping–gnawing–complete gnawing mechanism with increasing sawtooth angle and normal stress. The TFM mainly occurs at high sawtooth angles. This study provides a theoretical reference for the prediction and prevention of geological disasters.

With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data. It tries to integrate the dynamic public preferences, the individual interests and the physical road network space in the same spatiotemporal framework, ensuring that reasonable routes will be generated. A novel dual-layer mapping structure has been proposed to bridge the gap from brief individual preferences to specific entries of POIs (points-of-interest) inside realistic road networks. A case study on Changsha city has proven that the proposed method can not only flexibly plan people’s travel routes under different spatiotemporal backgrounds but also is close to people’s natural selection by the perception of the group.

Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with the support of auxiliary datasets, using thresholding methods and followed by morphological operations to further refine the results. However, auxiliary datasets may lose efficacy on large plain areas, whilst the parameters of morphological operations are hard to be decided in different situations. Here, a heuristic and automatic water extraction (HAWE) method is proposed to extract the water map from Sentinel-1 SAR data. In the HAWE, we integrate tile-based thresholding and the active contour model, in which the former provides a convincing initial water map used as a heuristic input, and the latter refines the initial map by using image gradient information. The proposed approach was tested on the Dongting Lake plain (China) by comparing the extracted water map with the reference data derived from the Sentinel-2 dataset. For the two selected test sites, the overall accuracy of water classification is between 94.90% and 97.21% whilst the Kappa coefficient is within the range of 0.89 and 0.94. For the entire study area, the overall accuracy is between 94.32% and 96.7% and the Kappa coefficient ranges from 0.80 to 0.90. The results show that the proposed method is capable of extracting water inundations with satisfying accuracy.

To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along network infrastructure simultaneously. In TOST, a flexible intersection-based semantic representation is designed to express movement typically constrained by urban road networks by combining sets of local semantic details along the time axis. A relational schema based on this model was instantiated against real datasets, which illustrated the effectivity of our proposed model.

With the continuous development of remote sensing(RS) technology, the surface information can be collected conveniently and quickly by using the popular unmanned aerial vehicle(UAV). The application of UAV low altitude RS technology in road safety in intelligent area has certain practical significance. It can provide safety warning for most drivers, and provide auxiliary decision-making for the road supervision department. Through the collection, processing, calculation and analysis of the road image, the UAV can find out the road obstacles with potential safety hazards, identify the road pit, calculate the radius and depth of the road pit through the digital mapping system, predict the accident risk according to different speed and provide scientific basis for the road safety monitoring. At the same time, UAV can provide repair scheme for damaged roads, estimate the quantity of materials needed for repair, and achieve the target of resource saving and efficiency improvement. The experimental results show that the UAV can not only provide scientific prediction information for driving safety, but also provide relatively accurate material consumption for road repair. [ABSTRACT FROM AUTHOR]