Expansion Path Research Articles

大学韩语课程思政的跨学科拓展路径探究

As a general foreign language course of College Korean language course, due to the curriculum, student characteristics, teaching methods and other reasons, the ‘Ke-Cheng Si-Zheng’ has not been well integrated into the teaching of College Korean language course, and in the research related to the ‘Ke-Cheng Si-Zheng’, the research related to the College Korean language course is in the embarrassing situation of being marginalized. Analyzing the current situation of ‘Ke-Cheng Si-Zheng’ in the College Korean language course, there are problems such as the lack of awareness of the ‘Ke-Cheng Si-Zheng’, a single and shallow angle of the content of the ‘Ke- Cheng Si-Zheng’, and a single teaching method of the ‘Ke-Cheng Si-Zheng’. This paper will break the limitations of the discipline itself, integrate the knowledge of different disciplines organically, and explore the path and direction of the interdisciplinary expansion of the contents of the ‘Ke-Cheng Si-Zheng’ with the goal of cultivating compound talents with interdisciplinary vision and ability based on the College Korean language course.

Study on the Development History and Expansion Path of Electric Vehicles in Henan Province

Study on the Development History and Expansion Path of Electric Vehicles in Henan Province

Research on Digital Economy Driving High Quality Development of China's Manufacturing Industry

With the profound changes in the global industrial economic situation, the industrial competition pattern is further adjusted, China's original reliance on the "demographic dividend" effect of the manufacturing industry expansion path is facing the challenge of low-end locking and marginalization, the urgent need to cultivate new kinetic energy. Get rid of the inherent low-cost competition and scale expansion mode, promote the digital transformation of the manufacturing industry, are the road to high-quality development of the manufacturing industry under the new development pattern. On the one hand, the digital economy has reconstructed the layout of the manufacturing industry chain in digital space and physical space, effectively unimpeded the domestic macrocycle; on the other hand, the digital economy can promote China's manufacturing industry to the high-end of the value chain, and win a larger profit space and the right to speak for the manufacturing industry to integrate into the international cycle. Therefore, under the new development pattern of internal and external double cycle, digital economy has become the new kinetic energy for the upgrading of China's manufacturing industry. This project combs through the domestic digital economy to drive the high-quality development of the manufacturing industry related literature, summarizes the economy to drive the high-quality development of the manufacturing industry measurement method, organizes the recent years of China's manufacturing industry development related research, consolidates the research foundation and expands the research perspective, constructs the "scale - structure", We construct a four-dimensional analytical framework of "scale-structure" and "circulation-external circulation" for the high-quality development of manufacturing industry, and explore the theoretical mechanism of digitalization-driven high-quality development of manufacturing industry. Through the use of econometric modeling methods to carry out empirical analysis of the digital economy driven manufacturing high quality, according to the empirical results, more scientific formulation of the digital economy driven China's manufacturing industry upgrading countermeasures recommendations.

Developing a new flexibility-oriented model for generation expansion planning studies of renewable-based energy systems

This paper presents a flexibility-oriented generation expansion planning (GEP) model with the goal of increasing renewable energy penetration. The model incorporates GEP and unit commitment while considering improved reserve requirements. Utilizing GEP and unit commitment results in considering short-term operational constraints. Furthermore, given the increase in renewable portfolio standards (RPS) and the need for accurate reserve requirements determination, it is essential to consider improved reserve requirements in the proposed model. Moreover, this model utilizes a metric that measures power system flexibility deficiency and leads to flexible power system expansion by integrating it into the objective function. The flexibility-oriented GEP model is developed using mixed-integer linear programming and applied to a scaled-sized case study representing the national power system of Iran with a twenty-year planning horizon. The results show that neglecting improved reserve requirements leads to plans short on flexibility, and considering these requirements substantially declines the upward flexibility violation costs by 68% under the base scenario and 87% under increasing non-hydro RPS scenario. Furthermore, using the flexibility metric coupled with improved reserve requirements changes the optimal path for power system expansion and reduces the risk of flexibility deficiency. Also, increasing RPS by 35% would strongly depend on developing fast-response dispatchable generation units in the years before renewable energy resources expansion; however it doesn’t impose a significant extra cost of planning and the total cost will increase just by 9.43%.

Identification of quartz cement in sandstone through deep learning segmentation of electron microscopy images

Quartz cementation is a crucial factor in controlling the petrophysical properties of sandstone reservoirs. However, reliable identification of sandstone cementation often requires petrographic analysis of rock thin sections using a combination of optical light microscopy, backscattered electron (BSE), and cathodoluminescence (CL) images. This process is currently carried out manually and is both costly and time consuming. In this study, we present the first attempt to automate this process by identifying sandstone cement through convolutional neural networks (CNNs). We used a combination of BSE and CL images acquired from sandstone thin sections sourced from formations in the US, Israel, and the Netherlands. For each image pair we created a labeled mask with 4 classes: (i) quartz grains; (ii) quartz cement; (iii) porosity; and (iv) Other phases. We developed a U-Net with a total of 10 layers: 5 layers for the contracting path and 5 layers for the expansive path. The model is trained to predict the segmentation mask for a given input of paired BSE and CL images. A high level of accuracy is achieved for quartz grains (91%), quartz cement (78%), and porosity (95%). By contrast, the Other phase class was poorly predicted due to a combination of mineral heterogeneity and low representation. Our results indicate that Deep Learning algorithms provide a promising avenue for the automation of quartz cement detection in sandstone images.

The Path of Meishan Cultural Renewal and Expansion Based on Data Association Rules

Abstract Metadata and data association rules are combined in this paper to create an aggregation framework for Meishan cultural digital resources. Meishan cultural digital resources are standardized through the use of metadata standards. The accuracy of metadata is evaluated in conjunction with trustworthiness. Metadata association rules are employed to evaluate the degree of association in Meishan cultural data. Enhance the efficiency of data association analysis by enhancing the Apriori algorithm. The algorithm is applied to the association analysis of Meishan culture data, and the degree of association between different digital resources and Meishan culture is determined by combining the data. The results show that the highest correlation with Meishan culture is the digital resources of documentaries and skills, with a correlation degree of 0.9, and the lowest correlation is the book resources, with a correlation degree of 0.65. The number of nodes in the period of 1-6 and the time consumed by each dataset is in the interval of (7000,1100).

Analysis of Interlayer Crack Propagation and Strength Prediction of Steel Bridge Deck Asphalt Pavement Based on Extended Finite Element Method and Cohesive Zone Model (XFEM–CZM) Coupling

The extended finite element method (XFEM) was employed for the computational modeling of internal defects within a bond layer. Furthermore, a cohesive zone model (CZM) was implemented to characterize the behavior of the bond layer in response to interactions at both the bond layer/steel plate and bond layer/asphalt paving layer interfaces. The coupling of XFEM and CZM was used for a comprehensive analysis of crack propagation within the bond layer as well as the assessment of phenomena associated with interfacial debonding and delamination. The feasibility and accuracy of the XFEM–CZM coupling method were verified by comparing it with the virtual crack closure technique (VCCT), CZM, XFEM–VCCT, and experiments. A double cantilever beam experimental model was established to simulate the process of interlayer-type cracks expanding from the inside of the bond layer to the interface between the bond layer and the upper and lower layers, causing debonding. This was undertaken to analyze the damage failure mechanism of interlayer-type cracks in asphalt paving layers of steel bridge decks; to discuss the impacts of the initial crack length, the interface stiffness, the interface strength, and the thickness of the bond layer on the performance of the overall interlayer bond strength; and to carry out the significance analysis. The results showed that the initial crack length, interface stiffness, and bond layer thickness had different effects on the expansion path of interlayer cracks. The interlayer strength decreased with an increase in the initial crack length and interface stiffness, increased with an increase in the interface strength, and decreased with an increase in the thickness of the bond layer. The interface stiffness had the most significant effect on the strength.

Automated diagnostic classification of diabetic retinopathy with microvascular structure of fundus images using deep learning method

Multi-class classification is a major concern in the research field, especially in medical image analysis. This work proposes a novel method for automatically segmenting the blood vessels and classifying Diabetic Retinopathy (DR) using fundus images. This system helps ophthalmologists with the early detection and grading of DR diseases. The main aim here is to detect any pathological changes happening in the retinal vascular structure for the development of DR, through which the patients can avoid undergoing expensive scans. The proposed system involves three different stages, including pre-processing, vessel segmentation, and classification. The input images are processed first to eliminate the noise, followed by green channel extraction and enhancement with Contrast Limited Adaptive Histogram Equalization (CLAHE) and gamma correction. Retinal Vascular Structure (RVS) segmentation is a major concern in this work as it is responsible for detecting the different stages of DR by detecting the presence of microaneurysms, haemorrhages, and exudates. The U-Net is used as a base architecture to develop the segmentation model. The contracting path in the U-Net contains four consecutive downsampling and upsampling layers with skip connections. After performing downsampling four times, information on the tiny blood vessels may be missed. Therefore, ResEAD2Net is introduced in this work, where the number of downsampling and upsampling layers is reduced to two instead of four, and two such contracting paths and expansion paths are added to the network. Thus, detailed semantic information can be retained with this structure. Residual blocks are included instead of convolution blocks to increase the computational speed. To include structural connectivity, the segmented output is passed through the proposed Regularized Random Walker (RRW) algorithm, which focuses on the broken blood vessels. Finally, the features are extracted from the vessel structure and passed through the Machine Learning (ML) classifier to predict the DR grading. The proposed method achieves better performance in segmentation with accuracy, sensitivity, specificity, and area under the curve values of 98.07%, 90.24%, 99.01%, and 97.51%, respectively, for the STARE dataset and 97.55, 90.07, 98.01, and 97.64, respectively, for the DRIVE dataset. It is also found that the features obtained from the segmentation and ML classifier outperforms the existing methods in multiclass classification by achieving accuracy, sensitivity, and specificity of 98.88%, 98.91%, and 98.29% with the Messidor-2 dataset.

Balancing urban expansion with a focus on ecological security: A case study of Zhaotong City, China

The conflict between socio-economic development and ecological environmental protection presents a significant challenge to achieving regional and urban sustainable development. In Zhaotong City, Yunnan Province, China, this conflict arises from the interplay between urban expansion and ecological environmental protection land, creating a fundamental tension between built-up and non-built-up ecosystems. This study focuses on investigating the spatial characteristics of expansion, constructing an ecological security pattern based on ecosystem service functions and sensitivity, and utilizing integrated models to simulate urban expansion paths and analyze their formation mechanisms. The results highlight that Zhaotong City has undergone rapid urban expansion, characterized by linear growth surrounding the central city, specifically in the east, north, and west directions. The expansion between urban areas exhibits a discontinuous dotted-star distribution, with concentrated urbanization and a distinct urban boundary observed in Zhaoyang District. In contrast, other areas experience scattered expansion, indicative of random land encroachment. Through simulation, three primary urban expansion axes are identified: the western town sprawl belt, central town development axis, and resource-rich town corridor. Ecological conflicts primarily arise in Zhaoyang District, Zhenxiong County, and Yanjin County. By combining these findings, the study analyzes the scenario pattern of urban expansion evolution in Zhaotong City, revealing a transition from the spreading spillover expansion of a single center (Zhaoyang and Ludian) during the circle expansion phase to a polycentric expansion and spreading spillover pattern centered around Zhaoyang District along three primary axial radial paths. In conclusion, this study contributes to our understanding of the challenges posed by the conflict between socio-economic development and ecological environmental protection in Zhaotong City. The findings underscore the importance of integrating ecological considerations into urban planning processes and highlight the need for sustainable development practices to achieve a harmonious coexistence between urban expansion and ecological security. The insights gained from this research offer guidance for fostering sustainable urban development in Zhaotong City and beyond.

한·중 냉각 형용사 대조 연구

Objectives The purpose of this study was to illuminate the semantic extension patterns of cooling adjectives in Korean and Chinese through a comparative study focusing on the Korean words ‘차다’, ‘차갑다’, ‘시리다’, ‘쌀쌀하다’, ‘서늘하다’, ‘싸늘하다’, and the Chinese word ‘涼’.
 Methods In this study, the definitions and characteristics of the original meanings, extended meanings, metaphor, and metonymy were first summarized. Then, a comparative analysis of the dictionary meanings of cooling adjectives in Korean and Chinese was conducted. Through this process, distinctions were made between the original meanings and extended meanings of these adjectives. Subsequently, corpus data were used to analyze the patterns of semantic extension of cooling adjectives in Korean and Chinese.
 Results The extended meanings of the Chinese word ‘涼’ and the Korean words ‘차다’, ‘차갑다’, ‘시리다’, ‘쌀쌀하다’, ‘싸늘하다’, and ‘서늘하다’ were found to encompass both similar and completely distinct meanings. However, the most important observation was that even when there are similar meanings, there are significant differences in the extension paths. The extension paths of the character 涼 mainly focus on abstract semantic extensions, and the verb ‘차다’ reveals not only abstract semantic extensions but also sensory extensions. Furthermore, when comparing the extended meanings of 涼 and ‘차갑다’, ‘쌀쌀하다’, ‘서늘하다’, and ‘싸늘하다’, it can be observed that while they share some similar extended meanings, the extension paths of ‘차갑다’, ‘쌀쌀하다’, ‘서늘하다’, and ‘싸늘하다’ are relatively diverse. Lastly, it can be confirmed that there is no similar meaning in the extended meanings of 涼 and ‘시리다’, and from the perspective of extension paths, both words share the commonality of extensions related to ‘state’ and ‘abstract entity’. Additionally, the Chinese character 涼 has additional extension paths related to ‘body parts’ and ‘area (space)’.
 Conclusions This study is meaningful in that when making sentences using adjectives for cold , the vocabulary is accurately selected and used by referring to the expansion path of the extended meaning of Korean and Chinese adjectives for cold.

Thermodynamics-Inspired Multi-Feature Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) is widely used in many fields such as detection and guidance systems and is of great research importance. However, small targets in infrared images are typically small, blurry, feature-poor, and prone to being overwhelmed by noisy backgrounds, posing a significant challenge for IRSTD. In this paper, we propose a thermodynamics-inspired multi-feature network (TMNet) for the IRSTD task, which extracts richer and more essential semantic features of infrared targets through cross-layer and multi-scale feature fusion, along with the assistance of a thermodynamics-inspired super-resolution branch. Specifically, it consists of an attention-directed feature cross-aggregation encoder (AFCE), a U-Net backbone decoder, and a thermodynamic super-resolution branch (TSB). In the shrinkage path, the original encoder structure is reconstructed as AFCE, which contains two depth-weighted multi-scale attention modules (DMA) and a cross-layer feature fusion module (CFF). The DMA and CFF modules achieve self-feature-guided multi-scale feature fusion and cross-layer feature interaction by utilizing semantic features from different stages in the encoding process. In thermodynamics, the difference in the formation of different heat between particles leads to heat transfer between objects, which inspired us to analogize the feature extraction process of gradually focusing the network’s attention to an infrared target under the constraints of the loss function to the process of heat transfer. On the expansion path, the TSB module incorporates the Hamming equation of thermodynamics to mine infrared detail features through heat transfer-inspired high-resolution feature representations while assisting the low-resolution branch to learn high-resolution features. We conduct extensive experiments on the publicly available NUAA-SIRSST dataset and find that the proposed TMNet exhibits excellent detection performance in both pixel-level and object-level metrics. This discovery provides us with a relatively dependable guideline for formulating network designs aimed at IRSTD.

Study on the Expansion of ICSID Arbitration Jurisdiction

With the development of the global economy, international investment gradually occupies an important position in the market, and at the same time it also brings some legal disputes, ICSID is the direct main body to resolve international investment disputes, while its arbitration jurisdiction is the prerequisite and the primary issue for the arbitral tribunal to resolve international investment disputes. At present, the expansion of ICSID's arbitration jurisdiction is an issue that needs to be addressed by the international community. This paper is divided into four parts, the first part of the overview starts from the main body ICSID, the second part analyzes the expansion path of ICSID's arbitration jurisdiction and its reasons, the third part puts forward the reform path of ICSID's arbitration jurisdiction, and the fourth part puts forward China's countermeasures.

Hybrid power generation plants: paths for expansion and feasibility in the use of renewable resources

In the last years, the participation of renewable sources in the global electro-energetic matrix has been around 25%, but there is still possibilities for expansion, both due to the availability of resources and the appeal of popular opinion in the face of the imminent scenario of climate change. Many studies have been developed in this direction and perhaps the biggest problems to be dealt with are: frequent intermittency, seasonal scarcity, configuring stochastic scenarios. The implementation of hybrid generation plants is a good solution to mitigate the aforementioned problems. The cooperative hybridization of solar photovoltaic, biomass thermal, wind and hydroelectric plants has been presented as an interesting option to mitigate the problems arising from uncertainties and thus enable the expansion of renewable natural resources in the electro-energetic matrix with operational safety.

Technical change, task reallocation, and wage inequality

Abstract This paper empirically investigates wage inequality within the group of skilled workers in the recent four decades in the USA using CPS data and finds evidence that the trend of wage growth of the top and bottom 10th percentile of skilled workers significantly diverged starting from 2000. Using a task-based framework of occupation, I find that the changing trend of wage inequality was entirely driven by one category of occupation, namely the nonroutine analytic occupation. Then, I consider in a model task reallocation between two broad task categories, namely the routine and abstract task, induced by an ongoing investment-specific technical change. In my model, the labor in the routine task is replaced by cheaper machines due to investment-specific technical change, then workers that are less productive in the abstract task enter abstract occupations. As a result, the wage inequality in the abstract task widens because of the reallocation of less productive workers from the routine task to the abstract task, that is, the “composition effect.” In addition, since economic agents tend to postpone the investment in machines after the ongoing investment-specific technical change takes place for a while, the expansion path of wage inequality is not linear but features an acceleration of wage dispersion in the middle of the technical change. The quantitative results suggest that the model is able to provide a well-matched timing and magnitude of the nonlinear expansion path in wage inequality that is observed in the data.

The Influence of Multi-Size Basalt Fiber on Cemented Paste Backfill Mechanical Properties and Meso-Structure Characteristics

As the mine enters the deep mining stage, there is a need to enhance the compressive strength and toughness of the backfill. The objective of this study is to examine the mechanical properties of cemented tailings backfill after the incorporation of multi-size fibers and to validate the toughening mechanism of basalt fibers (BFs). To achieve this, a series of basic mechanical property tests for multi-size BFs mixing were devised, accompanied by industrial computerized tomography (CT) scanning and discrete element simulation. This study shows that the compressive strength increases and then decreases with the increase of BF dosage at a certain percentage of each size, and the splitting tensile strength gradually increases with the increase of BF dosage. The compressive strength tends to decrease and then increase, and the splitting tensile strength increases and then decreases as the fiber size ratio changes. The distribution of cemented tailings backfill and BF within the discrete element model is random. A few BF cannot play a bridging role; however, a moderate amount of BF is relatively uniformly distributed in the model to form a network structure, which generates a bond between the particles and the matrix and can effectively limit the expansion path of cracks and enhance the toughness.

Long-term optimal capacity expansion planning for an operating off-grid PV mini-grid in rural Africa under different demand evolution scenarios

Using real-time load data and HOMER Pro's ‘multi-year’ optimization tool, this paper investigates the long-term cost optimal capacity expansion planning (CEP) for an overloaded photovoltaic (PV) mini-grid (MG) with storage batteries in off-grid rural Ethiopia over a 20-year planning horizon. Three distinct annual energy demand growth scenarios were considered: 0 % (fulfils the minimum load requirement), 5 %, and a 15 % from productive users only. In all scenarios, the generation mix consists of only solar energy and the maximum allowable capacity shortage (MACS) is limited to 10 %. The findings reveal that, in all scenarios, the largest capacity expansion is performed on the battery and PV systems, covering up to 73 % and 35 % of the total expansion costs, respectively. The annual unmet load fraction of the expanded MG system ranges from 5.9 % in scenario-3 to 9.4 % in scenario-1, and the cost of electricity (LCOE) ranges from $0.404/kWh in scenario-3 to $0.887/kWh in scenario-1. The results indicate that the scenario-3 expansion path is comparatively cost-effective and has the highest reliability; but it still falls short of fully satisfying the required load demand and is not financially viable. Surprisingly, increasing the reliability of the scenario-3 capacity expansion from 94 % to 100 % raises the MG's Net Present Cost by 37 %. The sensitivity analysis shows that the MACS, ambient temperature, and battery's depth of discharge significantly affect the cost and performance of the capacity expansion. The study demonstrates (a) there are significant trade-offs between minimizing MG expansion costs and maximizing reliability levels; (b) capacity expansion based solely on cost-minimization without considering key constraints and uncertainties (demand, cost, PV, and battery degradations) may not provide a practical and robust solution to severe reliability issues, (c) capacity expansion that supports demand from productive users increases the cost-effectiveness and bankability of isolated MGs.

Research on low-velocity impact characteristics of Z-pin reinforced quartz/phenolic laminates

The problem of in-plane damage caused by dynamic impact loading of composite laminates can be enhanced by implanting Z-pins. The effect of Z-pin implantation on the impact resistance of composite laminates is investigated experimentally, and the damage process is analyzed by establishing a low-velocity impact finite element model of Z-pin reinforced laminates. The test results show that the maximum impact contact force of Z-pin reinforced laminates can be increased by 8.23%, and the maximum contact force and absorbed energy of Z-pin reinforced samples both increase gradually with the increase of impact energy. The simulation results show that with the increase of impact energy, the expansion of impact damage is hindered by the Z-pin, and it reduces in-plane matrix damage. The expansion path of the interlaminar crack changes when it reaches the Z-pin enhancement area, and the shape of the interlaminar damage changes from circular to elliptical.

Enhanced strength-ductility synergy of laser additive manufactured stainless steel/Ni-based superalloy dissimilar materials characterized by bionic mechanical interlocking structures

The unique mechanism of toughness enhancement found in natural materials has served as inspiration for the development of a new generation of dissimilar metal materials (DMMs). Here we present a bionic mechanical interlocking structure strategy that stainless steel 316L and Inconel 625 DMMs with an enhanced strength-conductivity trade-off processed by laser additive manufacturing (AM), possessing an ultimate tensile strength of 675.64 MPa, and a uniform elongation of 33.6%. The incompatible deformation between SS316L (plastic deformation) and IN625 (non-deformation) contributes to overcoming the strength-ductility trade-off. The integration of alternating soft and hard non-uniform structures plays a pivotal role in effectively absorbing and dissipating the energy introduced by external loads during crack propagation. This geometric arrangement and multilevel structure at the interface notably reduces the effective stress intensity factor along the crack path, leading to added resistance against loading. The multi-strengthening mechanism coupled with the heterogeneous deformability gives the material excellent strength-ductility synergy, especially the bionic mechanical interlocking structure extending the crack expansion path, which helps to avoid premature failure of weak interface layers. These findings will provide new insights into the controlled design of high-performance DMMs, which highlights the potential to inherit merits from constitutive materials for better strength-ductility combinations.

Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization

Currently, video and digital images possess extensive utility, ranging from recreational and social media purposes to verification, military operations, legal proceedings, and penalization. The enhancement mechanisms of this medium have undergone significant advancements, rendering them more accessible and widely available to a larger population. Consequently, this has facilitated the ease with which counterfeiters can manipulate images. Convolutional neural network (CNN)-based feature extraction and detection techniques were used to carry out this task, which aims to identify the variations in image features between modified and non-manipulated areas. However, the effectiveness of the existing detection methods could be more efficient. The contributions of this paper include the introduction of a segmentation method to identify the forgery region in images with the U-Net model’s improved structure. The suggested model connects the encoder and decoder pipeline by improving the convolution module and increasing the set of weights in the U-Net contraction and expansion path. In addition, the parameters of the U-Net network are optimized by using the grasshopper optimization algorithm (GOA). Experiments were carried out on the publicly accessible image tempering detection evaluation dataset from the Chinese Academy of Sciences Institute of Automation (CASIA) to assess the efficacy of the suggested strategy. The results show that the U-Net modifications significantly improve the overall segmentation results compared to other models. The effectiveness of this method was evaluated on CASIA, and the quantitative results obtained based on accuracy, precision, recall, and the F1 score demonstrate the superiority of the U-Net modifications over other models.

Strain Field Evolution Analysis of Brittle Shale with Initial Fractures Based on DIC

Wellbore instability mainly occurs in shale formations, and it is of great significance to deeply analyze the characteristics of shale-failure behavior to evaluate the stability of the shale surrounding the well wall during drilling. Through a uniaxial compression experiment and DIC technology, the whole strain field of shale specimens with prefabricated holes and cracks under uniaxial compression is measured. The experimental data of load, displacement and strain field are analyzed comprehensively. The results show that the fracture location and expansion path of shale are closely related to the evolution of the strain field. The evolution of the strain field directly affects the failure behavior of the rock. Under the action of load, local high strain will first appear around the initial shale defects (pores and fractures), and stress concentration will occur. With the increase of load, cracks and failures will first appear in the local high-strain zone, and the failure will spread along the region and path and eventually lead to the overall failure of the rock. The establishment of a description method for shale-failure behavior through strain-field evolution can effectively analyze the crack behavior of shale with initial defects such as holes and cracks and provide theoretical and experimental bases for the stability evaluation of the shale surrounding the well wall, including shale-strength prediction and shale-failure mechanism.