Nonstationary Effects Research Articles

Unraveling nonlinear and spatial non-stationary effects of urban form on surface urban heat islands using explainable spatial machine learning

Under global warming, surface urban heat islands (SUHI) threaten human health and urban ecosystems. However, scant research focused on exploring the complex associations between urban form factors and SUHI at the county scale, compared with rich studies at the city scale. Therefore, this study simultaneously examined the nonlinear and spatial non-stationary association between SUHI and urban form factors (e.g., landscape structure, built environment, and industrial pattern) across 2321 Chinese counties. An explainable spatial machine learning method, combining the Geographically Weighted Regression, Random Forest, and Shapley Additive Explanation model, was employed to deal with nonlinearity, spatial non-stationary, and interpretability of modeling. The results indicate the remarkable spatial disparities in the relationship between urban form factors and SUHI. Landscape structure contributes the most in southern counties, while the built environment is more important in northeastern counties. The impact of building density and building height increases with the county size and becomes the main driver of urban heat in mega counties. Most urban form factors exhibit nonlinear impacts on SUHI. For example, urban contiguity significantly affects SUHI beyond a threshold of 0.93, while building density does so at 0.17. By comparison, the influence of shape complexity remains stable above a value of 7. Factors such as industrial density and diversity have a varied influence on SUHI between daytime and nighttime. The results of local explanations and nonlinear effects provide targeted regional mitigation strategies for urban heat.

How do heat waves affect the relationship between built environment patches of different compactness and land surface temperature?

The compactness of the urban built environment significantly affects land surface temperature (LST), especially during heat waves (HW). However, the mechanisms by which the configuration of key building patches in built environments of varying compactness drives LST are unclear. This study proposes a new research framework combining local climate zones (LCZ), spatial pattern type (SPT) and landscape index (LI) to reveal the impacts of key building patches on LST. Taking Shenyang as an example, we utilized the geographically weighted regression (GWR) method to reveal the non-stationary relationship between building patches with different compactness and LST during heat and non-heat waves, and an optimal parameters-based geographical detectors model (OPGDM) to explore the mechanisms by which the configuration of key building patches drives LST. The results show that HW enhances the spatially non-stationary effects of different types of building patches on LST. The configuration of key building patches in the open built environment drives LST more strongly than those in the compact built environment. The relationship between LIs and LST in key building patches exhibits diverse characteristics during heat and non-heat waves, so differentiated configuration optimization strategies are required for built environments of different compactness. The interactions of patch configurations also require emphasis, especially the patch complexity. The research findings help to formulate urban planning strategies from a climate adaptation and mitigation perspective to cope with the increasing frequency of extreme heat events.

Non-Stationary Effects of Current-Load on Electrochemical Behavior of La0.8Sr0.2MnO3- δ in Solid Oxide Electrolysis Cell

High-temperature solid oxide water electrolysis is a highly efficient and carbon-free technology employed for energy conversion, offering numerous advantages over traditional low-temperature water electrolysis methods. These benefits include fast electrode kinetics without the need for Pt group metals and the potential to operate in reversible regimes (fuel cell/steam electrolysis), attributed to the elevated operating temperature (i.e., 600-800 °C). However, the high operating temperature imposes stringent material requirements.While recent attention has been directed towards developing and characterizing new materials for both electrodes and electrolytes, a comprehensive understanding of the underlying reaction mechanisms remains incomplete, hindering the further advancement of the solid oxide cells.Despite the emergence of more advanced materials in recent years, the lanthanum-strontium-manganite La1-xSrxMnO3-δ (LSM) remains to be widely used for an oxygen electrode construction. This preference is due to its cost-effectiveness, high electrical conductivity, thermal and chemical stability, and compatibility with various solid electrolytes. Although LSM has been known for several decades, the reaction mechanism occurring on this oxygen electrode in a broad range of electrode potentials is inadequately described, with discrepancies arising among different authors. These inconsistencies are often linked to the evolving understanding of the non-stationary properties of the LSM lattice – the inner defect chemistry of LSM.Contemporary studies increasingly adopt the concept of ionic conductivity of LSM enabled by cathodic polarization to describe the oxygen reduction reaction. This shift from exclusive electron to ionic-electron conductivity enhances the performance of the LSM electrode, potentially causing discrepancies in the literature as experimental data may not align with theoretical descriptions. Conversely, for the oxygen evolution reaction during anodic polarization, a reversed reaction mechanism is considered without accounting for ionic conductivity of LSM under either stationary or non-stationary conditions. This approach may oversimplify the defect chemistry of LSM, particularly during transitions between operational regimes (i.e., fuel cell and electrolyser regimes). However, such changes are difficult to describe only by experimental approach. Here a combination with mathematical modelling seems to be promising approach allowing to gain deeper understanding.The primary objective of this study is to further investigate the impact of LSM defect chemistry on electrode performance, proposing a reaction scheme during both operating regimes and, crucially, during transitions between them.To address this goal, a series of experiments were conducted under both current-less and current-load conditions using laboratory button single cells. Various gas compositions and operating temperatures were used to determine whether LSM becomes a mixed ionic-electron conductor. Potentiostatic or potentiodynamic methods were used to clarify how this altered property influences individual operating regimes. The results indicate that cathodic polarization leads to a partial reduction of Mn4+ to Mn2+ in the crystalline lattice of LSM, creating oxygen vacancies through which oxide ions can be transported. Additionally, these lattice changes, though temporary, impact the oxygen evolution reaction region under certain conditions. A mathematical model incorporating parallel reaction pathways for both operating regimes supports these experimental results, predicting a cathodic potential at which ionic conductivity becomes viable and identifying conditions where partial reduction of Mn ions is no longer possible.This work contributes to understanding solid oxide cells, providing a solid foundation for more complex mathematical modeling of electrolysis cells. It underscores the necessity to fully comprehend the electrochemical behavior of electrode materials for the continued development of this technology.This work was supported by the Technology Agency of the Czech Republic under project no. TK04030143 (Advanced reversible system for hydrogen production on base of the high temperature solid oxide cell).

Spatiotemporally non-stationary evolution of groundwater levels in Poyang Lake Basin driven by meteorological and hydrological factors

The complex relationship between wet-dry transition in the Poyang Lake basin and groundwater storage significantly affects the lake's hydrology, downstream ecological state, and overall security along the Yangtze River in China. There is, however, a notable lack of systematic exploration into how various factors drive spatiotemporal variability in groundwater level (GWL). Using local indicators of spatial association (LISA), spatial non-stationarity models, and multi-source data, our analysis explores the spatial distribution of GWL and quantifies the influence of driving factors on its spatiotemporal non-stationarity at annual and monthly scales. We also compare driving factor contributions in hilly, plain, and local areas within the Poyang Lake basin. Our findings reveal significant local clustering of GWL, indicating substantial spatial autocorrelation and geographic heterogeneity in GWL. Influencing factors exhibit non-stationary effects on GWL at spatial and temporal scales, with precipitation (P), ground surface elevation (GSE), and soil moisture (SM) being primary contributors, generally exerting positive effects. SM contributes most during dry years and normal periods. P and the Palmer Drought Severity Index (PDSI) have greater impacts in hilly areas, while GSE shows the opposite trend. Rainfall is a source of groundwater recharge, with a lagged response observed in GWL to rainfall in this basin. The lag time is about 1–2 months. Evapotranspiration is not the dominant discharge pathway. The decrease in GWL during the dry season is mainly due to reduced precipitation recharge and increased lateral groundwater discharge from areas of high hydraulic head to areas of low hydraulic head.

Forecasting Future Vegetation Dynamics under SSP/RCP Pathways under Spatially Changing Climate and Human Activities Conditions

Vegetation dynamics result from the interaction between human activities and climate change. Numerous studies have investigated the contributions of human activities and climate change to vegetation cover dynamics using statistical methods. However, these studies have not focused much on the spatially non-stationary effects of human activities on vegetation cover changes and future trends. Taking the Three Gorges Reservoir (TGR) area as the case study area, it was divided into 32 combinations by considering the spatially varying effects of five factors related to human activity and climate change, including gross domestic product (GDP), population, land use change, precipitation, and temperature. Regression in terms of pixels was then performed for each combination at the pixel scale. The result showed that from 2001 to 2020, the annual average normalized digital vegetation index (NDVI) in the TGR area exhibited an upward trend (slope = 0.0051, p < 0.01), with the mean NDVI increasing from 0.53 to 0.64. Compared with the regression with climate variables, the proposed model improved the R2 value from 0.2567 to 0.6484, with the p-value in the t-test reduced from 0.2579 to 0.0056. It indicated that changes in vegetation were dominated by human activities and climate change in 48.77% and 3.19% of the TGR area, respectively, and 43.70% of the vegetation coverage was dominated by both human activities and climate change. This study also predicted the future NDVI according to the shared socioeconomic pathways (SSPs) and representative concentration pathway (RCP) scenarios provided by the Intergovernmental Panel on Climate Change. It suggests that, assuming future regional policies are the same as the historical policies in the TGR, the SSP5–8.5 scenario would have the highest and fastest growth in average NDVI, with the average NDVI increasing from 0.68 to 0.89, because of the large increase in the GDP, lower population in this scenario, and adequate hydrothermal conditions.

Non-stationary winds effects over large partially-open roofs: A CFD study regarding the role of unsteady aerodynamics

Non-stationary winds are increasingly attracting the attention of the scientific community and, despite not being explicitly considered in current design practice, they are known to produce important damages. In particular, the non-stationarity of such events has important consequences with respect to four main aspects which affect the definition of design wind loads: (i) the design wind speed definition, (ii) the wind profiles, (iii) the calculation of the structural response and (iv) the insurgence of unsteady aerodynamics effects. While the first three aspects already received considerable attention, the last one is still largely unclear and its relevance still to be assessed. In particular, for non-stationary winds such as macro- and micro-downbursts, the ramp-up time might become so short to trigger unsteady aerodynamic effects in the overall flow arrangement, which cannot be inferred from results obtained for stationary cases. In this paper, we propose a first investigation of such matter considering a large stadium roof using Large Eddy Simulations. Results show that, for the considered case, noticeable unsteady aerodynamic effects, which leads to an amplification of wind loads, can be triggered when the wind rump-up time is in the order of 30 s, and quickly decrease in importance for higher rump-up times.

LRFD methodology for river embankments against non-stationary flooding under climate change

Riverine floods have become increasingly prevalent on a global scale, posing significant risks to infrastructure systems and communities. The escalating impacts of climate change associated with the increase in rainfall intensities and frequencies necessitate the improvement of the existing design methodologies to account for the non-stationary climate change effects to ensure that the reliability is above the target level and mitigate future flood disasters. This paper presents a novel LRFD approach for river embankments subjected to extreme rainfall under non-stationary climate change effects. This approach introduces an additional partial factor to account for the effects of climate change. Precipitation and temperature projections are collected from various climate models considering several cases of emission scenarios. An integrated hydrological and hydraulic modeling of the analyzed river is carried out to estimate the associated time-variant river discharge and water surface elevation. The non-stationary extreme value associated with the maximum flood level is leveraged using the peak-over-threshold approach. The embankment reliability and the corresponding most probable points are evaluated using limit states associated with overtopping and slope failures. Based on the estimated and target reliability indexes, the design point for each random variable is assessed considering the cases with and without climate change effects. Finally, the partial factors associated with climate change effects are determined. As an illustrative example, the proposed framework is applied to the Ashida River in Fukuyama city of Japan.

Investigating the multiscale associations between urban landscape patterns and PM1 pollution in China using a new combined framework

Urban landscape patterns serve as the spatial carriers of emission sources and influencing factors of air contaminants, which inevitably impact submicron particle (PM1) pollution. However, most studies ignore the spatial nonstationary and nonlinear effects of urban landscape patterns on the PM1 concentration. In this study, a novel framework was developed that integrates a multiscale geographically weighted regression (MGWR) model and Shapley Additive exPlanations (SHAP) machine learning method to explore the spatial heterogeneity effect, relative contribution and nonlinear impact of landscape patterns on the PM1 concentration at the national and urban agglomeration scales. The results indicated that urban landscape patterns were closely related to PM1 pollution and exhibited obvious spatial differences. The disorderly expansion of urban built-up areas and an irregular urban morphology could aggravate PM1 pollution, while urban fragmentation and connectivity could impose reducing effects. Dispersed urban landscape patterns could reduce the PM1 concentration in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CDCQ) regions, whereas compact and continuous landscape patterns positively affected PM1 pollution mitigation in the Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZH) urban agglomerations. The impact of urban landscape patterns on PM1 pollution was greater in urban agglomerations than elsewhere. The ENN_MN landscape index exhibited the highest feature importance and interpretability. The threshold effects between the urban shape indicators and PM1 concentration were more complex than those with the other landscape indices. This critical knowledge provides a scientific basis for further understanding the correlation mechanism between PM1 pollution and the landscape pattern, urban sustainable planning and air pollution control.

Non-stationary Bayesian spatial model for disease mapping based on sub-regions.

This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model's ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence in different sub-regions. We derive a joint penalized complexity prior to the flexible local precision parameters to prevent overfitting and ensure contraction to the stationary model at a user-defined rate. The proposed methodology can be used as a basis for the development of various other non-stationary effects over other domains such as time. An accompanying R package fbesag equips the reader with the necessary tools for immediate use and application. We illustrate the novelty of the proposal by modeling the risk of dengue in Brazil, where the stationary spatial assumption fails and interesting risk profiles are estimated when accounting for spatial non-stationary. Additionally, we model different causes of death in Brazil, where we use the new model to investigate the spatial stationarity of these causes.

Forecasting with an N-dimensional Langevin equation and a neural-ordinary differential equation.

Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecasting techniques have received considerable attention, research on non-stationary methods is comparatively scarce, despite the common prevalence of non-stationary features in electricity markets. Specifically, existing non-stationary techniques will often aim to address individual non-stationary features in isolation, leaving aside the exploration of concurrent multiple non-stationary effects. Our overarching objective here is the formulation of a framework to systematically model and forecast non-stationary electricity-price time series, encompassing the broader scope of non-stationary behavior. For this purpose, we develop a data-driven model that combines an N-dimensional Langevin equation (LE) with a neural-ordinary differential equation (NODE). The LE captures fine-grained details of the electricity-price behavior in stationary regimes but is inadequate for non-stationary conditions. To overcome this inherent limitation, we adopt a NODE approach to learn, and at the same time predict, the difference between the actual electricity-price time series and the simulated price trajectories generated by the LE. By learning this difference, the NODE reconstructs the non-stationary components of the time series that the LE is not able to capture. We exemplify the effectiveness of our framework using the Spanish electricity day-ahead market as a prototypical case study. Our findings reveal that the NODE nicely complements the LE, providing a comprehensive strategy to tackle both stationary and non-stationary electricity-price behavior. The framework's dependability and robustness is demonstrated through different non-stationary scenarios by comparing it against a range of basic naïve methods.

Information Geometry Theoretic Measures for Characterizing Neural Information Processing from Simulated EEG Signals.

In this work, we explore information geometry theoretic measures for characterizing neural information processing from EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer's disease (AD) patients with both eyes-closed and eyes-open conditions. In particular, we employ information rates to quantify the time evolution of probability density functions of simulated EEG signals, and employ causal information rates to quantify one signal's instantaneous influence on another signal's information rate. These two measures help us find significant and interesting distinctions between healthy subjects and AD patients when they open or close their eyes. These distinctions may be further related to differences in neural information processing activities of the corresponding brain regions, and to differences in connectivities among these brain regions. Our results show that information rate and causal information rate are superior to their more traditional or established information-theoretic counterparts, i.e., differential entropy and transfer entropy, respectively. Since these novel, information geometry theoretic measures can be applied to experimental EEG signals in a model-free manner, and they are capable of quantifying non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity presented in real-world EEG signals, we believe that they can form an important and powerful tool-set for both understanding neural information processing in the brain and the diagnosis of neurological disorders, such as Alzheimer's disease as presented in this work.

Mechanisms of non-stationary influence of urban form on the diurnal thermal environment based on machine learning and MGWR analysis

The non-stationary impact of urban form at block scale on the diurnal thermal environment has not been extensively studied. To fill the gap, a comprehensive multi-scale research framework was constructed, integrating machine learning algorithms and multiscale geographically weighted regression (MGWR) analysis. The urban form types were extracted by machine learning algorithms from 2282 blocks of Dalian, a coastal city, and the contribution to the diurnal land surface temperature (LST) was evaluated. Based on MGWR, the non-stationary effects of urban form, human activity and spatial location on the diurnal LST were quantified, which indicated the contributing factors to the diurnal thermal differences among the form types. The result was as follows: 1) Blocks characterized by low vegetation and mid/low-rise buildings had the highest warming contribution for diurnal LST. 2) The impact of sky view factor (SVF) on diurnal temperature amplitude exhibited no significant spatial-temporal heterogeneity. 3) Building density had a prominent effect on diurnal temperature amplitude. 4) High-vegetation and open (SVF > 0.5) mid/mid-high/low-rise (15–50 m) buildings were recommended. This study provides a more precise basis for policymakers to develop climate adaptation strategies throughout day and night, particularly for coastal cities.

The molecular diversity of dissolved organic matter in forest streams across central Canadian boreal watersheds.

Small headwater streams can mobilize large amounts of terrestrially derived dissolved organic matter (DOM). While the molecular composition of DOM has important controls on biogeochemical cycles and carbon cycling, how stationary landscape metrics affect DOM composition is poorly understood, particularly in relation to non-stationary effects from hydrological changes across seasons. Here, we apply a combination of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and absorbance spectroscopy to characterize stream DOM from 13 diverse watersheds across the central Canadian boreal forests and statistically relate DOM compositional characteristics to landscape topography and hydrological metrics. We found that watershed runoff across different surface physiographies produced DOM with distinctly different chemical compositions related to runoff pH. Specifically, streams in sandy soil watersheds contained more abundant aromatic, nitrogenated and sulfurized fractions of DOM, likely due to a combination of lower soil capacity to absorb DOM than other soil types and high conifer forest coverage that generated acidic litterfall in more sandy watersheds. In contrast, streams with more neutral pH in watersheds with shallow soils had DOM resembling low oxidized phenolic molecules mainly due to increased brush/alder and deciduous vegetation coverage in relatively steeper watersheds. However, as precipitation and flows increased in the fall, the overall water chemistry of streams became more similar as runoff pH increased, the overall chemical diversity of DOM in streams decreased, and stream DOM resembled fresher, lower molecular weight lignin material likely originating from freshly produced leaf litter. Together, our findings show that during hydrologically disconnected periods, pH and landscape characteristics have important controls on the mobilization of aromatic DOM but that many landscape-specific characteristics in the Canadian boreal forest are less influential on DOM processing during wetter conditions where chemically similar, plant-derived DOM signatures are preferentially mobilized. These findings collectively help predict the composition of DOM across diverse watersheds in the Canadian boreal to inform microbial and contaminant biogeochemical processes in downstream ecosystems.

РАСЧЕТ ЭЛЕМЕНТОВ ЖЕЛЕЗОБЕТОННЫХ КОНСТРУКЦИЙ НА ОГНЕСТОЙКОСТЬ И ОГНЕСОХРАННОСТЬ В ПРОГРАММНОМ КОМПЛЕКСЕ СТАРКОН.

RCDiagra software as a part of the domestic STARKON software is presented. The software is designed to perform fire resistance and fire safety analysis of reinforced concrete beams and columns. The strength analysis is based on the nonlinear deformation theory and enables to take into account non-stationary thermal effects changing the concrete and reinforcement properties. To determine the ultimate strength, elastic modulus for concrete and reinforcement, ultimate relative deformations, a thermal analysis is performed in accordance with SP 468.1325800.2019 and MDS 21-2.2000. The thermal engineering analysis solve the problem of determining the temperature field in the beam cross-section under non-stationary thermal action, depending on the boundary conditions and thermophysical parameters of the material. It is assumed that the coefficients of thermal conductivity and specific heat capacity of concrete depend on the current temperature at the crosssection points.

Tsunami insurance portfolio optimization for coastal residential buildings under non-stationary sea level rise effects based on sample average approximation

Tsunami insurance portfolio optimization for coastal residential buildings under non-stationary sea level rise effects based on sample average approximation

Non-stationary temporal-spatio correlation analysis of information-driven complex financial dynamics

The explosion of information production provides a new perspective on investigating complex dynamic systems such as the financial markets. With large-scale historical data, a textual-based sentiment index is introduced to study the correlations between the external information and the price return in the stock market. In particular, a novel approach taking into account the non-stationary effect of the sentiment is proposed to compute the sentiment-return correlation function, and it reveals a non-zero correlation between the past sentiment and the future motion of the price return. Such a computation is then extended to a cross-correlation form which describes the correlations between different sentiment indexes and price returns. A stratified structure of the cross-correlation functions is observed. With the random matrix theory, the features of the stratified structure are quantitatively analyzed. Finally, an investment strategy is constructed based on the temporal correlation.

Understanding spatially nonstationary effects of natural and human-induced factors on land subsidence based on multi-temporal InSAR and multi-source geospatial data: a case study in the Guangdong-Hong Kong-Macao Greater Bay Area

ABSTRACTLand subsidence, a common geological phenomenon in deltaic regions, poses significant risks to infrastructures, environments, and human lives. Monitoring and understanding land subsidence are crucial for establishing resilient, adaptive, and sustainable environments. In this study, a robust multi-temporal interferometric synthetic aperture radar (MTInSAR) method was employed to monitor land subsidence in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) using 541 Sentinel-1 SAR images. Spatial auto- and cross-correlation analysis were applied to select the most reasonable explanatory variables from nine candidates in each city of the GBA. City-level geographically weighted regression (GWR) models were then constructed to explore the spatially nonstationary effects of natural and human-induced factors. The findings revealed subsidence velocities ranging from 0 to 83.7 mm/yr in the GBA from 2015 to 2021, with the dominant factors affecting subsidence varying among regions. Natural and human-related factors accounted for 51.13% and 48.87%, respectively. Further analysis of representative subsidence areas highlights the importance of continuous monitoring of ground deformation using MTInSAR, region-specific land subsidence mitigation strategies, and regular maintenance of urban infrastructure. These insights are valuable for policymakers and urban planners in comprehending the complex processes underlying land subsidence and informing decision-making processes for sustainable and resilient urban development.

Research on the influence of the non-stationary effect of the magnetorheological finishing removal function on mid-frequency errors of optical component surfaces.

With the continuous development of modern optical systems, the demand for full spatial frequency errors of optical components in the system is increasing. Although computer-controlled sub-aperture polishing technology can quickly correct low-frequency errors, this technology significantly worsens the mid-frequency errors on the surface of the component, which greatly inhibits the improvement of optical system performance. Therefore, we conducted in-depth research on the non-stationary effect of the removal function caused by the fluctuation in magnetorheological polishing and their influence on the mid-frequency errors of the component surface. We established a non-stationary profile model of the removal function and applied this model to simulate the distribution of mid-frequency errors on the surface of the processed component, considering the non-stationary effect. The simulation results showed that the non-stationary effect of the removal function weaken the mid-frequency ripple errors but increase other mid-frequency errors. Therefore, we first proposed the optimal single-material removal thickness corresponding to the non-stationary effect and experimentally verified the effectiveness of the optimal material removal thickness in suppressing mid-frequency errors. The experimental results showed that when the magnetorheological finishing single-material removal thickness is set to the optimal value, both the mid-frequency ripple errors and the mid-frequency RMS on the surface significantly decrease. Therefore, this work provides a basis for improving the existing magnetorheological finishing process and effectively suppressing the mid-frequency errors on the surface of processed components. It also provides theoretical and technical support for the magnetorheological processing and manufacturing of high-precision optical components. At the same time, the non-stationary effect and the corresponding analytical models has the potential to be extended to other polishing tools.

Establishment and experimental verification of a three-dimensional finite element model for residual stress in surface processing of Inconel 718 alloy by laser cladding

The local load and residual stress changes of conventional forged Inconel 718 alloy can be simulated in a two-dimensional (2D) cutting simulation model, but the type and distribution of global residual stress field of the alloy cannot be predicted during laser cladding layer cutting. To address the shortcomings of conventional 2D cutting simulation models for forging Inconel 718 alloy, this paper proposed a 3D closed-loop finite element model, and taking the dry-side milling process of Inconel 718 alloy laser cladding layer as an example, the generation mechanism and distribution of residual stress are predicted during the machining process. Further testing of surface, near-surface, and subsurface residual stresses was conducted to analyze the specific distribution of residual stresses in the workpiece. The results indicated that the uneven distribution of cutting stress and temperature on the workpiece had a significant non-stationary thermal-mechanical effect on the surface of the machined workpiece; the distribution of surface residual stress was uneven, with the significant tensile residual stress at the workpiece boundary. As the depth increases, the influence of mechanical effects dominated, and the compressive residual stress was generated on the subsurface of the workpiece. Residual stress testing was conducted on the surface/near-surface, and the experimental results were consistent with the simulation results of the closed-loop simulation model. In the subsurface, residual stress showed a similar trend of change. This study compensated for the shortcomings of conventional finite element simulation and provided the reference value for the application of three-dimensional finite element simulation in laser cladding materials.

Vegetation EVI changes and response to natural factors and human activities based on geographically and temporally weighted regression

The research on vegetation changes plays a crucial role in the assessment of ecosystem health, monitoring environmental changes, providing early warnings for natural disasters, and supporting decision-making for sustainable development. However, understanding the nonstationary characteristics of drivers affecting vegetation change remains challenging. This study used Enhanced Vegetation Index (EVI) data obtained through Google Earth Engine (GEE), Theil-Sen, and Mann-Kendall methods to analyze the spatial-temporal patterns and trends of vegetation changes in Sichuan, western China from 2000 to 2020. The Geographical and Temporal Weighted Regression (GTWR) method was applied to deal with spatial and temporal nonstationarity simultaneously. Results showed that vegetation cover in Sichuan was good overall, with medium and high vegetation covering more than 78% of the area. About 72.75% of the area showed an increasing trend in vegetation cover, and areas with extremely significant and significant EVI growth (p < 0.01 and 0.01 ≤ p < 0.05) accounted for 23.94% of the total area. The areas with significant increases in vegetation EVI were mainly distributed in northeast, east, southeast, central, and southwest in Sichuan, while the areas with significant decreases were mainly distributed in the central Sichuan plain urban agglomeration and western Sichuan plateau. GTWR addressed the nonstationary effect of the temporal dimension on the drivers of natural and human activities, with a fitted R2 of 0.846. The study identified climate, terrain, and human activities as the primary driving factors behind vegetation EVI fluctuations. Annual average temperature and precipitation, human activities, and slope had a positive impact on vegetation EVI changes, while solar radiation and aspect had a negative inhibitory effect. The effects of climate, terrain, and human activities on EVI changes exhibited significant spatial heterogeneity and clustering, resulting in either positive promotion or negative inhibition. This study provides an additional methodology to solve the nonstationary problem of vegetation change trends and their response mechanisms. The revealed changes in vegetation EVI and the spatiotemporal heterogeneity characteristics of their driving factors are important for fragile ecosystems to adapt to and mitigate the effects of natural changes and human activities. Revealing the variations in vegetation EVI and their underlying drivers can showcase diverse characteristics across regions and time periods, the presence of spatiotemporal heterogeneity holds great significance in comprehending the adaptive strategies employed by fragile ecosystems to mitigate the effects of natural fluctuations and human-induced activities.