Recreational Behavior and Spatial Characteristics of Urban Parks Based on Multi-Source Information: A Case Study of Xiaohong Stone Carving Park in Nanjing

Urban parks are an important part of urban recreational spaces,and the development of analysis framework of recreational behavior and space is helpful for the design of urban parks to better meet the activity and usage requirements of visitors.In this paper,Xiaohong Stone Carving Park in Nanjing was taken as the research object to explore the relationship between landscape space and user behavior for design feedback.The orthographic image technology of unmanned aerial vehicle(UAV)and machine deep learning were used to analyze users behavior activities in the park,and the spatial characteristics of each node in the park were evaluated by semantic differential scale to explore the potential causes of behavior activities.The results showed that①there were significant differences in the number of visitors on holidays and working days,and young people and children were the main users.②The users activity of the park differed greatly on the inner and outer tour roads,and the users showed stronger willingness to stay on the inner tour roads.Recreational activities received more attention than cultural ones.③The overall distribution of users was significantly affected by hydrophilicity,and the recreational behavior and activity type were affected by multiple factors which include the hydrophilicity,safety,accessibility of cultural relics and the amount of recreational facilities.Finally,the enhanced strategies such as improving the cultural tour system,establishing hydrophilic safety belts and optimizing recreational facilities were put forward.

Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.

Spatial evolution and spatial production of traditional villages from "backward poverty villages" to "ecologically well-off villages": Experiences from the hinterland of national nature reserves in China

With the rapid urbanization process,the space of traditional villages in China is undergoing significant changes.Studying the spatial evolution of traditional villages is significant in promoting rural spatial transformation and realizing rural revitalization and sustainable rural development.Based on the traceability analysis of spatial production theory,this paper constructed an analytical framework for the spatial production evolution of traditional villages,analyzed the spatial evolution process and characteristics of traditional villages by using buffer analysis,spatial syntax,and other research methods,and revealed the characteristics of the spatial production evolution of traditional villages and the driving mechanism.The results show that:(1)The village spatial formation and development follow the village life cycle theory and usually develop from embryonic villages to diversified and integrated villages;(2)The evolution of village spatial production is characterized by the diversity of material space,the sublimation of daily life space,and the integration of social system space and generalization of emotional space;(3)The evolution of village spatial production from backward and poor village to ecologically well-off village is influenced by a combination of factors;(4)The village has formed a spatial structure of"people-land-scape-culture-industry",realized comprehensive reconstruction and spatial reproduction.The study results reflect the spatial evolution characteristics of traditional villages in mountainous areas in a more comprehensive way,which helps to promote the protection and development of traditional villages in mountainous areas and,to a certain extent,provides a reference for the development of rural revitalization.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Enhancing transboundary natural tourism resources governance:unveiling the spatial pattern and its influencing factors

Tourism resources that span provincial boundaries in China play a pivotal role in regional development,yet effective governance poses persistent challenges.This study addresses this issue by constructing a comprehensive database of transboundary natural tourism resources(TNTR)through amalgamation of diverse data sources.Utilizing the Getis-Ord Gi^(*),kernel density estimation,and geographical detectors,we scrutinize the spatial patterns of TNTR,focusing on both named and unnamed entities,while exploring the influencing factors.Our findings reveal 7883 identified TNTR in China,with mountain tourism resources emerging as the predominant type.Among provinces,Hunan boasts the highest count,while Shanghai exhibits the lowest.Southern China demonstrates a pronounced clustering trend in TNTR distribution,with the spatial arrangement of biological landscapes appearing more random compared to geological and water landscapes.Western China,characterized by intricate terrain,exhibits fewer TNTR,concurrently unveiling a significant presence of unnamed natural tourism resources.Crucially,administrative segmentation influences TNTR development,generating disparities in regional goals,developmental stages and intensities,and management approaches.In response to these variations,we advocate for strengthening the naming of the unnamed transboundary tourism resources,constructing a geographic database of TNTR for government and establishing a collaborative management mechanism based on TNTR database.Our research contributes to elucidating the intricate landscape of TNTR,offering insights for tailored governance strategies in the realm of cross-provincial tourism resource management.

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Osteoarthritis(OA)is a debilitating degenerative disease affecting multiple joint tissues,including cartilage,bone,synovium,and adipose tissues.OA presents diverse clinical phenotypes and distinct molecular endotypes,including inflammatory,metabolic,mechanical,genetic,and synovial variants.Consequently,innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches.Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints,causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues.This issue has led to standardization difficulties and hindered the success of clinical trials.Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues,encompassing DNA,RNA,metabolites,and proteins,as well as their chemical properties,elemental composition,and mechanical attributes,can contribute to a more comprehensive understanding of the disease subtypes.Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment,providing a more holistic view of cellular function.Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various-omics lenses,such as genomics,transcriptomics,proteomics,and metabolomics,with spatial data.This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates.Furthermore,advanced imaging techniques,including high-resolution microscopy,hyperspectral imaging,and mass spectrometry imaging,enable the visualization and analysis of the spatial distribution of biomolecules,cells,and tissues.Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes.This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis.It explores their applications,challenges,and potential opportunities in the field of OA.Additionally,this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

Decoding complexity: the need to enhance precision and streamline spatial understanding in neuroscience

Neuroscience is the ultimate frontier in our quest for a comprehensive understanding of human behavior. Since its launch in 2009, the Human Connectome Project has emerged as a pioneering force, making heroic strides in elucidating the intricate correlation between structural information and the functioning of the human brain.

Unveiling the brain’s symphony:exploring the necessity and sufficiency of neural networks in behavior control

Since the pioneering work by Broca and Wernicke in the 19th century,who examined individuals with brain lesions to associate them with specific behaviors,it was evident that behaviors are complex and cannot be fully attributable to specific brain areas alone.Instead,they involve connectivity among brain areas,whether close or distant.At that time,this approach was considered the optimal way to dissect brain circuitry and function.These pioneering efforts opened the field to explore the necessity or sufficiency of brain areas in controlling behavior and hence dissecting brain function.However,the connectivity of the brain and the mechanisms through which various brain regions regulate specific behaviors,either individually or collaboratively,remain largely elusive.Utilizing animal models,researchers have endeavored to unravel the necessity or sufficiency of specific brain areas in influencing behavior;however,no clear associations have been firmly established.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Spatial and temporal evolution of electromagnetic pulses from solid target irradiated with multi-hundred-terawatt laser pulse inside target chamber

Giant electromagnetic pulses(EMPs) induced by high-power laser irradiating solid targets interfere with various experimental diagnoses and even damage equipment,so unveiling the evolution of EMPs inside the laser chamber is crucial for designing effective EMP shielding.In this work,the transmission characteristics of EMPs as a function of distances from the target chamber center(TCC) are studied using B-dot probes.The mean EMP amplitude generated by picosecond laser-target interaction reaches 561 kV m^(-1),357 kV m^(-1),395 kV m^(-1),and 341 kV m^(-1)at 0.32 m,0.53 m,0.76 m,and 1 m from TCC,which decreases dramatically from 0.32 m to 0.53 m.However,it shows a fluctuation from 0.53 m to 1 m.The temporal features of EMPs indicate that time-domain EMP signals near the target chamber wall have a wider full width at half maximum compared to that close to TCC,mainly due to the echo oscillation of electromagnetic waves inside the target chamber based on simulation and experimentation.The conclusions of this study will provide a new approach to mitigate strong electromagnetic pulses by decreasing the echo oscillation of electromagnetic waves inside the target chamber during laser coupling with targets.

Spatially resolved transcriptomic profiling of placental development in dairy cow

The placenta plays a crucial role in successful mammalian reproduction.Ruminant animals possess a semi-invasive placenta characterized by a highly vascularized structure formed by maternal endometrial caruncles and fetal placental cotyledons,essential for full-term fetal development.The cow placenta harbors at least two trophoblast cell populations:uninucleate(UNC)and binucleate(BNC)cells.However,the limited capacity to elucidate the transcriptomic dynamics of the placental natural environment has resulted in a poor understanding of both the molecular and cellular interactions between trophoblast cells and niches,and the molecular mechanisms governing trophoblast differentiation and functionalization.To fill this knowledge gap,we employed Stereo-seq to map spatial gene expression patterns at near single-cell resolution in the cow placenta at 90 and 130 days of gestation,attaining high-resolution,spatially resolved gene expression profiles.Based on clustering and cell marker gene expression analyses,key transcription factors,including YBX1 and NPAS2,were shown to regulate the heterogeneity of trophoblast cell subpopulations.Cell communication and trajectory analysis provided a framework for understanding cell-cell interactions and the differentiation of trophoblasts into BNCs in the placental microenvironment.Differential analysis of cell trajectories identified a set of genes involved in regulation of trophoblast differentiation.Additionally,spatial modules and co-variant genes that help shape specific tissue structures were identified.Together,these findings provide foundational insights into important biological pathways critical to the placental development and function in cows.

Integrated ribosome and proteome analyses reveal insights into sevoflurane-induced long-term social behavior and cognitive dysfunctions through ADNP inhibition in neonatal mice

A growing number of studies have demonstrated that repeated exposure to sevoflurane during development results in persistent social abnormalities and cognitive impairment.Davunetide,an active fragment of the activity-dependent neuroprotective protein(ADNP),has been implicated in social and cognitive protection.However,the potential of davunetide to attenuate social deficits following sevoflurane exposure and the underlying developmental mechanisms remain poorly understood.In this study,ribosome and proteome profiles were analyzed to investigate the molecular basis of sevoflurane-induced social deficits in neonatal mice.The neuropathological basis was also explored using Golgi staining,morphological analysis,western blotting,electrophysiological analysis,and behavioral analysis.Results indicated that ADNP was significantly down-regulated following developmental exposure to sevoflurane.In adulthood,anterior cingulate cortex(ACC)neurons exposed to sevoflurane exhibited a decrease in dendrite number,total dendrite length,and spine density.Furthermore,the expression levels of Homer,PSD95,synaptophysin,and vglut2 were significantly reduced in the sevoflurane group.Patch-clamp recordings indicated reductions in both the frequency and amplitude of miniature excitatory postsynaptic currents(mEPSCs).Notably,davunetide significantly ameliorated the synaptic defects,social behavior deficits,and cognitive impairments induced by sevoflurane.Mechanistic analysis revealed that loss of ADNP led to dysregulation of Ca^(2+)activity via the Wnt/β-catenin signaling,resulting in decreased expression of synaptic proteins.Suppression of Wnt signaling was restored in the davunetide-treated group.Thus,ADNP was identified as a promising therapeutic target for the prevention and treatment of neurodevelopmental toxicity caused by general anesthetics.This study provides important insights into the mechanisms underlying social and cognitive disturbances caused by sevoflurane exposure in neonatal mice and elucidates the regulatory pathways involved.

Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma

We propose a computing model in which individuals can automatically adjust their interaction intensity with their mentor according to the learning effect.This model is designed to investigate the cooperative dynamics of the spatial prisoner's dilemma.More specifically,when the cumulative payoff of a learner is more than his reference earning,he will strengthen his interaction with his mentor;otherwise,he will reduce it.The experimental results indicate that this mechanism can improve the emergence of cooperation in a networked population and that the driving coefficient of interaction intensity plays an important role in promoting cooperation.Interestingly,under a certain social dilemma condition,there exists a minimal driving coefficient that leads to optimal cooperation.This occurs due to a positive feedback effect between the individual's satisfaction frequency and the number of effective neighbors.Moreover,we find that the experimental results are in accord with theoretical predictions obtained from an extension of the classical pair-approximation method.Our conclusions obtained by considering relationships with mentors can provide a new perspective for future investigations into the dynamics of evolutionary games within structured populations.

Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

Spatial distribution of shallow landslides caused by Typhoon Lekima in 2019 in Zhejiang Province, China

In recent years, the coastal region of Southeast China has witnessed a significant increase in the frequency and intensity of extreme rainfall events associated with landfalling typhoons. The hilly and mountainous terrain of this area, combined with rapid rainfall accumulation, has led to a surge in flash floods and severe geological hazards. On August 10, 2019, Typhoon Lekima made landfall in Zhejiang Province, China, and its torrential rainfall triggered extensive landslides, resulting in substantial damage and economic losses. Utilizing high-resolution satellite images, we compiled a landslide inventory of the affected area, which comprises a total of 2,774 rainfallinduced landslides over an area of 2965 km2. The majority of these landslides were small to mediumsized and exhibited elongated, clustered patterns. Some landslides displayed characteristics of high-level initiation, obstructing or partially blocking rivers, leading to the formation of debris dams. We used the inventory to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to elevation, slope angle, faults, and road density. The landslides were predominantly located in hilly and low mountainous areas, with elevations ranging from 150 to 300 m, slopes of 20 to 30 degrees, and a NE-SE aspect. Notably, we observed the highest Landslide Number Density(LND) and Landslide Area Percentage(LAP) in the rhyolite region. Landslides were concentrated within approximately 4 km on either side of fault zones, with their size and frequency negatively correlated with distances to faults, roads, and river systems. Furthermore, under the influence of typhoons, regions with denser vegetation cover exhibited higher landslide density, reaching maximum values in shrubland areas. In areas experiencing significantly increased concentrated rainfall, landslide density also showed a corresponding rise. In terms of spatial distribution, the rainfall-triggered landslides primarily occurred in the northeastern part of the study area, particularly in regions characterized by complex topography such as Shanzao Village in Yantan Town, Xixia Township, and Shangzhang Township. The research findings offer crucial data on the rainfallinduced landslides triggered by Typhoon Lekima, shedding light on their spatial distribution patterns. These findings provide valuable references for mitigating risks and planning reconstruction in typhoon-affected area.

Coke behavior with H_(2)O in a hydrogen-enriched blast furnace:A review

Hydrogen-enriched blast furnace ironmaking has become an essential route to reduce CO_(2)emissions in the ironmaking process.However,hydrogen-enriched reduction produces large amounts of H_(2)O,which places new demands on coke quality in a blast furnace.In a hydrogen-rich blast furnace,the presence of H_(2)O promotes the solution loss reaction.This result improves the reactivity of coke,which is 20%-30%higher in a pure H_(2)O atmosphere than in a pure CO_(2)atmosphere.The activation energy range is 110-300 kJ/mol between coke and CO_(2)and 80-170 kJ/mol between coke and H_(2)O.CO_(2)and H_(2)O are shown to have different effects on coke degradation mechanisms.This review provides a comprehensive overview of the effect of H_(2)O on the structure and properties of coke.By exploring the interactions between H_(2)O and coke,several unresolved issues in the field requiring further research were identified.This review aims to provide valuable insights into coke behavior in hydrogen-rich environments and promote the further development of hydrogen-rich blast furnace ironmaking processes.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

Understanding the spatial interaction of ultrasounds based on three-dimensional dual-frequency ultrasonic field numerical simulation

A transient 3D model was established to investigate the effect of spatial interaction of ultrasounds on the dual-frequency ultrasonic field in magnesium alloy melt.The effects of insertion depth and tip shape of the ultrasonic rods,input pressures and their ratio on the acoustic field distribution were discussed in detail.Additionally,the spacing,angle,and insertion depth of two ultrasonic rods significantly affect the interaction between distinct ultrasounds.As a result,various acoustic pressure distributions and cavitation regions are obtained.The spherical rods mitigate the longitudinal and transversal attenuation of acoustic pressure and expand the cavitation volume by 53.7%and 31.7%,respectively,compared to the plate and conical rods.Increasing the input pressure will enlarge the cavitation region but has no effect on the acoustic pressure distribution pattern.The acoustic pressure ratio significantly affects the pressure distribution and the cavitation region,and the best cavitation effect is obtained at the ratio of 2:1(P15:P20).

FPGA-based position reconstruction method for neutron beam flux spatial distribution measurement in BNCT

A new measurement method for the spatial distribution of neutron beam flux in boron neutron capture therapy(BNCT)is being developed based on the two-dimensional Micromegas detector.To address the issue of long processing times in traditional offline position reconstruction methods,this paper proposes a field programmable gate array based online position reconstruction method utilizing the micro-time projection chamber principle.This method encapsulates key technical aspects:a self-adaptive serial link technique built upon the dynamical adjustment of the delay chain length,fast sorting,a coordinate-matching technique based on the mapping between signal timestamps and random access memory(RAM)addresses,and a precise start point-merging technique utilizing a circular combined RAM.The performance test of the selfadaptive serial link shows that the bit error rate of the link is better than 10-12 at a confidence level of 99%,ensuring reliable data transmission.The experiment utilizing the readout electronics and Micromegas detector shows a spatial resolution of approximately 1.4 mm,surpassing the current method’s resolution level of 5 mm.The beam experiment confirms that the readout electronics system can obtain the flux spatial distribution of neutron beams online,thus validating the feasibility of the position reconstruction method.The online position reconstruction method avoids traditional methods,such as bubble sorting and traversal searching,simplifies the design of the logic firmware,and reduces the time complexity from O(n2)to O(n).This study contributes to the advancement in measuring neutron beam flux for BNCT.

Spatial search weighting information contained in cell velocity distribution

Cell migration plays a significant role in physiological and pathological processes.Understanding the characteristics of cell movement is crucial for comprehending biological processes such as cell functionality,cell migration,and cell–cell interactions.One of the fundamental characteristics of cell movement is the specific distribution of cell speed,containing valuable information that still requires comprehensive understanding.This article investigates the distribution of mean velocities along cell trajectories,with a focus on optimizing the efficiency of cell food search in the context of the entire colony.We confirm that the specific velocity distribution in the experiments corresponds to an optimal search efficiency when spatial weighting is considered.The simulation results indicate that the distribution of average velocity does not align with the optimal search efficiency when employing average spatial weighting.However,when considering the distribution of central spatial weighting,the specific velocity distribution in the experiment is shown to correspond to the optimal search efficiency.Our simulations reveal that for any given distribution of average velocity,a specific central spatial weighting can be identified among the possible central spatial weighting that aligns with the optimal search strategy.Additionally,our work presents a method for determining the spatial weights embedded in the velocity distribution of cell movement.Our results have provided new avenues for further investigation of significant topics,such as relationship between cell behavior and environmental conditions throughout their evolutionary history,and how cells achieve collective cooperation through cell-cell communication.