Spatial Migration Research Articles

Primary Stability and Bone Contact Loading Evaluation of Suture and Screw based Coracoid Graft Fixation for Anterior Glenoid Bone Loss

Background: Reconstruction techniques for anterior glenoid bone loss have seen a trend from screws to suture-based fixations. However, comparative biomechanical data, including primary fixation and glenoid-graft contact pressure mapping, are limited. Hypothesis: Suture-based bone block cerclage (BBC) and suspensory suture button (SB) techniques provide similar primary fixation and cyclic stability to double-screw fixation but with higher contact loading at the bony interface. Study Design: Controlled laboratory study. Methods: In total, 60 cadaveric scapulae were prepared to simulate anterior glenoid bone loss with coracoid autograft reconstruction. Graft fixation was performed with 3 different techniques: (1) an interconnected all-suture BBC, (2) 2 SB suspensions, and (3) 2 screws. Initial compression was analyzed during primary fixation. Cyclic peak loading with 50 N and 100 N over 250 cycles at 1 Hz was performed with a constant valley load of 25 N. Optical recording and pressure foils allowed for spatial bone block tracking and contact pressure mapping at the glenoid-graft interface. Load-to-failure testing was performed at a rate of 1.5 mm/s with ultimate load and stiffness measured. Results: Initial graft compression was higher with screw fixation (141 ± 5 N) compared with suture-based fixations (P < .001), with BBC fixation providing significantly higher compression than SB fixation (116 ± 7 N vs. 91 ± 5 N; P < .001). Spatial bone block migration and ultimate failure load were similar between the BBC and screw groups. The SB group showed significantly increased bone block translation (3.1 ± 1.0 mm; P≤ .014) and rotation (2.5°± 1.4°; P≤ .025) and significantly lower ultimate failure load (180 ± 53 N) compared with the BBC (P = .046) and screw (P = .002) groups. Both suture-based fixations provided significantly increased graft-glenoid contact loading with higher pressure amplitudes (P≤ .032) and contact pressure after cyclic loading (+13%; SB: P = .007; BBC: P = .004) compared with screw fixation. Conclusion: Both SB and interconnected cerclage fixation improved dynamic contact loading compared with screw fixation in a biomechanical glenoid bone loss model. Cerclage fixation was biomechanically comparable with screw fixation but with a greater variability. SB fixation showed significantly lower primary fixation strength and greater bone block rotation and migration. Clinical Relevance: Suture-based bone block fixations improved graft-glenoid contact loading, but the overall clinical consequence on healing remains unclear.

S-scheme heterojunction of hollow corncob-like ZnIn2S4/LaFeO3 for water splitting and tetracycline degradation

Reasonable design of heterojunction photocatalysts with high-quality interfacial coupling is an effective way to improve the photocatalytic activity of semiconductors. Herein, we successfully decorated Zinc indium sulfide (ZnIn2S4, ZIS) on perovskite Lanthanum ferrite (LaFeO3, LFO) with more active sites by a pre-hydrothermal combined post-calcination method, and constructed S-scheme heterojunction photocatalyst with a unique hollow corncob-like morphology for efficient photocatalytic hydrogen production and tetracycline (TC) degradation. When the mass ratio of LFO is 35% and 15%, the ZIS/LFO photocatalyst exhibits the best hydrogen evolution rate and TC photodegradation performance, respectively. Notably, the optimum hydrogen production rate is 6 times that of pure ZIS with excellent cycling stability. The enhanced photoactivity can be explained by the hollow corncob-like morphology and the formed S-scheme heterojunction with close interface contact between ZIS and LFO, which significantly improves the spatial separation and migration efficiency of photoexcited carriers, while maintaining a high redox potential. Finally, it provides an effective support for the photocatalytic mechanism through calculation results of density functional theory. This work not only provides a novel construction strategy of photocatalysts for efficient photocatalytic hydrogen evolution and organic pollutant degradation, but also opens up a new insight for perovskite-modified S-scheme heterojunction.

Spatial Distribution and Migration Characteristic of Forchlorfenuron in Oriental Melon Fruit by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

Forchlorfenuron is a widely used plant growth regulator to support the pollination and fruit set of oriental melons. It is critical to investigate the spatial distribution and migration characteristics of forchlorfenuron among fruit tissues to understand its metabolism and toxic effects on plants. However, the application of imaging mass spectrometry in pesticides remains challenging due to the usually extremely low residual concentration and the strong interference from plant tissues. In this study, a matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) method was developed for the first time to obtain the dynamic images of forchlorfenuron in oriental melon. A quantitative assessment has also been performed for MALDI-MSI to characterize the time-dependent permeation and degradation sites of forchlorfenuron in oriental melon. The majority of forchlorfenuron was detected in the exocarp and mesocarp regions of oriental melon and decreased within two days after application. The degradation rate obtained by MALDI-MSI in this study was comparable to that obtained by HPLC-MS/MS, indicating that the methodology and quantification approach based on the MALDI-MSI was reliable and practicable for pesticide degradation study. These results provide an important scientific basis for the assessment of the potential risks and effects of forchlorfenuron on oriental melons.

An Improved UAV Bi-SAR Imaging Algorithm with Two-Dimensional Spatial Variant Range Cell Migration Correction and Azimuth Non-Linear Phase Equalization

The transmitter and receiver of unmanned aerial vehicle (UAV) bistatic synthetic aperture radar (Bi-SAR) are respectively carried on different UAV platforms, which has the advantages of flexible movement and strong concealment, and has broad application prospects in remote sensing fields. However, the range cell migration (RCM) and azimuth non-linear phase (ANP) of UAV Bi-SAR are seriously spatially variant along the range and azimuth directions, while the UAV Bi-SAR has a short operating range, complex trajectory and wide azimuth beam. Aiming at the problem that the RCM and ANP of UAV Bi-SAR in spotlight mode are difficult to correct and equalize due to the severe two-dimensional (2D) spatial variation, an RCM correction (RCMC) and ANP equalization (ANPE) method based on Doppler domain blocking is proposed. First, the azimuth spatial variance of RCM is eliminated by Doppler blocking, and the range spatial variant RCMC is realized by RNCS. Second, by combining Doppler blocking with azimuth nonlinear chirp scaling (ANCS), this method can adapt to ANPE with larger width and more severe spatial variation. At last, the criteria of Doppler blocking are given in detail, and the effectiveness of the proposed method is verified by UAV Bi-SAR real data and computer simulation.

Coupling or contradiction? The spatiotemporal relationship between urbanization and urban park system development in China

The urban park system (UPS) is an essential determinant of a city’s vitality and construction quality, playing an important role in the process of the high-quality construction of new urbanization. To explore the coupling and coordination relationship between UPSs and urbanization, a multidimensional evaluation system for urbanization and UPSs is constructed for 30 provinces in China from 2000 to 2020, and the indexes are calculated using the entropy weight method. Then, a coupling coordination degree (CCD) model and relative development degree (RDD) model are used to comprehensively analyze the spatiotemporal evolutionary pattern in the coupling coordination between systems from a systematic perspective to a holistic perspective. On this basis, the spatial clustering of the CCD is identified by spatial autocorrelation methods. The research results indicate the following: (1) The urbanization indexes and UPS index both improved over the research period and both exhibit a significantly higher index in eastern provinces than in other regions. (2) The CCD between systems generally improved to a higher level overall. The spatiotemporal patterns indicate that the CCD showed a trend of deepening development from the edge to the middle. (3) Regarding the RDD, there was a widespread lack of synergy between UPSs and urbanization systems. In 2020, most provinces showed that the development of UPSs lagged behind urbanization. (4) The cold and hot spots of spatial clustering revealed a spatial evolution and migration trend, albeit to a limited extent. The cold spots were always distributed in the west with a gradually shrinking scope, while the hot spots were concentrated in the east, with their scope gradually expanding.

Language Assimilation and Linguistic Diversity in India: Exploring the Role of Geography and Migration

Indian language assimilation, linguistic variety, geographical location, and population movement all have complex interrelationships with one another. The importance of investigating how these elements influence linguistic and cultural identities is explored. The investigation of language contact, linguistic variation, geographical context, and migratory patterns in India is crucial. The research highlights the role of geography in shaping language dynamics, as migration patterns impact linguistic assimilation in different regions of the country. It examines how migration to urban areas has contributed to the spread of English as a common language among diverse linguistic communities. A more accepting and peaceful society can be fostered by the application of this information in the form of policy, education, and social integration initiatives. Navigating a complex linguistic landscape, obtaining trustworthy data from diverse communities, and untangling the impact of geography and migration from multifaceted assimilation processes while maintaining cultural sensitivity are all obstacles to the study of language assimilation and linguistic diversity in India. The paper provides a complete method called Geo-Linguistic Computational Mapping Analysis (G-LCMA), which integrates spatial analysis, migration dynamics, and sociolinguistic aspects. The research intends to model potential scenarios of language absorption through computational simulation analysis. Policymakers and educators can benefit from this method since it provides information about the outcomes of linguistic encounters. There are repercussions for the future of language, culture, and social cohesion that stem from this study. This research helps India's linguistic landscape become more peaceful and welcoming by separating the intricate relationship between language, geography, and migration. &#x0D;

Spatial Migration Experience and Turnover Intention: Empirical Evidence from Japan

AbstractMigration experience has a profound impact on psychological health and well‐being, but relatively little is known about how spatial relocation affects turnover intention in the labor market. This study attempted to examine the effects of spatial migration experience on turnover intention. Using individual‐level survey data on people's migration experience and career development from Japan, we verified the central hypothesis that internal migration has positive effects on later turnover intention by employing both linear and ordered probit regressions. Further, several robustness checks were conducted in order to address endogeneity issues. We first corrected for the self‐selection bias in two ways, adding the number of companies worked for as a control variable to partly capture the individual psychological factors and estimating with the treatment effects model. Then a sub‐sample regression was used to eliminate the bias from active migration due to job change. All confirmed the positive effect of migration experience on turnover intention.

An Efficient Deep Learning-Based Workflow for CO2 Plume Imaging With Distributed Pressure and Temperature Measurements

Summary Geologic carbon dioxide (CO2) sequestration has received significant attention from the scientific community as a response to global warming due to greenhouse gas emissions. Effective monitoring of CO2 plume is critical to CO2 storage safety throughout the life cycle of a geologic CO2 sequestration project. Although simulation-based techniques such as history matching can be used for predicting the evolution of underground CO2 saturation, the computational cost of high-fidelity simulations can be prohibitive. Recent development in data-driven models can provide a viable alternative for rapid CO2 plume imaging. Here, we present a novel deep learning–based workflow that can efficiently visualize CO2 plume in near real time. Our deep learning framework utilizes field measurements, such as downhole pressure, distributed pressure, and temperature, as input to visualize the subsurface CO2 plume images. However, the high output dimension of CO2 plume images makes the training inefficient. We address this challenge in two ways: First, we output a single CO2 onset time map rather than multiple saturation maps at different times; second, we apply an autoencoder-decoder network to identify lower-dimensional latent variables that compress high-dimensional output images. The “onset time” is the calendar time when the CO2 saturation at a given location exceeds a specified threshold value. In our approach, a deep learning–based regression model is trained to predict latent variables of the autoencoder-decoder network. Subsequently, the latent variables are used as inputs of the trained decoder network to generate the 3D onset time image, visualizing the evolving CO2 plume in near real time. The power and efficacy of our approach are demonstrated using both synthetic and field-scale applications. We first validate the deep learning–based CO2 plume imaging workflow using a 2D synthetic example. Next, the visualization workflow is applied to a 3D field-scale reservoir to demonstrate the robustness and efficiency of the workflow. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at a monitoring well, flowing bottomhole pressure (BHP) data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. Our approach is also extended to efficiently evaluate the uncertainty of predicted CO2 plume images. Additionally, an efficient workflow for optimizing data acquisition and measurement type is demonstrated using our deep learning–based framework. The novelty of this work is the development and application of a unique and efficient deep learning–based subsurface visualization workflow for the spatial and temporal migration of the CO2 plume. The efficiency and flexibility of the data-driven workflow make our approach suitable for field-scale applications.

Source identification and migration fate of metal(loid)s in soil and groundwater from an abandoned Pb/Zn mine

Understanding the spatial distribution, source identification, and migration fate of toxic metals is crucial for managing the potential risks associated with metal(loid)s in abandoned Pb/Zn mines. This study provides a comprehensive analysis of the heterogeneous characteristics, contamination sources, and migration fate of metal(loid)s in both mine soil and groundwater. The results reveal that the abandoned mine soil is primarily contaminated with As and Pb, whereas groundwater in the mining and smelting area is mainly contaminated with Pb. The concentrations of As and Pb in the soil reached a maximum of 37.5 mg/kg and 289 mg/kg, respectively, significantly exceeding the local background values of 13.6 mg/kg for As and 29 mg/kg for Pb. The sources of soil contamination were attributed to historical smelting activities (31.4 %) for As, Cd, Hg, and Sb, while Pb and Mn were primarily derived from the ore-deposited belt (21.5 %). Machine learning predictions indicate that the migration of As in the soil can extend up to six meters or more, predominantly influenced by the presence of grit and silt. As a significant source of groundwater contamination, both soil As and Cd can infiltrate the groundwater through convection or diffusion processes. In conclusion, it is imperative to address the long-term release of heterogeneous metal ores in the soil of abandoned mine sites, as this can severely deteriorate the quality of both soil and groundwater.

The arc-scale spatial distribution of volcano erosion implies coupled magmatism and regional climate in the Cascades arc, United States

The morphology and distribution of volcanic edifices in volcanic terrains encodes the structure and evolution of underlying magma transport as well as surface processes that shape landforms. How magmatic construction and erosion interact on long timescales to sculpt these landscapes, however, remains poorly resolved. In the Cascades arc, distributed volcanic edifices mirror long-wavelength topography associated with underlying crustal magmatism and define the regional drainage divide. The resulting strong along- and across-arc modern precipitation gradients and extensive glaciation provide a natural laboratory for climate-volcano interactions. Here, we use 1,658 volcanic edifice boundaries to quantify volcano morphology at the arc-scale, and reconstruct primary edifice volumes to create first-order estimations of Cascades erosion throughout the Quaternary. Across-arc asymmetry in eroded volumes, mirroring similarly asymmetric spatial distribution of volcanism, suggests a coupling between magmatism and climate in which construction of topography enhances erosion by orographic precipitation and glaciers on million-year timescales. We demonstrate with a coupled landscape evolution and crustal stress model that mountain building associated with magmatism and subsequent orographically-induced erosion can redistribute surface loads and direct subsequent time-averaged magma ascent. This two-way coupling can thus contribute to Myr-scale spatial migration of volcanism observed in the Cascades and other arcs globally.

Multipatch stochastic epidemic model for the dynamics of a tick-borne disease

Spatial heterogeneity and migration of hosts and ticks have an impact on the spread, extinction and persistence of tick-borne diseases. In this paper, we investigate the impact of between-patch migration of white-tailed deer and lone star ticks on the dynamics of a tick-borne disease with regard to disease extinction and persistence using a system of Itô stochastic differential equations model. It is shown that the disease-free equilibrium exists and is unique. The general formula for computing the basic reproduction number for all patches is derived. We show that for patches in isolation, the basic reproduction number is equal to the largest patch reproduction number and for connected patches it lies between the minimum and maximum of the patch reproduction numbers. Numerical simulations for a two-patch deterministic and stochastic differential equation models are performed to illustrate the dynamics of the disease for varying migration rates. Our results show that the probability of eliminating or minimizing the disease in both patches is high when there is no migration unlike when it is present. The results imply that the probability of disease extinction can be increased if deer and tick movement are controlled or even prohibited especially when there is an outbreak in one or both patches since movement can introduce a disease in an area that was initially disease-free. Thus, screening of infectives in protected areas such as deer farms, private game parks or reserves, etc. before they migrate to other areas can be one of the intervention strategies for controlling and preventing disease spread.

Detailed View of the Seismogenic Structures and Processes of the 2022 Bayan Har Intraplate Earthquake Swarm on the East Margin of the Qinghai–Tibet Plateau

Abstract At 16:03 on 9 June 2022 (UTC), an Mw 5.5 earthquake followed by several Mw&amp;gt;4 events, including the largest event of Mw 5.8 within a few hours, occurred in the Maerkang area near the Caodeng Hot Spring Town, located in the south-central part of the Bayan Har plate on the eastern margin of the Qinghai–Tibet plateau. The earthquake swarm allows understanding the tectonic stress environment of the Bayan Har plate and is an example of a typical moderate-to-strong intraplate earthquake swarm. This article comprehensively analyzes the detailed seismogenic fault structure of the swarm by means of precise hypocenter relocation, focal mechanism inversion for Mw&amp;gt;4 earthquakes, inversion of the tectonic stress field in different regions of the Bayan Har plate, tidal strain calculation, and seismicity statistics. The results show that the swarm was not directly related to the nearby mapped Songgang fault, but rather resulted from the successive activation of a series of unknown faults. The precise hypocenter distribution, together with focal mechanism solutions of major earthquakes, illuminates five major seismogenic faults with conjugate relationships and stepover. Spatial and temporal migration of hypocenters, stress transfer, and tidal correlations demonstrate that cascade triggering, afterslip, and overpressured fluid might have jointly played a role in causing the earthquake swarm. As an output of this research, a set of verifiable datasets are provided as a basis for further in-depth research.

Multi-scaled attentive style transfer based on dilated convolution

Image style transfer aims to apply artists painting styles to various images. Many approaches seek different purposes, but a general tendency is to increase efficiency and enable arbitrary style inputs. The state-of-art method is the adaptive convolutional network which expands the process of feature mixture into a layer-wise adjustment that superior previous work by presenting results that is more aware of detailed structures. However, the encoding process that guides the entire stylized revision is unaware of multi-scaled information. We designed an improved version of the style feature encoding procedure in our work. With the introduction of dilated convolution with a different rate, the output stylized image is better at spatial texture migration and color distribution determination. We also come up with a hybrid objective that better measures the spatial dissimilarity between content and style features.

Spatial Distribution, Migration, and Ecological Risk of Cd in Sediments and Soils Surrounding Sulfide Mines—A Case Study of the Dabaoshan Mine of Guangdong, China

Acid mine drainage (AMD) resulting from metal sulfide mining activities can lead to contamination by potentially toxic elements (PTEs) primarily concentrated around the mining area and gradually spreading outward. However, ecological risks do not correspond directly to PTE concentrations, making it challenging to effectively manage the mining environment and accurately prevent potential ecological impacts. In this paper, we analyzed Cd levels in sediments, soils, and corresponding rice grains sampled from four villages near Dabaoshan Mine of Guangdong, China, in 2017. Our results reveal that Cd is the most prominent pollutant element, exhibiting significant enrichment and spatial heterogeneity in both soil and sediments and higher accumulation levels in rice grains compared to other PTEs. Cd concentrations in soil decrease from the tailings pond to the river terrace, with a slight increase after Taiping River joins and flows into the alluvial plain. However, the concentrations in sediments show the opposite trend. The bioconcentration factor (BCF) for Cd in agricultural soil from the river terrace is lower than that from the alluvial plain and the degree of exceeding the maximum permit level (MPL) of Cd in rice grains increases along the river. Mineral transformation and topography are important factors in controlling the geochemical behavior of PTEs. Remediation efforts alter the physicochemical properties of the river, resulting in the release of PTEs during schwertmannite transformation followed by their adsorption by clay minerals. Furthermore, the random forest (RF) analysis highlights that the bioavailability and potential ecological risk of Cd in soils are governed by the occurrence form of Cd in different topographies, mainly controlled by TFe2O3, Mn, and CaO in the river terrace and CaO, Al2O3/SiO2, and Mn in the alluvial plain. Therefore, considering the impact of topography on mineral compositions, physicochemical properties, and occurrence form of PTEs in soil and sediments is essential for assessing ecological risk in mining areas.

Spatial Distribution and Migration Mechanisms of Toxic Elements in Farmland Soil at Nonferrous Metal Smelting Site

Nonferrous metal smelting is a potential emission source of trace elements. However, it is vital to identify the dominant factors in determining toxic element (TE) spatial distribution and migration behaviors. We hypothesize that soil clay is the key factor in agricultural land around nonferrous metal smelting areas. Hence, this study focused on Qingyuan Town, a typical nonferrous metal smelting base. From this site, 95 soil samples (0–20 cm) were collected from cultivated land around the nonferrous metal smelters. Eight soil samples were analyzed for TE speciation and clay minerals in hot spot and non-hot spot areas following the TE distribution. A geographical detector (Geodor) showed that the distributions of total and exchangeable TE were affected by multiple factors (clay, CaO, and Fe2O3). X-ray diffraction (XRD) showed that the clay was mainly comprised of an illite and smectite mixed layer (67.13%), illite (15.38%), chlorite (9.25%), and kaolinite (8.25%). Moreover, correlation analysis showed that the exchangeable As was positively correlated with illite (R2 = 0.76, at p &lt; 0.01 level), kaolinite (R2 = 0.43, at p &lt; 0.01 level), and chlorite (R2 = 0.59, at p &lt; 0.01 level) in the hot spot, but negatively correlated with a mixed layer of illite and smectite (R2 = 0.83, at p &lt; 0.01 level). In contrast, the cases of Cd, Cu, Pb, and Zn presented an opposite tread with As. The positive matrix factorization (PMF) results showed that the contribution rate of nonferrous metal smelting to soil As was 42.90% and those of Cd, Cu, Pb, and Zn were 84.90%, 56.40%, 59.90%, and 59.20%, respectively. These results can provide guidance for controlling the TE risk associated with agricultural land management.

Using SWMM for emergency response planning: A case study evaluating biological agent transport under various rainfall scenarios and urban surfaces

To assist in emergency preparedness for a biological agent terrorist attack or accidental pathogen release, potential contaminant levels and migration pathways of spores spread by urban stormwater were evaluated using a Storm Water Management Model (SWMM) of U.S. Coast Guard Base Elizabeth City, North Carolina. The high temporal-spatial resolution SWMM model was built using spore concentrations in stormwater runoff from asphalt, grass, and concrete collected from a point-scale field study. The subsequent modeled contamination scenarios included a notional plume release and point releases mimicking the field study under three rainfall conditions. The rainfall scenarios included a 6-hour natural rainfall event on Dec. 8, 2021 and two design storms (2-year and 100-year events). The observed spore concentrations from asphalt and concrete from the actual field experiment were applied to calibrate the washoff parameters in the SWMM model, using an exponential washoff function. The calibrated washoff coefficient (c1) and exponent (c2) were 0.01 and 1.00 for asphalt, 0.05 and 1.45 for grass, and 2.45 and 1.00 for concrete, respectively. The calibrated SWMM model simulated spore concentrations in runoff at times and magnitudes similar to the field study data. In the point release modeled scenario, the concrete surface generated 55.6% higher average spore concentrations than asphalt. Similarly, in the field experiment, a 175% (p < 0.05) higher average spore concentration in surface runoff was observed from concrete than from asphalt. This study demonstrates how SWMM may be used to evaluate spore washoff from urban surfaces under different precipitation amounts, intensities, and durations, and how visualized spatial migration pathways in stormwater runoff may be used for emergency planning and remediation.

Migration of dissolved carbon on bare karst slopes in soil in response to natural rainfall events

Soil carbon in karst critical zones is an essential part of the global carbon pool, and the migration of dissolved carbon is a vital component of the carbon cycle in such regions. The unique “dual hydrological structure” makes the path and process of migration of soil dissolved carbon driven by rainfall unclear in karst slopes, which ultimately hinders the accurate evaluation of the carbon sink effect in such regions. Therefore, based on field monitoring of natural rainfall, this study evaluated the process and characteristics of the spatial migration of soil dissolved carbon in bare karst slopes and its response to natural rainfall (rainfall duration and amount). The results showed that the concentration of dissolved carbon transported in surface runoff was significantly lower than that transported within the soil layer; however, carbon loss displayed the opposite trend. Rainfall factors (rainfall amount and duration) and runoff volume significantly affected soil dissolved carbon, contributing 46.91% to soil dissolved carbon. Rainfall factors significantly affect soil dissolved carbon migration by influencing runoff. In addition, the hydrological path is an important factor affecting soil dissolved carbon transport. Our study shows that the soil and bedrock interface (SBI) and shallow karst fissures (SKF) are the main routes of dissolved carbon migration in soils in bare karst slopes. Dissolved carbon migrating along SKF is a vital contributor to the “missing carbon sink” in karst regions, and dissolved carbon migrating along the SBI is potentially an important contributor to the “missing carbon sink.” This study provides primary data and is expected to serve as a reference for revealing the mechanism of karst carbon migration and transformation and accurately evaluating the carbon sink effect in karst areas.

Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data

Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the Yellow River Delta (YRD) in China, and the soil water content was determined using the drying method. A gradient boosting decision tree (GBDT) model based on a tree-structured Parzen estimator (TPE) hyperparametric optimization was developed, and then the soil water content was predicted and mapped based on the soil texture and vegetation index from Sentinel-2 remote sensing images. The results of statistical analysis showed that the soil water content had a high coefficient of variation (55.30%), a non-normal distribution, and complex spatial variability. Compared with other models, the TPE-GBDT model had the highest prediction accuracy (RMSE = 6.02% and R2 = 0.71), and its mapping results showed that the areas with high soil water content were distributed on both sides of the river and near the estuary. Furthermore, the results of Shapley additive explanation (SHAP) analysis showed that the soil texture (PC2 and PC5), modified normalized difference vegetation index (MNDVI), and Sentinel-2 red edge position (S2REP) index provided important contributions to the spatial prediction of soil water content. We found that the hydraulic physical properties of soil texture and the vegetation characteristics (such as vegetation coverage, root action, and transpiration) are the key factors affecting the spatial migration and heterogeneity of the soil water content in the study area. The above results show that the TPE algorithm can quickly capture the hyperparameters that are most suitable for the GBDT model, so that the GBDT model can ensure prediction accuracy, reduce the loss function with less training data, and accurately learn of the nonlinear relationship between soil water content and environmental factors. This paper proposes a machine learning method for hyperparameter optimization that shows considerable potential to predict the spatial heterogeneity of soil water content, which can effectively support regional farmland soil and water conservation and high-quality agricultural development.

Connecting distinct realms along multiple dimensions: A meta-ecosystem resilience perspective

Resilience research is central to confront the sustainability challenges to ecosystems and human societies in a rapidly changing world. Given that social-ecological problems span the entire Earth system, there is a critical need for resilience models that account for the connectivity across intricately linked ecosystems (i.e., freshwater, marine, terrestrial, atmosphere). We present a resilience perspective of meta-ecosystems that are connected through the flow of biota, matter and energy within and across aquatic and terrestrial realms, and the atmosphere. We demonstrate ecological resilience sensu Holling using aquatic-terrestrial linkages and riparian ecosystems more generally. A discussion of applications in riparian ecology and meta-ecosystem research (e.g., resilience quantification, panarchy, meta-ecosystem boundary delineations, spatial regime migration, including early warning indications) concludes the paper. Understanding meta-ecosystem resilience may have potential to support decision making for natural resource management (scenario planning, risk and vulnerability assessments).

THE SPATIAL-TEMPORAL DIFFERENTIATION IN AIR POLLUTION AND INDUSTRY STRUCTURE — EVIDENCE FROM THE METROPOLITAN AREA OF BEIJING–TIANJIN–HEBEI URBAN IN CHINA

In developing countries, environmental problems are often intertwined with industrial spatial distribution and migration. This paper is the first in this field to apply the standard deviational ellipse (SDE) method to the analysis of spatial agglomeration and spatiotemporal evolution of air pollution in 13 cities in the Beijing–Tianjin–Hebei region (BTH). According to the industrial classification, each industry could be further divided into five sub-industries: labor-intensive, capital-intensive, technology-intensive, pollution-intensive, and the mining industry. The discussion includes an exploration of the relationship between environmental quality and the spatial distribution of different sub-industries, along with their dynamic changes in migration. This paper also describes the innovative use of the geographically weighted regression (GWR) to study the impact of the whole industry and sub-industries on the environment. The main conclusions of this paper are as follows: (i) Both air pollution and the industry have obvious spatial agglomeration characteristics. (ii) The spatial difference coefficients between the secondary industry and environmental pollution and between various sub-industries and environmental pollution are small, and there is spatial consistency. (iii) Capital-intensive industries represent a primary source of air pollution. In addition to the slowdown of the tertiary industry, the contribution of various types of sub-industries to environmental pollution displays an increasing trend.