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Quantifying watershed state is crucial for ecological management and sustainable development. Traditional methods, often based on multiple indicators and subjective weighting, struggle to objectively and comprehensively capture the inherent complexity of watershed ecosystems. Runoff, a key hydrological component, reflects watershed functioning, but most studies focus solely on runoff volume, limiting the ability to link runoff dynamics with surface conditions and broader system processes. This study introduced a novel approach that uses runoff complexity to represent watershed system-level state, bridging the gap between runoff dynamics and ecosystem functioning. The Hydrological Refined Composite Multiscale Entropy (Hydro_RCMFE) method was developed to quantify runoff complexity across various time scales. Combined with the Hydrological Complexity Transformer Model (HydroC_Trans) and Shapley Additive Explanations (SHAP), this method explored interactions between runoff complexity and influencing factors. It was applied to the Yanhe Watershed in China’s Loess Plateau, known for severe soil erosion and large-scale ecological restoration. The results revealed significant fluctuations in runoff complexity after 2005. Vegetation cover from the Grain for Green Program enhanced self-organization, buffering climatic variability while introducing instability. Urbanization further amplified runoff complexity, while landscape factors, such as aggregation and hydrological connectivity, had spatially and temporally varied effects, highlighting the need for tailored management strategies. Upstream, efforts should enhance climate resilience, increase vegetation cover—particularly grasslands—and improve landscape aggregation. Midstream and downstream strategies should prioritize optimizing hydrological connectivity, limiting impervious surface expansion, and restoring ecological functions.

Abstract Simultaneous multi-target detection and multi-site gene editing are two key factors restricting the development of disease diagnostic and treatment technologies. Despite numerous explorations on the source, classification, functional features, crystal structure, applications and engineering of CRISPR-Cas13a, all reports use the contiguous target RNA activation paradigm that only enables single-target detection in vitro and one-site gene editing in vivo. Here we propose a noncontiguous target RNA activation paradigm of Cas13a and establish a CRISPR-Cas13a Gemini System composed of two Cas13a:crRNA binary complexes, which can provide rapid, simultaneous, highly specific and sensitive detection of two RNAs in a single readout, as well as parallel dual transgene knockdown. CRISPR-Cas13a Gemini System are demonstrated in the detection of two miRNAs (miR-155 and miR-375) for breast cancer diagnosis and two small RNAs (EBER-1 and EBER-2) for Epstein-Barr virus diagnosis using multiple diagnostic platforms, including fluorescence and colorimetric-based lateral flow systems. We also show that CRISPR-Cas13a Gemini System can knockdown two foreign genes (EGFP and mCherry transcripts) in mammalian cells simultaneously. These findings suggest the potential of highly effective and simultaneous detection of multiple biomarkers and gene editing of multiple sites.

It is of great significance to accurately grasp the charge induction law of rock mass during load failure process for the prediction of dynamic disasters such as rock burst. On the basis of optimizing and improving the acquisition accuracy of charge monitor, laboratory monitoring tests were carried out on the charge induction rule of sandstone samples under uniaxial compression failure under load, and underground monitoring tests were carried out. The relationship between charge induction, stress drop and crack growth of sandstone samples under different loading rates and the charge induction characteristics in the area affected by abutment pressure of advanced working face were analyzed in detail. The research shows that: the formation, development and propagation of cracks are accompanied by charge induction signals during the process of loading failure, and the generation of precursor charge signals is earlier than the stress drop caused by loading failure. Under the loading rates of 0.005 mm/s, 0.015 mm/s and 0.020 mm/s, the law of charge signal in the failure process of rock samples varies with the loading rate. The higher the loading rate is, the richer the charge signal is, and the precursory charge signal appears more early. Field monitoring found that the maximum value of the average induced charge at each measuring point was located in the influence area of the advanced abutment pressure, and the average induced charge was consistent with the distribution trend of the advanced abutment pressure on the working face.

The environment of mountain ecosystems can change greatly in short distances as elevation increases. The effects of elevation change on the distribution and body size of carabid beetles were investigated at elevations of 750–2600 m in the Changbaishan Nature Reserve (Northeast China). The richness and abundance of carabid species decreased significantly as elevation increased. However, the change trends are different in forests and tundra. In the broad-leaved Korean pine forest and coniferous forest at low elevations, carabid beetle species have high richness and abundance. The community composition of carabid beetles was significantly different at different elevations and among different vegetation types. Some species only occurred at specific elevations. There were fewer indicator species in high-elevation areas, but Carabus macleayi Dejean, Nebria pektusanica Horratovich and Pterostichus jaechi Kirschenhofer were mainly found in high-elevation areas. The average body size of species in the carabid beetle community was negatively correlated with elevation. The sizes of the larger Carabus canaliculatus Adams and Carabus venustus Morawitz were negatively correlated with elevation. Their body sizes decreased obviously in the tundra at elevations above 2000 m. Changes in vegetation types at high elevations affect the distribution and body sizes of beetles along the elevation gradient. Some large carabid species may be smaller at high elevations where a unique insect fauna has developed. The body size and distribution range of the carabid may be the factors that affect body size reduction at high elevation. Although some high-elevation species also occur in low-elevation areas, the protection of species diversity in high-elevation areas should be emphasized in the context of global climate change. The results illustrate the mechanisms of carabid beetles’ response to elevation change and the need for carabid beetles’ diversity conservation under global climate change.

Abstract The proton exchange membrane (PEM) water electrolyzer has been considered a versatile approach for practical H2 production. However, the oxygen evolution reaction (OER) in acid media with complicated proton‐coupled electron transfer steps possesses sluggish kinetics and high reaction barriers, severely hindering the development of PEM water electrolyzers. Consequently, high‐efficient Ru‐ and Ir‐based catalysts have always been essential to accelerate the OER rate and lower the reaction barrier in PEM water electrolyzer. Therefore, it is very necessary to construct low‐cost catalysts with excellent electrocatalytic performances to replace these noble metal‐based OER electrocatalysts. In this review paper, a detailed discussion towards fundamentally comprehending the reaction mechanisms of OER was conducted. Accordingly, we proposed the principles of designing advanced OER electrocatalysts with enhanced performances and lowered costs. After that, recent developments in designing various acidic OER electrocatalysts were summarized. Meanwhile, the available regulation strategies about noble metals, nonprecious metals, and metal‐free nanomaterials were presented, which are promising for tuning the electronic structures, boosting the electrocatalytic performances, and reducing the costs of electrocatalysts. We also provided the existing challenges and perspectives of various OER electrocatalysts, hoping to promote the development of PEM water electrolyzers.

Abstract Background Atherosclerosis is driven by synergistic interactions between pathological biomechanical and lipid metabolic factors. Long noncoding RNAs (LncRNAs) have been implicated in atherogenesis. The purpose of this study was to investigate the potential mechanism of lncRNA AI662270 on macrophage cholesterol transport in atherosclerosis. Methods Apolipoprotein E deficiency (ApoE −/−) mice were fed a high fat diet for 16 weeks to construct atherosclerotic model, and the mice were injected with recombinant lentivirus carrying AI662270 gene to overexpress AI662270. Macrophages were cleared by liposomal clondronate in vivo. Fundamental experiments and functional assays, hematoxylin and eosin staining, oil red O staining and others, were performed to evaluate the function of AI662270 on atherogenesis. Peritoneal macrophages were treated with oxidized low density lipoprotein (ox-LDL) to simulate in vitro model. Mechanism assays, RNA-interacting protein immunoprecipitation, RNA–protein pulldown and others, were performed to study the regulatory mechanism of AI662270 in macrophages. Results The novel AI662270 was mainly enriched in macrophages, but not in endothelial cells, smooth muscle cells and fibroblasts of mouse atherosclerotic lesions and was upregulated by ox-LDL. Overexpression of AI662270 resulted in lipid accumulation, larger atherosclerotic plaques and cardiac dysfunction in vivo. After macrophages were removed, the pro-atherogenic effect of AI662270 disappeared. Downregulation of AI662270 in macrophages protected against foam cell formation by potentiating cholesterol efflux and reducing intracellular total cholesterol. The opposite effect was observed in macrophage-specific AI662270-overexpressed cells in vitro. AI662270 bound to adenosine triphosphate-binding cassette transporter A1 (Abca1) responsible for regulating cholesterol efflux in macrophages. Forced expression of AI662270 in macrophages decreased Abca1 expression. The reverse occurred when expression of AI662270 was repressed. Conclusion These findings reveal an essential role for AI662270 in atherosclerosis progression by regulating cholesterol efflux from macrophages.

Geomatics is an interdisciplinary subject. Many disciplines have teaching demands in this field. A new course on “Geomatics Technology” has been suggested by the Weiyang College of Tsinghua University of China for the major of “Mathematical and Scientific Basic Science+Civil, Hydraulic and Marine Engineering”. This paper offers a data-led geomatics teaching mode, developing a customized teaching cloud platform, to explore the cross-integrated innovative teaching methods. Teachers and students can assign and submit assignments on this platform. The platform constitutes a data flow with the data download, data processing and result sharing. It encourages communication among students in various majors, grades and units using data as the medium, from data processing to application upstream and downstream. In the “Geomatics Technology” course, geospatial data has emerged as a vital element of the multidisciplinary approach. This kind of teaching mode has been used in the postgraduate remote sensing course offered by Tsinghua University’s Department of Civil Engineering and Construction Management. Furthermore, the mode will be used for the first time in the autumn semester of 2022 in the undergraduate teaching of Weiyang College and civil engineering, to offer a novel idea for the reform of courses linked to geospatial informatics.

Effective monitoring techniques and equipment are essential for the prevention and control of coal and rock dynamic disasters such as rockburst. Based on the fact that there is charge generation during deformation and rupture of coal rock body and the charge signals contain a large amount of information about the mechanical process of deformation and rupture of coal rock, the rockburst charge sensing monitoring technology has been formed. In order to improve the charge sensing technology for monitoring and early warning of rockburst disasters, this paper develops a new generation of portable coal rock charge monitoring instrument on the basis of the original instrument and carries out laboratory and underground field application. The primary advancement involves enhancing the external structure of the sensor and increasing the charge sensing area, which can more comprehensively capture the charge signals from the loaded rupture of the coal rock body. The overall structure of the data acquisition instrument has been improved, the monitoring channels have been increased, and the function of displaying the monitoring data curve has been added, so that the coal and rock body force status can be grasped in time. The results of the experimental study show that the abnormal charge signals can be monitored during the rupture process of rock samples under loading, and the monitored charge signals are in good agreement with the sudden change of stress in the rock samples and the formation of crack extension. There is a precursor charge signal before the stress mutation, and the larger the loading rate is, the earlier the precursor charge signal appears. The charge monitoring instrument can monitor the charge signal of the coal seam roadway under strong mining pressure. In the zone of elevated overburden pressure, the amount of induced charge is large, and anomalously high value charge signals can be monitored when a coal shot occurs. The change trend of the charge at different measuring points of strike and inclination has a good consistency with the distribution of overrunning support pressure and lateral support pressure, which can reflect the stress distribution and the degree of stress concentration of the coal body through the size and location of the charge, foster early warning and analysis of rockburst, and provide target guidance for the prevention and control of rockburst. [ABSTRACT FROM AUTHOR]

Rapid urban expansion caused by vigorous urban population growth brought up various socioeconomic and eco-environmental problems, which have important ramifications for sustainable development across the world. Along with the accelerated urbanization process, accurate and realistic prediction of urban expansion is of great importance to optimize urban planning and urban development. This study proposed a new hybrid model, which combined the urban scaling law (USL) with the ANN-CA model to predict urban expansion. To employ urban scaling law in the model, we innovatively calculated the law exponent at a single-city scale. Based on USL, we estimated urban land demand in the future by panel data regression. Finally, we added the area constraint and ecological constraint into the ANN-CA model to simulate urban expansion spatially. This frame of urban expansion has been successfully applied in Shenzhen, of which the urban land area would increase from 816.45 km2 in 2020 to 842.48 km2 in 2025. By comparing this model with the traditional prediction method, we proved its effectiveness and accuracy. Besides, we found that the scaling exponent can reflect urbanization level and distinguish overconstructed cities.

Albeit hyperspectral image (HSI) classification methods based on deep learning have presented high accuracy in supervised classification, these traditional methods required quite a few labeled samples for parameter optimization. When processing HSIs, however, artificially labeled samples are always insufficient, and class imbalance in limited samples is inevitable. This study proposed a Transformer-based framework of spatial–spectral–associative contrastive learning classification methods to extract both spatial and spectral features of HSIs by the self-supervised method. Firstly, the label information required for contrastive learning is generated by a spatial–spectral augmentation transform and image entropy. Then, the spatial and spectral Transformer modules are used to learn the high-level semantic features of the spatial domain and the spectral domain, respectively, from which the cross-domain features are fused by associative optimization. Finally, we design a classifier based on the Transformer. The invariant features distinguished from spatial–spectral properties are used in the classification of satellite HSIs to further extract the discriminant features between different pixels, and the class intersection over union is imported into the loss function to avoid the classification collapse caused by class imbalance. Conducting experiments on two satellite HSI datasets, this study verified the classification performance of the model. The results showed that the self-supervised contrastive learning model can extract effective features for classification, and the classification generated from this model is more accurate compared with that of the supervised deep learning model, especially in the average accuracy of the various classifications.

Runoff complexity is an important indicator reflecting the sustainability of a watershed ecosystem. In order to explore the multiscale characteristics of runoff complexity and analyze its variation and influencing factors in the Yanhe watershed in China during the period 1991–2020, we established a new analysis method for watershed runoff complexity based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method for the decomposition of multiscale characteristics and the refined composite multiscale entropy (RCMSE) method for the quantification of the system complexity. The results show that runoff and its components all present multiscale complexity characteristics that are different from random signals, and the intermediate frequency modes contribute the most to runoff complexity. The runoff complexity of the Yanhe watershed has decreased gradually since 1991, and 2010 was a turning point of runoff complexity, when it changed from a decline to an increase, indicating that the ecological sustainability of this basin has improved since 2010, which was mainly related to the ecological restoration measures of the Grain for Green Project. This study expands the research perspective for analyzing the variation characteristics of runoff at the multiscale, and provides a reference for the study of watershed ecological sustainability and ecological management.

Satellite hyperspectral remote sensing has gradually become an important means of Earth observation, but the existence of various types of noise seriously limits the application value of satellite hyperspectral images. With the continuous development of deep learning technology, breakthroughs have been made in improving hyperspectral image denoising algorithms based on supervised learning; however, these methods usually require a large number of clean/noisy training pairs, a target that is difficult to meet for real satellite hyperspectral imagery. In this paper, we propose a self-supervised learning-based algorithm, 3S-HSID, for denoising real satellite hyperspectral images without requiring external data support. The 3S-HSID framework can perform robust denoising of a single satellite hyperspectral image in all bands simultaneously. It first conducts a Bernoulli sampling of the input data, then uses the Bernoulli sampling results to construct the training pairs. Furthermore, the global spectral consistency and minimum local variance are used in the loss function to train the network. We use the training model to predict different Bernoulli sampling results, and the average of multiple predicted values is used as the denoising result. To prevent overfitting, we adopt a dropout strategy during training and testing. The results of denoising experiments on the simulated hyperspectral data show that the denoising performance of 3S-HSID is better than most state-of-the-art algorithms, especially in terms of maintaining the spectral characteristics of hyperspectral images. The denoising results for different types of real satellite hyperspectral data also demonstrate the reliability of the proposed method. The 3S-HSID framework provides a new technical means for real satellite hyperspectral image preprocessing.

Mobile laser scanning (MLS) technology directly collects dense 3D coordinate information at expressway and satisfied with the demands of transportation-related road surveying. This paper aims to extract road curbs and road markings from MLS point clouds. The proposed method is based on the following approaches: 1) extracting and segmenting road surfaces and subsidiary facility point clouds from raw point clouds based on the GPS trajectory; 2) dynamically shifting the point clouds from the global coordinate system to block-based local coordinate systems; 3) estimating the curbs and centre points based on the block profiles and re-sampled point clouds; and 4) extracting road markings based on the passthrough-statistical-radius-filter. An application for an expressway and its interchange road is performed to test the proposed method. The mean deviation in the estimated road centres was 5.30 cm, which satisfies the demand for centimetre-scale road precision in expressway applications. The results illustrate that the proposed method is a precise alternative for expressway curb and marking extraction, and can be performed for horizontal analysis and lane navigation in intelligent transportation systems.

This paper presents a new method to identify the stress concentration degree and stress distribution characteristics in front of working face in coal mine, based on the close relationship between charge and stress during fracture of coal mass. This method overcomes many disadvantages of conventional stress-monitored methods. First, the stress and charge relationship of coal mass was established through damage theory and statistical strength theory. Then, the relationship between stress and charge was studied by a laboratory test, and finally, the field charge monitoring test was performed. The results show that there is a nonlinear relationship between loading stress (σ) and cumulative charge (Q), which can be represented by polynomials. The fitting results of the laboratory test between σ and Q conform to a cubic polynomial function, Q=aσ3+bσ2+cσ+d. It verifies the rationality of the theoretical relationship formula. The field monitoring results show that average charge is great and cumulative charge changes from rapid upward to sharp upward before roof falling. The magnitude and position of average charge and the upward trend of cumulative charge can be used to identify the stress concentration and stress distribution in front of working face, and the abnormal stress area can be predicted. The results can provide certain guidance for the forecast of rock burst in coal mine.

The article analyzes the practice of the WTO dispute resolution body and cases, which were considered even under the GATT for anti-dumping cases. The result of the analysis is systematization and determination of the general characteristics of the precedents of the above topics, as well as the definition of innovation in the new dispute resolution procedure. To put it more clearly, it should be emphasized that the procedure for settling trade disputes of the WTO Dispute Resolution Body is not entirely new, but has been in effect since the establishment of the organization, but because of article considers precedents that preceded the WTO disputes, there was an objective need to make a comparative analysis between the two procedures on the resolution of trade disputes. The authors distinguish two main innovations, which, in turn, consist of two elements and are mainly expressed in institutional and operational new introductions. Undoubtedly, such a reform of the procedure has a positive effect and increases the effectiveness of measures, which makes the WTO dispute resolution body a more attractive institution for parties to trade disputes. The features of the anti-dumping precedents are based on the specifics of the dispute resolution procedure, which is subject to the norms of the code, developed taking into account the complexities of disputes, the object of which is dumping. Therefore, in determining the general characteristics, the main categories were the stages of the dispute and already in stages, tendencies were derived. Key words: anti-dumping, GATT, WTO, WTO innovations.

Urban scaling laws describe powerful universalities of the scaling relationships between urban attributes and the city size across different countries and times. There are still challenges in precise statistical estimation of the scaling exponent; the properties of variance require further study. In this paper, a statistical regression method based on the maximum likelihood estimation considering the lower bound constraints and the heterogeneous variance of error structure, termed as CHVR, is proposed for urban scaling estimation. In the CHVR method, the heterogeneous properties of variance are explored and modeled in the form of a power-of-the-mean variance model. The maximum likelihood fitting method is supplemented to satisfy the lower bound constraints in empirical data. The CHVR method has been applied to estimating the scaling exponents of six urban attributes covering three scaling regimes in China and compared with two traditional methods. Method evaluations based on three different criteria validate that compared with both classical methods, the CHVR method is more effective and robust. Moreover, a statistical test and long-term variations of the parameter in the variance function demonstrate that the proposed heterogeneous variance function can not only describe the heterogeneity in empirical data adequately but also provide more meaningful urban information. Therefore, the CHVR method shows great potential to provide a valuable tool for effective urban scaling studies across the world and be applied to scaling law estimation in other complex systems in the future.

An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split into quarter sections and the quadtree structure is constructed. In this process, an improved fast calculation method for standard deviation of image is proposed, which significantly increases the speed of quadtree segmentation with standard deviation criterion. A spatial indexing structure was built using improved Morton encoding based on this structure, which provides the merging process with data structure for neighborhood queries. Then, in order to obtain the final segmentation result, we constructed a feature vector using both spectral and texture factors, and proposed an algorithm for region merging based on the region adjacency graph technique. Finally, to validate the method, experiments were performed on GeoEye-1 and IKONOS color images, and the segmentation results were compared with two typical algorithms: multi-resolution segmentation and Mean-Shift segmentation. The experimental results showed that: (1) Compared with multi-resolution and Mean-Shift segmentation, our method increased efficiency by 3–5 times and 10 times, respectively; (2) Compared with the typical algorithms, the new method significantly improved the accuracy of segmentation.

Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL) and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established.

Distinguishing and characterizing different landscape patterns have long been the primary concerns of quantitative landscape ecology. Information theory and entropy-related metrics have provided the deepest insights in complex system analysis, and have high relevance in landscape ecology. However, ideal methods to compare different landscape patterns from an entropy view are still lacking. The overall aim of this research is to propose a new form of spatial entropy (Hs) in order to distinguish and characterize different landscape patterns. Hs is an entropy-related index based on information theory, and integrates proximity as a key spatial component into the measurement of spatial diversity. Proximity contains two aspects, i.e., total edge length and distance, and by including both aspects gives richer information about spatial pattern than metrics that only consider one aspect. Thus, Hs provides a novel way to study the spatial structures of landscape patterns where both the edge length and distance relationships are relevant. We compare the performances of Hs and other similar approaches through both simulated and real-life landscape patterns. Results show that Hs is more flexible and objective in distinguishing and characterizing different landscape patterns. We believe that this metric will facilitate the exploration of relationships between landscape patterns and ecological processes.

A significant difficulty in urban studies is obtaining urban areas. Nighttime light (NTL) data provide efficient approaches to map urban areas. Previous methods have utilized visual particularities of cities with ancillary data to obtain the optimal thresholds. How cities behave differently from rural areas should be considered. A Zipf’s law-based method is proposed to bootstrap the optimal threshold based on the statistical properties of a Zipf’s law model on continuous thresholds at the country scale. In our method, the Zipf’s law model is utilized to quantify fractal, self-organized, and agglomeration behaviors of cities. The three-phase cluster dynamics are discovered and the abrupt transition between Phase 1 and Phase 2 denotes the rural-urban demarcation point. The urban areas are derived by the proposed method from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data in 2013 in China. An accuracy assessment is conducted to compare it with the GlobeLand30-2010 data and the overall accuracy has directly confirmed the effectiveness of the method. The validation using point of interest (POI) data verifies that the urban areas show strong responses to social interactions and production with R2 values of 0.91 and 0.92, respectively, implying that the city areas extracted by our method can be a proxy for human activities. Comparisons with existing methods validate the effectiveness and high degree of automation of the proposed method in mapping urban areas at the country level. According to our analyses, the Zipf’s law-based method shows great potential to provide a universal criterion to map urban areas from the perspective of the behaviors of urban systems without ancillary data, and a valuable tool for spatial and temporal urban studies.