Digital Imaging Technology Research Articles

Television and the Origins of Visual Pattern Recognition AI in Japan: a ‘Quest for a Seeing Machine’

Abstract This article traces the genealogy of contemporary data-driven computer vision to developments at the Japan Broadcasting Corporation (NHK) in the 1960s. Specifically, it examines NHK’s Visual and Auditory Information Science Unit and its role in the invention of the world’s first deep convolutional neural network. The use of television to collect viewer behaviour data enabled modelling of eye-brain information processing, in particular mechanisms of feature extraction. This, in turn, linked Fukushima Kunihiko’s formative work on signal compression to the development of a pattern recognition machine, resulting in the creation of the world’s first convolutional neural network. Recovering this history is important for two reasons. First, it helps counter a trend of ‘digital universalism’ that covertly homogenizes local differences into a single culture of artificial intelligence in the Cold-War-era US. Second, it reveals the largely ignored role of television in the genesis of digital image technologies and AI more broadly.

Automated Fingerprint Identification System

Abstract: AFIS is regarded as one of the greatest advancements in biometric systems as it enables fast and efficient identification of an individual by their distinct fingerprint designs. This system employs the use of digital imaging, finger-power techniques and other technologies in the storage and comparison of fingerprints. With AFIS, fingerprint images are all digitized making it easier to carry out crime investigations, border control and general identification of persons. The strength of AFIS systems is evidenced by the increased accuracy levels along with the capacity to accommodate numerous records and still allow for fast searches by fingerprinting matching to existing images for law enforcement on file. Some problems nevertheless persist which include the quality of fingerprints and environmental interferences, however machine learning and artificial intelligence improvements are still being incorporated into the systems to make them work even better. This paper investigates the system architecture and components, operational aspects, and prospects for development of the AFIS technology, which is essential in contemporary informational security

Utility of digital cell imaging as an aid to enhance pathologist peripheral blood smear review for routine and on call practice: A verification study

Abstract Introduction/Objective Microscopic examination of peripheral blood smear (PBS) provides important information to support the diagnosis of hematological diseases. However, manual microscopic examination of PBS is labor-intensive and cannot be done remotely. CellaVision is digital cell imaging technology that provides pre-classification and digital imaging of the red blood cells (RBCs), white blood cells (WBCs), and platelets, myeloid, lymphoid and erythroid precursors. Several studies have evaluated the performance of this technology in the hematology laboratory as tool to assist laboratory technologists; our study evaluates its use for pathologist review of peripheral blood smears both during routine and on call practice. Methods/Case Report Our study included 30 PBS and two hematopathologists with greater than 10 years’ experience in performing PBS review. We compared pathologist’s own impression of microscopic PBS review to their own impression using CellaVision software (intraobserver comparison). Additionally, pathologist to pathologist impression was compared for both methods (interobserver comparison). All 30 cases revealed abnormal findings including, but not limited to: pancytopenia, leukocytosis, leukopenia, anemia, thrombocytopenia, and thrombocytosis. Of the 30 cases, 5 “on-call” were cases when the pathologist had to come on–site for pathologist PBS review. Results (if a Case Study enter NA) The pathologists own PBS interpretation (intraobserver) by microscopy and CellaVision had a concordance of 96.7%. The pathologist to pathologist interpretation of both methods had a concordance of 96.7%.The one discrepant case was a smear with platelet clumping and normal platelet count. On chart review of this case, this discrepancy would have minimal impact on patient care. Conclusion Pathologist use of CellaVision in their routine practice may result in speedier PBS review, particularly in cases with leukopenia, and better communication with laboratory technologists. CellaVision is less reliable in identifying platelet clumps and should not be used in isolation in evaluating thrombocytopenia. Used with other relevant data, CellaVision has the potential to preempt in person microscopic review of PBS when on-call in certain situations.

Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives.

Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes.

A comparison study of pore structure and permeability of sandstone by BSE-SE images

The pore structure and permeability of geotechnical materials are critical parameters that guide underground engineering. With advancements in digital imaging technology, scanning electron microscopy (SEM) has emerged as a vital tool for examining the pore structure and permeability of these materials. SEM operates in two primary modes: backscattered electron imaging (BSE) and secondary electron imaging (SE), each of which emphasizes different aspects of the material’s structure. However, few studies have been conducted to elucidate the influence of these two modes on the quantification of geotechnical structural characteristics. This study undertakes a more comprehensive quantitative analysis of the structure and permeability of sandstones by juxtaposing the two modes. The findings indicate that the BSE mode excels in analyzing the structure and composition, whereas the SE mode highlights the surface morphology. In terms of image segmentation, BSE mode images are more conducive to effective segmentation. Although SE images permit viable segmentation when preprocessed, they tend to represent a greater number of discrete tiny pores. Furthermore, there exists a discernible correlation between pore size and shape, wherein larger pores exhibit heightened roughness and deviate more from sphericity. Notably, these larger pores predominantly contribute to the material's permeability. Given that the BSE mode more readily captures continuous pore structures, the permeability values derived from BSE images are significantly higher than those obtained from SE images. These findings hold profound implications for enhancing our comprehension of geotechnical materials' pore structure and permeability, thereby informing the strategic use of BSE and SE modes in related studies.

Shadows and signals: a brief history of medical imaging

Abstract Medical imaging is a revolutionary field that has transformed healthcare, providing clinicians with the ability to view the inside of the body without invasive procedures. This overview traces the evolution of the major medical imaging modalities from their inception to the advanced technologies used today. Starting with the serendipitous discovery of X-rays by Wilhelm Conrad Röntgen in 1895 the subsequent developments in various imaging modalities including fluoroscopy, computed tomography (CT), magnetic resonance imaging (MRI), nuclear medicine and ultrasound imaging are chronicled. The technological breakthroughs, pioneering scientists and clinicians involved, and the significant impact of these advancements on diagnostic medicine are discussed. Additionally the integration of digital imaging, artificial intelligence (AI) and machine learning technologies into medical imaging is considered together with their potential to revolutionise diagnostic procedures, enhance patient care and streamline healthcare delivery.

Research on the splitting tensile dynamic mechanics performance of post-high-temperature high-performance concrete

High-performance concrete (HPC) structures were affected by secondary hazards after a fire, research on its dynamic mechanics performance is vital for assessing post-disaster structural safety. Therefore, the mechanism which governs impact of coupling among high temperature, cooling mode and strain rate on the splitting tensile mechanics performance of HPC was delved; impact factors including five temperatures, two cooling modes and four strain rates were designed; digital image technology (DIC) and hydraulic servo instrument were adopted to investigate the splitting tensile mechanics performance of HPC with Brazilian disc. Failure mechanical parameters of HPC under varying operational conditions and full-field deformation parameters of the whole loading process were obtained. As indicated by research result, the splitting tensile strength of test specimens cooled naturally from 200 °C to 600 °C was higher than that of specimens cooled rapidly, while the splitting tensile strength under natural cooling from 800 ℃ decreased by 6 % compared with rapid cooling. At the same temperature, splitting tensile strength of HPC was gradually increased with the increase of strain rate, and Dynamic Increase Factor (DIF) was significantly increased, showing relatively high rate sensitivity. With increasing temperature, the increase in DIF for HPC declined somewhat. As revealed by the result of DIC analysis, at high temperature, damage was relatively mild at the development stage and entered the stage of abrupt change of strain field in a short time. With increasing strain rate, the overall damage duration of test specimen was significantly shortened, and cooling mode exerted no significant impact on the development of splitting tensile damage in HPC. The criterion for splitting tensile dynamic strength of post-high-temperature HPC was put forward on the basis of J criterion. Meanwhile, an analysis by backscattered electron (BSE) on microstructure of HPC revealed mechanism which governs the impact of high temperature and cooling mode on the splitting tensile dynamic mechanics performance of HPC. The research result serves as a theoretical basis for calculating and assessing the post-fire structural dynamic response of HPC.

Evaluating the Impact of Digital Mapping and 3D Imaging Technologies on the Restoration of Historical Sites in Southeast Asia

The preservation of cultural heritage sites is undergoing a transformation, propelled by advancements in digital and 3D technologies. This paper explores the integration and impact of such technologies, including digital mapping, 3D imaging, Artificial Intelligence (AI), and the Internet of Things (IoT), on the conservation of historical sites. By providing detailed case studies from Southeast Asia, this study examines the accuracy improvements, enhanced visualization capabilities, and increased public engagement facilitated by these technologies. It also addresses the technical, financial, and training challenges associated with their implementation. Furthermore, the paper discusses the potential of emerging technologies to revolutionize heritage conservation and predicts future trends in the field. The findings suggest that while there are significant benefits to the adoption of these technologies, careful consideration is needed to overcome the associated challenges and ensure sustainable integration into conservation practices.

ISK: Index steering kernel for multi-SWIR-image super-resolution

Multi-image super-resolution (SR) techniques reconstruct high-resolution (HR) images from multiple low-resolution (LR) counterparts, yet existing methods often fall short in reproducing high-frequency details when compared to true HR images, especially short-wave infrared (SWIR) images. This discrepancy, coupled with the technological and cost limitations of enhancing high-resolution imaging system performance through lens aperture size and sensor density, impedes the advancement of imaging systems and digital image technology. Addressing this, we propose the Index Steering Kernel (ISK) method, an innovation grounded in Gaussian process multi-image super-resolution, equivariant kernel theory, and steering kernel methods. Validated through comparative resolution chart tests using a SWIR camera (InGaAs detector), our method achieves a TV Line resolution of 60lp/mm in 4-image 2 × SR experiments, closely approaching the 65lp/mm of genuine high-resolution SWIR images and surpassing alternative approaches. In 9-image 3 × SR experiments, ISK consistently outperforms comparative methods. As a non-data-driven approach, ISK incorporates data compression designs, enabling 3 × SR of nine 640 × 512 SWIR images on a single NVIDIA GTX GeForce 1080Ti graphics card without image splitting, proving its computational efficiency while maintaining superior SR quality.

Intraoperative margin assessment during radical prostatectomy: is microscopy frozen in time or ready for digital defrost?

Intraoperative frozen section (IFS) is used with the intention to improve functional and oncological outcomes for patients undergoing radical prostatectomy (RP). High resource requirements of IFS techniques such as NeuroSAFE may preclude widespread adoption, even if there are benefits to patients. Recent advances in fresh-tissue microscopic digital imaging technologies may offer an attractive alternative, and there is a growing body of evidence regarding these technologies. In this narrative review, we discuss some of the familiar limitations of IFS and compare these to the attractive counterpoints of modern digital imaging technologies such as the speed and ease of image generation, the locality of equipment within (or near) the operating room, the ability to maintain tissue integrity, and digital transfer of images. Confocal laser microscopy (CLM) is the modality most frequently reported in the literature for margin assessment during RP. We discuss several imitations and obstacles to widespread dissemination of digital imaging technologies. Among these, we consider how the 'en-face' margin perspective will challenge urologists and pathologists to understand afresh the meaning of positive margin significance. As a part of this, discussions on how to describe, categorize, react to, and evaluate these technologies are needed to improve patient outcomes. Limitations of this review include its narrative structure and that the evidence base in this field is relatively immature but developing at pace.

Numerical simulations on compression behaviors of the laminated shale based on the digital image technology and the discrete element method

As an unconventional reservoir sedimentary rock, shale contains a series of layers and various microstructures that lead to complex mechanical properties, such as the anisotropy of stiffness and strength. This study is directed towards the anisotropy caused by the microstructures of the shale, employing the 2D particle flow code (PFC2D) to explore stiffness, strength, failure mode, and micro-crack evolution. More realistic microstructures and the calibration of microscopic parameters of the shale are reasonably considered through the computed tomography (CT) images and mineral analysis. The corresponding numerical simulation results are fully compared with the experimental results. In what follows, the sensitivity analysis is conducted on the key microscopic parameters and microstructure characteristics in numerical samples with laminated characteristics. The results show that the influence of microscopic parameters of the parallel bonding model on macroscopic parameters is related to the layering angle and the face type, and the microstructures and initial cracks of numerical samples can considerably affect the macroscopic mechanical behaviors of the laminated samples. Next, the effect of confining pressure on the mechanical properties of layered shale is also discussed based on the numerical results. These findings highlight the potential of this approach for applications in micro-scaled models and calibration of microscopic parameters to probe mechanical behaviors of the laminated rock.

Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising

ABSTRACT Demosaicking is a popular scientific area that is being explored by a vast number of scientists. Current digital imaging technologies capture colour images with a single monochrome sensor. In addition, the colour images were captured using a sensor coupled with a Colour Filter Array (CFA). Furthermore, the demosaicking procedure is required to obtain a full-colour image. Image denoising and image demosaicking are the two important image restoration techniques, which have increased popularity in recent years. Finding a suitable strategy for multiple image restoration is critical for researchers. Hence, a deep learning (DL) based image denoising and image demosaicking is developed in this research. Moreover, the Autoregressive Circle Wave Optimization (ACWO) based Demosaicking Convolutional Neural Network (DMCNN) is designed for image demosaicking. The Quantum Wavelet Transform (QWT) is used in the image denoising process. Similarly, Quantum Wavelet Transform (QWT) is used to analyse the abrupt changes in the input image with noise. The transformed image is then subjected to a thresholding technique, which determines an appropriate threshold range. Once the threshold range has been determined, soft thresholding is applied to the resulting wavelet coefficients. After that, the extraction and reconstruction of the original image is carried out using the Inverse Quantum Wavelet Transform (IQWT). Finally, the fused image is created by combining the results of both processes using a weighted average. The denoised and demosaicked images are combined using the weighted average technique. Furthermore, the proposed QWT+DMCNN-ACWO model provided the ideal values of Peak signal-to-noise ratio (PSNR), Second derivative like measure of enhancement (SDME), Structural Similarity Index (SSIM), Figure of Merit (FOM) of 0.890, and computational time of 49.549 dB, 59.53 dB, 0.963, 0.890, and 0.571, respectively.

Application of improved and efficient image repair algorithm in rock damage experimental research

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm’s operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis.

Integrating Fresnelet Transform and Spiking Cortical Model for robust medical image cryptosystem in zero-watermarking

Medical imaging is essential in modern healthcare as it enables accurate diagnosis, planning of therapies, and monitoring of various medical problems. The security and integrity of medical images have become of utmost importance due to the growing dependence on digital imaging technologies. Zero-watermarking is a technique used in digital image processing where the watermark is not embedded into the medical image instead, a key is generated for security purposes to achieve the information process. In the context of medical images, preserving visual fidelity and diagnostic accuracy is of utmost importance, making zero-watermarking an attractive approach for safeguarding sensitive healthcare data. This paper proposes a zero-watermarking system that utilizes the Frensnelet Transform and Spiking Cortical Model (FT-SCM) for extracting image features and enhancing the system’s resilience against various attacks. The proposed system uses FT-SCM techniques to generate a perceptual hash of size 1 × 64. The proposed cryptosystem is used to encrypt the watermark for better security. Later, the encrypted watermark and perceptual hash are merged to create a zero-watermark image (key). The proposed system yields an average PSNR, SSIM, and NC values of 57.07 dB, 0.99, and 0.75, respectively, which demonstrates an improved performance from the state-of-the-art zero-watermarking approaches. Also, the proposed cryptosystem is analysed and compared using security metrics. Security assessments demonstrate the proposed image cryptosystem approach’s efficiency, security, and realizability because of key generation in the confusion and diffusion process, which gives a chaotic nature compared to state-of-the-art approaches encryption methods.

Experimental Study on Surface Segregation Law of Three-Dimensional Accumulation Body of Sand and Stone Particles by Binary Mechanism

Sand and stone materials are one of the important raw materials for infrastructure construction. In recent years, natural sand resources have decreased rapidly. In order to protect rivers and DAMS and ecological balance, local governments have increased the control power of natural sand mining, and replacing natural sand with mechanically broken parent rock formation mechanism sand has become an inevitable trend of the industry development. During the crushing process of parent rock, sand particles of different particle size range are produced, and particle segregation occurs in the process of stacking in the yard, which brings great trouble to the calculation of particle grading and material sample detection, and hinders the large-scale application of machine-made sand. In this paper, three-dimensional stacking tests of irregular particles were carried out to identify the particle distribution on the surface of the stack by digital image technology, analyze the relationship between particle size composition of particle mixture and the particle distribution of the accumulation body, and study the distribution law and segregation characteristics of the accumulation body. The experimental results show that the mixture with different coarse particle content will produce the corresponding distribution on the surface of the accumulation body after the accumulation. On the surface of the accretion body, the content of coarse particles is vertically distributed along the height of the accretion body. The content of coarse particles in the lower part of the accretion body is greater than that in the upper part of the accretion body. The overall segregation coefficient on the surface of the pile decreases with the increase of coarse particle content, and the variation is the largest in the lower part of the pile surface with the initial coarse particle content. Based on the test results in this paper, it provides a basis for formulating the sampling and inspection method of machine-made sand, ensuring the quality of machine-made sand and improving the utilization rate of machine-made sand.

Joint Metric Learning-Based Class-Specific Representation for Image Set Classification.

With the rapid advances in digital imaging and communication technologies, recently image set classification has attracted significant attention and has been widely used in many real-world scenarios. As an effective technology, the class-specific representation theory-based methods have demonstrated their superior performances. However, this type of methods either only uses one gallery set to measure the gallery-to-probe set distance or ignores the inner connection between different metrics, leading to the learned distance metric lacking robustness, and is sensitive to the size of image sets. In this article, we propose a novel joint metric learning-based class-specific representation framework (JMLC), which can jointly learn the related and unrelated metrics. By iteratively modeling probe set and related or unrelated gallery sets as affine hull, we reconstruct this hull sparsely or collaboratively over another image set. With the obtained representation coefficients, the combined metric between the query set and the gallery set can then be calculated. In addition, we also derive the kernel extension of JMLC and propose two new unrelated set constituting strategies. Specifically, kernelized JMLC (KJMLC) embeds the gallery sets and probe sets into the high-dimensional Hilbert space, and in the kernel space, the data become approximately linear separable. Extensive experiments on seven benchmark databases show the superiority of the proposed methods to the state-of-the-art image set classifiers.

HoloDiffusion: Sparse Digital Holographic Reconstruction via Diffusion Modeling

In digital holography, reconstructed image quality can be primarily limited due to the inability of a single small aperture sensor to cover the entire field of a hologram. The use of multi-sensor arrays in synthetic aperture digital holographic imaging technology contributes to overcoming the limitations of sensor coverage by expanding the area for detection. However, imaging accuracy is affected by the gap size between sensors and the resolution of sensors, especially when dealing with a limited number of sensors. An image reconstruction method is proposed that combines physical constraint characteristics of the imaging object with a score-based diffusion model, aiming to enhance the imaging accuracy of digital holography technology with extremely sparse sensor arrays. Prior information of the sample is learned by the neural network in the diffusion model to obtain a score function, which alternately constrains the iterative reconstruction process with the underlying physical model. The results demonstrate that the structural similarity and peak signal-to-noise ratio of the reconstructed images using this method are higher than the traditional method, along with a strong generalization ability.

Paradigm Shift in Rhinoplasty with Virtual 3D Surgery Software and 3D Printing Technology.

Most Asians have a nose with a short columella and a low dorsum; augmentation rhinoplasty using implants is commonly performed in Asian countries to achieve a taller and more well-defined nasal dorsum. However, the current knowledge is insufficient to fully understand the various subjective desires of patients, reflect on them during surgery, or to objectively analyze the results after surgery. Advances in digital imaging technologies, such as 3D printing and 3D scanning, have transformed the medical system from hospital-centric to patient-centric throughout the medical field. In this study, we applied these techniques to rhinoplasty. First, we used virtual 3D plastic surgery software to enable surgical planning through objectified numerical calculations based on the visualized data of the patient's medical images rather than simple virtual plastic surgery. Second, the customized nasal implant was manufactured by reflecting the patient's anatomical shape and virtual 3D plastic surgery data. Taken together, we describe the surgical results of applying these rhinoplasty solutions in four patients. Our experience indicates that high fidelity and patient satisfaction can be achieved by applying these techniques.

Based on an Overview of Crack Detection

This Crack detection, as an important part of concrete structural health monitoring, aims to reflect the stress state and damage degree of the structure. Traditional concrete crack detection mainly relies on manual visual identification, which has low detection efficiency and accuracy. In addition, manual visual detection has problems such as being greatly affected by lighting conditions, being unable to cover overhead locations such as bridge towers and high piers, and being highly subjective. In recent years, in order to solve the above problems, researchers at home and abroad have developed concrete crack detection equipment based on digital image technology, such as inspection vehicles equipped with high-resolution cameras, unmanned aerial vehicles, crawling robots, and so on. Meanwhile, efficient and accurate crack detection algorithms are the basis for realizing accurate crack identification. How to balance the detection speed and accuracy has been the focus of academic attention. This paper reviews the research progress of concrete crack detection equipment based on digital image technology at home and abroad in recent years, including camera platform, calibration method, preprocessing, traditional and deep learning algorithms, crack feature extraction, image stitching, and 3D representation and monitoring of cracks. In addition, the deficiencies in the research are summarized.

Experimental study of the effect of crack distribution on the failure mechanism of sandstone specimens based on inclination angles and number of parallel flaws

Discontinuous joints are prevalent in engineered rock masses and play a significant role in the stability of the rock mass. This study aims to analyze the impact of the inclination angle and number of prefabricated flaws on the crack evolution and failure pattern of sandstone specimens. Uniaxial compression tests, along with acoustic emission technology and digital image technology, were employed to monitor and analyze the effects. The findings indicate that: (1) With the increase in the flaw inclination angle, the damage mode of the specimen transitions from tensile to compressive-shear failure. The localized high-strain region on the surface of the specimen predicts the propagation path for the formation of macroscopic cracks. (2) When the number of prefabricated flaws is small, the flaws mainly expand through tensile wing cracks. As the number of flaws increases, the inner flaw tip does not produce cracks. Instead, the failure of the entire specimen occurs along the direction of the outer flaw's tensile wing crack, with the inner flaw running through it. (3) The winged tensile crack is the first crack to appear in all rock samples, regardless of the flaw initiation angles. Finally, the stress intensity factor at the crack tip under uniaxial compression conditions, without considering the closure effect, was expressed based on fracture mechanics theory. The crack initiation angle was then calculated. The results of the theoretical calculation of the initiation angle were found to be consistent with the test results. These research findings can serve as theoretical references and provide insights into the failure mechanisms of cracked rocks and the development of disaster control methods in rock engineering.