Spatial Temporal Evolution Research Articles

Laser interaction with undercritical foams of different spatial structures

The interaction of high-power laser pulses with undercritical foams produced by different techniques but with the same average density is studied at the PALS laser facility. The spatial–temporal evolution of X-ray emission is observed using an X-ray streak camera, electron and ion temperatures are measured by X-ray spectroscopy, and hot-electron production is characterized by monochromatic X-ray imaging. Transmission of a femtosecond laser probe pulse through foams is observed in the near and far fields. In spite of large differences in pore size and foam structure, the velocity of ionization front propagation is quite similar for all the foams studied and is slower than that in a homogeneous material of the same average density. The ion temperature in the plasma behind the ionization front is a few times higher than the electron temperature. Hot-electron production in plastic foams with small pores is strongly suppressed compared with that in solid targets, whereas in foams produced by additive manufacturing, it is significantly increased to the level observed in bare copper foil targets.

A Physics-Informed Auto-Learning Framework for Developing Stochastic Conceptual Models for ENSO Diversity

Abstract Understanding El Niño–Southern Oscillation (ENSO) dynamics has tremendously improved over the past few decades. The ENSO diversity in spatial pattern, peak intensity, and temporal evolution is, however, still poorly represented in conceptual ENSO models. In this paper, a physics-informed auto-learning framework is applied to derive ENSO stochastic conceptual models with varying degrees of freedom. The framework is computationally efficient and easy to apply. Once the state vector of the target model is set, causal inference is exploited to build the right-hand side of the equations based on a mathematical function library. Fundamentally different from standard nonlinear regression, the auto-learning framework provides a parsimonious model by retaining only terms that improve the dynamical consistency with observations. It can also identify crucial latent variables and provide physical explanations. This methodology successfully reconstructs the equations of a realistic six-dimensional reference ENSO model based on the recharge oscillator theory from its data. A hierarchy of lower-dimensional models is derived, and their representation of ENSO (including its diversity) is systematically assessed. The minimum model that represents ENSO diversity is four-dimensional, with three interannual variables describing the western Pacific thermocline depth, the eastern and central Pacific sea surface temperatures (SSTs), and one intraseasonal variable for westerly wind events. Without the intraseasonal variable, the resulting three-dimensional model underestimates extreme events and is too regular. A limited number of weak nonlinearities in the model are essential in reproducing the observed extreme El Niño events and the observed nonlinear relationship between eastern and western Pacific SSTs. Significance Statement This study develops a physics-informed auto-learning approach to improve the modeling and understanding of El Niño–Southern Oscillation (ENSO), a major climate phenomenon influencing global weather and climate. The auto-learning framework explores the causality between key processes to systematically produce stochastic conceptual models with different climate factors that simulate the diversity of observed ENSO events. The key finding is that the minimal model for characterizing the ENSO diversity (with the least number of climate factors) is a four-variable model capturing thermocline depth, sea surface temperatures, and wind bursts that can reproduce intensity and spatial pattern variation. This advancement provides an interpretable tool to identify the minimum sufficient processes governing ENSO behavior for improved predictability.

The spatial-temporal evolution characteristics and influencing factors of the coupling between the industrial chain and innovation chain in China

Exploring the theoretical logic, development characteristics and improvement paths of the deep coupling of innovation chains and industrial chains (two-chain) is of great importance for building a modern industrial system and developing productive forces. However, empirical evidence on the spatial-temporal characteristics and influencing factors of two-chain coupling is still lacking. In this study, the degree of coupling coordination model is applied to assess the degree of two-chain coupling in China using provincial panel data from 2013 to 2022. In addition, spatial analysis is applied to investigate the spatial-temporal evolution characteristics of the variables in question. On this basis, the fuzzy set qualitative comparative analysis (fsQCA) method is applied to identify influential factors. The results indicate that: (1) Temporally, the coupling level of the two-chain shows a consistent upward trend and gradually develops a distribution pattern characterized by high values in the east and weak values in the west. (2) Spatially, there is a significant positive spatial correlation between the two-chain coupling. (3) In terms of drivers, the increase in two-chain coupling is attributed to multiple factors. Three models can be identified: human-driven, capital-driven and information-driven, which are influenced by the cooperation between government and market. The novelty of this article lies in its exploration of the theoretical logic and developmental characteristics of two-chain coupling, while empirically demonstrating its driving factors. This study provides a new perspective for governments and relevant departments to improve two-chain linkages and promote economic development.

Spatial-Temporal Evolution of Carbon Sequestration and Emission Reduction Capacity of Agricultural Cultivation in Different Functional Grain Areas

The capacity of agricultural cultivation for carbon sequestration and emission reduction plays a key role in advancing the green and sustainable development of agriculture and achieving both emission reduction and carbon sink objectives. This study constructs an index for agricultural carbon sequestration and emission reduction capacity using spatial autocorrelation analysis, Theil index, and kernel density estimation, to thoroughly explore spatiotemporal changes and regional differences in carbon sequestration and emission reduction capacity across China and its different grain functional areas. Major findings include: (1) From 2000 to 2020, China’s agricultural carbon sequestration and emission reduction capacity exhibited a fluctuating upward trend, consistent in grain main production areas, while production–consumption and main marketing areas showed a fluctuating decline. (2) From 2000 to 2020, positive spatial correlation in carbon sequestration and emission reduction capacity increased at both national and regional levels, particularly in the main production and production–consumption areas, while the main consumption areas demonstrated a trend toward negative correlation. (3) The Theil index of China’s agricultural carbon sequestration and emission reduction capacity showed an overall fluctuating increase, with interregional differences contributing over 54%, significantly surpassing intra-regional contributions. Moreover, the main producing regions are the main source of the overall national variance. (4) Kernel density analysis highlights that there are significant differences in the distribution and evolution of carbon sequestration and emission reduction capacity among different grain functional areas, and the overall development is unbalanced. Therefore, each region needs to formulate tailored measures to improve agricultural carbon sequestration efficiency, promote regional sustainable development, and achieve carbon reduction goals.

Assessing the Gap: Resource Efficiency of Institutionalised Elderly Care in Urban and Rural China

ObjectivesThe allocation of resources in elderly care institutions is directly linked to the well-being, quality of life and sustainable development of public health for the elderly. However, a disparity in elderly care resources between urban and rural areas exists. Therefore, this study aimed to explore resource allocation efficiency in China's urban and rural institutional elderly care. Study DesignEmploying a mixed-methods approach, the research integrated quantitative analysis with spatial-temporal assessments to investigate spatial-temporal evolution patterns of resource allocation efficiency. MethodsA Projection Pursuit Evaluation Model (PPM) utilising an Accelerated Genetic Algorithm was applied to measure the input and output of urban and rural elderly care institutions. Data Envelopment Analysis (DEA) was used to visualise the static and dynamic efficiency. Spatial analysis techniques determined the spatial evolution of resources. ResultsSignificant disparities in resource allocation between urban and rural areas were identified, with rural areas facing declining numbers of elderly care institutions despite having a larger ageing population. The efficiency analysis revealed room for improvement in both urban and rural settings, emphasising the need to strengthen management and technology in elderly care institutions. ConclusionsAddressing the challenges of elderly care in China requires policy reform, enhanced government support and stimulation of private investment, particularly in rural areas. A shift towards quality-focused elderly care and strategic investments is crucial for equitable and efficient service provision across China.

Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm

China’s total carbon emissions account for one-third of the world’s total. How to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060 is an important policy orientation at present. Therefore, it is of great significance to analyze the characteristics and driving factors of temporal and spatial evolution on the basis of effective calculation and prediction of carbon emissions in various provinces for promoting high-quality economic development and realizing carbon emission reduction. Based on the energy consumption data of 30 provinces in China from 2000 to 2021, this paper calculates and predicts the total carbon emissions of 30 provinces in China from 2000 to 2035 based on ARIMA model and BP neural network model, and uses ArcGIS and standard elliptic difference to visually analyze the spatial and temporal evolution characteristics, and further uses LMDI model to decompose the driving factors affecting carbon emissions. The results show that: (1) From 2000 to 2035, China’s total carbon emissions increased year by year, but the growth rate of carbon emissions gradually decreased; The carbon emission structure is “secondary industry > residents’ life > tertiary industry > primary industry”, and the growth rate of carbon in secondary industry and residents’ life is faster, while the change trend of primary industry and tertiary industry is smaller; (2) The spatial distribution of carbon emissions in China’s provinces presents a typical pattern of “eastern > central > western” and “northern > southern”, and the carbon emission centers tend to move to the northwest; (3) The regions with high level of digital economy, advanced industrial structure and new quality productivity have relatively less carbon emissions, which has significant group difference effect; (4) The intensity effect of energy consumption is the main factor driving the continuous growth of carbon emissions, while the per capita GDP and the structure effect of energy consumption are the main factors restraining carbon emissions, while the effects of industrial structure and population size are relatively small. Based on the research conclusion, this paper puts forward some policy suggestions from energy structure, industrial structure, new quality productivity and digital economy.

Evolution of Rice Cultivar Performance Across China: A Multi-Dimensional Study on Yield and Agronomic Characteristics over Three Decades

Rice (Oryza sativa L.) is a staple food crop for over half of the world’s population, with China being the largest producer. However, the growth rate of rice yield per hectare has slowed in recent years, emphasizing the need for in-depth studies on the evolution of rice cultivar performance. This study presents a comprehensive analysis of the yield and key agronomic traits of rice cultivars across China over three decades, utilizing data from 11,811 cultivar trials conducted between 1990 and 2023. We assessed the spatial distribution and temporal evolution of rice cultivar performance, exploring regional differences and the interplay between agronomic traits and environmental factors. Our results reveal significant variations in growth duration, plant height, grains per panicle, thousand-grain weight, effective panicle number, and seed setting rate across different regions. Temporal trends showed diverse patterns of improvement, with some regions experiencing rapid advancements (up to 1.42% annual yield increase in Jiangxi Province of Central China) and others nearing yield plateaus (0.16% in Jilin Province and 0.45% in Heilongjiang Province of Northeast China). Correlation analysis between agronomic traits and grain yield highlighted the complex relationships and potential for further genetic gains through targeted breeding. This study underscores the importance of region-specific breeding strategies to optimize rice production in the face of environmental challenges and yield ceilings. The insights gained provide a scientific basis for future rice cultivar development and regional agricultural policies aimed at enhancing sustainability and efficiency in China’s diverse rice-growing regions.

Spatial–Temporal Evolution Pattern of Soil Erosion and Its Dominant Factors on the Loess Plateau from 2000 to 2020

Global changes have led to significant changes in soil erosion on the Loess Plateau. Soil erosion leads to the degradation of land resources and a decline in soil fertility, adversely affecting agricultural production and the socioeconomic situation. Therefore, revealing the spatiotemporal evolution patterns of soil erosion in the Loess Plateau region and investigating the influencing factors that contribute to soil erosion are crucial for its management and restoration. In this study, the RUSLE monthly model and the Geodetector model were utilized to reveal the spatiotemporal trends of soil erosion in the Loess Plateau from 2000 to 2020 and to determine the dominant influencing factors in different periods. The main results are as follows: (1) From 2000 to 2020, the soil erosion in the Loess Plateau initially weakened and then intensified, indicating that precipitation and precipitation intensity have different effects on surface soil. (2) From 2000 to 2015, the area experiencing slight and mild erosion increased. This is attributed to the increase in vegetation coverage in the Loess Plateau region, which has alleviated soil erosion in the area. (3) From 2000 to 2020, zones of severe soil erosion were mainly located in the cities of Yan’an and Yulin and their surrounding areas. The gravity center of soil erosion shifted northwestward from Yan’an City overall, indicating an improvement in the soil erosion conditions in the Yan’an area. (4) The predominant level of soil erosion across different land-use types was slight erosion, accounting for over 40%. This may be a result of forestry ecological projects that effectively reduce soil loss. (5) In slope zones of 0–5°, slight erosion accounted for the largest area proportion. As the slope increased, the area proportion of severe and extremely severe erosion also increased. This is attributed to the protective role of vegetation on soil in gentle slope areas. (6) From 2000 to 2020, vegetation was the dominant single factor influencing the spatiotemporal changes in soil erosion, while the interactions between vegetation and land use had the largest explanatory power, indicating that changes in land-use types partially affect variations in vegetation coverage. Our research findings could provide important data support for soil erosion control and eco-environment restoration in the Loess Plateau region.

Time-Dependent Evolution and Source Heterogeneities of Ocean Island Basalts from a Weak Plume, São Jorge, Azores

Abstract The geochemical composition of Ocean Island Basalts (OIBs) from the Azores reflects the spatial distribution, shape, and temporal evolution of small-scale geochemical heterogeneities within their mantle plume source. Here, we investigate the time-related evolution of volcanism at São Jorge Island, Central Azores. New field observations, a magnetic survey, 40Ar/39Ar and 14C ages and geochemical data indicate that the fissural volcanic activity at São Jorge produced at least four main mafic volcanic complexes (V. C.). The oldest V. C., São João, produced the thickest lava piles at ca. 1.3 Ma in the eastern part of the island. After a period of quiescence, the Serra do Topo V. C. was produced at ca. 0.8-0.5 Ma in the central part of the island. The Rosais V. C. was emplaced between ca. 0.4 and 0.1 Ma on the entire island. Finally, the Holocene Manadas V.C. volcanism became active in the western part of the island and includes three historic eruptions (1580, 1808 and 1964 CE). Magmas were formed at low melting degrees from a peridotitic mantle with possible minor contributions of recycled components. Olivine compositions and whole-rock trace element ratios discard a significant contribution from pyroxenitic source rocks. Melting temperatures (ca. 1420-1480 °C) were slightly higher than those of the ambient upper mantle. The four V.C. are characterized by distinct geochemical compositions in terms of incompatible trace elements and Sr-Nd-Pb isotopic ratios. The oldest V.C., São João, is characterized by Pb isotopic compositions (e.g., markedly negative Δ7/4 and 8/4 values) plotting well below the Northern Hemisphere Reference Line (NHRL). The Upper Pleistocene (Rosais V.C.) lavas from the north-western cliffs have compositions similar to enriched mantle (EM) end-member basalts (e.g., high 207Pb/204Pb at moderate 206Pb/204Pb; high Ba/Nb), which are rare among northern hemisphere OIBs. Finally, high 206Pb/204Pb (up to 20), reflecting contribution from a HIMU-type component characterizes the Holocene Manadas lavas and is occasionally found in lavas from other volcanic complexes from 1.3 to 0.1 Ma. These findings indicate that magmas from São Jorge and the nearby Central Azores islands were sourced from a strongly heterogeneous mantle plume, which displayed localized filaments of heterogeneous material that were rapidly exhausted (in ca. 0.2 Ma). The dominant component at São Jorge and in the Central Azores in general appears to be the HIMU-type endmember, which instead is not significant in the Eastern Azores. Possibly, the Central and Eastern Azores were produced by distinct branches of the Azores mantle plume, as would also be consistent with previous tomographic studies.

Analysis of the Spatial-Temporal Characteristics and Driving Factors of Cultivated Land Fragmentation Under the Expansion of Urban and Rural Construction Land: A Case Study of Ezhou City

A systematic understanding of the spatial-temporal evolution patterns of cultivated land fragmentation (CLF), its driving factors, and its relationship with the expansion of urban and rural construction land is essential for identifying strategies to mitigate CLF in rapidly urbanizing regions. This study combined landscape fragmentation with ownership fragmentation, analyzing CLF through three dimensions: resource endowment, spatial concentration, and convenience of utilization, with eight selected indicators. By comparing village-level data from 2013 to 2022, we explored the key drivers of CLF and its conflicts with urban and rural construction land expansion. The findings indicate a clear spatial variation in village-level CLF in Ezhou, characterized by low fragmentation in the northwest and northeast, and high fragmentation in the southwest and central regions. This pattern is in contrast to Ezhou’s economic development, which decreased progressively from east to north and south. Over the study period, village-level CLF in Ezhou evolved from being primarily moderately and relatively severely fragmented to predominantly severely and relatively severely fragmented, with an overall declining trend and more pronounced polarization. At the same time, the CLF within the village region demonstrated notable spatial clustering features, with a rapid increase observed between 2013 and 2022. It was also discovered that CLF is driven by various factors, with the main influences being the proportion of construction land, land use intensity, and population density. Cultivated land is the main source of both urban construction land (UCL) and rural construction land (RCL), with average contribution rates of 46.47% and 62.62%, respectively. This research offers empirical evidence for rapid urbanization and serves as a critical reference for rural revitalization and coordinated urban–rural development, with potential guidance for future policy formulation and implementation.

Analysis of spatial and temporal characteristics and evolution of green total factor productivity in agriculture in the lower Yellow River basin

The construction of ecological barriers in the Yellow River Basin represents a significant step toward reducing agricultural carbon emissions, achieving carbon neutrality, and reaching carbon peaking in China. The diverse agrarian development objectives of various regions within the basin have resulted in a heterogeneous approach to greening agriculture. Therefore, this paper will evaluate the development of carbon sink agriculture across 34 cities and municipalities in the lower Yellow River basin from 2008 to 2021 based on the EBM-GML model, and analyze the spatial-temporal evolution of agricultural green total factor productivity (AGTFP) in each region through the application of the Moran index, kernel density estimation, and spatial Markov chain analysis. The results demonstrate that agricultural carbon emissions in the Lower Yellow River Basin gradually decreased throughout the study period. Furthermore, overall carbon emission efficiency improved, indicating significant potential for further emission reduction. In addition, Agricultural Green Technology Progress (AGTC) has become a primary driver of AGTFP growth, while Agricultural Green Technology Efficiency (AGEC) has demonstrated a gradual upward trend. Locally, most areas are weakly connected and display an isolated development trend. The results of the kernel density analysis demonstrate a notable degree of mobility in the distributional dynamics of AGTFP growth, characterized by a gradual narrowing of the gap between locations. The transfer of (AGTFP) types in the lower reaches of the Yellow River Basin is stable, with a noticeable “club convergence” phenomenon, while geographical conditions significantly influence the transfer of AGTFP types in this region. Based on long-term trend predictions, the future trajectory of AGTFP in the lower Yellow River Basin appears optimistic and is expected to improve progressively, with the overall distribution tending toward equilibrium.

Spatial-Temporal Evolution and Determinants of Green Economy Efficiency: An Integrated Analytical Approach

In response to global environmental challenges and the need for sustainable development, this study investigates the spatiotemporal evolution and determinants of green economy efficiency (GEE) across 30 Chinese provinces from 2005 to 2021. By employing a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model, kernel density estimation, and spatial Markov chain analysis, this study provides a comprehensive assessment of GEE, incorporating both spatial and temporal dimensions. The analysis reveals a positive overall trend in GEE, with notable regional disparities, as eastern provinces exhibit significantly higher efficiency levels compared to central and western regions. Additionally, GEE improvements have been observed nationwide, although the central and western regions lag. Key factors influencing GEE include energy intensity, economic development, urbanization, industrialization, transport infrastructure, and government intervention, with distinct regional variations in their impact. The findings suggest that optimizing economic growth, reducing energy intensity, and fostering green urbanization and industrialization are essential strategies for enhancing GEE. This research contributes to the theoretical understanding of green economy efficiency and offers valuable policy recommendations for achieving balanced regional development and sustainable growth in China.

A complex network analysis of groundwater wells in and around the Doñana Natural Space, Spain

This paper proposes the use of Complex Network Analysis tools to study the spatial distribution and temporal evolution of groundwater abstraction wells within a region of interest. To that end a weighted undirected network connecting wells within a certain distance was built. The edge weights take into account the distance between the wells as well as their corresponding abstraction volumes. In addition to the conventional complex network metrics, two specific abstraction-based centrality indexes are proposed, based on the total impact of a groundwater abstraction well on its surrounding wells and on the total impact of surrounding wells on a specific location. The proposed approach has been applied to the area of the Doñana Natural Space, one of the most important wetlands in Europe. The topology of the network reveals a large number of small, disconnected components and a few large components. This is reflected also in the degree distribution, which follows a Power Law distribution. The network has a large average clustering coefficient, a small average path length and a low level of centralisation. Global and average local network efficiency are also high. The network is assortative in terms of degree-degree and strength-strength correlations. Eigenvector centrality of the nodes reveals the location of influential wells.

Error and Traffic State Analysis of Floating Car Data

Abstract. Floating car technology is widely applied in traffic information collection and has become a common method for real-time and dynamic monitoring of urban traffic conditions. Using floating car data from Shanghai as the research object, this paper analyzes the positioning error of floating cars and finds that most errors are within 15 meters. The error is significantly affected by vehicle speed but less by time, and the error decreases as the road grade increases. Speed and travel time ratio are used as parameters to assess the spatiotemporal traffic state, exploring the spatial distribution and temporal evolution of speed, as well as the spatiotemporal distribution of traffic congestion. In the spatial distribution of speed, clustering analysis is used to deduce the temporal distribution pattern of speed on the same road segment. Based on this, corresponding traffic management measures can be applied to different road segments and at different times.

Spatial distribution and temporal evolution of wall-stabilized DME/O2 premixed cool flames

The low-temperature oxidation of dimethyl ether (DME) was investigated in premixed wall-stabilized cool flames at two equivalence ratios (ϕ) of 0.2 and 0.5. Using a time-of-flight mass spectrometry (TOF-MS) coupled with gas chromatography (GC), the spatial distributions of major intermediate species, including DME, CH2O (formaldehyde), CO, CO2, and CH3OCHO (methyl formate), were quantified under well-controlled boundary conditions. Moreover, the temporal evolutions of multiple intermediate species in the wall-stabilized cool flame ignition process were measured via TOF-MS, while the wall temperature was gradually ramped up from 550 K to 730 K. Several kinetic models were examined herein to assess the estimated low-temperature reactivity of DME by comparing the one-dimensional axisymmetric simulation results with the experimental data. Wall-stabilized cool flame structures at equivalence ratios ϕ of 0.2 and 0.5 were quantitatively examined with the major intermediate species. It is found that the kinetic models reasonably predict the onset of the reaction zone near the wall. Among these models, Kurimoto et al.’s model gives better predictions for the distributions of CH2O and CO, which are characteristic species of cool flames. In addition, time-resolved measurements of the unsteady cool flames identified the negative temperature coefficient (NTC) turnover points for different species across various temperature regions. It is also found that the Kurimoto et al. model still indicates a slightly higher reactivity of DME in the low-temperature range, resulting in earlier DME consumption and a shift of NTC window to lower temperatures at ϕ = 0.2.

Spatial-temporal analysis and trend prediction of regional crop disease based on electronic medical records

Intelligent diagnosis of individual crop diseases has matured. How to understand the evolution patterns and predict regional disease trends remains a significant challenge. Plant Electronic Medical Records (PEMRs) offer valuable spatial-temporal characteristics about crop diseases, presenting a new opportunity for predicting the occurrence of regional diseases. In this study, we used a large prescription database from Beijing (2018–2021) to reframe regional disease prediction as a time series forecasting task. Firstly, to analyze spatial-temporal evolution patterns, we use ArcGIS to extract key information and identify potential connections between different disease occurrence points. Then, we developed a novel deep learning combined model SV-CBA, which combines Seasonal and Trend decomposition (STL) with Variational Mode Decomposition (VMD) to identify trend, seasonal, and residual components, and re-decomposes the residuals. STL-VMD can capture long-term trends and periodic variations while managing nonlinear and volatile characteristics. The CNN-BiLSTM-Attention model calculates disease trends by linearly integrating predictions of each sub-series. To reduce computational complexity while maintaining predictive performance, we propose an improved simplified attention mechanism. Our model demonstrates superior performance in both comparative and ablation experiments using the PEMRs dataset, outperforming numerous other models. This study provides accurate disease trend predictions, aiding farmers and regional managers in agricultural production management.

Spatial-Temporal Evolution Analysis of Regional MWPIIs and the Development Path under the Integrated Strategy in the Yangtze River Delta, China

Spatial-Temporal Evolution Analysis of Regional MWPIIs and the Development Path under the Integrated Strategy in the Yangtze River Delta, China

A numerical study on the oscillatory dynamics of tip vortex cavitation

In this paper, we numerically study the mechanism of the oscillatory flow dynamics associated with the tip vortex cavitation (TVC) over an elliptical hydrofoil section. Using our recently developed three-dimensional variational multiphase flow solver, we investigate the TVC phenomenon at Reynolds number $Re = 8.95 \times 10^5$ via dynamic subgrid-scale modelling and the homogeneous mixture theory. To begin, we examine the grid resolution requirements and introduce a length scale considering both the tip vortex strength and the core radius. This length scale is then employed to non-dimensionalize the spatial resolution in the tip vortex region, the results of which serve as a basis for estimation of the required mesh resolution in large eddy simulations of TVC. We next perform simulations to analyse the dynamical modes of tip vortex cavity oscillation at different cavitation numbers, and compare them with the semi-analytical solution. The breathing mode of cavity surface oscillation is extracted from the spatial-temporal evolution of the cavity's effective radius. The temporally averaged effective radius demonstrates that the columnar cavity experiences a growth region followed by decay as it progresses away from the tip. Further examination of the characteristics of local breathing mode oscillations in the growth and decay regions indicates the alteration of the cavity's oscillatory behaviour as it travels from the growth region to the decay region, with the oscillations within the growth region being characterized by lower frequencies. For representative cavitation numbers $\sigma \in [1.2,2.6]$ , we find that pressure fluctuations exhibit a shift of the spectrum towards lower frequencies as the cavitation number decreases, similar to its influence on breathing mode oscillations. The results indicate the existence of correlations between the breathing mode oscillations and the pressure fluctuations. While the low-frequency pressure fluctuations are found to be correlated with the growth region, the breathing mode oscillations within the decay region are related to higher-frequency pressure fluctuations. The proposed mechanism can play an important role in developing mitigation strategies for TVC, which can reduce the underwater radiated noise by marine propellers.

A new chemical networked system: spatial-temporal evolution and control

Abstract This paper constructs a scale-free chemical network based on the Gierer-Meinhardt (GM) system and investigates its Turing instability. We establish a fractional-order single-node GM system with delay and design a fractional-order proportional derivative (PD) control strategy for the issue of bifurcation control. Using delay as bifurcation parameter, the existence of Hopf bifurcation is proven, and control over bifurcation threshold points is achieved through a fractional-order PD control strategy. For the scale-free chemical network based on the GM system, we obtain the condition of how the Turing instability occurs. We derive how the number of edges for the new nodes changes the stability of the network-organized system and investigate the relationship between degrees of nodes and eigenvalues of the network matrix. We give the instability condition caused by diffusion in the network-organized system. Finally, the numerical simulations verify analytical results.

Spatial–Temporal Evolution and Driving Force Analysis of Blue–Green Space in the Chengdu–Chongqing Economic Circle, China

In the rapid process of urbanization, revealing the patterns and driving forces behind the evolution of blue–green spaces holds significant value for optimizing urban blue–green environments. This study systematically investigates the spatial–temporal evolution characteristics and driving forces of blue–green space in the Chengdu–Chongqing Economic Circle from 1990 to 2020, utilizing GIS technology, landscape pattern analysis, and geographic detectors. The research findings indicate the following: (1) The area of blue–green space in the study area exhibits a general trend of initial growth followed by decline, with significant changes occurring between 2010 and 2020. (2) The fragmentation degree of blue–green space is gradually increasing, while connectivity among landscapes is decreasing; however, there has been an increase in landscape distribution uniformity. More than 90% of blue–green spaces expanded mainly through adjacency patterns. (3) In examining driving forces, it was found that temperature, topographic relief, elevation, population density, and construction intensity are the primary driving factors. Notably, the influence of natural factors has diminished over time while human social factors have significantly intensified. This study offers solutions for optimizing the configuration of blue–green spaces within the Chengdu–Chongqing Economic Circle. It also serves as a reference case for promoting high-quality urbanization in developing countries undergoing rapid urbanization.