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The National Forestland "One Map" applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in "One Map", taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for "One Map" (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in "One Map" and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data. [ABSTRACT FROM AUTHOR]

The color–area ratio in ethnic minority areas is one way to perceive cultural elements visually. The openness of spaces, sense of rhythm, and richness of color affect people's emotions and induce different psychological perceptions. Despite many ethnic minority areas being more colorful than the main traits of Han, there is no systematic quantitative study for the color elements in ethnic minority areas' landscapes, not to mention the research on the color–area ratio, main and auxiliary colors and embellishments, and layouts. Therefore, this paper studies the color–area ratio of Xiangcheng County in the Tibetan area of Ganzi Prefecture in Sichuan Province. Colors are extracted and quantitatively analyzed from six different aspects using the semantic differential (SD) method and color quantitative analysis method. In this way, low-scored (B group) and high-scored (A group) color landscape samples were extracted from the landscape image library and quantitatively analyzed by ColorImpact V4.1.2. The results show that the ethnic minority group's color layout is characterized by richer colors and stronger contrasts than the Han group. This paper contributes to academic scholarship regarding color culture in ethnic minority areas. It also provides theoretical support for preserving ethnic minority groups' cultural heritage and practical insights into color planning for urban and landscape designs. [ABSTRACT FROM AUTHOR]

China's argillaceous limestone reservoir has a lot of oil and gas resources, and hydraulic fracturing of the argillaceous limestone reservoir faces many difficulties. The first problem is that the heterogeneity of the argillaceous limestone reservoir is strong, and it is difficult to optimize fracturing parameters. The second problem is that there are a lot of natural fractures in the argillaceous limestone reservoir, which leads to a lot of fracturing fluid loss. The third problem is that the closure pressure of the argillaceous limestone reservoir is high, and the conductivity of fractures decreases rapidly under high closure pressure. The last problem is that the fracture shape of the argillaceous limestone reservoir is complex, and the law of proppant migration is unclear. The main research methods in this paper include reservoir numerical simulation, fluid-loss-reducer performance evaluation, flow conductivity tests and proppant migration visualization. To solve the above problems, this paper establishes the fracturing productivity prediction model of complex lithology reservoirs and defines the optimal hydraulic fracturing parameters of the argillous limestone reservoir in the Sichuan Basin. The 70/140 mesh ceramide was selected as the fluid loss additive after an evaluation of the sealing properties of different mesh ceramides. At the same time, the hydraulic fracture conductivity test is carried out in this paper, and it is confirmed that the fracture conductivity of 70/140 mesh and 40/70 mesh composite particle-size ceramics mixed according to the mass ratio of 5:5 is the highest. When the closure pressure is 40 MPa, the conductivity of a mixture of 70/140 mesh ceramic and 40/70 mesh ceramic is 35.6% higher than that of a mixture of 70/140 mesh ceramic and 30/50 mesh ceramic. The proppant migration visualization device is used to evaluate the morphology of the sand dike formed by the ceramsite, and it is clear that the shape of the sand dike is the best when the mass ratio of 70/140 mesh ceramsite and 40/70 mesh ceramsite is 6:4. The research results achieved a good stimulation effect in the SC1 well. The daily oil production of the SC1 well is 20 t, and the monitoring results of the wide-area electromagnetic method show that the fracturing fracture length of the SC1 well is up to 129 m. [ABSTRACT FROM AUTHOR]

The scheduling of rescue aircraft needs to be studied in depth because of its criticality for the general aviation rescue of forest fires. This paper constructs a collaborative schedule optimization model for general aviation rescue under the condition of multiple aircraft, from multiple rally points to multiple fire points, targeting the shortest rescue time and the lowest rescue cost in the context of forest fires based on the simulation verification of a forest fire that broke out simultaneously in multiple locations in Liangshan Prefecture, Sichuan Province, China. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm was used to find the optimal set of solutions satisfying the objective function: four feasible solutions. Then, the optimal solution was solved based on the weighted TOPSIS method, which was the optimal solution for this rescue task. The simulation results show that unnecessary flight times can be reduced by optimizing the schedule plan. Under the premise of ensuring rescue timeliness, the utilization rate of rescue aircraft was improved, and rescue costs were further reduced. The presented work provides a theoretical reference for the efficient development of general aviation rescue. [ABSTRACT FROM AUTHOR]

The plum is a kind of delicious and common fruit with high edible value and nutritional value. The accurate and effective detection of plum fruit is the key to fruit number counting and pest and disease early warning. However, the actual plum orchard environment is complex, and the detection of plum fruits has many problems, such as leaf shading and fruit overlapping. The traditional method of manually estimating the number of fruits and the presence of pests and diseases used in the plum growing industry has disadvantages, such as low efficiency, a high cost, and low accuracy. To detect plum fruits quickly and accurately in a complex orchard environment, this paper proposes an efficient plum fruit detection model based on an improved You Only Look Once version 7(YOLOv7). First, different devices were used to capture high-resolution images of plum fruits growing under natural conditions in a plum orchard in Gulin County, Sichuan Province, and a dataset for plum fruit detection was formed after the manual screening, data enhancement, and annotation. Based on the dataset, this paper chose YOLOv7 as the base model, introduced the Convolutional Block Attention Module (CBAM) attention mechanism in YOLOv7, used Cross Stage Partial Spatial Pyramid Pooling–Fast (CSPSPPF) instead of Cross Stage Partial Spatial Pyramid Pooling(CSPSPP) in the network, and used bilinear interpolation to replace the nearest neighbor interpolation in the original network upsampling module to form the improved target detection algorithm YOLOv7-plum. The tested YOLOv7-plum model achieved an average precision (AP) value of 94.91%, which was a 2.03% improvement compared to the YOLOv7 model. In order to verify the effectiveness of the YOLOv7-plum algorithm, this paper evaluated the performance of the algorithm through ablation experiments, statistical analysis, etc. The experimental results showed that the method proposed in this study could better achieve plum fruit detection in complex backgrounds, which helped to promote the development of intelligent cultivation in the plum industry. [ABSTRACT FROM AUTHOR]

Spatial population distribution data is the discretization of demographic data into spatial grids, which has vital reference significance for disaster emergency response, disaster assessment, emergency rescue resource allocation, and post-disaster reconstruction. The random forest (RF) model, as a prominent method for modeling the spatial distribution of population, has been studied by many scholars, both domestically and abroad. Specifically, research has focused on aspects such as multi-source data fusion, feature selection, and data accuracy evaluation within the modeling process. However, discussions about parameter optimization methods during the modeling process and the impact of different optimization methods on modeling accuracy are relatively limited. In light of the above circumstances, this paper employs the RF model to conduct research on population spatialization with multi-source spatial information data. The study primarily explores the differences in model parameter optimization achieved through random search algorithms, grid search algorithms, genetic algorithms, simulated annealing algorithms, Bayesian optimization based on Gaussian process algorithms, and Bayesian optimization based on gradient boosting regression tree algorithms. Additionally, the study investigates the influence of different optimization algorithms on the accuracy of population spatialization modeling. Subsequently, the model with the highest accuracy is selected as the prediction model for population spatialization. Based on this model, a spatial population distribution dataset of Sichuan Province at a 1 km resolution is generated. Finally, the population dataset created in this paper is compared and validated with open datasets such as GPW, LandScan, and WorldPop. Experimental results indicate that the spatial population distribution dataset produced by the Bayesian optimization-based random forest model proposed in this paper exhibits a higher fitting accuracy with real data. The Coefficient of Determination (R2) is 0.6628, the Mean Absolute Error (MAE) is 12,459, and the Root Mean Squared Error (RMSE) is 25,037. Compared to publicly available international datasets, the dataset generated in this paper more accurately represents the spatial distribution of the population. [ABSTRACT FROM AUTHOR]

Although atmospheric CO2 concentrations collected by satellites play a crucial role in understanding global greenhouse gases, the sparse geographic distribution greatly affects their widespread application. In this paper, a hybrid CNN and spatiotemporal Kriging (CNN-STK) model is proposed to generate a monthly spatiotemporal continuous XCO2 dataset over China at 0.25° grid-scale from 2015 to 2020, utilizing OCO-2 XCO2 and geographic covariates. The validations against observation samples, CAMS XCO2 and TCCON measurements indicate the CNN-STK model is effective, robust, and reliable with high accuracy (validation set metrics: R2 = 0.936, RMSE = 1.3 ppm, MAE = 0.946 ppm; compared with TCCON: R2 = 0.954, RMSE = 0.898 ppm and MAE = 0.741 ppm). The accuracy of CNN-STK XCO2 exhibits spatial inhomogeneity, with higher accuracy in northern China during spring, autumn, and winter and lower accuracy in northeast China during summer. XCO2 in low-value-clustering areas is notably influenced by biological activities. Moreover, relatively high uncertainties are observed in the Qinghai-Tibet Plateau and Sichuan Basin. This study innovatively integrates deep learning with the geostatistical method, providing a stable and cost-effective approach for other countries and regions to obtain regional scales of atmospheric CO2 concentrations, thereby supporting policy formulation and actions to address climate change. [ABSTRACT FROM AUTHOR]

With the increasing frequency of emergencies in recent years, the emergency response capacity of the emergency management system needs to be improved. Based on safety stock strategy, this paper proposes a multilevel siting model on the topic of mobile emergency response. We modeled the emergency response needs during emergencies by incorporating the population distribution of each region. The uncertainty of emergencies is modeled by aggregating the frequency of crises in each region over the past 20 years. The site selection model minimizes contingency logistics costs that include transshipment, deployment, inventory, and safety stock costs. In this paper, the IA (Immune Algorithm) is optimized to solve the constructed emergency site selection model. The experiments on the model were carried out with data from the area of Chongqing, Sichuan Province. The number of logistics centers and distribution storage warehouses was tested. The influence of safety stock strategy on the total cost of emergency logistics was analyzed. The research results found that the cost of safety stock is negatively related to the cost of transshipment. In addition, the total cost of emergency logistics has a lower bound. Adding distribution and storage warehouses does not further reduce the total emergency logistics cost. [ABSTRACT FROM AUTHOR]

Aiming at the problem of ultra-low-frequency oscillation (ULFO) in power systems, with an oscillation frequency lower than 0.1 Hz, an oscillation suppression control method is proposed. Firstly, the mathematical modeling of the system is carried out, and a cascade correction control method of the governor of hydropower generators is proposed. Secondly, using the ability of photovoltaic (PV) active power to adjust the system frequency, an additional controller for PV active power is proposed. Then, a unified frequency model considering the two controllers is established for multi-hydro and multi-PV systems, and the structural singular value method is used to coordinate the design of the parameters of the hydropower and PV generator controllers to achieve robust performance. Finally, based on a time-domain simulation of single-hydro and single-PV systems and an actual demonstration system located in Sichuan Province, China, the results show that the control method proposed in this paper has a good suppression effect on ultra-low-frequency oscillation under different working conditions, and meets the requirements of robust performance. [ABSTRACT FROM AUTHOR]

Landslides constitute a significant hazard to human life, safety and natural resources. Traditional landslide investigation methods demand considerable human effort and expertise. To address this issue, this study introduces an innovative landslide segmentation framework, EMR-HRNet, aimed at enhancing accuracy. Initially, a novel data augmentation technique, CenterRep, is proposed, not only augmenting the training dataset but also enabling the model to more effectively capture the intricate features of landslides. Furthermore, this paper integrates a RefConv and Multi-Dconv Head Transposed Attention (RMA) feature pyramid structure into the HRNet model, augmenting the model's capacity for semantic recognition and expression at various levels. Last, the incorporation of the Dilated Efficient Multi-Scale Attention (DEMA) block substantially widens the model's receptive field, bolstering its capability to discern local features. Rigorous evaluations on the Bijie dataset and the Sichuan and surrounding area dataset demonstrate that EMR-HRNet outperforms other advanced semantic segmentation models, achieving mIoU scores of 81.70% and 71.68%, respectively. Additionally, ablation studies conducted across the comprehensive dataset further corroborate the enhancements' efficacy. The results indicate that EMR-HRNet excels in processing satellite and UAV remote sensing imagery, showcasing its significant potential in multi-source optical remote sensing for landslide segmentation. [ABSTRACT FROM AUTHOR]

Fracture network fracturing is pivotal for achieving the economical and efficient development of shale gas, with the connectivity among fracture networks playing a crucial role in reservoir stimulation effectiveness. However, flow back data that reflect fracture network connectivity information are often ignored, resulting in an inaccurate prediction of the effective fracture network volume (EFNV). The accurate calculation of the EFNV has become a key and difficult issue in the field of shale fracturing. For this reason, the accurate shale gas effective fracture network volume inversion method needs to be improved. Based on the flow back characteristics of fracturing fluids, a tree-shaped fractal fracture flow back mathematical model for inversion of EFNV was established and combined with fractal theory. A genetic algorithm workflow suitable for EFNV inversion of shale gas was constructed based on the flow back data after fracturing, and the fracture wells in southern Sichuan were used as an example to carry out the EFNV inversion. The reliability of the inversion model was verified by testing production, cumulative gas production, and microseismic results. The field application showed that the inversion method proposed in this paper can obtain tree-shaped fractal fracture network structure parameters, fracture system original pressure, matrix gas breakthrough pressure, fracture compressibility coefficient, reverse imbibition index, equivalent main fracture half length, and effective initial fracture volume (EIFV). The calculated results of the model belong to the same order of magnitude as those of the HD model and Alkouh model, and the model has stronger applicability. This research has important theoretical guiding significance and field application value for improving the accuracy of the EFNV calculation. [ABSTRACT FROM AUTHOR]

Old landslides in the Loess Plateau, Northwest China usually occurred over a relatively long period, and their sizes are usually smaller compared to old landslides in the alpine valley areas of Sichuan, Yunnan, and Southeast Tibet. These landslide areas may have been changed either partially or greatly, and they are usually covered with vegetation and similar to their surrounding environment. Therefore, it is a great challenge to detect them using high-resolution remote sensing images with only orthophoto view. This paper proposes the optimal-view and multi-view strategic hybrid deep learning (OMV-HDL) method for old loess landslide detection. First, the optimal-view dataset in the Yan'an area (YA-OP) was established to solve the problem of insufficient optical features in orthophoto images. Second, in order to make the process of interpretation more labor-saving, the optimal-view and multi-view (OMV) strategy was proposed. Third, hybrid deep learning with weighted boxes fusion (HDL-WBF) was proposed to detect old loess landslides effectively. The experimental results with the constructed optimal-view dataset and multi-view data show that the proposed method has excellent performance among the compared methods—the F1 score and AP (mean) of the proposed method were improved by about 30% compared with the single detection model using traditional orthophoto-view data—and that it has good detection performance on multi-view data with the recall of 81.4%. [ABSTRACT FROM AUTHOR]

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as 'exposures', which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, 'exposure risk' denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method's efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O   (m × n × p) to O   (m × n) . [ABSTRACT FROM AUTHOR]

Rural operation system reform is vital in promoting rural revitalisation and agricultural and rural modernisation in China. Based on the sample data of 153 counties in Sichuan Province from 2012 to 2020, this paper constructs a PSM-DID evaluation model to analyse the effectiveness of rural operation system reform on agricultural development from the perspective of policy action mechanism, variable differentiation and other aspects. The results show that: (1) rural operation system reform has significantly boosted the economic development of the counties implementing the pilot reform of the rural operation system; (2) rural operation system reform mainly has two components: rural land system reform and agriculture organisation innovation; both have an impact on county economic and social development, but the degrees of impact are different; (3) the effectiveness of the rural operation system reform policy has a specific time lag. The results of this paper enrich the effectiveness evaluation of the reform policy on economic development and have policy implications for further promotion of agricultural and rural reform in China. [ABSTRACT FROM AUTHOR]

Under the background of tighter resource and environmental constraints, whether and how land transfer can promote the green development of agriculture has become a realistic question that needs to be answered urgently. This paper analyzes the internal mechanism between land transfer and agricultural green production by using property rights theory and sustainable development theory. Taking the data of the "investigation on household energy consumption and green agricultural development" in Sichuan Province in 2022 as a sample, it empirically analyzes the impact of land transfer on agricultural green production by using OLS and 2SLS models. The results show that: (1) land inflow significantly improves the level of agricultural green production, with a unit impact of 22.3%; (2) whereas land outflow will inhibit agricultural green production, with a unit impact of 5.46%; (3) the family's long-term agricultural labor, social capital, migrant experience, non-agricultural income, and household clean energy use have a promoting effect on agricultural green production; (4) age, education level, health level and agricultural subsidies inhibit agricultural green production; (5) the heterogeneity analysis found that the inflow of land would significantly promote the level of green agricultural production of farmers who have environmental awareness, have been village cadres, have purchased agricultural insurance, and have not suffered from agricultural disasters; (6) agricultural training, farmers' digital literacy, and agricultural related loans have a positive and strengthened regulatory role in the impact of land transfer on agricultural green production. Based on this, this paper gets policy enlightenment from the government, market, and farmers. [ABSTRACT FROM AUTHOR]

Climate change has led to more frequent extreme weather events, and various natural disasters have posed risks to the operation of transmission lines. Line failures caused by natural disasters are unpredictable and bring additional maintenance work. Therefore, this paper proposes a transmission line risk reliability assessment method that considers the combined effects of multiple natural disasters. This method establishes a theory of disaster risk quantification that considers the probability of the occurrence of the risk, the degree of the impact of the risk on the line, and the severity of the risk disaster. The risk weights for different natural disasters are calculated by combining a hierarchical analysis and entropy weighting methods. The example of a transmission line risk assessment under the combined effects of multiple natural disasters for a Sichuan region verifies the proposed method's effectiveness. The results show that the method effectively assesses the operational risk to transmission lines under the combined effects of natural disasters. The assessment results can be used for disaster recovery and line risk prevention. [ABSTRACT FROM AUTHOR]

Lacustrine shale has garnered significant attention due to its significant resource potential. The Jurassic shale in the Sichuan Basin is an important target for lacustrine shale exploration in China. However, previous studies have predominantly focused on the Da'anzhai member of the Ziliujing Formation, and little attention has been paid to the shale of other strata. This paper aims to address this gap by investigating the Jurassic Dongyuemiao member in the Sichuan Basin. The study systematically characterizes the geological properties of the Dongyuemiao shale system, reconstructs the paleosedimentary environment, identifies the key factors influencing organic matter (OM) enrichment and physical properties, and assesses its resource potential through comparative analysis. Our results show that the Dongyuemiao shale was deposited in an oxic and semi-humid sedimentary environment characterized by intense weathering conditions. The enrichment of OM in the shale is primarily controlled by redox conditions and salinity, with redox conditions playing the most crucial role in OM accumulation. In terms of pore system characterization, clay mineral-associated pores dominate the pore types in the Dongyuemiao shale, while two types of organic matter-associated pores are also widely observed. The development of framework grain-associated pores is limited and only present in certain siliceous and carbonate minerals. The shales of the Dongyuemiao member and the Da'anzhai member exhibit slight differences in TOC content. However, the kerogen in the Dongyuemiao member displays higher hydrocarbon generation potential, and the Dongyuemiao shale exhibits more favorable pore structure parameters. Overall, the Dongyuemiao shale does not exhibit any significant disadvantages compared to the Da'anzhai shale. Therefore, it deserves greater attention in future exploration endeavors. The research findings presented in this paper provide a solid theoretical foundation for expanding the exploration scope of lacustrine shale in the Sichuan Basin. [ABSTRACT FROM AUTHOR]

Numerous scholars have used traditional thermal anomaly extraction methods and time series prediction models to study seismic anomalies based on longwave infrared radiation data. This paper selected bidirectional long short-term memory (BILSTM) as the research algorithm after analyzing and comparing the prediction performance of five time series prediction models. Based on the outgoing longwave radiation (OLR) data, the time series prediction model was used to predict the infrared longwave radiation values in the spatial area of 5° × 5° at the epicenter for 30 days before the earthquake. The confidence interval was used as the evaluation criterion to extract anomalies. The examples of earthquakes selected for study were the Yangbi Ms6.4-magnitude earthquake in Yunnan on 21 May 2021 and the Luding Ms6.8-magnitude earthquake in Sichuan on 5 September 2022. The results showed that the observed values of the Yangbi earthquake 15 to 16 days before the earthquake (5 May to 6 May) exceeded the prediction confidence interval over a wide area and to a large extent. This indicates a strong and concentrated OLR anomaly before the Yangbi earthquake. The observations at 27 days (9 August), 18 days (18 August), and 8 days (28 August) before the Luding earthquake exceeded the prediction confidence interval in a local area and by a large extent, indicating a strong and scattered OLR anomaly before the Luding earthquake. Overall, the method used in this paper extracts anomalies in both spatial and temporal dimensions and is an effective method for extracting infrared longwave radiation anomalies. [ABSTRACT FROM AUTHOR]

Forest fires have frequently occurred and caused great harm to people's lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogeneous data. There is a lack of a method that can effectively extract features required by machine learning-based forest fire predictions from multi-source spatio-temporal data. This paper proposes a forest fire prediction method that integrates spatio-temporal knowledge graphs and machine learning models. This method can fuse multi-source heterogeneous spatio-temporal forest fire data by constructing a forest fire semantic ontology and a knowledge graph-based spatio-temporal framework. This paper defines the domain expertise of forest fire analysis as the semantic rules of the knowledge graph. This paper proposes a rule-based reasoning method to obtain the corresponding data for the specific machine learning-based forest fire prediction methods, which are dedicated to tackling the problem with real-time prediction scenarios. This paper performs experiments regarding forest fire predictions based on real-world data in the experimental areas Xichang and Yanyuan in Sichuan province. The results show that the proposed method is beneficial for the fusion of multi-source spatio-temporal data and highly improves the prediction performance in real forest fire prediction scenarios. [ABSTRACT FROM AUTHOR]

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas. [ABSTRACT FROM AUTHOR]

Siliceous minerals of the Dengying Formation in the Gaoshiti–Moxi area in the central Sichuan Basin exhibit four types of quartz crystals (cryptocrystalline quartz, chalcedony, microcrystalline quartz, and megacrystalline quartz) and three structural types: cryptocrystalline, microcrystalline, and mosaic (laminated mosaic, window-hole interrupted mosaic, and arc-laminated mosaic). Siliceous minerals have a great influence on the storage performance of the reservoirs in the Dengying Formation. According to the petrophysical parameters of the Dengying Formation and porosity intersection diagrams, the siliceous dolomite and the reservoirs have low impedance characteristics, which makes it difficult to distinguish between them and leads to difficulties in the characterization and prediction of the reservoirs. The transverse wave velocity is favorable for reservoir characterization. Currently, the main method used to estimate the transverse wave velocity is petrophysical modeling, which establishes a relationship between the elastic and physical parameters of the reservoir. In this paper, the siliceous minerals in the dolomite in the study area are regarded as solid inclusions, and the calculation method of the rock matrix modulus is improved by using solid replacement. Then, an improved petrophysical model is constructed by combining the KT (Kuster–Toksöz) model, the DEM (Discrete Element Method) model, the Gassmann equation, and the Wood equation. The transverse wave velocity is estimated using the improved model under the constraint of the longitudinal wave velocity. The shapes of the transverse wave velocity curves obtained by the improved model and the deviations from the measured velocities are significantly better than those of the Xu–Payne model and other models. The results show that the improved model can effectively estimate the transverse wave velocity of the reservoir in this area, which provides a basis for future reservoir predictions in this area. [ABSTRACT FROM AUTHOR]

With the increase of shale gas resource exploitation in our country during recent decades, the situations of low gas production, fast production decline rate, and low flowback rate have been appearing in field production. It is an urgent problem to be solved in shale gas production and it is therefore necessary to study the interaction of the shale gas reservoir and the detained fracturing fluid. In this paper, the Longmaxi Formation shale samples of Sichuan Basin were selected for a water invasion experiment. The fracture propagation law, the water invasion front location, and the water invasion thickness of deep and shallow shale reservoirs after water invasion were compared and analyzed by CT scanning technology. Based on the analysis of the experimental mechanism, a numerical simulation model was established. The dimensionless permeability and thickness of the fracturing fluid invasion layer were introduced to analyze the positive and negative effects of fracturing fluid retention on the reservoir. The results show that during the hydraulic fracturing of shale gas wells, fracturing fluid can quickly enter the complex fracture network, and then slowly enter the shale matrix under various mechanisms to form the fracturing fluid invasion layer. Compared with shallow shale reservoirs, deep shale reservoirs have lower porosity and permeability, which propagates microfractures in the matrix induced by fracturing fluid retention, and results in a smaller fracturing fluid invasion layer thickness. Both the negative effect of fracturing fluid retention on shale damage and the positive effect of microfracture formation and propagation exist simultaneously. The higher the dimensionless fracturing fluid invasion layer permeability, the more complex the fracture network formed in the fractured reservoir will be, resulting in a longer stable production period and a better development effect. When the dimensionless fracturing fluid invasion layer permeability is greater than 1, that is, when the positive effect of fracturing fluid retention is greater, and the thicker the dimensionless fracturing fluid invasion layer is, the better the development effect will be. Combining reservoir characteristics and fracture development, the key to obtaining high productivity of a shale gas well is to optimize the soaking time and the speed of flowback in order to extend the stable production period. In this paper, the characteristics of the fracturing fluid invasion layer and the influence of fracturing fluid retention on gas well productivity are deeply studied, which provides a certain theoretical basis for the optimization of shale gas extraction technology and the improvement of the gas–water two-phase productivity prediction method for fractured horizontal wells. [ABSTRACT FROM AUTHOR]

Promoting quality certification of agricultural products is a matter of the sustainable development of agriculture and the transformation of the global agri-food system, and cooperatives are an important carrier to achieve the above policy goals. However, few scholars have included external resources and cooperative quality certification behavior in an analytical framework from the perspective of resource embedding. This study combines resource dependence theory and embeddedness theory, based on survey data from 230 agricultural cooperatives in six counties of Sichuan Province, China. This paper constructs a theoretical analysis framework of government resources, market resources and cooperative quality certification, and uses binary Logit regression model to empirically study the correlation between external resources and cooperative quality certification. The results show that: (1) Cooperatives do not receive a high proportion of external resource support, with 30% and 47% receiving support from the government and market respectively; the proportion of cooperatives carrying out product quality certification was relatively low, about 29.57%. (2) Government resources and market resources have a significant positive effect on improving the quality certification of cooperatives, and the promotion effect of the market is greater than that of the government. (3) Mechanism analysis shows that government resources promote quality certification in cooperatives mainly through regulations on agricultural inputs, while market resources can jointly play a role on regulations on agricultural inputs and market expectations for certified agricultural products. [ABSTRACT FROM AUTHOR]

Farmers' participation in public affairs is the combined effect of informal and formal institutions. In order to improve the current situation of low willingness of Chinese farmers to participate in living environment improvement, and to enhance the motivation of farmers and the effectiveness of living environment improvement, this paper incorporates the clan network in the informal institution and the perceivable authority of village chiefs in the formal institution into a unified analytical framework, divides the clan network into two dimensions (scale and strength) and explores the influence of the clan network and the perceivable authority of village chiefs on farmers' participation in rural living environment improvement. Finally, an empirical test is conducted using survey data from 683 farming households in the provinces of Henan, Shaanxi and Sichuan. The results show that the strength of the clan network and the perceivable authority of the village chiefs have a significant positive effect on the farmers' participation in rural living environment improvement, while the scale of the clan network does not have a significant effect on farmers' behavior. The perceivable authority of the village chief has a positive moderating effect between the clan network and farmers' participation in rural living environment improvement, while the heterogeneity analysis shows that the positive effect of the clan network on farmers' participation in the rural living environment is reduced for those who have worked outside, far away from the village. It is suggested that the protection of traditional clan relations should be emphasized, and the positive role of clan agents and their influence should be effectively utilized in promoting the improvement of the rural living environment at the grassroots level. Moreover, it is necessary to carry out timely training of professional knowledge and skills for grassroots leaders, propagandize and interpret policies, thus increasing the enthusiasm of farmers to participate in the improvement of the rural living environment. [ABSTRACT FROM AUTHOR]

The relationship between government intervention and cooperative development has always been a source of controversy in the developing world. This paper aims to examine the rationale and successful conditions of government intervention to promote cooperative development in poor areas of rural China. In the context of the "targeted poverty alleviation" program (2015–2020), a government-led campaign covering all poverty-stricken villages in west China, cooperative development was listed by the central government as a criterion for evaluating successful intervention at the county government level. Accordingly, the central questions of this paper are: why is government intervention necessary to initiate a process of cooperative development in poor areas of China; and under what conditions can government intervention be successful, leading to sustainable cooperative development? Bearing in mind the complexity of government intervention with mixed results, both successful and failed, the above questions are addressed through a case study of XM Beekeeping Cooperative, representing one type of successful government intervention in poverty-stricken and ethnic-minority-dominated regions of China. Overall, government intervention is crucial in building cooperative ecosystems in poor regions of China. However, government intervention is not invariable because the approaches can be modified to accommodate the effect of the intervention. [ABSTRACT FROM AUTHOR]

Several possible lithosphere–atmosphere–ionosphere coupling mechanisms before earthquake occurrence are presented in the literature. They are described by several models with different interaction channels (e.g., electromagnetic, mechanics, chemical, thermal), sometimes in conflict with each other. In this paper, we search for anomalies six months before the Lushan (China) 2013 earthquake in the three geo-layers looking for a possible view of the couplings and testing if one or another is more reliable to describe the observations. The Lushan earthquake occurred in China's Sichuan province on 20 April 2013, with a magnitude of Mw = 6.7. Despite the moderate magnitude of the event, it caused concern because its source was localized on the southwest side of the same fault that produced the catastrophic Wenchuan event in 2008. This paper applies a geophysical multi-layer approach to search for possible pre-earthquake anomalies in the lithosphere, atmosphere, and ionosphere. In detail, six main increases in the accumulated seismic stress were depicted. Anomalous geomagnetic pulsations were recorded in the Chengdu observatory, sometimes following the increased stress. Atmosphere status and composition were found to be anomalous in several periods before the earthquake, and, spatially, the anomalies seem to appear firstly far from the upcoming earthquakes and later approaching the Longmenshan fault where the Lushan earthquakes nucleated. The Formosat-3 data identified interesting anomalies in the altitude or electron content of the ionospheric F2 peak in correspondence with seismic and atmospheric anomalies 130 days before the earthquake. In addition, the total electron content showed high anomalous values from 12 to 6 days before the earthquake. We compared the anomalies and tried to explain their correspondences in different geo-layers by the lithosphere–atmosphere–ionosphere coupling models. In particular, we identified three possible couplings with different mechanisms: a first, about 130 days before the earthquake, with a fast (order of one day) propagation delay; a second, about 40 days before the earthquake occurrence, with a propagation delay of few days and a third from 2.5 weeks until one week before the event. Such evidence suggests that the geo-layers could interact with different channels (pure electromagnetic or a chain of physical-chemical processes) with specific propagation delays. Such results support the understanding of the preparation for medium and large earthquakes globally, which is necessary (although not sufficient) knowledge in order to mitigate their impact on human life. [ABSTRACT FROM AUTHOR]

In the face of the new reform of China's "unified electricity market", the frequency regulation ancillary service market in each region of China is still divided between using the MCP and PAB pricing mechanisms. The purpose of this paper is to explore the different impact the laws of the above two pricing mechanisms have on the frequency regulation market and to provide relevant suggestions for this electricity market reform. This paper simulates the competitive activities of the frequency regulation market based on the multi-agent simulation model, conducts a comparative experiment by changing the pricing mechanism to a single variable based on the rules of the Sichuan frequency regulation market in China, and concludes that MCP can make the market fast and stable, and that its market settlement price is low, which is suitable for the "unified electricity market". Although PAB makes the market settlement price high, it can ensure the retention of high-performance units in the market, and the stable settlement price makes this model able to accurately reflect the "price signal", making it suitable for late adoption in the "unified electricity market". [ABSTRACT FROM AUTHOR]

In Sichuan Province, China, most coal seams that are mined are steeply inclined; their roadways' surrounding rocks are asymmetric, with non-equilibrium deformations and unstable anchorage structures, thus making major safety hazards highly likely. Using field observations and a universal distinct element code (UDEC) numerical simulation method, this paper analyzed the time-dependent failure of the ventilation roadway of Working Face 1961 of the Zhaojiaba Mine, revealing the preconditions for such damage and a bidirectional deterioration mechanism for the deformation as well as stress of surrounding rocks. Moreover, this paper built an anchorage mechanical model for the thick layer of the roadway roof and proposed a cross-boundary anchor-grouting (CBAG) differential support technique. Calculations proved that the new support was particularly effective in restraining the expansion of tension cracks, thus preventing the slipping and dislocation deformations of rock masses on the curved roof side. The feedback of engineering applications showed that the maximum development depths of cracks in the arc roof and straight inclined roof of the roadway 150 m behind the working face are only 1.5 m and 1.10 m, decreasing by 61.3% and 47.6%, respectively, compared with the primary support. The proposed technology offers an overall thick-layer bearing structure for the surrounding rocks of roadways, effectively restraining the non-equilibrium large deformations of roadways in steeply inclined coal seams. [ABSTRACT FROM AUTHOR]

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. [ABSTRACT FROM AUTHOR]

With the continuous emphasis of the country on the construction of ecological civilization, promoting the utilization of straw resources has become an important measure to achieve green agricultural development. Based on the survey data of 540 households in Sichuan Province and under the guidance of the theory of planned behavior, this paper constructed the IV-Probit model to explore the impact of land transfer on the straw resource utilization of households. The results show that: (1) land transfer in and land transfer out can significantly promote the utilization of straw resources by farmers. (2) Heterogeneity analysis showed that land transfer in had a positive effect on crop straw utilization of the new generation and large-scale farmers. (3) Land transfer in can promote the utilization of straw resources by improving farmers' economic cognition and efficacy cognition; land transfer out can promote the utilization of straw resources by improving farmers' efficacy cognition. Accordingly, the government should improve the land transfer market, increase technology propaganda, and create an excellent policy environment to promote farmers' participation in straw resource utilization. [ABSTRACT FROM AUTHOR]

In this paper, based on Sina Weibo data, a natural language processing (NLP) analysis method was used to analyze the temporal and spatial sequence characteristics of people's attention and the characteristics of text content with the help of microblogs posted by people within 6 days after the 2022 Lushan M6.1, Maerkang M5.8 and Luding M6.8 earthquakes. Moreover, the same analysis method was used on the content of comments on microblogs posted by official media outlets within 6 days after the earthquakes to analyze the changes in people's sentiments and the differences in the sentiments in various regions, and the influencing factors were also analyzed. The results of this research show the following: In terms of the spatial and temporal distributions, people's attention was affected by the earthquakes themselves and their social impacts, and the first 2 h was often a period of an outbreak of attention, with the publishing areas mainly concentrated in Sichuan and Guangdong. In terms of people's sentiments, the overall microblogging sentiment of the three earthquakes was positive, and the sentiment value of the people in Sichuan was generally low compared with that of the people in the other regions. Not only was the fluctuation in sentiment affected by the influence of the region, but it was also positively related to the sentiment of official microblogs. The results of this research provide reference for guiding people's sentiments after earthquakes in the new media era. [ABSTRACT FROM AUTHOR]

The Sichuan Basin is a significant region for exploration and development of shale gas in China, and it is essential to clarify the impact of deep shale gas reservoir parameters on cost-effective development at scale to ensure national energy security. Rock physics modeling is a significant means of communicating the physical and elastic parameters of rocks. A rock physics modeling method applicable to fractured shale gas reservoirs is proposed for the current situation of complex fluid relationships in shale gas reservoirs and unclear characteristics of gas identification seismic response. In this paper, based on the Self Consistent Approximation (SCA) model and the differential effective medium (DEM) model, the anisotropic source of shale is used as a starting point to add bound water, kerogen, clay, and brittle minerals, the Schoenberg linear slip theory is used to add fracture disturbance effects, and then the Brown–Korringa model is used to perform fluid replacement under anisotropic conditions. Finally a rock physics model applicable to fractured shale gas reservoirs is obtained, and the established rock physics model is used for analysis of elastic parameters, Thomsen parameters, and fracture weakness parameters. Rock physics tests were performed on shale in southern Sichuan as an example. The experimental results show that the model established by the process can accurately invert the longitudinal and transverse wave velocities of the shale, which can provide a conceptual basis for the study of fractured shale gas reservoirs. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we describe the well-preserved fossil seed cones of Nothotsuga found in the Mula Basin, Litang County, Sichuan Province, southwestern China. By detailed morphological comparisons, a new fossil species, Nothotsuga mulaensis Z. Li et J. L. Dong sp. nov. is named. This discovery suggests that Nothotsuga had a more northern distribution during the Miocene. In addition, it suggests that the Mula region had a humid and warm climate during this period. The ancient landform presents an intermountain lake basin, which would have been surrounded by higher mountains. After the Miocene, due to the uplift of the Tibetan Plateau, Nothotsuga was either unable to adapt to the extinction of the local environmental or was able to migrate to several scattered places in southern China. In this paper, we describe a new fossil species, Nothotsuga mulaensis Z. Li & J.L. Dong sp. nov. The discovery of the fossil species was based on well-preserved fossil seed cones that were found in the Mula Basin in Xiamula village, Litang County, Sichuan Province, southwestern China. The shapes of these fossils were characterized by ovate seed cones, rhombic or suborbicular scales with auriculate bases, and the bracts were ligulate-spathulate in shape. This finding suggests that Nothotsuga once had a wide distribution range in China and that it also inhabited the eastern Tibetan Plateau (TP). Nothotsuga mulaensis was distributed in an intermountain lake basin, at altitudes from 2000 to 2300 m, in a warm and humid environment. This finding also suggests that the eastern TP may have provided good habitat for Nothotsuga during the Miocene. In addition, we propose that the uplift, accompanied by the severe cooling and aridification that occurred after the Miocene, caused the disappearance of this species of Nothotsuga in the eastern TP. [ABSTRACT FROM AUTHOR]

As flexible resources, cascaded hydropower stations can regulate the fluctuations caused by wind and photovoltaic power. Constructing pumped-storage units between two upstream and downstream reservoirs is an effective method to further expand the capacity of flexible resources. This method transforms cascaded hydropower stations into a cascaded pumped-hydro-energy storage system. In this paper, a flexibility reformation planning model of cascaded hydropower stations retrofitted with pumped-storage units under a hybrid system composed of thermal, wind, and photovoltaic power is established with the aim of investigating the optimal capacity of pumped-storage units. First, a generative adversarial network and a density peak clustering algorithm are utilized to generate typical scenarios to deal with the seasonal fluctuation of renewable energy generation, natural water inflow, and loads. Then, a full-scenario optimization method is proposed to optimize the operation costs of multiple scenarios considering the variable-speed operation characteristics of pumped storage and to obtain a scheme with better comprehensive economy. Meanwhile, the proposed model is retransformed into a mixed-integer linear programming problem to simplify the solution. Case studies in Sichuan province are used to demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The frequent fluctuation of pork prices has seriously affected the sustainable development of the pork industry. The accurate prediction of pork prices can not only help pork practitioners make scientific decisions but also help them to avoid market risks, which is the only way to promote the healthy development of the pork industry. Therefore, to improve the prediction accuracy of pork prices, this paper first combines the Sparrow Search Algorithm (SSA) and traditional machine learning model, Classification and Regression Trees (CART), to establish an SSA-CART optimization model for predicting pork prices. Secondly, based on the Sichuan pork price data during the 12th Five-Year Plan period, the linear correlation between piglet, corn, fattening pig feed, and pork price was measured using the Pearson correlation coefficient. Thirdly, the MAE fitness value was calculated by combining the validation set and training set, and the hyperparameter "MinLeafSize" was optimized via the SSA. Finally, a comparative analysis of the prediction performance of the White Shark Optimizer (WSO)-CART model, CART model, and Simulated Annealing (SA)-CART model demonstrated that the SSA-CART model has the best prediction of pork price (compared with a single decision tree, R2 increased by 9.236%), which is conducive to providing support for pork price prediction. The accurate prediction of pork prices with an optimized machine learning model is of great practical significance for stabilizing pig production, ensuring the sustainable growth of farmers' income, and promoting sound economic development. [ABSTRACT FROM AUTHOR]

As a typical rock, shale's reservoir depth is about 1500–4000 m, and the temperature of the shale reservoir at this depth is 150 °C. Therefore, in order to study the dynamic strength of shale at this temperature, it is necessary to consider the effects of temperature and strain rate on the dynamic strength of shale, and then establish the damage constitutive model of shale. This paper took black shale from the Sichuan Basin as the research object, combined it with the separated Hopkinson bar experiment and temperature control system, and conducted the Hopkinson bar experiment on shale at room temperature, 60 °C, 90 °C, 120 °C, and 150 °C, and at three groups of air pressures of 0.2 MPa, 0.3 MPa, and 0.4 MPa. The stress–strain curves of shale at the same strain rate and different temperature and at the same temperature and different strain rate were obtained. In the temperature difference range of this experiment, the dynamic strength of the sample presented two opposite trends (increasing and decreasing) with the increase in temperature, which was determined via the direction of the bedding. The peak strength linearly increased with the increase in strain rate. Based on the Weibull statistical distribution and the D–P failure criterion, a statistical damage constitutive model of shale dynamic strength considering the effects of temperature and strain rate was obtained. By modifying the parameters F0 and m, the dynamic strength statistical damage constitutive model of shale was in good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

The ion-adsorption rare earth deposit developed on the Mosuoying granite in the Panxi area of southwestern China represents a significant advancement in the exploration of ion-adsorption rare earth deposits in Sichuan. Being the first and currently the sole ion-adsorption rare earth deposit in Sichuan, studying its rare earth mineralization characteristics holds great importance. This paper aims to investigate the geochemical properties of the Mosuoying granite and its overlying weathered crust using rock geochemical methods based on field geological investigations. The findings reveal that the deposit belongs to the light rare earth type, with the ore-forming parent rock attributed to the high-potassium calc-alkaline series. It exhibits a high rock REE content ranging from 419 to 578 ppm, indicating favorable mineralization potential. Hydrothermal alteration reduces the REE content of the parent rock, leading to a notable increase in the LREE/HREE ratio, thus impacting the partitioning of rare earth elements and subsequent ore formation. The distribution characteristics of rare earth elements in each layer of the weathered crust are controlled by the parent rock and exhibit a light rare earth distribution pattern. The completely weathered layer is the main enrichment zone for rare earth elements, and the migration and enrichment patterns of rare earth elements in the weathered crust are evident. From the semi-weathered layer to the completely weathered layer, all REEs were gained, with a higher degree of migration for LREE. From the completely weathered layer to the clay layer, all REEs were lost, and the vertical distribution of rare earth content shows a "low-high-low" pattern. [ABSTRACT FROM AUTHOR]

Incorporating wind energy on a large scale into power systems presents challenges for the operation and control of the grid. To enhance the safety of power grid operation, accurate short-term forecasting of wind power is necessary, as it minimizes the impact of randomness. Considering the uncertainty and prediction issues associated with wind power, this paper introduces a CNN–GRU ultra-short-term wind power prediction model. This model relies on multichannel signals, including data such as wind speed, wind direction, climate conditions, and historical power outputs collected from wind farms. These data types contribute to the formation of a comprehensive multichannel signal for wind power. Following that, the CNN method extracts both global and partial features from these signals. Concurrently, features are extracted from past power outputs based on their time series. These features are then combined with the ones obtained from the convolution process. Subsequently, these combined features are input into a fully connected network. This step is crucial for blending multichannel information and generating forecast results. To validate the model, it was tested using data from a wind farm located in a specific region of Sichuan Province. According to our experimental results, the model demonstrates a high level of accuracy in computation and robust generalization ability. [ABSTRACT FROM AUTHOR]

As a digital innovation tool in the field of architecture and engineering, BIM technology can improve the efficiency and quality of projects and also realize all-round information sharing and collaboration throughout the project life cycle. Most of the ancient buildings in China are wooden structures, which often face problems such as damage, corrosion, and insect infestation, but because of their high cultural value, the maintenance of ancient buildings is always subject to problems. The effective combination of BIM technology and ancient buildings is conducive to the all-round, detailed, and in-depth maintenance and protection of ancient buildings. The Han family compound is located in Shangli Ancient Town, Sichuan Province, which is a well-preserved Qing dynasty architectural complex in an ancient town. However, under the dual effects of nature and man, a fire in the Republic of China period, an earthquake in 2008, and tourism development in recent years have caused the ancient buildings to become damaged and collapse, and they tend to assimilate with modern buildings, facing the dilemmas of maintenance and protection. Therefore, this paper is dedicated to exploring the feasibility of combining BIM technology with the maintenance and protection of the Han Family Courtyard and summarizing the specific application of BIM technology in the repair and protection of the Han Family Courtyard through domestic and international cases, so that the Han Family Courtyard can be developed in a sustainable way. [ABSTRACT FROM AUTHOR]

The prediction and optimization of the rate of penetration (ROP) for horizontal wells are more complicated than for vertical wells, but most of the current ROP prediction studies are for vertical wells, which cannot be adapted to the complex drilling characteristics of horizontal wells. To this end, this paper proposes a data knowledge dual-driven horizontal well ROP prediction method. Firstly, the drilling characteristics of horizontal wells are analyzed, showing that the horizontal wells ROP prediction model cannot be modeled using surface measurement data; secondly, based on the analysis of horizontal well drilling characteristics, a physical model-based horizontal well ROP modeling data pre-processing method is proposed by introducing the drag and torque model. Finally, a data knowledge dual-driven horizontal well ROP prediction method is proposed in conjunction with data-driven algorithms. The proposed horizontal well ROP prediction method is applied to the A1~A4 wells in the Sichuan area. Compared with the conventional data-driven ROP prediction method, the prediction accuracy of this method is improved by 30%. The proposed method can provide a basis for the intelligent optimization and management of ROP during the drilling of horizontal wells. [ABSTRACT FROM AUTHOR]

Based on various velocity fields from Global Positioning System (GPS) data over nearly 20 years in the Sichuan–Yunnan region, this paper calculated the strain rate field and its spatiotemporal characteristics by using an improved least squares collocation method. We evaluated the calculated strain field by extensively discussing the impact of non-tectonic factors on the calculation. Subsequently, we described the present-day strain rate features and their spatiotemporal variations. The results indicate the necessity of considering the influence of non-tectonic factors when calculating the strain rate field by using GPS velocity data. Widespread strain accumulation is observed in the Sichuan–Yunnan region, and significant second strain rate invariant with an average value of 33.1 nanostrain/yr primarily occurs along the eastern boundary faults of the Sichuan–Yunnan rhomboid block, specifically the Xianshuihe–Anninghe–Zemuhe–Xiaojiang fault systems. These fault systems also demarcate the zones with negative and positive dilation strain. According to the spatiotemporal variations of strain rate fields, the northern and southern segments of the Xianshuihe Fault, the Anninghe Fault and its eastern adjacent faults, and the Xiaojiang Fault are undergoing intensifying strain. Consequently, these zones should be paid more attention due to their relatively higher seismic risk in the Sichuan–Yunnan region. [ABSTRACT FROM AUTHOR]

Highlights: 1. How to quantitative evaluation of mechanical properties of shale in Da'anzhai section combined with triaxial mechanical experiments? The compressive strength of dry rock samples and soaked rock samples were tested by triaxial mechanical experiments. According to the stress-strain curves, the changes of compressive strength, elastic modulus and Poisson's ratio before and after hydration were analyzed. 2. How to establish a mathematical model of shale fracability that comprehensively considers brittleness index, horizontal differential stress, and fracture toughness is proposed? Fracability index is proportional to brittleness index and horizontal differential stress, and inversely proportional to fracture toughness. Young 's modulus, Poisson's ratio and tensile strength in fracability index model are related to rock water saturation, so the fracturing index is also related to rock hydration. 3. What is the effect of shale hydration on fracability and fracture pressure? As the water saturation rate of the rock gradually decreases, the FI of the formation rock de-creases Combined with the variation law of water saturation rate, it shows that after the shale around the well is hydrated, the fracture pressure of the formation decreases Accurate evaluation of shale oil reservoir fracability helps avoid blind fracturing and ensures efficient fracturing. However, the current evaluation of the fracability index rarely considers the impact of hydration caused by drilling fluid invasion during drilling. The results of a rock triaxial mechanical test conducted to evaluate the mechanical properties of shale oil reservoirs are reported in this paper. Based on the results, we developed a comprehensive evaluation method of shale oil reservoir fracability that considers hydration; the effects of the brittleness index, horizontal difference stress, and fracture toughness; and the law of water phase intrusion into shale oil reservoirs. The research results show that the average compressive strength decreased by 37.99%, the average elastic modulus decreased by 53.36%, and the average Poisson's ratio increased by 68.75% after being soaked for 48.00 h at 80 °C and 30.00 MPa. The water saturation rate at the borehole wall was the highest; with the extension to the stratum, it gradually decreases to the original water saturation rate of the formation, while the fluctuation radius gradually increases with time. The Young's modulus and fracture toughness decrease, the Poisson's ratio increases, and the fracability index reaches a maximum value at the wellbore (i.e., the highest water saturation rate), indicating that the strength of the hydrated rock decreases and it can be easily fractured. The case analysis shows that the optimal fracturing position of the Da'anzhai Section of Well NC2H is around 2600 m deep. After the hydration occurs, the fracture initiation pressure of the formation is reduced from the original value of 72.31 MPa to 66.80 MPa. This indicates that when hydration decreases, the formation fracture pressure also increases. The research presented in this paper can be used to optimize fracture location and set a reasonable fracturing pressure. [ABSTRACT FROM AUTHOR]

Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall deformation monitoring. The traditional GB-InSAR real-time processing method is to process the whole data set or group in time sequence. This type of method takes up a lot of computer memory, has low efficiency, cannot meet the timeliness of slope monitoring, and cannot perform deformation prediction and disaster warning forecasting. In response to this problem, this paper proposes a GB-InSAR time series processing method based on the LSTM (long short-term memory) model. First, according to the early monitoring data of GBSAR equipment, the time series InSAR method (PS-InSAR, SBAS, etc.) is used to obtain the initial deformation information. According to the deformation calculated in the previous stage and the atmospheric environmental parameters monitored, the LSTM model is used to predict the deformation and atmospheric delay at the next time. The phase is removed from the interference phase, and finally the residual phase is unwrapped using the spatial domain unwrapping algorithm to solve the residual deformation. The predicted deformation and the residual deformation are added to obtain the deformation amount at the current moment. This method only needs to process the difference map at the current moment, which greatly saves time series processing time and can realize the prediction of deformation variables. The reliability of the proposed method is verified by ground-based SAR monitoring data of the Guangyuan landslide in Sichuan Province. [ABSTRACT FROM AUTHOR]

The geological storage of CO2 is a critical technique for reducing emissions, which significantly contributes to the mitigation of the greenhouse effect. Currently, CO2 is often geologically stored in coal seams, hydrocarbon reservoirs, and saline aquifers in order to store CO2 and improve the oil and gas recovery simultaneously. Shale formations, as candidates for CO2 storage, are drawing more attention because of their rich volumes. CO2 storage through shale formations in the Sichuan Basin, China, has tremendous potential because of the readily available CO2 injection equipment, such as abandoned shale gas wells. Therefore, we review the potential of using these wells to store CO2 in this paper. Firstly, we review the status of the geological storage of CO2 and discuss the features and filed applications for the most studied storage techniques. Secondly, we investigate the formation properties, shale gas field development process, and characteristics of the abandoned wells in the Sichuan Basin. Additionally, after carefully studying the mechanism and theoretical storage capacity, we evaluate the potential of using these abandoned wells to store CO2. Lastly, recommendations are proposed based on the current technologies and government policies. We hope this paper may provide some insights into the development of geological CO2 storage using unconventional reservoirs. [ABSTRACT FROM AUTHOR]

The giant panda (Ailuropoda melanoleuca) is a relic species in China and a flagship species in the field of endangered wildlife conservation. The conservation of the giant panda's habitat has gained widespread attention for this reason. Historically, Chinese Giant Panda Nature Reserves are surrounded by communities and the resource utilization behavior of households disturbs the giant panda habitat. Changes in these communities and in Giant Panda Nature Reserves began around 2010, with the feminization and aging of the farm labor force. These changes brought different resource utilization behaviors that led to different evolutionary tendencies in giant panda habitats. This research study assesses the impact of these tendencies based on data from the fourth survey of the giant panda in the Sichuan Province and from geographic information data. The paper aims to uncover the internal mechanisms of farmers' resource utilization behavior in terms of the changes wrought to giant panda habitats. The paper simulates the future habitat of the giant panda based on the LUCC (land use/cover change) model to identify anticipated changes in future landscape patterns and the habitat quality of giant pandas under the current scenarios. The paper analyzes the spatial-temporal change of landscape patterns through the land use transfer matrix, based on the Markov model. The results of the scenario analyses illustrate the spatial and temporal difference in habitat quality. The driving mechanism for landscape pattern change is explored using the logistic regression model. The paper simulates the variation tendency of giant panda habitats under differential labor force structures and resource utilization behavior based on the CA (cellular automata) model, with the robustness of the results verified by participatory experiment. Through four scenarios of simulated farm labor force structure and resource utilization behavior, results suggest that the quality of giant panda habitats in the future will be relatively high when workforce feminization and aging is intense and resource utilization behavior is weakened. The simulated results confirm that the current agricultural labor force structure can reduce the intensity of resource utilization behavior. In this scenario, disturbance to giant panda habitats would decrease and their quality would improve. [ABSTRACT FROM AUTHOR]

Fast, accurate, and non-destructive large-scale detection of sweet cherry ripeness is the key to determining the optimal harvesting period and accurate grading by ripeness. Due to the complexity and variability of the orchard environment and the multi-scale, obscured, and even overlapping fruit, there are still problems of low detection accuracy even using the mainstream algorithm YOLOX in the absence of a large amount of tagging data. In this paper, we proposed an improved YOLOX target detection algorithm to quickly and accurately detect sweet cherry ripeness categories in complex environments. Firstly, we took a total of 2400 high-resolution images of immature, semi-ripe, and ripe sweet cherries in an orchard in Hanyuan County, Sichuan Province, including complex environments such as sunny days, cloudy days, branch and leaf shading, fruit overlapping, distant views, and similar colors of green fruits and leaves, and formed a dataset dedicated to sweet cherry ripeness detection by manually labeling 36068 samples, named SweetCherry. On this basis, an improved YOLOX target detection algorithm YOLOX-EIoU-CBAM was proposed, which embedded the Convolutional Block Attention Module (CBAM) between the backbone and neck of the YOLOX model to improve the model's attention to different channels, spaces capability, and replaced the original bounding box loss function of the YOLOX model with Efficient IoU (EIoU) loss to make the regression of the prediction box more accurate. Finally, we validated the feasibility and reliability of the YOLOX-EIoU-CBAM network on the SweetCherry dataset. The experimental results showed that the method in this paper significantly outperforms the traditional Faster R-CNN and SSD300 algorithms in terms of mean Average Precision (mAP), recall, model size, and single-image inference time. Compared with the YOLOX model, the mAP of this method is improved by 4.12%, recall is improved by 4.6%, F-score is improved by 2.34%, while model size and single-image inference time remain basically comparable. The method in this paper can cope well with complex backgrounds such as fruit overlap, branch and leaf occlusion, and can provide a data base and technical reference for other similar target detection problems. [ABSTRACT FROM AUTHOR]

Vegetation net primary productivity (VNPP) is the main factor in ecosystem carbon sink function and regulation of environmental processes. However, NPP data products have data missing in some areas, which affects the availability and overall accuracy level of data. Therefore, we adopted the Factor Analysis Backpropagation neural network model (FA-BP model) to acquire a high-accuracy and high-reliability NPP result without missing or empty areas by using a series of easily accessible datasets, such as meteorological data and remote sensing data. We selected the western Sichuan region as the study area and carried out a VNPP time series estimation from 2000 to 2016. Comparative simulations also verify the accuracy of the time series estimation results: The Pearson correlation r of VNPP prediction results ranged from 0.807 to 0.917, the mean absolute error ranged from 29.1 to 38.9, the root mean square error was between 37.3 and 51.8, and the mean relative error varies from 0.10 to 0.14. Further analysis shows that the spatial pattern of estimated VNPP during the past 17 years in western Sichuan shows a decreasing trend from southeast to northwest. Besides, the VNPP time series is generally on an upward trend in this period. The increasing and decreasing areas of VNPP values in the study area accounted for 81.42% and 18.58%, respectively. Moreover, we find that temperature dominates the change of VNPP in the whole western Sichuan region. The data processing method and experimental results presented in this paper can provide a reference for accurately acquiring VNPP and related studies on natural resources and climate change. [ABSTRACT FROM AUTHOR]

For emergency rescue and damage assessment after an earthquake, quick detection of seismic landslides in the affected areas is crucial. The purpose of this study is to quickly determine the extent and size of post-earthquake seismic landslides using a small amount of post-earthquake seismic landslide imagery data. This information will serve as a foundation for emergency rescue efforts, disaster estimation, and other actions. In this study, Wenchuan County, Sichuan Province, China's 2008 post-quake Unmanned Air Vehicle (UAV) remote sensing images are used as the data source. ResNet-50, ResNet-101, and Swin Transformer are used as the backbone networks of Mask R-CNN to train and identify seismic landslides in post-quake UAV images. The training samples are then augmented by data augmentation methods, and transfer learning methods are used to reduce the training time required and enhance the generalization of the model. Finally, transfer learning was used to apply the model to seismic landslide imagery from Haiti after the earthquake that was not calibrated. With Precision and F1 scores of 0.9328 and 0.9025, respectively, the results demonstrate that Swin Transformer performs better as a backbone network than the original Mask R-CNN, YOLOv5, and Faster R-CNN. In Haiti's post-earthquake images, the improved model performs significantly better than the original model in terms of accuracy and recognition. The model for identifying post-earthquake seismic landslides developed in this paper has good generalizability and transferability as well as good application potential in emergency responses to earthquake disasters, which can offer strong support for post-earthquake emergency rescue and disaster assessment. [ABSTRACT FROM AUTHOR]

Wide area landslide detection is a major international research hotspot in the field of geological hazards, and the integration of multi-temporal optical satellite images and spaceborne interferometric synthetic aperture radar (InSAR) appears to be an effective way to realize this. In this paper, a technical framework is presented for wide area landslide detection: (i) multi-temporal satellite optical images are used to detect landslides with distinguishable geomorphological features; (ii) Generic Atmospheric Correction Online Service (GACOS) assisted InSAR stacking is employed to generate annual surface displacement rate maps in radar line of sight using satellite SAR images from both ascending and descending tracks, which are in turn utilized to automatically detect active landslides from ground motion using hotspot analysis, and (iii) the distribution characteristics of the detected landslides are investigated by examining their relationships with topographic and hydrological factors. Three expressways in Sichuan Province, China—namely the Yakang (Ya'an-Kangding), Yaxi (Ya'an-Xichang), and Lushi (Luding-Shimian) expressways—and their surrounding regions (a total area of approximately 20,000 square kilometers) were chosen as the study area. A total of 413 landslides were detected, among which 320 were detected using multi-temporal satellite optical images, and 109 were detected using GACOS-assisted InSAR stacking. It should be noted that only 16 landslides were detected by both approaches; these landslides all exhibited not only obvious geomorphological features but also ground motion. A statistical analysis of the topographic and hydrological factors shows that of the detected landslides: 81% are distributed at elevations of 1000–2500 m, over 60% lie within the elevation range of 100~400 m, and 90% present with medium and steep slopes (20°~45°), and 80% are located within areas seeing an annual rainfall of 950~1050 mm. Nine landslides were found to pose potential safety hazards to the expressways. The research findings in this paper have directly benefitted the Sichuan expressways; equally important, it is believed that the technical framework presented in this paper will provide guidance for hazard mitigation and the prevention of transportation hazards in the future. [ABSTRACT FROM AUTHOR]

Low-resistivity shales are widely developed in the Sichuan Basin. The production of low-resistivity shale gas reservoirs ranges from high to low to none. The existing methods for gas-content prediction cannot accurately predict the gas content of low-resistivity shale. This increases the risk of shale-gas exploration. To prove that the random forest algorithm has apparent advantages in predicting the gas content of low-resistivity shale and reducing the risks associated with shale-gas exploration and development, three prediction methods were selected in this paper to compare their effects. The first method is known as the grey-correlation multiple linear regression method. Low-resistivity shale-gas content logging series were optimized using the grey-correlation approach, and then the low-resistivity shale-gas-content prediction model was established using the multiple linear regression method. The second method we selected was the resistivity method. The improved water-saturation model was used to predict the water saturation of low-resistivity shale, and then the gas content of low-resistivity shale was predicted based on the free-gas content and the adsorbed-gas-content model. The random forest algorithm was the third method we selected. Fourteen logging series were used as input data and the measured gas content was used as supervised data to train the model and to apply the trained model to the gas-content prediction. The findings demonstrated that the grey-correlation multiple regression method had poor accuracy in predicting gas content in low-resistivity shale; The resistivity method accurately predicted water saturation, and the predicted gas content was higher than the actual gas content. Because the random forest algorithm accurately predicted low-resistivity shale-gas content, its use in the Sichuan Basin was advantageous. The selection of a low-resistivity shale-gas-content prediction model was guided by the research findings. [ABSTRACT FROM AUTHOR]