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Abstract To study the influence of cyclic disturbance stress on the mechanical behavior of coal during mining, a gas containing coal fluid-solid coupling servo seepage experimental system was used to conduct experimental research on the acoustic emission (AE) characteristics of gas containing coal under two stress paths of graded cyclic loading and unloading. The AE characteristics of coal damage and failure processes under different cyclic stress paths were analyzed. The research results indicate that: (1) The overall characteristics of AE signals for both graded cyclic loading and unloading paths are basically the same. With increasing of the amount of graded cyclic loading or unloading, the AE count reaches its maximum value when reaching failure, and the cumulative ringing calculation of AE increases exponentially. (2) The AE signals under the graded cyclic loading or unloading path exhibit obvious zoning characteristics. In the low and medium stress regions, the AE signal basically satisfies the Kaiser effect, while reaching the high stress region before failure, the AE signal exhibits a significant Felicity effect. (3) The concentration coefficient of AE and the intensity coefficient of the Kaiser effect have been newly defined. They are used to quantitatively characterize the extent of the Kaiser effect of AEs under graded cyclic stress. It was found that as the variation of graded cyclic stress increases, the concentration coefficient and Kaiser effect intensity coefficient both show a decreasing trend. (4) Combining the AF and RA values of AE, it was found that the coal failure signals of the two stress paths were basically similar, that is, the overall failure was mainly tensile failure, and the signals of cyclic unloading tensile failure were significantly more than those of cyclic loading. The AE signal characteristics studied in this article are of great significance for predicting coal power disasters.

Abstract In this paper, we investigate the thermodynamic properties of the charged AdS Gauss–Bonnet black holes and their associations with the Lyapunov exponent. The chaotic features of the black holes and the isobaric heat capacity characterized by the Lyapunov exponent are studied to reveal the thermodynamic stability of the black hole phases. By considering both the timelike and null geodesics, we find that the relationship between the Lyapunov exponent and the Hawking temperature can accurately represent the features of the Small/Large phase transition and even the triple point. We also reveal the properties of the difference in the Lyapunov exponent as an order parameter. It is demonstrated that there is a negative correlation between the Lyapunov exponent and the size of the black hole shadow, which can be used to bridge the thermodynamic properties and the shadow of black holes.

Remote sensing technology plays an indispensable role in the monitoring of grassland biodiversity, with developments towards a space–sky–ground integrated monitoring network. At present, there is an urgent need to summarize the progress of existing research, systematically grasp the overall development direction, and provide theoretical and application guidance for the in-depth promotion of grassland biodiversity management. The objective of this study was to systematically summarize the progress of remote sensing for grassland biodiversity monitoring. First, a bibliographic analysis of the relevant studies from 2000 to 2022 was conducted, and then a systematic exposition was made from three aspects, namely, remote sensing observation platforms and sensors, the dimensions and indicators of grassland biodiversity observation and the main methods. Finally, the current problems and future development directions were summarized. The study found that the current monitoring of grassland biodiversity in the region still has major problems, such as an insufficient integration of multisource remote sensing, a need to enrich the monitoring level, and a limited analysis method. It is recommended that a space–sky–ground integrated monitoring network be constructed in the future to facilitate the systematic monitoring of grassland biodiversity across multiple dimensions, scales and trophic levels. Furthermore, the analysis methods and techniques should be strengthened. The results of this study can contribute to a deeper understanding of the application of remote sensing technology in grassland biodiversity monitoring and provide scientific references for related research.

Abstract Various surgical methods have so far been developed for treating rectovaginal fistula (RVF), each with its own advantages and disadvantages. The lack of standardized animal models of RVF is a major reason for the failure to establish a unified and effective surgical method for the treatment of RVF. This study aimed to explore the feasibility of an RVF animal model by magnetic compression and compare it with the traditional modeling method. Thirty-two female Japanese white rabbits were randomly divided into four groups: A, B, C, and D, based on how the rectovaginal septum was treated. The operation time, intraoperative blood loss, and model success rate of each group were determined. The experimental animals were euthanized 2 weeks after the operation. Their rectovaginal septum specimens were obtained. RVF was observed by the naked eye. The fistula size was measured. Histological changes of fistula were observed by hematoxylin and eosin and Masson staining. All rabbits completed the RVF model and survived 2 weeks after the operation. Groups A and B had no bleeding, while groups C and D had

Abstract This study investigates the stability of an artificial dam used in an underground reservoir in a coal mine under periodic weighting imposed by overlying rock strata. For this purpose, cyclic loading and unloading tests with different stress amplitudes were designed. Differences in the mechanical performance of the artificial dam with and without overlying strata were analyzed using a uniaxial compression test. The mechanical properties of the structure under constant-amplitude cyclic loading and unloading were characterized. Further, the law of influence of stress amplitude on stability was discussed. A formula for predicting the mechanical performance of the artificial dam with its overlying rocks (hereafter referred to as the complex) was finally derived and was suitable for clarifying the law of damage in the complex under cyclic loading and unloading. The results showed that the complex had changed the internal structure of rocks. The strength and deformation of the complex were intermediate to that of either single structure. All three underwent brittle failure. During the constant-amplitude loading and unloading tests, the hysteresis loop could be divided into three phases, namely, sparse, dense, and sparse again, with a shift in the turning point in rock deformation memory effect. As the stress amplitude increased during the test, the damping ratio of the specimens decreased, and the area of the hysteresis loop increased non-linearly. The dynamic elastic modulus decreased first and then increased. The confidence interval for the formula fitted based on the test results was above 97%. Damage to the complex caused by constant-amplitude loading and unloading could be divided into three stages. An increase in peak stress served as a catalyst for the evolution of small cracks within the specimens into median and large cracks, thereby accelerating the damage process.

Abstract Vesicovaginal fistula lacks a standard, established animal model, making surgical innovations for this condition challenging. Herein, we aimed to non-surgically establish vesicovaginal fistula using the magnetic compression technique, and the feasibility of this method was explored using eight female Beagle dogs as model animals. In these dogs, cylindrical daughter and parent magnets were implanted into the bladder and vagina, respectively, after anesthesia, and the positions of these magnets were adjusted under X-ray supervision to make them attract each other, thus forming the structure of daughter magnet-bladder wall-vaginal wall-parent magnet. Operation time and collateral damage were recorded. The experimental animals were euthanized 2 weeks postoperatively, and the vesicovaginal fistula gross specimens were obtained. The size of the fistula was measured. Vesicovaginal fistula was observed by naked eye and under a light microscope. Magnet placement was successful in all dogs, and remained in the established position for the reminder of the experiment. The average operation time was 14.38 min ± 1.66 min (range, 12–17 min). The dogs were generally in good condition postoperatively and were voiding normally, with no complications like bleeding and urine retention. The magnets were removed from the vagina after euthanasia. The vesicovaginal fistula was successfully established according to gross observation, and the fistula diameters were 4.50–6.24 mm. Histological observation revealed that the bladder mucosa and vaginal mucosa were in close contact on the internal surface of the fistula. Taken together, magnetic compression technique is a simple and feasible method to establish an animal model of vesicovaginal fistula using Beagle dogs. This model can help clinicians study new surgical techniques and practice innovative approaches for treating vesicovaginal fistula.

Disulfiram is widely applied for treating chronic alcoholism. In recent years, researchers have proposed specific anti-cancer mechanisms by disulfiram, such as arresting the activity of acetaldehyde dehydrogenase （ALDH） and boosting intracellular reactive oxygen species （ROS）（reactive oxygen species）. A higher concentration of ROS suppressed the activity of nuclear factor kappa-B （NF-κB） and promoted the conjugation between ROS and nuclear protein localization protein 4 （nuclear protein localization protein 4）. Conjugation of NPL4, an inhibition of FROUNT protein and anti-cancer activity of disulfiram have been demonstrated in various cancer models. Anti-glomerular basement membrane glomerulonephritis is one type of acute glomerulonephritis. Upon a definite diagnosis, it should be promptly treated for relieving symptoms and improving outcomes. Disulfiram could antagonize C-C chemokine receptor type 2 and C-C chemokine receptor type 5 through suppressing C-C chemokine receptor type 2/C-C chemokine receptor type 5, CCR-2/CCR-5） and FROUNT protein interacted to suppress the migration, aggregation and activation of macrophages for alleviating anti-glomerular basement membrane type glomerulonephritis. It indicated that disulfiram had some potential therapeutic value. This review focused upon the molecular mechanism of disulfiram. And disulfiram may become a new agent for anti-glomerular basement membrane glomerulonephritis.

The evaluation of ecosystem quality (EQ) is crucial for sustainable regional development. Existing studies have often focused on a single aspect of the evaluation of EQ, which cannot capture the whole picture of changes in EQ. Therefore, an urgent need exists to construct a comprehensive evaluation system for regional EQ and clarify the relationship between EQ and sustainable development level (SDL). This study takes the West Liao River Basin as an example, constructs a regional EQ evaluation index system based on the “pattern-process-service” framework, evaluates and analyzes the spatial and temporal evolution of EQ as well as the relationship between EQ and SDL in the basin from 2000 to 2020. The regional SDL is also analyzed by combining the indicator system with the United Nations Sustainable Development Goals (SDGs), analyzing the relationship between watershed EQ and SDL and its driving mechanism by using the coupling coordination degree model and Geodetector method and then exploring the strategies for sustainable management of watershed ecosystems. The spatial variation of EQ in the study area from 2000 to 2020 was found to be significant and characterized by increasing inter-annual changes; regions with a high EQ tended to have higher SDLs and SDL evaluation indicators contributed more to the coupling and coordination between EQ and SDL than EQ evaluation indicators. The study developed a regional EQ evaluation system under the perspective of ecosystem services, constructed a county-scale SDL evaluation system, and revealed the relationship between regional EQ and SDL. The evaluation results can provide decision-making references for sustainable regional management. In addition, the EQ evaluation system developed in this study is generalized and objective, thus being applicable to other watershed regions with complex landscape patterns.

Solar power generation is an effective way to reduce carbon emissions and has a wide range of applications worldwide. China's newly installed photovoltaic capacity has ranked first in the world in recent years. Timely and accurate monitoring of the spatiotemporal distribution characteristics of solar power plants is essential to optimize China's renewable energy power distribution and achieve carbon reduction targets. However, long-term solar panel (SP) datasets are still lacking. Based on the Google Earth Engine platform, this study proposed a fine extraction method framework of SPs in large and complex geographical environments by integrating stratified sampling and zonal modeling and obtained the first comprehensive dataset of SP distribution in China covering 2000–2022 to fill the gap in relevant research and practice. It has been verified that the F1-score of the SP datasets was higher than 0.87, which could meet the research needs. On this basis, this study revealed the spatiotemporal variation and development trends of SPs in China over the past 20 years. The proposed method framework can serve as a scientific reference for relevant research, while the interpreted SP datasets and analysis results can provide a basis for regional decision making and related research in the context of future climate change.

The main content of this study is the human-machine collaborative design research, taking the car body design as the carrier. The research framework focused on two phases of car body design process, that of design ideation and evaluation. In the ideation stage, we trained an imperfect Deep Convolutional Generative Adversarial Network (DCGAN) model that just could generate blur automobile images as the blur design motherboards for the iterative sketching, which had design uncertainties and blanks, thus activating designers’ subjective initiative and aesthetic intuition to provide more creative deepen sketches. We leveraged motherboards to address uncertainty through sketching and aesthetic intuition, refining options and ultimately selecting an optimal design. In the evaluation phase, we initially constructed a parametric 3D model with 20 parameters based on the optimal design, and invited 32 designers conducting participatory design experiments, getting 1024 human-designed schemes. Following this, we administered an online survey to assess the aesthetic qualities of a total of 1024 design schemes. Leveraging the collected score data (The first round of surveys engaged 279 participants, while the second round involved 73 participants), we trained an Artificial Neural Network (ANN) model to serve as an aesthetic evaluation score predictor for unknown parameter configurations. The machine could evaluate designs autonomously, thus selecting best design from 20,000 schemes generated randomly by machine. We utilized the parametric design converting sketching images to the numeric parameters, switching the qualitative ideation to the quantitative evaluation, thus achieving aesthetic evaluation and optimization. This study explores the relationship between human cognitive intuition and machine intelligence and how they can collaborate with each other.

The recommendation accuracy of traditional product recommendation systems is insufficient. Therefore, an improved deep factor decomposition machine algorithm combining adaptive regularization and attention mechanisms is proposed, and big data components are integrated to enable the algorithm to support more data input types. The publicly available datasets Criteo and Avazu from the Kaggle competition were selected for testing experiments in the study. The experimental results are as follows. During the training phase, after the convergence of each model, the loss function value of the model designed in this study is 1.26, which is the lowest among all comparison models. Moreover, when the number of recommended products is 7, the overall recommendation effect of each model is the best. The area under the curve of the subject operation characteristic curve of the model designed in this study on the Criteo dataset is 0.809, which is significantly higher than all comparison models. It is believed that this model has higher recommendation accuracy and can be used in application scenarios that require higher recommendation quality.

Abstract Bergenia purpurascens is an important medicinal, edible and ornamental plant. It generally grows in high-altitude areas with complex climates. There have been no reports about how B. purpurascens survives under cold stress. Here, the B. purpurascens under low temperature were subjected to transcriptomics analysis to explore the candidate genes and pathways that involved in the cold tolerance of B. purpurascens. Compared with the control treatment, we found 9,600 up-regulated differentially expressed genes (DEGs) and 7,055 down-regulated DEGs. A significant number of DEGs were involved in the Ca2+ signaling pathway, mitogen-activated protein kinase (MAPK) cascade, plant hormone signaling pathway, and lipid metabolism. A total of 400 transcription factors were found to respond to cold stress, most of which belonged to the MYB and AP2/ERF families. Five novel genes were found to be potential candidate genes involved in the cold tolerance of B. purpurascens. The study provide insights into further investigation of the molecular mechanism of how B. purpurascens survives under cold stress.

Camouflaged object detection (COD) is a challenging task, aimed at segmenting objects that are similar in color and texture to their background. Sufficient multi-scale feature fusion is crucial for accurately segmenting object regions. However, most methods usually focus on information compensation, overlooking the difference between features, which is important for distinguishing the object from the background. To this end, we propose the cross-level iterative subtraction network (CISNet), which integrates information from cross-layer features and enhances details through iteration mechanisms. CISNet involves a cross-level iterative structure (CIS) for feature complementarity, where texture information is used to enrich high-level features and semantic information is used to enhance low-level features. In particular, we present a multi-scale strip convolution subtraction (MSCSub) module within CIS to extract difference information between cross-level features and fuse multi-scale features, which improves the feature representation and guides accurate segmentation. Furthermore, an enhanced guided attention (EGA) module is presented to refine features by deeply mining local context information and capturing a broader range of relationships between different feature maps in a top-down manner. Extensive experiments conducted on four benchmark datasets demonstrate that our model outperforms the state-of-the-art COD models in all evaluation metrics.

Cloud contamination significantly impairs optical remote sensing images (RSIs), reducing their utility for Earth observation. The traditional cloud removal techniques, often reliant on deep learning, generally aim for holistic image reconstruction, which may inadvertently alter the intrinsic qualities of cloud-free areas, leading to image distortions. To address this issue, we propose a multi-stage frequency attention network (MFCRNet), a progressive paradigm for optical RSI cloud removal. MFCRNet hierarchically deploys frequency cloud removal modules (FCRMs) to refine the cloud edges while preserving the original characteristics of the non-cloud regions in the frequency domain. Specifically, the FCRM begins with a frequency attention block (FAB) that transforms the features into the frequency domain, enhancing the differentiation between cloud-covered and cloud-free regions. Moreover, a non-local attention block (NAB) is employed to augment and disseminate contextual information effectively. Furthermore, we introduce a collaborative loss function that amalgamates semantic, boundary, and frequency-domain information. The experimental results on the RICE1, RICE2, and T-Cloud datasets demonstrate that MFCRNet surpasses the contemporary models, achieving superior performance in terms of mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), validating its efficacy regarding the cloud removal from optical RSIs.

To simulate the impact of the periodic wetting of the groundwater reservoir water level on the mechanical performance of the groundwater reservoir dam, we made a coal pillar-artificial dam assembly with a vertical interface, designed uniaxial compression tests with different number of wetting cycles, and considered two cycles as a gradient to explore the factors that affect assembly strength. We determined the strength model of coal pillar-artificial dam assembly in the groundwater reservoir after cyclic wetting and studied the mass variation induced by cyclic wetting. In addition, we analyzed the trends in the strength and deformation of the groundwater reservoir dam after cyclic wetting, proposed three failure modes, and clarified the internal mechanism for the cyclic wetting of the groundwater reservoir dam. The results demonstrated that with the increase in the number of wetting cycles, the internal water content, hydration reaction, and damage to the specimen were significantly enhanced and became more interrelated. The proposed cyclic wetting strength model of the groundwater reservoir dam can effectively represent the three-stage evolution law of the specimen strength curve. The compaction stage in the stress-strain curve of the cyclic wetting specimen is gradually extended, the straight line segment is shortened, the yield stage is also gradually lengthened, and the yield platform is obvious. The specimen failure patterns can be classified as unilateral columnar splitting, bilateral columnar splitting, and conical failure. The concrete will form ettringite and thaumasite with load-bearing capacity during the wetting process, and subsequently, C–S–H groups to invade the coal pillar.

Abstract Under the carbon neutrality goal, coal enterprises must seek breakthroughs from abandoned mines, develop new resources in the new era, turn problems into countermeasures, and participate in the carbon emissions market, for contributing to the accomplishment of the national strategic goal of carbon neutrality. To this end, we investigated the relevant national policies and regulations to clarify the boundaries disclosed by the carbon information of enterprises, understood the development direction of carbon storage in abandoned mines, and clarified the transformation and development of carbon storage in abandoned mines. We made a few suggestions: (1) China should learn from its past experience and other countries to develop the energy industry with Chinese characteristics and reform the economic system. (2) Coal enterprises must actively respond to the national carbon information disclosure policy, clarify their own responsibilities and carbon emission boundaries. (3) It is necessary to proactively obtain advanced knowledge and plan carbon storage pathways for abandoned mines. (4) Development problems of coal enterprises should be deduced using cases. The ‘dual carbon’ goals should be achieved steadily step-by-step. (5) Three measures, i.e. improving the existing resource structure, coordinating the information of abandoned mines, and promoting the cultivation of scientific and technological talents.

Verticillium wilt (VW) is a common soilborne disease of cotton. It occurs mainly in the seedling and boll-opening stages and severely impairs the yield and quality of the fiber. Rapid and accurate identification and evaluation of VW severity (VWS) forms the basis of field cotton VW control, which has great significance to cotton production. Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses, which require abundant time and professional expertise. Remote and proximal sensing using imagery and spectrometry have great potential for this purpose. In this study, we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values, in situ images, and spectra of 361 cotton canopies. To estimate cotton VWS values at the canopy scale, we developed two deep learning approaches that use in situ images and spectra, respectively. For the imagery-based method, given the high complexity of the in situ environment, we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes (CFS) dataset with over 1000 images for each scene-unit type. We performed pretrained convolutional neural networks (CNNs) training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy. The results showed that the DarkNet-19 model achieved satisfactory performance in CFS classification and VWS values estimation (R2 = 0.91, root-mean-square error (RMSE) = 6.35%). For the spectroscopy-based method, we first designed a one-dimensional regression network (1D CNN) with four convolutional layers. After dimensionality reduction by sensitive-band selection and principal component analysis, we fitted the 1D CNN with varying numbers of principal components (PCs). The 1D CNN model with the top 20 PCs performed best (R2 = 0.93, RMSE = 5.77%). These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

The establishment of ecological security patterns (ESPs) has emerged as a significant approach to address the inherent conflict between ecological preservation and economic advancement. Existing studies commonly only focus on the supply of ecosystem services (ESs) but pay little attention to the demand for ESs and less attention to ecosystem service flow (ESF), which leads to the limited satisfaction of ecological space planning with the needs of human society. This study takes the West Liao River basin as an example to design a comprehensive ecological security pattern construction and optimization method. This study quantified the supply and demand of soil conservation (SC), carbon fixation (CF), water purification (WP), windbreak and sand fixation (WS) and water provision (FW) five key ESs in the study area from 2000 to 2020, and discussed the construction and optimization strategy of the ESPs from the perspective of “supply-flow-demand”. The results indicated that a total of 25 ecological source areas have been identified, with an optimized total area of 3.57×104km2, with 53 ecological corridors, including 15 primary corridors, 38 secondary corridors, 31 ecological checkpoints, and 16 ecological obstacle points. Based on the spatial distribution of ecological sources, an ecological security pattern of “four zones, three belts, and multiple branches” has been constructed. The distribution of ecological sources in the watershed is mainly on the forest and grassland in the northern, central, eastern, and southwestern regions, ecological pinch points is predominantly observed in the central and western regions, and the first-class ecological corridors are predominantly located in the western region. Research has shown that there is a mismatch between the supply and demand of ecosystem services within the study area. The use of the “supply-flow-demand” framework can more accurately identify ecological sources that traditional methods have not been able to identify. This research finding will contribute to enhancing the comprehension of the ecosystems within the West Liao River basin. This study aims to explore and optimize the construction methods of ESPs, which have certain universality and reference value, and can provide new paths and decision support for the construction of future regional ecological security patterns.

Executives' carbon cognition (ECC) is an important driving factor for enterprises to achieve carbon performance (CP) through low-carbon actions, but the existing research has not explored the mechanism between them. Based on the data of 440 Chinese listed companies from 2012 to 2021, we used text mining method to construct the index of ECC, and obtained noteworthy results through regression analysis. The results indicated that ECC has a significant positive impact on corporate low-carbon actions (CLA); CLA has a significant positive impact on CP; CLA plays a mediating role in the relationship between ECC and CP; in addition, firm size negatively moderates the positive impact of ECC on CLA; firm size negatively moderates the mediating effect of CLA on the relationship between ECC and CP. The research conclusions deepen the understanding of the relationship between ECC and CP in the context of carbon neutrality era, and provide theoretical guidance for guiding companies to actively implement low-carbon actions. At the same time, this study introduces the text mining method into the research of ECC, which provides a reference for future research in this field.

The ecosystem services (ESs) are the extremely important basis for the identification and optimization of ecological security patterns (ESPs). Nonetheless, the great majority of existing research on ESPs has concentrated on the ESs supply, ignoring the ESs demand and flow from social systems, which hinders the connection between ecological and social systems. Consequently, using remote sensing products, meteorological observations and statistical yearbooks and other multisource data, this study analysed supply–demand matching characteristics of ESs in the West Liaohe River Basin in China based on assessment of six key ESs from 2010 to 2020, including water yield, food production, livestock breeding, soil loss by water, soil loss by wind, and net primary production, identified the ESPs in combination with the perspective of ESs supply–demand, and discussed the ecological compensation scheme for ESPs construction based on the ESs flow. The research results indicated that the supply and demand of ESs within the study area was mismatched. Therefore, identifying ecological sources solely through the high supply areas of ESs might not effectively meet the needs of human society. By expanding the scope of alternative ecological sources and taking into account the distance between alternative sources and high demand areas, it was helpful to improve the rationality and scientificity of the identification of ESPs. There was a threshold effect in identification of ecological sources and extraction of ecological corridors, but there was no significant impact on overall spatial distribution of ESPs. The results of this research will help to promote the realization of the relationship between ESs and ESPs and better guide the development of regional socio-ecological coupling systems.

Carbon neutralization of land use and land cover (LULC) has become an important way for countries to cope with future climate change. Existing studies focus on the quantification and analysis of historical and current carbon storage and emissions but lack an understanding of future LULC carbon storage and emissions, which limits the practical guiding value of research findings for regional dynamic management and scientific decision-making. In this study, the temporal and spatial distribution patterns of carbon dynamics in different ecosystems, such as forest, cropland and grassland, in the West Liao River Basin (WLRB) since 1990 were quantitatively assessed. Grey multiple objective programming (G-MOP) and patch‑generating land use simulation (PLUS) models were used to predict the LULC distribution, carbon emissions and carbon storage under the sustainable development scenario (SDS) in the WLRB in 2030 and 2060, and a comparative analysis was performed with other development scenarios. The overall area of grassland and forest ecosystems showed an upwards trend from 1990 to 2020, while the area of farmland ecosystems slightly decreased. In the western part of the WLRB, net ecosystem production (NEP) showed high aggregation, and low NEP showed an aggregation effect in the urban area. Under the economic development scenario (EDS) in 2030 and 2060, cropland and impervious layers will be further expanded, carbon emissions will be the highest, and the carbon storage amount will be the lowest. Under the ecological protection scenario (EPS), the water area and forest area increased significantly, carbon emissions were the lowest, and carbon storage was the highest. Under the SDS scenario, the areas of cropland, forest, impervious layer and water body are moderate, and the carbon budget is better than that under the natural development scenario (NDS). Under these three scenarios, the barren area is lower than that of the natural development scenario. Considering the economic benefit, ecological benefit and carbon budget, the SDS scenario has the best performance. This study provides a new perspective for discussing the future carbon budget status of small-scale watershed areas and combines the goals of “sustainable development” and “dual carbon”. It provides a data-supported basis for discussing the diversified ecological compensation mechanism of the WLRB from the perspective of carbon neutrality as well as insights for the formulation of regional LULC optimization management policies.

AbstractBased on the true triaxial dynamic anisotropic permeability model of coal and rock, a multi field coupling mathematical model of structurally anisotropic coal seams under true triaxial stress was established, and a numerical simulation experiment of porous dry gas drainage in anisotropic coal seams was conducted using the COMSOL Multiphysics numerical simulation software. It was found that with an increase in the permeability anisotropy ratio, the greater the ratio of the long and short axes of the elliptical pressure isoline was, the denser was the gas streamline in the dominant seepage direction. As the permeability anisotropy ratio increased, the gas extraction volume and rate decreased. When the permeability was isotropic, the gas drainage volume of the uniformly arranged rectangular boreholes was the largest. However, when the permeability was anisotropic, the gas extraction volume and rate were the largest along the y-direction layout (perpendicular to the dominant seepage direction), followed by the rhombus layout, and the smallest along the x-direction layout. Based on the test results, an optimal arrangement principle of multi holes pumping boreholes along the vertical dominant seepage direction was proposed.

Food provides abundant nutrients for human beings, but also has sensory functions and physiological regulation. Lipids are the main components of food as well as the important structural and functional components of cells. Nevertheless, lipids are easily oxidized by different ways, such as thermal oxidation and air oxidation. Lipid oxidation has adverse effects on food quality and human health. Therefore, efforts should be made to reduce lipid oxidation and improve its stability. This review focuses on important knowledge about lipid oxidation, including the concept of lipids and lipid oxidation, the main pathways and mechanisms of lipid oxidation, factors affecting lipid oxidation, strategies to improve the stability of lipid oxidation, and the recent research progress of lipid oxidation in food science and nutritional health.

DHA-enriched eggs have gained popularity due to their unique nutritional value, but their flavor can be challenging for some consumers to accept. The study analyzed the correlation of lipids and flavors in DHA-enriched egg yolks using comprehensive lipidomics and volatile compound analysis. The results showed that 411 lipids were detected in two egg yolk samples. Among them, 148 lipid species, including 48 DHA-containing lipids, were significantly higher in DHA-enriched egg yolks than in Common ones (P ​

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in F1-scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model’s performance, emphasizing the effectiveness of these components in capturing detailed contextual cues.

Background and objectivesA totally implantable venous access port (TIVAP) is a commonly used intravenous infusion device for patients receiving chemotherapy or long-term infusion therapy. To improve the success rate of one-time insertion of the Huber needle, we developed a novel Y-Z magnetic TIVAP (Y-Z MTIVAP), which we produced using three-dimensional printing technology.Materials and methodsThe Y-Z MTIVAP includes a magnetic port body and a magnetic positioning device. For testing, we established four venous port implantation models using the two types of TIVAPs and two implantation depth ranges (≤5 mm and >5 mm). Twenty nurses performed Huber needle puncture with the four models, and we recorded the number of attempts required for successful needle insertion, the operation time, and the operator's satisfaction.ResultsThe success rate for one-time needle insertion with the Y-Z MTIVAP was significantly higher than that with the traditional TIVAP at either depth range (100% vs. 75% at ≤5 mm, p = 0.047; 95% vs. 35% at >5 mm, p 5 mm, p = 0.025), but the success rate with the Y-Z MTIVAP was not significantly affected (100% vs. 95%, p = 1.000). The operation time with the Y-Z MTIVAP was significantly shorter than that with the traditional TIVAP at either depth range (both p

This paper proposed an innovative method for aesthetic education by integrating shape grammar and neuroaesthetics. Aesthetic education can be divided into bottom-up aesthetic cultivation and top-down knowledge education, which correspond to the characteristics of shape grammar and neuroaesthetics, respectively. In this study, we redefined the state space of traditional shape grammar by replacing the computer-dominated label set with the affective-dominated emotion set of neuroaesthetics. This resulted in a neuroaesthetic shape grammar that is led by the designer and complementary to human intuition and algorithmic logic. We validated this method through practical design cases in the field of aesthetic education.

Grassland ecosystems are an important part of terrestrial ecosystems. It is of great significance to fully tap the potential of grassland carbon sinks to achieve the goal of carbon neutrality in the region and even worldwide. Previous studies have focused on analyzing the temporal and spatial distributions of grassland carbon sink capacity and lacked a systematic discussion of grassland carbon source/sink management. Taking three grasslands in the Inner Mongolia Autonomous Region of China as an example, this study characterized grassland carbon source/sink capacity through net ecosystem productivity (NEP), combined with a soil respiration model, Sen + MK (Mann-Kendall) trend test method and other methods, revealed the spatiotemporal distribution characteristics of NEP and carbon emissions (CEs) in the study area from 2000 to 2019, and used spatiotemporal and geographical weighted regression (GTWR) model to clarify the potential mechanism of grassland sink increase. Suggestions for grassland carbon source/sink management are also discussed. The study found that the CEs in the study area from 2000 to 2019 could be roughly divided into three stages: slow growth period (2000–2009), rapid growth period (2009–2011) and stable development period (2011–2019). Although grassland carbon sinks could not completely offset regional CEs, grassland ecosystems had a very large carbon sink potential, which could make important contributions to the realization of regional carbon neutrality goals. Grassland carbon sources/sinks were jointly affected by climate change and human activities, and the degree of impact varied from year to year, with the impact of human activities changing more significantly. On this basis, the study puts forward policy recommendations from four aspects: regional priority, dynamic monitoring and management, control of grazing intensity and implementation of ecological compensation, and it proposed a sustainable development pattern of “carbon trading-ecological compensation-grassland sink increase”. The research results have important practical guiding significance for the management of grassland sink increases and regional carbon neutral development strategies in Inner Mongolia under the background of climate change.

The coal and rock mass in water-rich mines with underground waterproof coal-and-rock pillars will be inevitably eroded by groundwater. The mechanical properties and interface failure mode of coal–rock mass under high-moisture conditions directly affect the mine stability and operation safety. This study performed uniaxial compression tests of samples from coal, rock, and their combination with different water contents, the evolution law of mechanical properties and acoustic emission (AE) damage characteristics of coal–rock combinations (C-RC) with different water contents were studied. The C-RC interface failure modes at different water contents were clarified. The results showed that the deterioration of average peak strength of coal, rock, and C-RC was obvious with increased water content. The deterioration degree in the descending order was as follows: C-RC > coal mass > rock mass. The maximum AE ringing number first increased rapidly and then sharply dropped, while the AE cumulative ringing number dropped slowly and then rapidly, reflecting the internal crack propagation patterns in C-RC. The failure mechanism of water-bearing C-RC was related to coal–rock strength ratio and the water-bearing effect of coal–rock interface. When the water-bearing state of C-RC changed from dry to saturated, the macro-failure mode showed the law of only coal fracture (0%)—minor fracture at the coal–rock interface (4%)—both coal and rock fracture (8%)—only coal fracture (12%). With increased water content, the water-bearing effect of C-RC interface gradually prevailed in the C-RC failure pattern.

Low-mass-ratio ( q ) contact binary systems are progenitors of stellar mergers such as blue stragglers or fast-rotating FK Com stars. In this study, we present the first light curve analysis of two newly identified low mass-ratio contact binary systems, TIC 55007847 and TIC 63597006, that are identified from TESS. Both stars are classified as A-subtype contact binaries. We obtained the precise orbit periods for the two objects by using the O − C method, i.e., P = 0.6117108 day for TIC 55007847 and P = 0.7008995 day for TIC 63597006, and found an obvious periodic signal in the O − C curve of TIC 63597006. We suggest that the periodic signal comes from a third body. We further use the Markov chain Monte Carlo method with PHOEBE to derive the photometric solutions for the two binaries. The photometric solution for this object shows that the contribution of the third body is about 6%. Our analysis revealed that TIC 55007847 has an extremely low mass ratio of q = 0.08. By calculating the ratio of spin angular momentum to the orbital angular momentum J _s / J _o , we found that TIC 55007847 is very close to the instability threshold with J _s / J _o = 0.31, indicating that it may merge into a single, fast-rotating star in the future. For TIC 63597006, q = 0.14 and J _s / J _o = 0.15. This object is in a relatively stable evolutionary status at present.

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing images (RSIs). However, conventional methods predominantly focus on learning representations within the spatial domain, often resulting in suboptimal discriminative capabilities. Given the intrinsic spectral characteristics of RSIs, it becomes imperative to enhance the discriminative potential of these representations by integrating spectral context alongside spatial information. In this paper, we introduce the spectrum-space collaborative network (SSCNet), which is designed to capture both spectral and spatial dependencies, thereby elevating the quality of semantic segmentation in RSIs. Our innovative approach features a joint spectral–spatial attention module (JSSA) that concurrently employs spectral attention (SpeA) and spatial attention (SpaA). Instead of feature-level aggregation, we propose the fusion of attention maps to gather spectral and spatial contexts from their respective branches. Within SpeA, we calculate the position-wise spectral similarity using the complex spectral Euclidean distance (CSED) of the real and imaginary components of projected feature maps in the frequency domain. To comprehensively calculate both spectral and spatial losses, we introduce edge loss, Dice loss, and cross-entropy loss, subsequently merging them with appropriate weighting. Extensive experiments on the ISPRS Potsdam and LoveDA datasets underscore SSCNet’s superior performance compared with several state-of-the-art methods. Furthermore, an ablation study confirms the efficacy of SpeA.

Abstract Magnetic compression anastomosis, also known as magnamosis, is a safe and feasible method for digestive tract anastomosis. However, the pathological process involved in magnamosis of the digestive tract has not been investigated. This study aimed to establish the stages of digestive tract magnamosis in a rat model. Eighty-four Sprague–Dawley albino rats (200–250 g) were randomly divided into 14 groups (n = 6 per group). All rats underwent colonic magnamosis. Starting from postoperative day (POD) 1, one group of rats was sacrificed every other day to obtain the specimens. Burst pressure at the anastomotic site of each specimen was examined. Gross and histological examination of the anastomotic site was performed to establish the stages of the digestive tract magnamosis. Colonic magnamosis was successfully performed in all rats and the mean anastomosis time was 5.62 ± 0.91 min. The postoperative survival rate was 100%. The lowest anastomotic burst pressure was 78.33 ± 3.44 mmHg on POD3. The anastomotic burst pressure gradually increased and stabilized on POD21. Macroscopic and histological examination showed that the anastomotic mucosal and serosal layer did not heal on POD1. The serosal layer of the anastomosis healed by adhesion on POD3, and the mucosal layer began to heal on POD3-11 and was established by POD21. According to the anastomotic bursting pressure, digestive tract magnamosis can be staged into the magnetic maintenance, fragile, strengthening, and stable phases, which on histology correspond to the serosal adhesion formation, serosal healing, mucosal healing, and stereotyping, respectively.

Abstract Background The contribution of autophagy to cancer therapy resistance remains complex, mainly owing to the discrepancy of autophagy mechanisms in different therapy. However, the potential mechanisms of autophagy-mediated resistance to icotinib have yet to be elucidated. Methods The effect of autophagy in icotinib resistance was examined using a series of in vitro and in vivo assays. The results above were further verified in biopsy specimens of lung cancer patients before and after icotinib or gefitinib treatment. Results Icotinib increased ATG3, ATG5, and ATG7 expression, but without affecting Beclin-1, VPS34 and ATBG14 levels in icotinib-resistant lung cancer cells. Autophagy blockade by 3-MA or silencing Beclin-1 had no effects on resistance to icotinib. CQ effectively restored lung cancer cell sensitivity to icotinib in vitro and in vivo. Notably, aberrantly activated STAT3 and highly expressed FOXM1 were required for autophagy induced by icotinib, without the involvement of AMPK/mTOR pathway in this process. Alterations of STAT3 activity using genetic and/or pharmacological methods effectively affected FOXM1 and ATG7 levels increased by icotinib, with altering autophagy and icotinib-mediated apoptosis in resistant cells. Furthermore, silencing FOXM1 impaired up-regulated ATG7 induced by STAT3-CA and icotinib. STAT3/FOXM1 signalling blockade also reversed resistance to icotinib in vivo. Finally, we found a negative correlation between STAT3/FOXM1/ATG7 signalling activity and epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) treatment efficacy in patients undergoing EGFR-TKIs treatment. Conclusions Our findings support that STAT3/FOXM1/ATG7 signalling-induced autophagy is a novel mechanism of resistance to icotinib, and provide insights into potential clinical values of ATG7-dependent autophagy in icotinib treatment. Graphical abstract

The concept of wen 文 has multiple meanings, but it plays an exceptionally important role in the development of Confucianism and Chinese philosophical terms. Pre-Qin Confucianism 先秦儒学 and Song Confucianism (Song dynasty Neo-Confucians 宋代儒学/宋代新儒家) are two important representatives in the history of Confucianism. Confucius has insisted that although the heaven is not going to destroy wen, wen must exist in everyone’s xing 性, and only when placed within a community can it develop. In Pre-Qin Confucianism, wen completed the transformation from the long established social political structure of the Zhou dynasty and its corresponding moral principles to the consciousness of consummate conduct and ritual propriety based on human instinct and humanistic rationality. Song dynasty Neo-Confucians inherited this Confucian mission and developed the spirit of wen in their period. They emphasized the necessity of learning classics and then writing articles to get closer to the heart-mind of sages and then to build a cultural community together. Both Pre-Qin Confucianism and Song Confucianism have been applying their viewpoints to achieve a community, which is “this culture of ours” 斯文.

Automatically extracting water bodies is a significant task in interpreting remote sensing images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance in processing RSIs, which have been widely used for fine-grained extraction of water bodies. However, it is difficult for the extraction accuracy of CNNs to satisfy the requirements in practice due to the limited receptive field and the gradually reduced spatial size during the encoder stage. In complicated scenarios, in particular, the existing methods perform even worse. To address this problem, a novel boundary-guided semantic context network (BGSNet) is proposed to accurately extract water bodies via leveraging boundary features to guide the integration of semantic context. Firstly, a boundary refinement (BR) module is proposed to preserve sufficient boundary distributions from shallow layer features. In addition, abstract semantic information of deep layers is also captured by a semantic context fusion (SCF) module. Based on the results obtained from the aforementioned modules, a boundary-guided semantic context (BGS) module is devised to aggregate semantic context information along the boundaries, thereby enhancing intra-class consistency of water bodies. Extensive experiments were conducted on the Qinghai–Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. The results demonstrate that the proposed BGSNet outperforms the mainstream approaches in terms of OA, MIoU, F1-score, and kappa. Specifically, BGSNet achieves an OA of 98.97% on the QTPL dataset and 95.70% on the LoveDA dataset. Additionally, an ablation study was conducted to validate the efficacy of the proposed modules.

The detection of long-period binary stars containing a δ Scuti variable is helpful to study the formation and evolution of binaries in the intermediate-mass region. The phase modulation and the frequency modulation caused by the light travel effect make it possible to find such systems that are hard to be detected by other methods. In this paper, we combined Kepler and TESS data to extend the observation span to more than 12 yr. Time-frequency analysis was applied on some δ Scuti variables observed by both Kepler and TESS. After careful phase modulation analysis, we succeeded in detecting a binary system with an orbital period as long as 3924 days. The 525 s light travel time along the line of sight indicates that the hidden companion of KIC 3440495 is a stellar object. To date, it is the longest-period binary confirmed by the phase modulation method. The method described in this article can be applied to more Kepler and TESS pulsating variables in the future.

Removing duplicate proposals is a critical process in pedestrian detection, and is usually performed via Non-Maximum Suppression (NMS); however, in crowded scenes, the detection proposals of occluded pedestrians are hard to distinguish from duplicate proposals, making the detection results inaccurate. In order to address the above-mentioned problem, the authors of this paper propose a Multi-Attribute NMS (MA-NMS) algorithm, which combines density and count attributes in order to adaptively adjust suppression, effectively preserving the proposals of occluded pedestrians while removing duplicate proposals. In order to obtain the density and count attributes, an attribute branch (ATTB), which uses a context extraction module (CEM) to extract the context of pedestrians, and then, concatenates the context with the features of pedestrians in order to predict both the density and count attributes simultaneously, is also proposed. With the proposed ATTB, a pedestrian detector, based on MA-NMS, is constructed for pedestrian detection in crowded scenes. Extensive experiments are conducted using the CrowdHuman and CityPersons datasets, and the results show that the proposed method outperforms mainstream methods on AP (average precision), Recall, and MR−2 (log-average miss rate), sufficiently validating the effectiveness of the proposed MA-NMS algorithm.

Water body extraction is a typical task in the semantic segmentation of remote sensing images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional methods in mining visual features; however, due to the inherent convolutional mechanism of the network, spatial details and abstract semantic representations at different levels are difficult to capture accurately at the same time, and then the extraction results decline to become suboptimal, especially on narrow areas and boundaries. To address the above-mentioned problem, a multiscale successive attention fusion network, named MSAFNet, is proposed to efficiently aggregate the multiscale features from two aspects. A successive attention fusion module (SAFM) is first devised to extract multiscale and fine-grained features of water bodies, while a joint attention module (JAM) is proposed to further mine salient semantic information by jointly modeling contextual dependencies. Furthermore, the multi-level features extracted by the above-mentioned modules are aggregated by a feature fusion module (FFM) so that the edges of water bodies are well mapped, directly improving the segmentation of various water bodies. Extensive experiments were conducted on the Qinghai-Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. Numerically, MSAFNet reached the highest accuracy on both QTPL and LoveDA datasets, including Kappa, MIoU, FWIoU, F1, and OA, outperforming several mainstream methods. Regarding the QTPL dataset, MSAFNet peaked at 99.14% and 98.97% in terms of F1 and OA. Although the LoveDA dataset is more challenging, MSAFNet retained the best performance, with F1 and OA being 97.69% and 95.87%. Additionally, visual inspections exhibited consistency with numerical evaluations.

Abstract Hypertensive disorder in pregnancy is a disease that occurs during pregnancy. We aimed to analyze the morbidity and maternal and infant outcomes with respect to the hypertensive disorder in pregnancy in China in 2018. Clinical data of 38 590 cases from 161 hospitals were retrospectively collected. The differences in morbidity and maternal and infant mortality among the major regions and provinces were compared. The overall national average morbidity was 4.74%, and the ratios of gestational hypertension, preeclampsia, eclampsia, chronic hypertension, and chronic hypertension with superimposed preeclampsia were 29.17%, 55.02%, 0.66%, 6.53%, and 8.62%, respectively. The overall maternal mortality was 0.61/100 000, and the case fatality was 0.13%. Morbidity associated with hypertensive disorder in pregnancy was 7.74% in North China, 6.62% in Northwest China, 6.40% in Central China, 5.83% in Northeast China, 4.28% in East China, 3.85% in South China, and 2.88% in Southwest China. The morbidity in each province was 1.62‐11.28%. The overall perinatal mortality was 3.59% (81.09% for stillbirths; 18.91% for neonatal deaths). Perinatal mortality decreased with increasing gestational weeks from 24 to 37 + 6 weeks. Perinatal mortality for delivery at 32 weeks of gestation in all regions of the country was

Uniaxial compression tests were carried on 12 concrete specimens with six different diameter holes using a rigid test machine, and the stress–strain relationship was analyzed in different hole diameter specimens. The effects of different hole diameters on specimen compression strength, elastic modulus, and Poisson’s ratio were studied, and the failure form and instability mode of concrete specimens with holes of different diameters were evaluated. The results show that the larger the compression strength of the specimen, the larger the axial and horizontal strains. As the hole diameter increased from 0 to 50 mm, the compression strength and elastic modulus reduced. The decreasing trend slowed down, and the relationships between the hole diameter and compression strength, and elastic modulus could be represented by negative liner functions. The Poisson ratio of the specimen increased in waves with the increase in hole diameter from 0 to 50 mm. A crack in the concrete specimen with 0–20 mm diameter hole started at the upper and lower diagonal angles of the hole wall; a crack in the concrete specimen with 30–50 mm diameter hole started at the left and right parts of the hole wall. The instability mode of concrete specimens with horizontal holes of different diameters was divided into shear dislocation instability and planar splitting instability.

Semantic segmentation of remote-sensing imagery strives to assign a pixel-wise semantic label. Since encoder-decoder networks have demonstrated tremendous success in natural image semantic segmentation, the adoption and extension of this kind of method are transferring such superior performance for the problems in remote-sensing. Facing the high-altitude angle of imaging and complex and diverse ground objects of remote-sensing data, it is necessary to strengthen the features’ distinguishability by enhancing the network’s capability. Nevertheless, the existing methods suffer from the structural stereotype, leveraging the short-range and long-range contextual information insufficiently. Attempting to address the problems mentioned above, a hierarchical self-attention embedded neural network with dense connection for remote sensing image semantic segmentation (HSDCN) is proposed. In the encoder stage, multiple self-attention modules (SAM) are embedded to model pixel-wise and channel-wise relationships at various scales hierarchically, making the representations more refined and discriminative. Then the dense connections are used to fuse the heterogeneous features. Thus, the network could produce logical and reasonable clues for labeling pixels. The extensive experiments are conducted on ISPRS Vaihingen and Potsdam benchmarks. And the results reveal significant improvements in comparison with other state-of-the-art methods.

Automatic detection of workers wearing safety helmets at the construction site is essential for safe production. Aiming at the problem of low recognition rate caused by factors such as background and light in the automatic detection of safety helmets using traditional machine learning methods, this paper proposes an object detection framework that combines Online Hard Example Mining (OHEM) and multi-part combination. In our framework, we first use the multi-scale training and the increasing anchors strategies to enhance the robustness of the original Faster RCNN algorithm to detect different scales and small object. Then, the OHEM is to optimize the model to prevent the imbalance of positive and negative samples. Finally, the person wearing the helmet and its parts (helmet and person) are detected by improved Faster RCNN. The multi-part combination method uses the geometric information of the detection objects to determine if a worker is wearing a helmet. Experiments show that compared with the original Faster RCNN, the detection accuracy is increased by 7%. It also has better detection performance for partial occlusion and different-size objects, showing good generalization and robustness.

Pan-sharpening is a significant task that aims to generate high spectral- and spatial- resolution remote-sensing image by fusing multi-spectral (MS) and panchromatic (PAN) image. The conventional approaches are insufficient to protect the fidelity both in spectral and spatial domains. Inspired by the robust capability and outstanding performance of convolutional neural networks (CNN) in natural image super-resolution tasks, CNN-based pan-sharpening methods are worthy of further exploration. In this paper, a novel pan-sharpening method is proposed by introducing a multi-scale channel attention residual network (MSCARN), which can represent features accurately and reconstruct a pan-sharpened image comprehensively. In MSCARN, the multi-scale feature extraction blocks comprehensively extract the coarse structures and high-frequency details. Moreover, the multi-residual architecture guarantees the consistency of feature learning procedure and accelerates convergence. Specifically, we introduce a channel attention mechanism to recalibrate the channel-wise features by considering interdependencies among channels adaptively. The extensive experiments are implemented on two real-datasets from GaoFen series satellites. And the results show that the proposed method performs better than the existing methods both in full-reference and no-reference metrics, meanwhile, the visual inspection displays in accordance with the quantitative metrics. Besides, in comparison with pan-sharpening by convolutional neural networks (PNN), the proposed method achieves faster convergence rate and lower loss.

The strong disturbance of upward mining of the short-distance multiseam results in frequent fractures in the coal seam to be mined, jeopardizing the operation safety due to the instability of the surrounding rock of the overlying stope and risks of coal and gas outburst. A lucrative alternative is the downward mining of multiseam, which wide implementation is limited by the lack of reliable data on stress evolution characteristics. In this paper, the evolution, nonlinear interaction, and superposition characteristics of mining-induced stress after multiple pressure relief in the short-distance multi-seam were investigated by the numerical simulation and physical simulation, taking the endowment and mining technology characteristics of group B short-distance multiseam in Pan’er Coal Mine in Anhui Province of China as an example. The numerical and physical models of multiple (2—5 times) mining activities in pressure relief multiseam (with five main coal layers) under downward mining are constructed and implemented with the FLAC3D software and 1 : 100 scaling, respectively. The results showed that a pressure relief arch, which bears and transfers stresses, is formed on the coal seam’s roof and floor. In downward mining, the local arch expands to the long arch, which bearing capacity within the influence range of multiple mining disturbances is weak, and the arch structure is prone to instability. If a coal pillar is left in the upper coal seam after its mining, the lower coal seam mining will switch from the large-scale low-stress area under the goaf to the small-scale high-stress area of nonlinear interaction of the multiple mining stresses with the stress under the coal pillar.

The key to the construction of underground reservoirs in abandoned mines is the construction of coal pillar-artificial dams, and the choice of bonding parameters between the coal pillars and artificial dams is the deciding factor that determines the engineering stability. Based on the analysis of the force state of coal pillar-artificial dams, the influence of the interface angle was analyzed. Seven sets of coal pillar-artificial dam specimens were prepared and a PFC3D numerical model was constructed to carry out the uniaxial compression test without lateral pressure. Based on the strength, deformation, and energy evolution characteristics of the coal pillar-artificial dam, the influence of the angle of the coal pillar-artificial dam interface on the performance of the specimen was analyzed. The research results showed that the force state of the coal pillar-artificial dam can be divided into three types. The composite simulation curve showed obvious postpeak viscosity. The compressive strength, peak strain, and average dissipated energy curves of the coal pillar-artificial dam showed a unimodal trend that first increased and then decreased. The total energy and elastic energy of the coal pillar-artificial dam showed an increasing trend during loading. The simulated initiation stress and damage stress of the coal pillar-artificial dam specimens were intermediate to that of the coal pillars and the artificial dams, which first increased and then decreased with the increase in inclination, reaching the peak at 70°.

Due to the proposal of China’s carbon neutrality target, the traditional fossil energy industry continues to decline, and the proportion of new energy continues to increase. New energy power systems have high requirements for peak shaving and energy storage, but China’s current energy storage facilities are seriously insufficient in number and scale. The unique features of abandoned mines offer considerable potential for the construction of large-scale pumped storage power stations. Several countries have reported the conversion of abandoned mines to pumped storage plants, and a pilot project for the conversion of an underground reservoir group has been formalized in China. A feasibility study that considered the natural conditions, mine conditions, safety conditions, and economic benefits revealed that the construction of pumped storage power stations using abandoned mines could ameliorate several economic, ecological, and social problems, including resource utilization, ecological restoration, and population resettlement. The construction of pumped storage power stations using abandoned mines not only utilizes underground space with no mining value (reduced cost and construction period), but also improves the peak-load regulation and energy storage urgently needed for the development of power grid systems. Combined with the underground space and surface water resources of the Shitai Mine in Anhui, China, a plan for the construction of a pumped storage power station was proposed. The challenges faced by the current project were evaluated, further research suggested, and demonstration projects established in order to help achieve carbon peaking and carbon neutrality goals.

Grassland ecosystem services (GES) are closely related to human well-being, and an accurate understanding of the driving mechanisms and relative contributions of climate change and human activities on GES is beneficial for grassland sustainable management. Current studies have focused on analyzing the impacts of climate factors on GES, but few studies have included grazing pressure to comprehensively assess the complex impacts of climate change and human activities. This study systematically analyzed the spatiotemporal patterns of climate factors, grazing pressure, and ecosystem services in Xilingol typical steppe from 2000–2019, and thoroughly explored the driving mechanisms and relative contributions of climate change and grazing pressure on GES over the past 20 years. The study points out that although the overall ecosystem services in the study area have improved over the past 20 years, driven by climate change and favorable human activities, grazing pressure management should be further strengthened to improve the resilience of ecosystem services to unfavorable climate conditions due to the high uncertainty of future climate conditions. The research results can help to improve the understanding of the complex relationship between climate change, land use and GES, and provide recommendations for future grassland use planning.

Grassland degradation seriously threatens the ability of grassland to provide ecosystem services. Grassland ecological restoration and sustainable management decision making depends on an accurate understanding of the impacts of grassland degradation on ecosystem services. Based on the assessment of grassland degradation and four key ecosystem services, including the net primary production (NPP), ecosystem carbon pool (EC), soil conservation (SC), and soil loss by wind (SL), the impacts of grassland degradation on ecosystem services and their relationships were analyzed. The impacts of climate change and grazing pressure on the relationship between grassland degradation and ecosystem services were revealed. Based on the “climate change and grazing pressure-grassland degradation-ecosystem services” network, the study puts forward specific suggestions on grassland ecological restoration and sustainable management under the premise of fully balancing ecological restoration and stakeholder relationships. The results showed that grassland degradation had a significant impact on ecosystem services and their relationships, but it varied with the types of ecosystem services. Although the degraded grassland in the study area has been in a state of recovery and ecosystem services have been improving in the past 20 years, the degradation of grassland in some areas has intensified, and there are still ecological risks, so it is necessary to continue to carry out ecological restoration work. On this basis, taking the local conditions into consideration, grassland ecological restoration and sustainable management policy suggestions were proposed. The study can provide a scientific reference for ecological protection and sustainable development in arid and semi-arid areas, and help to improve human well-being.

Extracting water bodies is an important task in remote sensing imagery (RSI) interpretation. Deep convolution neural networks (DCNNs) show great potential in feature learning; they are widely used in the water body interpretation of RSI. However, the accuracy of DCNNs is still unsatisfactory due to differences in the many hetero-features of water bodies, such as spectrum, geometry, and spatial size. To address the problem mentioned above, this paper proposes a multiscale normalization attention network (MSNANet) which can accurately extract water bodies in complicated scenarios. First of all, a multiscale normalization attention (MSNA) module was designed to merge multiscale water body features and highlight feature representation. Then, an optimized atrous spatial pyramid pooling (OASPP) module was developed to refine the representation by leveraging context information, which improves segmentation performance. Furthermore, a head module (FEH) for feature enhancing was devised to realize high-level feature enhancement and reduce training time. The extensive experiments were carried out on two benchmarks: the Surface Water dataset and the Qinghai–Tibet Plateau Lake dataset. The results indicate that the proposed model outperforms current mainstream models on OA (overall accuracy), f1-score, kappa, and MIoU (mean intersection over union). Moreover, the effectiveness of the proposed modules was proven to be favorable through ablation study.