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Abstract Background In resource-based cities, long-term irrational exploitation of resources has caused severe damage to ecosystem functions, mainly manifested in the significant decline of biodiversity, land degradation, water pollution, and the deterioration of air quality. This has led to a significant decline in the cities' sustainable development capabilities. Establishing and optimizing an ecological spatial network (ESN) can promote the effective transmission of material energy and enhance the ecosystem functions, which holds fundamental importance in ensuring the ecological integrity of the region and promoting sustainable urban development. In this study, by combining the ecological environment with the landscape to determine the ecological sources, we constructed the ESN of Shenmu City, a mining city, based on the minimum cumulative resistance (MCR) model, and conducted a correlation analysis between the topological structure of the ESN and the significance of ecosystem functions. Then, the optimization strategy scheme based on ecosystem functions was proposed. Finally, robustness was used to determine the effect before and after optimization. Results The results showed that the high-value ecosystem service areas in Shenmu City were predominantly located in the central and western parts, with the highest value in the southeast. There was a strong correlation between the importance of ecosystem functions and the degree and feature vector of ecological nodes. Conclusions The ESN can be optimized effectively by adding stepping stone nodes and new corridors. Through the robustness of the optimized ESN, we found that the optimized network has more robust connectivity and stability and can show better recovery ability after ecological function damage. This research presents an effective method for the construction and optimization of the ESN in the mining area and provides a theoretical basis for realizing the sustainability of the mining economy, regional development, and ecological protection in Shenmu City.

Predicting computational intensity (CI) is essential for domain decomposition and load balance in parallel geoprocessing. However, traditional CI prediction is limited in capturing the heterogeneity of spatial domain, leading to poor accuracy and load imbalance. Leveraging recent advancements in deep learning from Artificial Intelligence (AI), this paper proposes a deep learning-based approach for predicting CI and enhancing domain decomposition, which reduces the dependency on expert knowledge through automatic feature learning. In the approach, Convolutional Neural Networks are employed to capture the heterogeneity of spatial domain, encompassing structural, distribution, and topological characteristics. A fully connected layer is then utilized for CI prediction and optimized domain decomposition. Comparative experiments were implemented between the proposed approach and three traditional methods, using two cases: spatial intersection on vector data and peak perilousness assessment. The results demonstrate that the proposed approach achieves a speedup ratio of 19.8 and a parallel efficiency of 0.82. The findings highlight the advantages of the proposed approach in terms of parallel performance and usability. This study serves as a valuable reference for illustrating how deep learning can enhance parallel geoprocessing, providing a roadmap for applying deep learning techniques to geocomputations and fostering further advancements of AI GIS.

Vegetation, as a crucial carbon sink, is facing extensive degradation under the mounting pressures of urbanization and excessive resource exploitation, exacerbating the imbalance between carbon sources and sinks. In response, the ecological spatial network has emerged as a comprehensive conservation strategy to establish and maintain connectivity and interactions among diverse ecosystems, ensuring the continuous provision of ecological services and preservation of biodiversity. A pivotal indicator in this context is vegetation carbon use efficiency (CUE), which elucidates the relationship between CO2 assimilation through photosynthesis and biomass growth. In this study, utilizing remote sensing data, the Yellow River Basin (YRB) was selected as a case study to analyze vegetation CUE through the Carnegie Ames Stanford Approach (CASA) and Light Use Efficiency-Normalized Difference Water Index (LUE-NDWI) models. Furthermore, the Morphological Spatial Pattern Analysis (MSPA) method and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were employed to delineate ecological sources for woodlands, shrublands, and grasslands, while the Minimum Cumulative Resistance (MCR) model was used to identify ecological corridors, forming an ecological spatial network within the YRB. Subsequently, the interrelationship between topological metrics and vegetation CUE was analyzed, and optimization strategies were proposed based on the significance of the structure and function. The findings revealed that: (1) Vegetation CUE exhibited a spatially decreasing trend from the western to eastern regions, with spatial patterns correlated with vegetation types, temperature, and precipitation distribution; (2) The ecological spatial network demonstrated a denser configuration in the upper and middle reaches and a sparser and shorter pattern in the lower reaches, with seamless connectivity among all regions; (3) Significant correlations were observed between topological metrics and vegetation CUE, prompting the adoption of ecological construction and protection measures when functional importance is deficient, and enhancing the connectivity of ecological sources through the augmentation of ecological corridors when structural importance is lacking. The implementation of these strategies is expected to bolster the ecological functions of the network, facilitate carbon cycling, and exert a positive and far-reaching impact on the sustainable development of the ecological environment in the YRB.

Watershed ecosystems play a crucial role in the global carbon cycle, and the feedback effects of carbon cycling will have significant implications for future climate change. Therefore, this study utilizes MODIS data to estimate the vegetation carbon use efficiency (CUE) of the Yangtze River Basin (hereafter referred to as the YRB) aiming to explore its spatiotemporal patterns. Additionally, partial correlation analysis is employed to investigate the driving forces of climate and ecosystem types on vegetation CUE. Furthermore, an ecological network is constructed to examine the impact of CUE changes on the stability of the ecosystems.The results indicate that the CUE values in the vegetation ecosystems of the YRB exceed the threshold of 0.5, a constant CUE value commonly used in numerous ecological models. However, even within the same geographical region, different ecosystem types exhibit distinct CUE values, underscoring the complexity of the ecological landscape.Over the past 15 years, there has been an observable declining trend in CUE for the vegetation ecosystems within the YRB. Moreover, future projections suggest a continued decrease in CUE at the pixel scale, with land use changes possibly serving as a primary contributing factor. Interestingly, within the same ecosystem, higher temperatures and reduced precipitation appear to enhance CUE, implying that under adverse environmental conditions, plants may enhance their efficiency in converting atmospheric carbon into terrestrial biomass.Ultimately, an ecological network is established to investigate the influence of CUE changes on the stability of the ecosystems. Notably, alterations in CUE within the upstream ecological source areas of the Yangtze River exhibit a particularly pronounced impact on the overall ecosystem stability.These research findings hold paramount significance, deepening our understanding of carbon cycling variations in the YRB, while also providing valuable insights for safeguarding the ecological environment and formulating strategies to address the challenges posed by climate change.

Abstract Chronic kidney disease (CKD) has asymptomatic early stages, whereby early detection is crucial to prevent its complications and progression. Creatinine and cystatin C (cysC) assays are known for assessing kidney function but there are limited point‐of‐care diagnostics which are rapid, precise, and easy to use. Here, high resistivity silicon conductometric sensors for detection of creatinine and cysC with a 10 min sample incubation is introduced. The sensors provide resistance‐based signals that can be quantified and measured wirelessly. The sensors successfully detect creatinine and cysC in both phosphate buffer saline (PBS) and artificial saliva in the nanomolar range, being able to distinguish their critical concentrations at 8.8 and 20 nm, respectively, for diagnosis of early stage of CKD. The detection limit for both creatinine and cysC is determined as 0.01 nm which is more than 500× and 1000× times lower than critical concentrations for the two biomarkers, respectively. Finally, these sensors are incorporated into a battery‐free, miniaturized electronic device for wireless biomarker detection as a proof‐of‐concept demonstration of a point‐of‐care tool for assessing kidney functionality.

The changes in the landscape structure within resource-dependent urban areas is influenced by a confluence of factors, potentially resulting in a decreased landscape ecological quality and a heightened ecological vulnerability. Landscape ecological quality directly influences the flow and transmission of ecological functions, rendering it a pivotal consideration in the development of landscape ecological networks. In this study, an investigation was conducted utilizing land-use data spanning from 2005 to 2020, encompassing the city of Linfen in Shanxi Province, China. A landscape ecological risk assessment model was formulated to analyze the spatiotemporal attributes of landscape ecological risk during this timeframe. Acknowledging the underlying connection between ecosystem resilience and landscape ecological quality, the 2020 landscape ecological risk assessment served as the basis for the extraction of ecological source areas using the Minimum Spanning Tree with Path Algorithm (MSPA), and the corridors were established using the Minimum Cumulative Resistance (MCR) model. This framework facilitated the construction of a landscape ecological network, and the complex network analysis was employed to scrutinize the network’s topological properties. The regions within the study area exhibiting high and extremely high ecological risk displayed an initial increase followed by a subsequent decrease, culminating in an overall reduction in risk levels. The areas surrounding urban development and mining activities displayed relatively heightened risk levels, while the regions characterized by forests and grasslands experienced relatively lower risks. The shifts in risk within the study area were predominantly attributed to various factors, including mining activities, urban expansion, governmental policies, alterations in land-use types, and village migrations. Through the optimization of the landscape ecological network using edge addition strategies and a subsequent comparison of network robustness pre- and post-optimization, it was discerned that the edge addition strategy notably improved the efficiency of energy transfer and interconnectivity among network nodes. This ecological risk assessment model, in conjunction with the development of a prospective ecological network, lends a valuable theoretical support to the enhancement of landscape structure within resource-dependent urban areas and the establishment of comprehensive ecological security systems.

Abstract Matrix Metalloproteinases (MMPs) have been demonstrated to be essential in facilitating the migration and metastasis of clear cell renal cell carcinoma (ccRCC). However, the ability of the MMP family to predict clinical outcomes and guide optimal therapeutic strategies for ccRCC patients remains incompletely understood. In this investigation, we initially conducted a thorough examination of the MMP family in pan-cancer. Notably, MMPs exhibited distinctive significance in ccRCC. Following this, we undertook an extensive analysis to evaluate the clinical value of MMPs and potential mechanisms by which MMPs contribute to the progression of ccRCC. A novel stratification method and prognostic model were developed based on MMPs in order to enhance the accuracy of prognosis prediction for ccRCC patients and facilitate personalized treatment. By conducting multi-omics analysis and transcriptional regulation analysis, it was hypothesized that SAA1 plays a crucial role in promoting ccRCC migration through MMPs. Subsequently, in vitro experiments confirmed that SAA1 regulates ccRCC cell migration via the ERK-AP1-MMPs axis. In conclusion, our study has explored the potential value of the MMP family as prognostic markers for ccRCC and as guides for medication regimens. Additionally, we have identified SAA1 as a crucial factor in the migration of ccRCC.

Forests are vital for terrestrial ecosystems, providing crucial functions like carbon sequestration and water conservation. In the Yellow River Basin, where 70% of forest coverage is concentrated in the middle reaches encompassing Sichuan, Shaanxi, and Shanxi provinces, there exists significant potential for coal production, with nine planned coal bases. This study centered on Jincheng City, Shanxi Province, a representative coal mining area in the Yellow River Basin, and combined the MSPA analysis method and MCR model to generate the five-period forest ecological network of Jincheng City from 1985 to 2022 under the background of coal mining and calculate the degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality; the correlation between the four centralities and carbon sequestration ability is further explored. Simultaneously, employing the RAND-ESU algorithm for motif identification within forest ecological networks, this study integrates the ecological policies of the research area with the specific conditions of the coal mining region to optimize the forest ecological network in Jincheng City. Findings reveal the following. (1) Forest ecological spatial networks: Forest ecological networks exhibit robust overall ecological connectivity in the study area, with potential ecological corridors spanning the region. However, certain areas with high ecological resistance hinder connectivity between key forest ecological nodes under the background of coal mining. (2) Correlation between topological indices and carbon sequestration ecological services: From 1985 to 2022, the carbon sequestration capacity of Jincheng City’s forest source areas increased year by year, and significant positive correlations were observed between degree centrality, betweenness centrality, eigenvector centrality with carbon sequestration ecological services, indicating a strengthening trend over time. (3) Motif Recognition and Ecological Network Optimization: During the study, four types of motifs were identified in the forest ecological network of Jincheng City based on the number of nodes and their connections using the RAND-ESU network motif algorithm. These motifs are 3a, 4a, 4b, and 4d (where the number represents the number of nodes and the letter represents the connection type). Among these, motifs 3a and 4b play a crucial role. Based on these motifs and practical considerations, network optimization was performed on the existing ecological source areas to enhance the robustness of the forest ecological network.

The transformation of the landscape structure in mining cities is influenced by a combination of various factors, resulting in a decline in the ecological quality of the landscape and an increased vulnerability of the ecological environment. The quality of landscape ecology directly impacts the flow and transmission of ecosystem functions, underscoring the need for careful consideration in the establishment of landscape ecological networks. In this study, an analysis was conducted using land-use data from 2005 to 2020 in Linfen City, Shanxi Province, China. A network optimization strategy was established, focusing on the capacity for ecosystem self-restoration and the connectivity of ecological patches. Additionally, the spatiotemporal characteristics of landscape ecological risk between 2005 and 2020 were examined. Taking into account the influence of the ecological value of the landscape on the resilience of the ecosystem, the Minimum Cumulative Resistance (MCR) model was employed to construct the landscape ecological network. The structural characteristics of the landscape ecological network were explored using complex network methods. In addition, an optimization strategy based on ecosystem functionality and connectivity (EC) was adopted, and by comparing the connectivity and robustness of the network before and after the optimization, it was found that the method enhanced the smoothness of energy transfer and interconnectivity among nodes of the network, and significantly enhanced the stability of the ecological network. Within the study area, local levels of risk increased due to coal mining and urban expansion. (The maximum value in 2005 was 335.) However, overall risk levels improved with the progress of land reclamation efforts. (The maximum value in 2020 was 325.) Higher risks were observed in the vicinity of urban construction and mining areas, while forested and grassland areas exhibited relatively lower risks. The changes in risk within the study area were primarily influenced by factors such as mining activities, urban expansion, government policies, changes in land use types, and village relocation. The findings of this study provide theoretical support for the optimization of the landscape structure in mining cities, the construction of systems of ecological security as well as the restoration of ecosystems.

With the increasing use of large-screen portable devices and the prevalence of list-based user interfaces, it has become critically important to design list interfaces that are visually appealing and user-friendly across various devices and screen sizes. The rules for adapting list-based user interfaces on large screens warrant investigation. Thus, the present study aimed to determine the responsive list width that can enhance visual search efficiency and improve user experience on portable devices with different widths of screen. Two experiments were conducted, in which we manipulated the width of single-column and parent-child lists on portable devices with medium- (Experiment 1; N = 80) and large-width screens (Experiment 2; N = 41), varying the range of list width from very narrow to very wide. Results show that for the single-column lists on a medium-width screen, users demonstrated the highest level of preference and gave the highest ratings for satisfaction and visual aesthetics when the lists were moderately wide. For the single-column lists on a large-width screen, users preferred both the moderately-narrow and moderately-wide lists. However, for parent-child lists, the results show that both the moderately-wide and very-wide lists were favored on both the medium-width and large-width screens. These findings may be attributed to users’ preference for the appropriate white space on different screens, thereby providing useful guidelines for the responsive design of lists on portable devices.

Land use/cover change (LUCC) is the primary source of carbon storage changes in the ecosystem. Up to now, there are few studies about the impacts and driving mechanisms of LUCC for carbon storage in the ecosystem at spatial–temporal scales. Characterizing LUCC of the Yellow River Basin (YRB) and its role in carbon storage are very important and necessary to elucidate the results of human activities on ecosystems. The policies to address potential future risks should be formulated in advance to achieve effective development. In the paper, we regarded the YRB as the study area, analyzed its LUCC during 2000 to 2020, predicted land use patterns in 2040 under the scenarios of natural trend (NT), ecological degradation (ED), and ecological restoration (ER) using Markov model with Patch-generating Land Use Simulation (PLUS) model, and quantified carbon storage in the ecosystems over the last 20 years and under future scenarios according to Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The outcome was as follows: (1) During 2000 to 2040, LUCC in the YRB changed markedly, with cropland being transformed into woodland, grassland and built-up land; (2) During 2000 to 2040, carbon storage in the YRB was on an upward trend with a mean annual increase of 1.93×106Mg C, and woodland was the answer to increasing carbon storage, while unused land could induce carbon storage to decrease; (3) Carbon storage in the YRB varied to different degrees under three scenarios, but under the premise of not causing large-scale damage, the conversion of built-up land was an important means of improving carbon storage, greatly enhancing the carbon sequestration efficiency and capacity of the YRB. In conclusion, the future environmental management of the YRB should be continuously oriented to ecological protection and low-carbon development, so that carbon storage in the basin will be able to develop in a benign direction.

Two new compounds, named rhizoaspergillin A (1) and rhizoaspergillinol A (2), were isolated from the mangrove endophytic fungus Aspergillus sp. A1E3, associated with the fruit of Rhizophora mucronata, together with averufanin (3). The planar structures and absolute configurations of rhizoaspergillinol A (2) and averufanin (3) were established by extensive NMR investigations and quantum-chemical electronic circular dichroism (ECD) calculations. Most notably, the constitution and absolute configuration of rhizoaspergillin A (1) were unambiguously determined by single-crystal X-ray diffraction analysis of its tri-pivaloyl derivative 4, conducted with Cu Kα radiation, whereas those of averufanin (3) were first clarified by quantum-chemical ECD calculations. Rhizoaspergillin A is the first orsellinic acid–ribose–pyridazinone-N-oxide hybrid containing a unique β-oxo-2,3-dihydropyridazine 1-oxide moiety, whereas rhizoaspergillinol A (2) and averufanin (3) are sterigmatocystin and anthraquinone derivatives, respectively. From the perspective of biosynthesis, rhizoaspergillin A (1) could be originated from the combined assembly of three building blocks, viz., orsellinic acid, β-D-ribofuranose, and L-glutamine. It is an unprecedented alkaloid-N-oxide involving biosynthetic pathways of polyketides, pentose, and amino acids. In addition, rhizoaspergillinol A (2) exhibited potent antiproliferative activity against four cancer cell lines. It could dose-dependently induce G2/M phase arrest in HepG2 cells.

Lightweight semantic segmentation promotes the application of semantic segmentation in tiny devices. The existing lightweight semantic segmentation network (LSNet) has the problems of low precision and a large number of parameters. In response to the above problems, we designed a full 1D convolutional LSNet. The tremendous success of this network is attributed to the following three modules: 1D multi-layer space module (1D-MS), 1D multi-layer channel module (1D-MC), and flow alignment module (FA). The 1D-MS and the 1D-MC add global feature extraction operations based on the multi-layer perceptron (MLP) idea. This module uses 1D convolutional coding, which is more flexible than MLP. It increases the global information operation, improving features’ coding ability. The FA module fuses high-level and low-level semantic information, which solves the problem of precision loss caused by the misalignment of features. We designed a 1D-mixer encoder based on the transformer structure. It performed fusion encoding of the feature space information extracted by the 1D-MS module and the channel information extracted by the 1D-MC module. 1D-mixer obtains high-quality encoded features with very few parameters, which is the key to the network’s success. The attention pyramid with FA (AP-FA) uses an AP to decode features and adds a FA module to solve the problem of feature misalignment. Our network requires no pre-training and only needs a 1080Ti GPU for training. It achieved 72.6 mIoU and 95.6 FPS on the Cityscapes dataset and 70.5 mIoU and 122 FPS on the CamVid dataset. We ported the network trained on the ADE2K dataset to mobile devices, and the latency of 224 ms proves the application value of the network on mobile devices. The results on the three datasets prove that the network generalization ability we designed is powerful. Compared to state-of-the-art lightweight semantic segmentation algorithms, our designed network achieves the best balance between segmentation accuracy and parameters. The parameters of LSNet are only 0.62 M, which is currently the network with the highest segmentation accuracy within 1 M parameters.

Many regions on the globe are at risk of soil erosion, especially in the ecologically fragile areas of the Qinghai-Tibet Plateau. Enhancing the soil conservation capacity of regional vegetation is one of the main ways to address soil erosion. However, most studies ignore the relationship between the spatial distribution structure of vegetation and soil conservation capacity, and the construction of ecological spatial networks is an effective means to study the spatial distribution structure of vegetation. In this study, we extracted the ecological spatial network of Southeast Tibet by the minimum resistance model (MCR) and combined the ecological network with complex network theory to calculate the topological indices of the ecological spatial network and ecological nodes. Subsequently, the soil conservation capacity of ecological nodes is calculated based on the USLE model, and the relationship between it and topological indices is analyzed. It was shown that the degree, closeness centrality, betweenness centrality and eigenvector centrality of forest nodes showed a positive linear correlation with their soil conservation capacity, and the clustering coefficient of grassland nodes showed a positive linear correlation with their soil conservation capacity. This study calculates habitat quality based on the Invest model. We propose a Integrated Soil Conservation and Habitat Quality Evaluation (ISHE) Model to determine the direction of network optimization and propose an optimization strategy. We analyze the network robustness and soil conservation capacity and find that the optimized network robustness and soil conservation capacity are significantly improved. This study provides strategies and methods to protect ecologically vulnerable areas, optimize the spatial distribution of vegetation, and enhance soil conservation capacity. In addition, this can provide a theoretical reference for planning national ecological projects. We use the ISHE model to filter out three types of nodes that need to be focused on optimization and propose different optimization strategies.

Semantic segmentation can address the perceived needs of autonomous driving and micro-robots and is one of the challenging tasks in computer vision. From the application point of view, the difficulty faced by semantic segmentation is how to satisfy inference speed, network parameters, and segmentation accuracy at the same time. This paper proposes a lightweight multi-dimensional dynamic convolutional network (LMDCNet) for real-time semantic segmentation to address this problem. At the core of our architecture is Multidimensional Dynamic Convolution (MDy-Conv), which uses an attention mechanism and factorial convolution to remain efficient while maintaining remarkable accuracy. Specifically, LMDCNet belongs to an asymmetric network architecture. Therefore, we design an encoder module containing MDy-Conv convolution: MS-DAB. The success of this module is attributed to the use of MDy-Conv convolution, which increases the utilization of local and contextual information of features. Furthermore, we design a decoder module containing a feature pyramid and attention: SC-FP, which performs a multi-scale fusion of features accompanied by feature selection. On the Cityscapes and CamVid datasets, LMDCNet achieves accuracies of 73.8 mIoU and 69.6 mIoU at 71.2 FPS and 92.4 FPS, respectively, without pre-training or post-processing. Our designed LMDCNet is trained and inferred only on one 1080Ti GPU. Our experiments show that LMDCNet achieves a good balance between segmentation accuracy and network parameters with only 1.05 M.

The Yellow River Basin serves as a crucial ecological barrier in China, emphasizing the importance of accurately examining the spatial distribution of forest carbon stocks and enhancing carbon sequestration in order to attain “carbon peaking and carbon neutrality”. Forest patches have complex interactions that impact ecosystem services. To our knowledge, very few studies have explored the connection between these interactions and carbon stock. This study addressed this gap by utilizing complex network theory to establish a forest ecospatial network (ForEcoNet) in the Yellow River Basin in which forest patches are represented as nodes (sources) and their interactions as edges (corridors). Our objective was to optimize the ForEcoNet’s structure and enhance forest carbon stocks. First, we employed downscaling technology to allocate the forest carbon stocks of the 69 cities in the study area to grid cells, generating a spatial distribution map of forest carbon density in the Yellow River Basin. Next, we conducted morphological spatial pattern analysis (MSPA) and used the minimum cumulative resistance model (MCR) to extract the ForEcoNet in the basin. Finally, we proposed optimization of the ForEcoNet based on the coupling coordination between the node carbon stock and topological structure. The results showed that: (1) the forest carbon stocks of the upper, middle, and lower reaches accounted for 42.35%, 54.28%, and 3.37% of the total, respectively, (2) the ForEcoNet exhibited characteristics of both a random network and a scale-free network and demonstrated poor network stability, and (3) through the introduction of 51 sources and 46 corridors, we optimized the network and significantly improved its robustness. These findings provide scientific recommendations for the optimization of forest allocation in the Yellow River Basin and achieving the goal of increasing the forest carbon stock.

In recent years, the accelerated pace of urbanization has increased patch fragmentation, which has had a certain impact on the structure and ecological environment of forest–grass ecological networks, and certain protection measures have been taken in various regions. Therefore, studying the spatiotemporal changes and correlations of ecological service functions and forest–grass ecological networks can help to better grasp the changes in landscape ecological structure and function. This paper takes the Wuding River Basin as the research area and uses the windbreak and sand fixation service capacity index, soil conservation capacity, and net primary productivity (NPP) to evaluate the ecological service capacity of the research area from the three dimensions of windbreak and sand fixation, soil conservation, and carbon sequestration. The Regional Sustainability and Environment Index (RSEI) is used to extract ecological source areas, and GIS spatial analysis and the minimum cumulative resistance (MCR) model are used to extract potential ecological corridors. Referring to complex network theory, topology metrics such as degree distribution and clustering coefficient are calculated, and their correlation with ecological service capacity is explored. The results show that the overall ecological service capacity of sand fixation, soil fixation, and carbon sequestration in the research area in 2020 has increased compared to 2000, and the ecological flow at the northern and northwest boundaries of the river basin has been enhanced, but there are still shortcomings such as fragmented ecological nodes, a low degree of clustering, and poor connectivity. In terms of the correlation between topology indicators and ecological service functions, the windbreak and sand fixation service capacity index have the strongest correlation with clustering and the largest grasp, while the correlation between soil conservation capacity and eigencentrality is the strongest and has the largest grasp. The correlation between NPP and other indicators is not obvious, and its correlation with eccentricity and eigencentrality is relatively large.

The destruction of vegetation structure and quantity leads to the weakening of the carbon sequestration capacity of the ecosystem. Building an ecological spatial network is a potent method for studying vegetation spatial distribution structures. The relationship between the spatial distribution structure of vegetation networks and carbon sequestration, as approached from the perspective of complex network theory, is understudied. This study uses the minimum resistance model (MCR) and morphological spatial pattern analysis (MSPA) to study the eco-space network and ecological node spatial structure and topological characteristics of vegetation in the ecologically sensitive area of the middle reaches of the Yellow River (ESAMRYR). Based on the Carnegie-Ames-Stanford approach (CASA) model, the vegetation Net Primary Productivity (NPP) of the study area is calculated, and the ecological carbon sequestration function of the ecological node is estimated, and the relationship between the ecological node and the topological indicators is analyzed. The study shows that the forest land carbon storage in the regions situated toward the south and east of the Yellow River ecologically sensitive area is the highest, accounting for twice the proportion of the area, and is very important in terms of increasing carbon storage. Most of the ecological sources in the study area have a higher topological importance than functional importance, and the sources with low coordination are mainly distributed in the southwest and northeast. We construct a topology and function coupling optimization model (TFCO) to explore the coordination between vegetation structure and carbon sequestration function, to determine the network optimization direction, and to propose optimization solutions. Analysis of network robustness and carbon sequestration capacity shows that the sturdiness and carbon sequestration of the enhanced network are significantly improved. This study provides strategies and methods for protecting ecological sensitive areas, optimizing vegetation spatial distribution, and enhancing carbon sequestration capacity.

Abstract To obtained fungal resources with excellent tolerance and accumulation capacity to rare earth yttrium ions (Y3+), rare earth ore samples were collected and used for microbial screening. A fungus hyper-resistant to Y3+ was obtained and the effects of the fungus in three physiological states (growth process, mycelial pellets with physiological activity and the fungus powder after being ground) on the Y3+ accumulation were investigated. The Y3+ resistant fungus was identified as Penicillium sp. ZD28, and its mycelium pellets (about 1 mm in diameter) showed poor ability to accumulate Y3+ with an adsorption capacity of less than 81 μmol/g. However, the fungus was able to remove 99% of Y3+ during the growth process, at an initial concentration of less than 600 μM. Bioaccumulation of Y was observed on the cell surface of the ZD28 strain by elemental mapping using scanning electron microscopy-energy dispersive X-ray spectroscopy. The adsorbent (the dry fungal powder) had a remarkable adsorption property for Y3+ that was greater than 455 μmol/g in conditions of 465 μM

Increasing fire-induced bridge failures are demanding more precise behavior prediction for the bridges subjected to fires. However, current numerical methods are limited to temperature curves prescribed for building structures, which can misestimate the fire impact significantly. This paper developed a framework coupling the computational dynamics (CFD) method and finite element method (FEM) to predict the performance of fire-exposed bridges. The fire combustion was simulated in CFD software, Fire Dynamic Simulator, to calculate the thermal boundary required by the thermomechanical simulation. Then, the adiabatic surface temperatures and heat transfer coefficient were applied to the FEM model of the entire bridge girder. A sequential coupled thermomechanical FEM simulation was then carried out to evaluate the performance of the fire-exposed bridge, thermally and structurally. The methodology was then validated through a real fire experiment on a steel beam. The fire performance of a simply supported steel box bridge was simulated using the proposed coupled CFD-FEM methodology. Numerical results show that the presented method was able to replicate the inhomogeneous thermomechanical response of box bridges exposed to real fires. The girder failed due to the buckling of a central diaphragm after the ignition of the investigated tanker fire in no more than 10 min. The framework presented in this study is programmatic and friendly to researchers and can be applied for the estimation of bridges in different fire conditions.

In order to investigate the motion feature of alien phytoplankton in the Bohai Bay area, this manuscript builds a two-dimensional tide hydrodynamic model coupled with a particle tracking model to simulate the alien phytoplankton movement trajectory and the diffusion processes with different specific growth rates in three major ports of Bohai Bay (Caofeidian port, Tianjin port, and Huanghua port). The results show that the movement of alien phytoplankton is mainly affected by the tidal circulation near the port, and the diffusion trend is basically consistent with the residual flow in Bohai Bay. The distribution density of alien phytoplankton is directly affected by the specific growth rate of the population and is positively related to specific growth rate. The released alien phytoplankton in the three major ports are all concentrated around the ports area. The largest distribution density is in Tianjin port, and the possibility of red tide disasters is also greatest here compared with the other two major ports. It is necessary to strengthen the monitoring of alien organisms in the port area and actively prevent alien phytoplankton from entering Bohai Bay through ship ballast water.

Generation of light with desirable amplitude and phase profiles with nonlinear wavefront shaping is important for optical technologies. Here, the authors demonstrate nonlinear wavefront shaping with three-dimensional nonlinear photonic crystals formed by ultrafast-light-induced ferroelectric domain inversion approach.

In this paper, we study the problem of solving Seal’s type partial integro-differential equations (PIDEs) for the classical compound Poisson risk model. A data-driven deep neural network (DNN) method is proposed to calculate finite-time survival probability, and an alternative scheme is also investigated when claim payments are exponentially distributed. The DNN method is then extended to the numerical solution of generalized PIDEs. Numerical approximation results under different claim distributions are given, which show that the proposed scheme can obtain accurate results under different claim distributions.

Two-dimensional piezoelectric materials hold promise for nano-electromechanical technologies, yet it is challenging to prepare them in large areas with high sample homogeneity. Syed et al. surface print GaPO4 sheets with unit cell thickness over centimetres using a liquid metal-based synthesis process.

We derive an integral representation of the price formulas for European options whose terminal payoff involves path-dependent lookback variable. The intricacies in the derivation procedures using the partial differential equation techniques stem from the degenerate nature of the pricing models, where the lookback state variables appear only in the auxiliary conditions but not in the governing differential equations. We also derive a parity relation between the price functions of the floating strike and fixed strike lookback options.