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Huanma paper-cut is the representation of the first batch of provincial intangible cultural heritage masterpieces of Sichuan province. In recent years, Huanma Town in Cangxi County of Sichuan Province has continuously explored and trans⁃ formed traditional techniques into a cultural industry, seeking a market for the development of Huanma paper-cutting industry and realizing the transformation of the industrial structure of“prospering the town with culture and promoting literature with classics”. At present, Huanma’s paper-cutting inheritance work faces problems such as insufficient follow-up, fewer papercut inheritance craftsmen, lack of in-depth theoretical research and deviations in development positioning. In the period of rural revitalization, the protection project of Huanma paper-cut should start from improving the“intangible cultural heritage”man⁃ agement mechanism, cultivating and protecting the inheritors of paper-cutting skills, increasing the promotion of intangible cul⁃ tural heritage, creating a market brand of paper-cutting culture, and strengthening the construction of paper-cutting schoolbased courses, etc. Aspects of systematic construction, so as to promote its sound development. [ABSTRACT FROM AUTHOR]

With the high yield of many wells represented by Well JT1 in the Maokou Formation, has catalyzed a surge in exploration activities along the platform margin facies of the Maokou Formation in central Sichuan and further showed the significant exploration potential of the Maokou Formation in the northern slope. However, the fracture cave body of the Maokou Formation exhibits a high degree of development, strong longitudinal and horizontal heterogeneity, large formation pressure differences, and drilling events such as gas kicks and lost circulation occur frequently, which seriously affects the efficient implementation of drilling. Understanding the spatial distribution of the three-pressure in the formation can help better deal with and solve the above problems. Therefore, in order to help the safe, high-quality and rapid drilling of the Maokou Formation in the study area, and enhance the efficiency of oil and gas development, this paper explores the research on the prediction method of the three-pressure of jointing well-seismic data based on the geomechanical experimental data and the actual drilling data. In the process of prediction of pore pressure, this study found that the pore pressure and formation velocity in the study area have an exponential relationship. In order to enhance the applicability of the Filippone's method in the study area and improve the prediction accuracy of pore pressure, the linear relationship between pore pressure and formation velocity in the Filippone's method is modified to an exponential relationship, and a pore pressure prediction model suitable for the work area was established. Based on the Mohr–Coulomb criterion and Huang's model, the prediction models of collapse pressure and fracture pressure applicable to the study area were established, respectively. Then, the elastic parameters were obtained through pre-stack inversion, and the three-pressure bodies were calculated based on the elastic parameter bodies. The results indicate that: (1) The three-pressure prediction method of the jointing well-seismic data in this paper can predict the formation's longitudinal and transverse pressure anomaly zones in advance. (2) The Maokou Formation in the study area is characterized by abnormally high pressure, to balance the pressure of the high-ground formation, high-density drilling fluid is necessary. (3) The prediction results of three-pressure in this paper are highly consistent with the actual drilling engineering events, which verifies the reliability of the three-pressure prediction results presented in this study. The results of the study can provide a basis for decision-making in drilling geological design, such as the determination of drilling fluid density, the evaluation of borehole stability and other engineering problems that require support from three-pressure data. [ABSTRACT FROM AUTHOR]

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]

Purpose: The purpose of this paper is to propose a method that can calculate the transportation infrastructure network service capacity enhancement given by planned transportation infrastructure projects construction. Managers can sequence projects more rationally to maximize the construction effectiveness of infrastructure investments. Design/methodology/approach: This paper designed a computational network simulation software to generate topological networks based on established rules. Based on the topological networks, the software simulated the movement path of users and calculated the average travel time. This software allows the adjustment of parameters to suit different research objectives. The average travel time is used as an evaluation index to determine the most appropriate construction sequence. Findings: In this paper, the transportation infrastructure network of Sichuan Province in China was used to demonstrate this software. The average travel time of the existing transportation network in Sichuan Province was calculated as 211 min using this software. The high-speed railways from Leshan to Xichang and from Xichang to Yibin had the greatest influence on shortening the average travel time. This paper also measured the changes in the average travel time under two strategies: shortening the maximum and minimum priorities. All the transportation network optimisation plans for Sichuan Province will be somewhere between these two strategies. Originality/value: The contribution of this research are three aspects: First, a complex network analysis method that can take into account the differences of node elements is proposed. Second, it provides an effective tool for decision makers to plan transportation infrastructure construction. Third, the construction sequence of transportation infrastructure development plan can effect the infrastructure investment effectiveness. [ABSTRACT FROM AUTHOR]

To overcome the reliance on large samples and high-quality data in existing evaluation methods, while also improving evaluation efficiency and accuracy, this paper proposes a method for rapid landslide hazard assessment. This method utilizes existing research findings and specific analytical techniques for the study area to conduct rapid assessments. Taking the landslide in Yongxing Town, Mingshan Area, Ya'an City, Sichuan Province as an example, the Analytic Hierarchy Process (AHP) is combined with the Information Value (IV) method, Certainty Factor (CF) method, and Frequency Ratio (FR) method from previous studies, The AHP-IV and AHP-FR methods assess the study area as a moderately hazardous zone, while the AHP-CF method assesses it as a slightly hazardous zone. Affected by the strong 2013 Lushan earthquake, the landslide in the study area caused permanent damage. Field investigation results show that the landslide hazard in the study area is moderate, and the AHP-IV and AHP-FR methods are more consistent with the actual field results. The AHP-CF method, due to not considering the water system factor and having certain errors in its discrimination method, leans towards a safer assessment, The results of the three evaluation methods are somewhat consistent. [ABSTRACT FROM AUTHOR]

In order to explore the impact of education and training of farmers' professional cooperatives on agricultural green production, this paper uses the Oprobit model to analyze the impact of the time, methods, and content of cooperative education and training on green production willingness based on 534 micro-survey data of cooperative members in Sichuan Province. And then the instrumental variable method (IV-Oprobit) is used for endogeneity treatment and robustness test. The results show that: (1) Cooperative education and training time can significantly promote the green production willingness of members. Specifically, for every additional hour of education and training, the probability of "very willing" to participate in green production increases by 1.7%; (2) the four types of education and training methods of cooperative project demonstration, on-site training, bulletin board publicity, and online information publicity are all positively affecting the willingness of members to green production, and on-site training is the most effective method; (3) the three education and training contents of cognition, emotion and skills all positively affect the green production intention of members, and cognition is the most effective content; (4) the methods and contents of cooperative education and training have an interactive effect on the influence of members' green production willingness. Based on this, this paper proposes policy suggestions to optimize the functions, training methods, and content design of green production education and training in cooperatives. [ABSTRACT FROM AUTHOR]

Introduction: Fingered citron slices possess significant nutritional value and economic advantages as herbal products that are experiencing increasing demand. The grading of fingered citron slices plays a crucial role in the marketing strategy to maximize profits. However, due to the limited adoption of standardization practices and the decentralized structure of producers and distributors, the grading process of fingered citron slices requires substantial manpower and lead to a reduction in profitability. In order to provide authoritative, rapid and accurate grading standards for the market of fingered citron slices, this paper proposes a grading detection model for fingered citron slices based on improved YOLOv8n. Methods: Firstly, we obtained the raw materials of fingered citron slices from a dealer of Sichuan fingered citron origin in Shimian County, Ya'an City, Sichuan Province, China. Subsequently, high-resolution fingered citron slices images were taken using an experimental bench, and the dataset for grading detection of fingered citron slices was formed after manual screening and labelling. Based on this dataset, we chose YOLOv8n as the base model, and then replaced the YOLOv8n backbone structure with the Fasternet main module to improve the computational efficiency in the feature extraction process. Then we redesigned the PAN-FPN structure used in the original model with BiFPN structure to make full use of the high-resolution features to extend the sensory field of the model while balancing the computation amount and model volume, and finally we get the improved target detection algorithm YOLOv8-FCS. Results: The findings from the experiments indicated that this approach surpassed the conventional RT-DETR, Faster R-CNN, SSD300 and YOLOv8n models in most evaluation indicators. The experimental results show that the grading accuracy of the YOLOv8-FCS model reaches 98.1%, and the model size is only 6.4 M, and the FPS is 130.3. Discussion: The results suggest that our model offers both rapid and precise grading for fingered citron slices, holding significant practical value for promoting the advancement of automated grading systems tailored to fingered citron slices. [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]

In the early twentieth century, traditional handicraft was challenged by the latest technology in China. It is reflected by ethnic costumes combining new and old, as in the waistcoat of this study. This waistcoat made at Gyalrong Tibetan area in Sichuan, China, displays unique local features in terms of its brilliant colors and comprehensive craftsmanship. This study employs techniques such as optical microscopy, infrared spectroscopy, scanning electron microscope and high performance liquid chromatography-mass spectrometry to investigate various aspects of this waistcoat, including its fabrics and dyes. The results showed that the waistcoat was primarily made of cotton and silk, with a bamboo paper layer, and that silk as well as twisted gold and silver threads were employed for the embroidery. Various embroidery techniques were applied, with patterns, color combinations, and characteristics being consistent with those of Tibetan and Shu (蜀) embroidery. In terms of dyeing technology, a wide range of colors were achieved through multi-step dyeing processes using natural dye stuffs like pagoda bud, and synthetic dyes like magenta. These findings indicates that modern technologies were well integrated into traditional garment manufacture in the early twentieth century in China. [ABSTRACT FROM AUTHOR]

The forest-based wellness industry, as a rapidly growing sector that integrates various business forms with extensive coverage and an extended industrial chain, is undergoing rapid development due to the increasing aging population and people’s suboptimal health conditions. As a forerunner in developing the forest-based wellness industry, Sichuan province is known for its early development, proactive efforts, diverse models, and significant impact in this industry. It has achieved certain milestones in terms of top-level design, pilot demonstration, standardized guidance, and public awareness campaigns to promote the development of this industry. Therefore, this paper utilizes Sichuan as a case study to systematically summarize and analyze the key practices made by the province in promoting the rapid development of the industry by investigating the development trajectory of the forest-based wellness industry. Additionally, it examines the development trends of this industry from the perspectives of supply, demand, and consumption. Finally, this paper proposes several measures to facilitate the high-quality development of the forest-based wellness industry. These measures encompass nurturing specialized talent in forest-based wellness, enhancing market players’ capabilities in this domain, conducting extensive research on technologies that promote this industry, actively seeking support from relevant policies, and promoting integrated development across diverse sectors. [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]

Slope stability estimation is an engineering problem that involves several parameters. The problems of low accuracy of the model and blind data preprocessing are commonly existent in slope stability prediction research. To address these problems, 10 quantitative indicators are selected from 135 field cases to improve the accuracy of the model. These indicators were analyzed and visualized to examine their reliabilities after preprocessing. Combined with random forest (RF), particle swarm optimization (PSO), and least squares support vector machine (LSSVM) algorithms, a hybrid prediction model that the RF–PSO–LSSVM model is proposed for identifying slope stability, and its reliability is verified by other prediction models that SVM, logistic regression, decision trees, k-nearest neighbor, naive Bayes, and linear discriminant analysis. Besides, the importance score of each indicator in the prediction of slope stability is discussed by employing the RF algorithm. The research results show that the proposed hybrid model exhibits the best accuracy and superiority in slope stability prediction than other models in this paper, which its values of the best fitness, area under the curve, T-measure, and accuracy are 98.15%, 96.4%, 96.55%, and 95.82%, respectively. The most influential factors affecting slope stability are precipitation and gravity, and the slope type and pore water ratio are identified as the least significant factors in this paper. The results provide a novel approach toward slope stability prediction in the field of geotechnical engineering. [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]

Natural fractures are widely developed and distributed in tight sandstone reservoirs in the western Sichuan Basin, China, influenced by complex tectonic movements and diagenetic processes. These natural fractures serve as the main flow channels and important storage spaces in such reservoirs. Understanding their development characteristics and controlling factors determines the positioning and production efficiency of tight gas wells. This paper first explores the types and characteristics of natural fractures in the second member of the Upper Triassic Xujiahe Formation (T3x2) in the western Sichuan Basin through outcrop investigations, core observations, image log interpretations, and thin section analyses. We study the main influencing factors controlling the development and distribution of tectonic shear fractures by combining the characterization of fracture attributes. The results show that the tight sandstone reservoirs in the T3x2 mainly contain tectonic fractures, diagenetic fractures, and overpressure fractures, among which shear fractures in tectonic fractures are the main types. There are four sets of tectonic shear fractures in the study area, oriented in the E-W, N-S, NE-SW, and NW-SE directions. The dip angles of fractures range from 20° to 80°, with an average dip angle of 53°, predominantly consisting of high-angle fractures. The lengths of fractures are mainly within 100 cm, with a mean value of 47.53 cm, and 76.2% of fractures have lengths less than 50 cm. Fracture apertures range from 2.51 to 163.19 μM, with an average of 30.54 μM. The proportion of effective fractures in tectonic shear fractures reaches 85.7%. The development of tectonic shear fractures is primarily influenced by lithology, rock mechanics stratigraphy, diagenetic processes and facies, and faults. Lithology stands as the fundamental factor influencing the degree of fracture development in tight reservoirs. In rocks with the same composition, as the grain size decreases, the degree of fracture development increases. Simultaneously, with the rise in muddy content, fracture development diminishes in siltstone, argillaceous siltstone, silty mudstone, and mudstone. The formation and distribution of fractures are governed by rock mechanics stratigraphy, primarily manifested as a decrease in fracture density with an increase in rock mechanics layer thickness within a certain range. Diagenetic processes affect the abundance of tectonic shear fractures by influencing the mechanical properties of rocks. The stronger the compaction and cementation, the poorer the physical properties, and the higher the brittleness of the rocks, resulting in a greater abundance of tectonic shear fractures. Rocks with strong compaction and robust cementation facies exhibit high brittleness, rendering them more susceptible to fracturing under identical stress conditions. Fractures are highly prevalent in both the hanging wall and footwall of faults. As the distance from the fault increases, the linear density of fractures significantly decreases, owing to the uneven distribution of stress perturbation caused by fault activity. [ABSTRACT FROM AUTHOR]

Carbonate reservoirs are characterized by abundant reserves and are currently focal points for development in oil and gas producing regions such as the Ahdab oilfield, Tarim Basin, Sichuan Basin, and Ordos Basin. The primary method for exploiting carbonate reservoirs is waterflooding. However, due to the complex pore structure and pronounced heterogeneity of carbonate rocks, the waterflooding process often leads to an unclear distribution of remaining oil and low waterflooding recovery efficiency, significantly impacting the stable and high production of carbonate reservoirs. This paper presents a two-phase flow model of oil and water in distinct pore structures by integrating fluid flow equations and interface tracking equations. It visually represents the waterflooding process at the pore scale, elucidates the distribution and formation mechanism of remaining oil, and discusses the mechanism of microscopic displacement efficiency change. The study reveals that: 1) After waterflooding, the distribution patterns of remaining oil can be categorized into dead-end remaining oil, pressure balance remaining oil, wall-bound remaining oil, Jamin effect remaining oil, and water-encapsulating remaining oil, which are governed by microscopic pore structure, wettability, and preferential flow paths; 2) From the perspective of actual reservoir displacement efficiency, intergranular pores > intergranular dissolved pores > visceral foramen > mould pore, with this trend being more pronounced under hydrophilic wetting conditions; 3) Given the oil-wet to strong oil-wet wettability characteristics of these carbonate rocks, capillary forces pose significant resistance during waterflooding. The conclusion underscores the importance of leveraging the reservoir's microscopic pore structure and wettability characteristics for actual oil wells, elucidating the evolutionary law of the mechanical mechanism of oil-water interface advancement, clarifying oil-water percolation characteristics at the pore scale, and understanding the microscopic displacement physical mechanism, all of which are crucial for guiding the design of schemes aimed at enhancing reservoir recovery efficiency. [ABSTRACT FROM AUTHOR]

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics. [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 advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation. [ABSTRACT FROM AUTHOR]

As one of the common physical materials in Chinese archaeological excavations, ancient bronzes are an essential basis for studying the development of Chinese bronze culture, which is of great significance for exploring the development law of ancient human civilization and reconstructing ancient human society. As China's infrastructure advances, the number of bronzes unearthed by archaeological excavations continues to increase. However, environmental damage to artefacts is very complex, whether the buried environment of the artefacts or the above-ground environment when the artefacts are unearthed, leading to different health problems for the excavated bronzes. A scientific assessment of these bronzes needs to be carried out prior to extraction to inform staff how they should be extracted, moved, and transported and how they should be restored afterwards. In response to the above problems, this paper takes excavated bronzes from archaeological sites as the research object and, by analysing and studying the relevant industry standards and the disease characteristics of bronzes, establishes a three-tier indicator framework for assessing the health of bronzes in a layer-by-layer refinement and proposes quantitative indicators with typical correlations. Through extensive research and testing, we screened out efficient, non-destructive, convenient and reliable assessment and testing methods and assessment models that combine subjective and objective aspects suitable for archaeological sites. On this basis, the paper achieves a scientific and practical assessment of the health status of bronzes excavated from archaeological sites. After repeated experiments, a set of comprehensive methods for quickly and conveniently assessing the health status of excavated bronzes was proposed for the first time and successfully applied to the archaeological excavation site of Sanxingdui site in Guanghan City, Sichuan Province, China. [ABSTRACT FROM AUTHOR]

The paper used the location entropy index, spatial gini coefficient and barycenter model to analyze the temporal and spatial changes of Chinese animal husbandry industry agglomeration from 2002 to 2021. On this basis, the fixedeffects model was constructed to explore the influencing factors of the animal husbandry industry agglomeration. The results showed that from 2002 to 2021, the agglomeration degree of Chinese animal husbandry industry exhibited a continuous upward trend. The focus of the animal husbandry industry agglomeration had been concentrated in Henan and Sichuan for a long time. The animal husbandry industry gradually gathered from the eastern to the central region. The technology input, industry correlation, economic development level and policy environment all had a positive impact on the animal husbandry industry agglomeration, while the number of labor force had no significant impact on the animal husbandry industry agglomeration. Therefore, the paper puts forward some suggestions to accelerate the clustered development of animal husbandry industry, stimulate the shift of high-qualitanimal husbandry industry elements to the centralregion, and strengthen the policy support for the animal husbandry industry agglomeration, so as to provide a reference for promoting the high-quality development of animal husbandry industry. [ABSTRACT FROM AUTHOR]

Accurate delineation of debris-flow-endangered areas (e.g., the maximum runout distance) is a necessary prerequisite for the debris-flow risk assessment and countermeasures design. Recently, machine-learning models have been proved to be an effective tool in predicting debris-flow parameters. However, existing machine-learning models are generally developed based on a very limited number of observation data, which may result in the predictive model overfitting or underfitting. How to develop a robust model for accurate forecasting of debris-flow-endangered areas still remains a difficult task. This paper proposes a hybrid method for predicting debris-flow hazard zone by integrating machine-learning algorithms and an empirical regression model. The proposed method takes the calculated maximum runout distance obtained from the empirical model as supplementary inputs to increase the amount of training data to construct hybrid machine-learning models. Three commonly used machine-learning models (i.e., multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM)) are developed based on the training datasets of a specific debris basin. Then, these three machine-learning models are combined with an empirical relationship developed using the same training datasets to generate corresponding hybrid models. Finally, the performance metrics (i.e., coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE)) of the proposed hybrid models are comprehensively investigated and compared with the single predictive model (i.e., MARS, RF, SVM, and the empirical model) under fivefold cross-validation. The proposed method is illustrated using 134 channelized debris-flow events in Sichuan province, China. Results show that compared with the three individual machine-learning models, hybridization of machine-learning algorithms and the empirical model results in R2, RMSE, and MAE improved by 70.5%, 32.9%, and 41.1%, respectively. In contrast to the empirical model, the R2, RMSE, and MAE value of the proposed hybrid models are improved by 29.6%, 22.3%, and 32.5%, respectively. The proposed hybrid models generally perform better than the single machine-learning and the empirical model, providing a promising tool for accurate forecasting of a debris-flow-endangered area. [ABSTRACT FROM AUTHOR]

Previous studies mainly focused on the effect of single social learning on farmers' safety behavior and rarely explored the mechanism of social learning on green control techniques. Using survey data from 608 farmers in Sichuan Province, this paper empirically analyzes this mechanism. The results showed that the adoption of green control techniques in the sample area was not promising; although 94.41% of the surveyed farmers adopted the techniques, the average number of practices adopted was only 2.88 out of seven possible practices. Social learning facilitates the adoption level of green control techniques by farmers. From the marginal effect, the probability of "many techniques adopted" increased by 6.692% for each unit of increase in social learning. Moreover, it is also discovered that environmental literacy plays a bridging role in the process by which social learning influences the adoption level of green control techniques, i.e., social learning can act on the adoption level through environmental literacy. Finally, we found that a favorable market environment is conducive to the conversion of farmers' environmental literacy into green control techniques. The facilitating effect of environmental literacy on the adoption level is further enhanced when there is improvement in access to materials need for such techniques, ease of selling produce, and price stability. This paper makes important additions to the research field of social learning influencing the agricultural technology adoption and is an extension of social learning theory with important implications for green agriculture. [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]

As one of the most important meteorological elements, temperature is an indispensable meteorological parameter for the atmospheric correction of spaceborne LiDAR ranging. Given a limited number of surface meteorological observation stations, the temperature values for all region of LiDAR observation need to be interpolated using appropriate spatial interpolation methods. In this paper, based on the monthly surface observation values in individual years (1981–2010) of Sichuan province observation stations, we firstly analyze the effects of three common interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). To solve the problem of low interpolation accuracy in severely undulating terrain area, an improved gradient distance inverse square method based on the adiabatic lapse rate (GIDS-ALR) is proposed. The experimental results show that the GIDS-ALR has an obvious improvement in the effect of severely undulating terrain, where the absolute error has been improved by more than 43% in average. Additionally, the temperature-interpolated MAE is reduced by more than 30%. The effectiveness and applicability of the proposed method is verified in this paper. [ABSTRACT FROM AUTHOR]

The air quality is directly related to people's lives. This paper selects air quality data of Sichuan Province as the research object, and explores the inherent characteristics of air quality from the perspective of complex network theory. First, based on the complexity of network topology and nodes, a community detection algorithm which combines the clustering idea with principal component analysis (PCA) algorithm and self-organization competitive neural network (SOM) is designed (CSP). Compared with the classic community detection algorithm, the result proves that the CSP algorithm can accurately dig out a better community structure. Second, based on the strong correlation distance and strong correlation coefficient of the air quality network, the Sichuan Air Quality Complex Network (SCCN) was constructed. The SCCN is divided into five communities using the CSP algorithm. Combining the characteristics of each community and the Hurst coefficient, it is found that the air quality inside the community has long-term memory. Finally, based on the idea of time-dependent cross-correlation, this paper analyzes the cross-correlation of AQI time series of different stations in each community, constructs a directed air quality cross-correlation network combined with complex network theory, and locates the important pollution sources in each region of Sichuan Province according to the topological structure of the network. The work of this paper can provide the corresponding theoretical support and guidance for the current environmental pollution control. [ABSTRACT FROM AUTHOR]

Taking the Longmaxi deep-marine shale gas reservoir in Zigong region as the research target, this paper aimed to characterize the nano-scale pore structure and investigate the reservoirs' heterogeneity based on fractal theory. By conducting a series of experimental studies, mainly including TOC, XRD, gas adsorption (N2 and CH4), we were able to clarify the main controlling factors for the heterogeneity of deep shale pore structure. Our results indicated that the deep marine shale possessed a significant amount of organic matter, as the average TOC value is 3.68%. The XRD analysis results show that quartz and clay were the main mineral types, and the total content of these two minerals averaged 77.5%. Positive correlations were observed between TOC and quartz, while TOC decreases as the clay mineral increases, this discovery indicating that quartz is biogenic. Based on FHH (Frenkele-Halseye-Hill) method, by using the LTNA adsorption isotherms, we took relative pressure P/P0 = 0.5 as the boundary, then two separate fractal dimension were deduced, D1 and D2 represent the fractal characteristics of small and large pores, respectively. Our study revealed that both D1 and D2 demonstrated positive correlations with N2 adsorption pore volume and adsorption specific surface area, while negatively correlated with the adsorption average pore diameter. Moreover, the two fractal dimensions showed positive associations with TOC and quartz and negative associations with clay. Additionally, D1 and D2 also demonstrated a positive correlation with Langmuir volume. The presence of micropores was found to significantly influence the formation of an irregular pore structure in shale. As the pore size decreased, the adsorption specific surface area increased, resulting in a more intricate pore structure, and the fractal dimension of the pores elevated, ultimately. This intricate structure is beneficial for the accumulation of shale gas. These research findings offer valuable insights for the comprehensive assessment of deep shale gas, and enrich our knowledge of enrichment mechanisms in deep shale gas reservoirs. [ABSTRACT FROM AUTHOR]

Background: Health has effects on children's academic performance. Qi deficiency is generally used to assess an individual's health in the Chinese traditional medicine theory. This study explores the effects of qi deficiency on children's academic performance and examines whether mental fatigue mediates these effects. Methods: A total of 550 students aged 10-13 in fifth-grade were surveyed in a big primary school in Sichuan Province in November 2023 using paper-pencilbased questionnaires. Qi deficiency and mental fatigue were assessed, and exam scores in Chinese and Mathematics were recorded. Pearson's correlation and linear regression analyses were used to test the mediation model and hypotheses. Results: The fifth-grade students had mild qi deficiency (M = 2.09) and a mild state of mental fatigue (M = 2.38) on a five-point Likert scale. The average exam scores in Mathematics and Chinese were 70.07 and 74.44 points out of 100, respectively. Qi deficiency was associated with Mathematics scores (r = -0.37, p < 0.01) and Chinese scores (r = -0.30, p < 0.01), and mental fatigue (r = 0.47, p < 0.01). Furthermore, mental fatigue was associated with Mathematics scores (r = -0.46, p < 0.01) and Chinese scores (r = -0.34, p < 0.01). Linear regression analyses showed that qi deficiency significantly predicted Mathematics scores (β = -0.26, p < 0.01), Chinese scores (β = -0.19, p < 0.01), and mental fatigue (β = 0.41, p < 0.01). When qi deficiency was controlled for, mental fatigue significantly predicted Mathematics scores (β = -0.28, p < 0.01) and Chinese scores (β = -0.17, p < 0.01). Conclusion: The mediation model and hypotheses were well supported, indicating that mental fatigue mediated the influence of qi deficiency on academic performance of fifth-grade students. Furthermore, the mediation effect of mental fatigue on Mathematics scores was a little stronger than that on Chinese scores. [ABSTRACT FROM AUTHOR]

Efficient exploration of geothermal resources is the basis of exploitation and utilization of geothermal resources. In recent years, Geographic Information System (GIS) has been increasingly used for the exploration owing to its power ability to integrate and analyze multiple sources of data related to the formation of geothermal resources, such as geology, geophysics, and geochemistry. Correctly understanding the control effect of evidence factors on geothermal resources is the premise and basis of whether the prediction results of evidence weight model are accurate. Traditionally, the conventional weight of evidence model assume that each evidence factor exerts a uniform controlling effect on the formation and distribution of geothermal resources. However, recent research indicates significant variations in the controlling ability of factors such as faults and granites, influenced by factors like activity levels and crystalline ages. Yet, studies addressing this differential control are lacking. To address this gap, we propose a series of weight of evidence models using abundant geological, geophysical, and geothermal data from the western Sichuan plateau, a high-temperature geothermal hotspot in China. This study aims to investigate the impact of varying controlling abilities of evidence factors on the evaluation model, with faults and granites as a case. Performance metrics include prediction rate, success rate index, receiver operating characteristic curve (ROC) and prediction rate of geothermal well. The findings of this research reveal that the weight of evidence model developed through the methodology outlined in this study exhibits superior performance compared to the conventional weight of evidence model. This superiority is evidenced by higher prediction rates, success indices, prediction rate of geothermal wells, and larger AUC values of ROC. Among these models, the weight of evidence model considering both fault and granite classification have the best performance in model evaluation indicators, with a prediction rate of 22.528 and a success index of 0.015408 in the very high potential area. The prediction rate and success index of the high potential area are 3.656 and 0.0025, respectively, and the prediction rate and success index of the middle potential area are 1.649 and 0.001128, respectively, and the AUC value is 0.808, indicating that the model has good accuracy. In terms of geothermal well prediction, the total prediction rate of geothermal favorable areas based on fault and granite classification evidence weight model is as high as 47.0526. Therefore, when constructing the weight of evidence model, the influence of the difference control of evidence factors on the formation of geothermal resources should be fully considered. These results underscore the effectiveness of the proposed methodology in enhancing the predictive accuracy and reliability of geothermal resource assessment in this study. Based on the prediction results of the weight of evidence model considering both fault and granite classification, four favorable geothermal areas with abundant surface heat display are identified in this paper, namely Kangding, Litang, Batang and Ganzi-Dege. In addition, the relatively weak surface heat display areas such as Jiulong, Daofu, Luhuo and Derong also show high geothermal potential. Some attention should be paid to geothermal exploration in the future. [ABSTRACT FROM AUTHOR]

Recent exploration has revealed that the Middle Triassic Leikoupo Formation 32 Submember (T2/32) in the Sichuan Basin contains unconventional marl reservoirs with significant natural gas potential. Due to limited cores, old wells, and conventional logs, however, the lithological understanding of T2/32 is incomplete and relies solely on inaccurate mud logs. This lack of lithological foundation challenges geology and petroleum research. To identify complex lithology, this paper presents a double-hierarchical workflow to identify seven types of lithology using logs. The first order distinguishes salt, anhydrite, and marl, while the second order further subdivides marl into anhydrite marl, argillaceous limestone, shaly limestone, and limy shale. Different rocks' logging response characteristics are summarized based on quantity-limited cores and micro-resistivity imaging logs. Lithological identification of 2D and 3D plots is established using sensitive GR, DEN, and RT. Corresponding identification standards are built in two hierarchies. According to these standards, the lithology of T2/32 is identified in a total of 119 wells. Finally, the lithological characteristics of vertical, horizontal, and plane are discussed in the research area. The research results may aid in comprehending the entire lithological characteristics of the complex marl reservoir in T2/32 of the Sichuan Basin. It would help the exploration potential of petroleum systems in turn. [ABSTRACT FROM AUTHOR]

Small-scale landslides that occur at unexpected locations and seasons can cause severe losses of life and property. Unusually, the Shidang landslide occurred prior to the rainy season in Jinkouhe County, Sichuan Province, China, resulting in 19 deaths and missing persons. In this paper, landslide characteristics and causes are revealed through a field investigation, laboratory experiments, and numerical calculations. The results show that the Shidang landslide was a small-scale landslide with a total volume of approximately 19500 m3, originating from hollow colluvial deposits. The geomorphological hollow above the landslide provided topographic conditions for the collection of subsurface runoff. Groundwater flow was transported downward along dolomite and accumulated in the area above shale, with a low conductivity. Groundwater table rise combined with the decrease in the soil strength led to landslide occurrence. Improper siting of residential areas and insufficient vigilance against post-rainfall landslides caused this tragedy. Considering the post-disaster lessons, it is strongly recommended that in the future, more attention should be given to the investigation of landslides triggered by groundwater, especially in determining landslide hydrological system boundaries. In mountainous areas where the developable space is limited, areas with hollow landforms are potentially at risk of geohazards. This paper could provide a reference for the prevention and management of similar landslides and new insights for updating landslide investigation systems and residential housing plans. [ABSTRACT FROM AUTHOR]

There are significant challenges in attracting young people's attention to the protection and inheritance of intangible cultural heritage (ICH). Even Sichuan opera, a highly interactive and entertaining local cultural representative, struggles with inheritance issues. To solve this problem, this paper utilizes Augmented Reality (AR) technology to achieve a virtual try-on of Sichuan opera costumes and interactive face transformation for Sichuan opera. By virtual modeling, skeleton binding, weight setting of Sichuan opera dan-role costumes, and rendering mapping using body tracking and ray tracing as well as cloth simulation techniques, the AR try - on of Sichuan opera costumes is realized. Additionally, the representative facial masks of Sichuan opera's face-changing and role types are digitally reconstructed in 3D, and AR interactive face transformation is accomplished through visual programming. Experimental results demonstrate that in the AR environment, real-time matching and stable interaction among human body movements, costumes, and facial masks are achieved. By conducting AR experiments and surveys among the youth group, it is found that compared to traditional methods, AR technology is more effective in capturing the attention and recognition of the younger generation. This is beneficial for the inheritance and development of intangible cultural heritage within this demographic. [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]

In this paper, taking the Wufeng Formation-Longmaxi Formation shales in the southeastern region of the Sichuan Basin as an example, the thin section, X-ray, geochemical testing and TIMA techniques are used to systematically study the source analysis and characterization of quartz fractions, and in turn, the mechanism of the development of highly brittle shale reservoirs is revealed. The results of the study show that the high quartz mineral content segments are well stacked with the organic matter enriched segments. Excess silica occurs in the lower part of the Wufeng -Longmaxi Formation, and some samples in the middle and upper part of the Wufeng and Longmaxi Formation have excess silica deficit; the K2O/Na2O ratio is generally >1, reflecting that the reservoir quality is affected by the biogenic silica. Land- sourced detrital quartz grains, on the other hand, are significantly larger and are generally distributed over a range of tens to more than a hundred micrometers, often with clay minerals and organic matter around the periphery. Biogenic siliceous fossils are mainly developed in the lower part of the Wufeng-Longmaxi Formation, dominated by biogenic siliceous remains such as radiolarians. TIMA test results show that quartz belongs to the mineral type with absolutely dominant content ratio in the bottom samples of the Wufeng-Longmaxi Formation and reflects the characteristics of fine-grained-muddy/ carbonaceous horizontal laminar deposition. There are three different types of quartz grain distribution patterns in shale reservoirs, including excess silica type, multi-source silica type, and land-source silica type. The layers of highly brittle shale reservoirs are all at the bottom of Wufeng-Longmaxi Formation, which is rich in biogenic silica, and at the same time, they are also in the active layers of montmorillonite and other abundant volcanic activities, and they are developed in the closed basins as well as the deep-water land scaffolds, so that the reservoirs have a good self-sealing; at the same time, the combination of microcrystalline quartz+organic matter makes the reservoirs have an excellent mechanical brittleness. [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]

This paper aims to investigate the seismic motion characteristics of bedrock and overburden layer slope with the prototype model taken from slopes in the Zheduo Mountain in Northwest Plateau of Sichuan Province, China. Based on dimensional analysis and similarity principle, two model tests with different slope angles were carried out. A transfer function analysis method was proposed to interpret the results from shaking table tests. After eliminating trend terms and signal filtering, the time-domain acceleration was transformed into frequency domain. Then the transfer function was calculated by an average periodic chart. The variation of transfer function from different positions was analyzed by Pearson correlation coefficient, and the least square iteration method was used for modal analysis. The effect of seismic intensity on the dynamic response was highlighted. It is found that the transfer function obviously changes when the slopes are destroyed. Results from modal analysis show that the natural frequency decreases with the increase of the excitation intensity, and the damping ratio increases due to slope damage. [ABSTRACT FROM AUTHOR]

The living heritage approach seeks to link heritage properties with the living dimensions attributed by local communities - tangible and intangible heritage. However, how living heritage can be further understood and managed in post-disaster traditional settlements has yet to be explored adequately. This paper discusses the concept of living heritage embedded in post-earthquake planning and reconstruction of traditional settlements in Jiuzhaigou Valley, a World Heritage property in Sichuan Province. The thematic analysis method is employed to conduct both deductive and inductive content analysis of governmental administrative documents on post-earthquake reconstruction policies and practices. The results demonstrate that the continuity of heritage and the continuity of community are the two essential attributes conceptualizing living heritage by developing a people-centered approach to resilience-building in post-disaster traditional settlements. The paper broadens the concept of living heritage by incorporating the discussion of heritage and traditional settlements into a post-disaster context. The living heritage approach can be further elaborated into an integrated heritage management approach relying on community values and empowerment, to promote urban and rural conservation and urbanization policy-making and practices worldwide. [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]

This paper explored features of hedges in college students' English writing at different language levels and their differences from native students' writing. The 2000 writing texts by students from 3 key universities and 8 ordinary universities in Chongqing and Sichuan Province were offered by China Wordnet Company who initiated English writing campaign in 2016. The result showed that compared with native students, students from key and ordinary universities used more hedges, relied on a narrow range of hedges and offered stronger commitments to statements. Language level did not play a decisive role in these aspects. [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]

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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]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Albinism is a kind of anomalous hypopigmentation with a complete white pelage, and it is completely devoid of melanin in the skin, hair follicles and eyes. The present paper firstly reports two cases of adult mole shrews (Anourosorex squamipes) with albinism, which were found in Sichuan province of southwestern China. The two female mole shrews show a complete white pelage. Stained by the soil, the albinistic mole shrews show a yellowish white color. Besides the same morphological characteristics, all the body parameters of the two albinistic mole shrews are within the parameter range of the six normal mole shrews captured at the same site. [ABSTRACT FROM AUTHOR]