Research on the Spatio-Temporal Evolution and Driving Forces of Green Spaces in the Central Urban Area of Zunyi City

Green space,as a medium for carrying out urban functions and guiding urban development,is becoming a scarce resource along with the urbanization process and the intensification of environmental problems.In the face of the spatial mismatch between high demand and low supply,it is of great significance to clarify the evolution mechanism of green space to undertake national spatial planning,protect the natural strategic resources in the urban fringe area,and promote the sustainable development of the“three living spaces.”The study focuses on the Zunyi City Center,selecting the 20 years of rapid development following its establishment as a city as the study period.It explores the dynamic evolution of green space and the main driving forces during different periods using remote-sensing image data.The study shows that from 2003 to 2023,the total scale of green space has an obvious decreasing trend along with the expansion of the urban built-up area.A large amount of arable land is being converted to construction land,resulting in a sudden decrease in arable land area.In the past 10 years,the comprehensive land use dynamics have accelerated.Still,the spatial difference has gradually narrowed,indicating that the overall development intensity of Zunyi City’s central urban area has increased.There is a gradual spread of the trend to the hilly areas.The limiting effect of the mountainous natural environment on the city’s development has gradually diminished under the superposition of external factors,such as economic development,industrial technological upgrading,and policy orientation so the importance of the effective protection and rational utilization of urban green space has become more prominent.

Spatio-temporal evolution of the coordinated development of high-tech industry agglomeration and resource-environmental carrying capacity in the Yellow River Basin

Under the background of new infrastructure,the Yellow River Basin’s superior growth cannot be separated originating with the synergistic effect of scientific and technological inventiveness and ecological civilization construction.In light of the coupling coordination analysis of the coordination effect of provincial high-tech industry agglomeration and resource carrying capacity in the Yellow River Basin from 2009 to 2021,The evolution of the geographical and temporal pattern of development was investigated using the Moran index and kernel density estimation.The results show that the agglomeration of high-tech industries in the Yellow River Basin presents a development trend of seek improvement in stability,and there is a good coupling and coordination throughout the progression of scientific and technological innovation and the loading capacity of the resource,from the viewpoint of a time series.From the perspective of spatial pattern distribution,the whole basin aims at the lower reaches,accelerates the optimization of digital industry and promotes Yellow River Basin development of superior quality through innovation support and increase of input,and based on policy guidance.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Epidemic Characteristics and Spatio-Temporal Patterns of HFRS in Qingdao City,China,2010-2022

Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022.Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed.Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%.The male:female ratio was 2.8:1.75.3%of patients were aged between 16 and 60 years old,75.3%of patients were farmers,and 11.6%had both“three red”and“three pain”symptoms.The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak.The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou.The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak.Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity.The typical symptoms of“three red”and“three pain”in patients with HFRS were not obvious.

Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

Warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography

High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Mechanical properties and energy evolutions of burst-prone coal samples with holes and fillings

During the mining process of impact-prone coal seams,drilling pressure relief can reduce the impact propensity of the coal seam,but it also reduces the integrity and strength of the coal mass at the side of the roadway.Therefore,studying the mechanical properties and energy evolution rules of coal samples containing holes and filled structures has certain practical significance for achieving coordinated control of coal mine rockburst disasters and the stability of roadway surrounding rocks.To achieve this aim,seven types of burst-prone coal samples were prepared and subject to uniaxial compression experiments with the aid of a TAW-3000 electro-hydraulic servo testing machine.Besides,the stress–strain curves,acoustic emission signals,DIC strain fields and other data were collected during the experiments.Furthermore,the failure modes and energy evolutions of samples with varying drilled hole sizes and filling materials were analyzed.The results show that the indexes related to burst propensity of the drilled coal samples decline to some extent compared with those of the intact one,and the decline is positively corelated to the diameter of the drilled hole.After hole filling,the strain concentration degree around the drilled hole is lowered to a certain degree,and polyurethane filling has a more remarkable effect than cement filling.Meanwhile,hole filling can enhance the strength and deformation resistance of coal.Hole drilling can accelerate the release of accumulated elastic strain energy,turning the acoustic emission events from low-frequency and high-energy ones to high-frequency and low-energy ones,whereas hole filling can reduce the intensity of energy release.The experimental results and theoretical derivation demonstrate that hole filling promotes coal deformability and strength mainly by weakening stress concentration surrounding the drilled holes.Moreover,the fillings can achieve a better filling effect if their elastic modulus and Poisson’s ratio are closer to those of the coal body.

Spatio-temporal Evolution of the Rural Regional System and Its Evolution Mechanism in Huang-Huai-Hai Area of China

The problem of rural development arises from the evolution of rural regional system.It is urgent to deepen the research on the evolution process and mechanism of rural regional system.However,there are relatively few studies on rural development from the perspective of the evolution process,driving mechanism and evolution mechanism of rural regional system.Therefore,this study took Huang-Huai-Hai Area for example,started with the systematicness of the rural regional system,the spatio-temporal pattern and driving mechanism of rural regional system evolution,and further summarized and refined the evolution mechanism of the rural regional system.The methods of spatial pattern analysis,gray correlation degree and geographical detection were adopted.The results showed that the problems in rural areas were often dominated by one factor and produced by the joint action of many factors.Factors such as county urbanization,county economy,county public service,agricultural mechanization,surrounding cities and convenient transportation will affect the evolution of rural regional systems.Based on the evolution of the elements in the rural regional system,the evolution types of rural regional system can be divided into decline type,equilibrium type and growth type.This study can provide a reference for understanding the process of rural rise and fall and can also guide rural revitalization and rural sustainable development.

Relationship between spatio-temporal evolution of soil pH and geological environment/surface cover in the eastern Nenjiang River Basin of Northeast China during the past 30 years

To illuminate the spatio-temporal variation characteristics and geochemical driving mechanism of soil pH in the Nenjiang River Basin,the National Multi-objective Regional Geochemical Survey data of topsoil,the Second National Soil Survey data and Normalized Difference Vegetation Index(NDVI)were analyzed.The areas of neutral and alkaline soil decreased by 21100 km^(2)and 30500 km^(2),respectively,while that of strongly alkaline,extremely alkaline,and strongly acidic soil increased by 19600 km^(2),18200 km^(2),and 15500 km^(2),respectively,during the past 30 years.NDVI decreased with the increase of soil pH when soil pH>8.0,and it was reversed when soil pH<5.0.There were significant differences in soil pH with various surface cover types,which showed an ascending order:Arbor<reed<maize<rice<high and medium-covered meadow<low-covered meadow<Puccinellia.The weathering products of minerals rich in K_(2)O,Na_(2)O,CaO,and MgO entered into the low plain and were enriched in different parts by water transportation and lake deposition,while Fe and Al remained in the low hilly areas,which was the geochemical driving mechanism.The results of this study will provide scientific basis for making scientific and rational decisions on soil acidification and salinization.

Multi-Scale Location Attention Model for Spatio-Temporal Prediction of Disease Incidence

Due to the increasingly severe challenges brought by various epidemic diseases,people urgently need intelligent outbreak trend prediction.Predicting disease onset is very important to assist decision-making.Most of the exist-ing work fails to make full use of the temporal and spatial characteristics of epidemics,and also relies on multi-variate data for prediction.In this paper,we propose a Multi-Scale Location Attention Graph Neural Networks(MSLAGNN)based on a large number of Centers for Disease Control and Prevention(CDC)patient electronic medical records research sequence source data sets.In order to understand the geography and timeliness of infec-tious diseases,specific neural networks are used to extract the geography and timeliness of infectious diseases.In the model framework,the features of different periods are extracted by a multi-scale convolution module.At the same time,the propagation effects between regions are simulated by graph convolution and attention mechan-isms.We compare the proposed method with the most advanced statistical methods and deep learning models.Meanwhile,we conduct comparative experiments on data sets with different time lengths to observe the predic-tion performance of the model in the face of different degrees of data collection.We conduct extensive experi-ments on real-world epidemic-related data sets.The method has strong prediction performance and can be readily used for epidemic prediction.

Spatio-Temporal Change of Dispersal Areas of Greater Kudu (Tragelaphus strepsiceros) in Lake Bogoria Landscape, Kenya

Decline in wildlife populations is manifest globally, regionally and locally. A wildlife decline of 68% has been reported in Kenya’s rangelands with Baringo County experiencing more than 85% wildlife loss in the last four decades. Greater Kudu (Tragelaphus strepsiceros) is endemic to Lake Bogoria landscape in Baringo County and constitutes a major tourist attraction for the region necessitating use of its photo on the County’s logo and thus a flagship species. Tourism plays a central role in Baringo County’s economy and is a major source of potential growth and employment creation. The study was carried out to assess spatio-temporal change of dispersal areas of Greater Kudu (GK) in Lake Bogoria landscape in the last four years for enhanced adaptive management and improved livelihoods. GK population distribution primary data collected in December 2022 and secondary data acquired from Lake Bogoria National Game Reserve (LBNGR) for 2019 and 2020 were digitized using in a Geographic Information System (GIS). Measures of dispersion and point pattern analysis (PPA) were used to analyze dispersal of GK population using GIS. Spatio-temporal change of GK dispersal in LBNR was evident thus the null hypothesis was rejected. It is recommended that anthropogenic activities contributing to GK’s habitat degradation be curbed by providing alternative livelihood sources and promoting community adoption of sustainable technologies for improved livelihoods.

Traveling Wave Solutions of a SIR Epidemic Model with Spatio-Temporal Delay

In this paper, we studied the traveling wave solutions of a SIR epidemic model with spatial-temporal delay. We proved that this result is determined by the basic reproduction number R0and the minimum wave speed c*of the corresponding ordinary differential equations. The methods used in this paper are primarily the Schauder fixed point theorem and comparison principle. We have proved that when R0>1and c>c*, the model has a non-negative and non-trivial traveling wave solution. However, for R01and c≥0or R0>1and 0cc*, the model does not have a traveling wave solution.

Experimental investigation on failure process and spatio-temporal evolution of rockburst in granite with a prefabricated circular hole

To study the mechanism of rockburst and its spatio-temporal evolution criterion,a rockburst simulation experiment was performed on granite specimens,each with a prefabricated circular hole,under different lateral loads.Using micro camera,acoustic emission(AE)system,and infrared thermal imager,the AE characteristics and thermal radiation temperature migration were studied during the rockburst process.Then,the failure mode and damage evolution of the surrounding rock were analyzed.The results demonstrate that increasing the lateral load can first increase and then reduce the bearing capacity of the hole.In this experiment,the hole failure process could be divided into four periods:quiet,particle ejection,stability failure and collapse.Correspondingly,the AE signals evolved from a calm stage,to have intermittent appearance;then,they were continuous with a sudden increase,and finally increased dramatically.The failure of the surrounding rock was mainly tensile failure,while shear failure tended to first increase and then decrease.Meanwhile,damage to the hole increased gradually during the particle ejection period,whereas damage to the rockburst mainly occurred in the stability failure period.The thermal radiation temperature migration exhibited warming in shallow parts,inward expansion,cooling in the shallow parts with free surface heating,inward expansion,a sudden rise in temperature of the rockburst pits,and finally specimen failure.The initial reinforcement support should fully contribute to surface support.Furthermore,an appropriate tensile capacity and good energy absorption capacity should be established in support systems for high-stress roadways.

Spatio-temporal Evolution and Driving Factors of the High-quality Development of Provincial Tourism in China

Accelerating the promotion of high-quality development of tourism(HQDT) is of great significance to the sustainable development of tourism. This paper defined the concept of HQDT, and then built an evaluation system for HQDT measurement to analyze the spatio-temporal evolution characteristics of China’s HQDT based on provincial panel data from 2010 to 2019, using Geodetector to explore the similarities and differences between driving factors of HQDT and tourism development scale(TDS). The results show that:1) Taking the development concepts of innovation, coordination, green, openness and sharing as the guidance, and considering the organic unity of quantity and quality, the evaluation index system of the HQDT consists of six dimensions of economic stability, innovation driving, coordination and linkage, green and sustainability, openness and cooperation, and sharing and harmony, which respectively represent the basis, momentum, means, orientation, direction and purpose of the HQDT;2) The level of China’s HQDT shows an upward trend, presenting the characteristics of eastern region > central region > western region > northeastern region in 2019. The regional differences in China’s HQDT show a downward trend, and the intra-regional differences have replaced the inter-regional differences as the main source of regional differences;3) China’s HQDT shows the characteristics of higher in the east and lower in the west along the Hu line, while the improvement speed of HQDT shows the characteristics of faster in the west and slower in the east, making the decline of east-west differentiation of China’s HQDT and the movement of the gravity center towards southwest;4) Both HQDT and TDS are obviously driven by tourism capital investment and regional consumption. In terms of differences, the HQDT is more driven by government guidance, innovation driving force, and opening up, while the main driving factors of TDS are more biased toward capital elements and hardware facilities, including informatization, tourism resource, traffic, and eco-environment.

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.

Spatio-temporal Evolution of Marine Fishery Industry Ecosystem Vulnerability in the Bohai Rim Region

The building of the ocean power strategy and the implementation of the blue agriculture plan urgently need to strengthen the sustainable development of marine fishery.Taking vulnerability as the starting point, this paper constructs the vulnerability index system of marine fishery industry ecosystem from the aspects of sensitivity and response capacity, and combines the entropy method with the Topsis to comprehensively analyze the spatio-temporal evolution characteristics of vulnerability of marine fishery industry ecosystem in the Bohai Rim Region from 2001 to 2015.The results show that: 1) In the time dimension, from 2001 to 2015, the vulnerability of the marine fishery industry ecosystem in the Bohai Rim Region shows a fluctuant and degressive trend;2) In the spatial dimension, the spatial distribution of the marine fishery industry ecosystem vulnerability in the Bohai Rim Region presents the gradient characteristics which shows high vulnerability in the east and low vulnerability in the west.According to the evolution track of the system’s vulnerability level, the vulnerability of the marine fishery industry ecosystem is divided into ‘declining’ and ‘stable’ types of evolutionary structures;3) The development of marine fishery in the Bohai Rim Region needs to be derived from the marine fishery’s ecological environment and the industrial development mode and structure, which can improve the marine environment remediation efforts, optimize the marine fishery industry structure, vigorously focus on pelagic fishery, and enhance the introduction of marine fishery’s science and technology talents, etc.Then, the marine fishery’s development in the Bohai Rim Region will be moving in the green, circular and sustainable direction.

Spatio-temporal evolution of ecologically-sustainable land use in China's Loess Plateau and detection of its influencing factors

Ecological land(Eco-land) is a basic resource for human beings to survive, and eco-land use is a strategy, a way to manage the land resource. So, ecologically-sustainable land use is essential for human beings to survive. This paper investigates the spatiotemporal characteristics and mechanisms of urban-rural eco-land using a new and innovative integration way based on eco-land change data in China's Loess Plateau(LP) prefecture level cities and explores factors of eco-land change. The spatial difference characteristic of eco-land among different level cities in the LP is that: small cities > big cities > middle cities. From 2009 to 2016, the eco-land in the LP from the perspective of urban-rural areas has changed significantly. Significant differences of urban-rural eco-land were identified among various urban growth types, and all the cities in the LP were further classified into four types based on eco-land change trend, with type A and B cities identified as the vital zone and major zone. Taking the eco-fragile region Loess Plateau(LP) as an example, our results demonstrated that the migrants to cities in LP could relieve ecological pressures and promote restoration of ecological vegetation. We have demonstrated that urbanization and the influence of government policy can be discerned through the quantification of the spatial-temporal change of eco-land and suggest that combining both urban and rural eco-land can support more effective land use decisions and provide theoretical basis for the practical application of urban planning, policy-making and sustainable development. What's more, governments should strive to population mobility and restore vegetation to sustain this fragile ecological environment.

Spatio-temporal Evolution Characteristics and Driving Mechanism of the New Infrastructure Construction Development Potential in China

With the advent of the era of big data and artificial intelligence, new infrastructure construction(NIC) has attracted the attention of many countries. The development of NIC provides an opportunity to bridge the digital divide and narrow the regional gap,providing continuous impetus to further promote economic development. Here, we considered 31 provincial-level administrative units in China(not including Hong Kong, Macao, and Taiwan of China due to data unavailable) and established comprehensive evaluation indicators for the development potential of NIC. Afterward, we used the entropy-weight TOPSIS model to determine the development potential of NIC and analyze its spatio-temporal evolution characteristics. Furthermore, the GeoDetector model was applied to explore the driving mechanism of the NIC development potential. The conclusions were as follows: (1) The Chinese NIC development potential is generally low. The eastern China was the region with the highest development potential year by year, while the development potential in the central China was found to be in an accelerating phase. (2) The evolution of the Chinese NIC development potential’s spatial pattern has been characterized by an inland extension and coastal agglomeration. Moreover, we identified a superior development zone, a rising development zone, an inferior development zone, and a declining development zone. (3) The scope of Chinese NIC development potential agglomeration areas has gradually expanded and its degree has gradually deepened. The range of high-value agglomeration in eastern area gradually expanded and its degree gradually deepened. (4) Investment in innovative talents appears as the core factor affecting the Chinese NIC development potential. Whether acting alone or synergistically with other factors, its promoting effect on Chinese NIC development potential is the strongest.