In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach. Using image frame information obtained from a camera to derive pose estimates, commonly known as odometry, has been a key research topic in the field of Simultaneous Localization and Mapping (SLAM) . SLAM teAt present, visual odometry, typically serving as the front-end component of visual SLAM, is witnessing increasing adoption in more accessible mobile devices, like smartphones. This allows for the integration of more practical functionalities, thereby placing a greater emphasis on the real-time performance and lightweight operation of odometry algorithms. The core technical challenge is to achieve better accuracy and robustness with limited hardware computing resources. The odometry process known as bundle adjustment is employed to establish the correspondence between the two-dimensional pixel points in the image frames captured by the camera and the corresponding three-dimensional map points . Many soOptical flow is based on the variations in image brightness to estimate pixel-level motion. It assumes that the pixel intensities remain constant between adjacent frames during motion. Based on this assumption, optical flow calculates the motion vector for each pixel by tracking the changes in brightness across the image. On the other hand, the feature point method relies on extracting a key point from the image for motion estimation. These feature points typically possess unique positional and descriptor information, which enables them to exhibit good matching properties across different frames.The feature point method generally incurs higher computational complexity com-pared to optical flow. This is primarily due to the intricate processes involved, such as feature extraction, feature matching, and feature tracking. Firstly, feature point extraction requires processing and calculations across the entire image. Secondly, the extracted feature points need to be matched with corresponding points in other frames, involving distance or similarity computations between features. Lastly, the feature point method estimates camera motion by tracking the movement of feature points across consecutive frames, necessitating matching and association operations. In contrast, optical flow operates at the pixel level, eliminating the need for feature extraction, matching, and tracking, thus resulting in a lower computational burden.Compared to optical flow, the feature point method generally exhibits higher precision and robustness under normal circumstances. By extracting feature points and performing feature matching, the feature point method can provide more accurate camera motion estimation. Feature point possess unique positional and descriptor information, rendering them highly distinguishable and resilient during the matching process. On the other hand, optical flow operates at the pixel level, allowing for the estimation of motion vectors for each individual pixel. In cases of smooth motion and favorable lighting conditions, optical flow can deliver reasonably accurate results. However, optical flow is prone to failure in scenarios involving occlusions, texture deficiencies, or rapid motion, which may result in imprecise estimation outcomes.Both optical flow and feature point methods possess unique advantages, and the fusion of these approaches has been a prominent area of academic research. One approach is to initially employ optical flow for rapid and coarse pose estimation between images, facilitating quick matching ,9. SubseMotivated by this, this paper proposes a robust and integrated visual odometry framework exploiting optical flow and feature point methods that leverages optical flow tracking to accelerate the feature point matching process and obtain better-matched feature points by utilizing high-quality feature point selection. This will allow for accurate pose estimation using fewer feature points. When the performance of optical flow matching is poor, the odometry system switches to a global feature point method with a filtering mechanism to ensure both accuracy and robustness. By employing this approach, the system aims to mitigate the limitations of optical flow and improve the overall performance of the visual odometry.The speed and accuracy of our odometry are experimentally verified and compared with the front-end odometry of the classical ORB_SLAM3 solution. A robust and integrated visual odometry framework exploiting the optical flow and feature point method exhibits more than double the speed of the front-end visual odometry in ORB_SLAM3. Despite the increased speed, the method maintains a similar level of measurement accuracy and robustness as the front-end visual odometry in ORB_SLAM3, which is based on the classical feature point method framework.Currently, the main methods for recovering camera poses and scene structures can be categorized into direct and feature point methods. For feature point-based visual odometry, the standard approach to solve this problem involves extracting a set of prominent image features in each image, using feature descriptors for continuous frame matching, and recovering camera motion and structure stably using pairwise polar geometry. Finally, poses and structures are optimized by minimizing the reprojection error. Most visual SLAM algorithms follow the basic idea presented in the literature , which hHowever, visual SLAM systems based on feature point methods have some drawbacks, including the need to handle robust estimate techniques corresponding to errors due to slow feature extraction and matching per frame ,15. FurtThe direct method-based visual SLAM system estimates structure and motion directly by minimizing the error in the pixel-level intensity of the image . It utilDue to the extreme complementarity between the advantages and disadvantages of the feature point method and the direct method, there are some schemes that combine the strengths of both methods. For example,  improvedThe proposed robust and integrated visual odometry framework exploiting the optical flow and feature point method utilizes optical flow tracking to accelerate the traditional feature point method, improving the speed of odometry pose optimization while still retaining the global feature point method odometry to ensure robustness in the case of poor optical flow tracking.The proposed odometry method utilizes optical flow and feature fusion to collect observation information from adjacent image frames. Initially, the object’s pose is estimated using optical flow, and key points are extracted from the image frames. Then, optical flow tracking is utilized to establish the matching relationship between neighboring frames, and to obtain a rough position estimation. Meanwhile, the matching relationship between neighboring frames established using optical flow tracking will also be used as the initial matching relationship for the subsequent local feature point method odometry. Tracking quantity judgment is introduced to evaluate the effectiveness of the optical flow matching results, based on the number of successfully matched key points.If the tracking quantity judgment determines that the number of key points is sufficient, the system proceeds to the local feature point odometry module. In this module, key point descriptors are computed, and high-quality feature points are selected for pose estimation using geometric constraints. On the other hand, if the number of successfully matched key points in the optical flow is too small, the odometry system switches to the global feature point odometry module. In this module, uniform distribution key point quadtree selection is performed to achieve an even distribution of key points. Subsequently, feature point matching is conducted, followed by pose estimation using geometric constraints to obtain an optimized pose. The specific data-processing flow is illustrated in The object collects its own observation information in the form of image frames into the optical flow tracking module; it first extracts all the key points in the image frames, and then performs optical flow tracking matching on the key points in adjacent image frames to obtain the key point optical flow tracking matching relationship between adjacent image frames.The object feeds its own observation information into the optical flow tracking module and initially extracts all the key points from the image. The optical flow tracking module employs an improved version of the FAST key point detection method, which incorporates pre-detection based on FAST detection . Common Therefore, the detection method in our proposed odometry takes into account the advantages of the above two methods. This method requires 9 consecutive pixels and a total of more than 12 pixels need to exceed the detection threshold, which requires both to ensure that most of the better pixels pass the threshold detection (9 consecutive points) and to ensure that the key points that pass threshold detection are good . The detection method in our proposed odometry still retains the pre-detection part  of the FAST-12 key point detection method. Although a small number of pixels that meet the requirements cannot pass the pre-detection, the pre-detection can identify most of the pixels that do not meet the requirements in advance, which greatly improves the efficiency of key point detection. And the number of pixels that meet the requirements, but do not pass pre-detection, is a low proportion of all pixels that meet the requirements; so, the impact on critical point detection is quite limited.In order to test the actual operation effect of different FAST key point extraction methods, we selected a colorful image frame from the rgbd_dataset_freiburg1_desk series of the TUM dataset, so as to obtain more key points and then better analyze the difference between the extracted key point detection method in our proposed odometry and those of the traditional FAST-9 and FAST-12. Through the actual test, we obtained the following data: FAST-9 key point detection method extracts 1032 feature points, FAST-12 key point detection method extracts 332 feature points, and key point detection method in our proposed odometry extracts 587 feature points. As it can be seen in All key points are matched between adjacent frames using optical flow tracking after they are extracted from the image frames. In our odometry, the Lucas–Kanade optical flow method  is used,In the actual optical flow tracking process, our proposed odometry divides the neighboring key points in an image frame into multiple windows and assumes that the key points within each window have the same motion. The distribution of feature points is closely related to the number of windows. Taking part of the image frame in The quality of the window division directly affects the quality of the optical flow tracking; if the window is too little and too dense, it may lead to the poor accuracy of the photometric error minimization results solved by the least squares method. If the window is too much and dense, it may lead to redundant feature information and excessive computation of photometric error minimization, thus affecting the efficiency of optical flow tracking. The key points extracted using the method proposed in the previous section are more uniformly distributed in the image frames, which is conducive to dividing the image frames into a more appropriate number of windows in the optical flow tracking process, thus ensuring the accuracy of the photometric error minimization solution and taking into account the efficiency of the optical flow tracking process.If a sufficient number of key points are matched successfully by optical flow tracking, the input is then passed to the local feature point odometry module. First, the key point descriptors are calculated for the completed optical flow tracking, and then high-quality feature point selection is performed to extract only a few high-quality feature points from the image frames. Finally, the local feature point pose estimate is completed using geometric constraint relations to obtain the optimized pose.The matching relationship between key points is established through optical flow tracking in the optical flow tracking module, and the descriptors of successfully matched feature points are computed for the subsequent steps’ selection of high-quality feature points. In our proposed odometry, the BRIEF descriptor with rotational invariance is utilized. The optical flow method of our proposed odometry is not based on all pixels in the image frame but the key point after screening; so, usually, the key points for the success of optical flow tracking are usually two to three times that of the basis for this judgment, which greatly reduces the amount of calculation in the subsequent steps.H in Equation (1). Secondly, the ratio of the Hamming distance between two successfully tracked feature point descriptors and the Hamming distance between each feature point and its neighboring feature point descriptors, which is N in Equation (1). Lastly, the absolute value of the angle between the optical flow tracing vector between two feature points and the average optical flow tracing vector between all successfully tracked feature points in adjacent image frames, which is V in Equation (1).After the computation of descriptors for the tracked key points, the selection of high-quality feature points was required. We used the score in Equation (1) to describe the quality of the feature points. The selection process considers the following three aspects: Firstly, the Hamming distance between the descriptors of two successfully tracked feature points in adjacent image frames, which is H, N, and V. Each part is based on the metric of the best feature point in each image frame as the numerator, and the metric of the current feature point as the denominator, so that the larger the fraction, the better the score. The smaller Hamming distance indicates a higher likelihood that the two feature points correspond to the same spatial map point. The smaller ratio implies that the pair of feature points represents the correspondence between adjacent areas in a more representative manner. The smaller the absolute value of the angle, the better the tracking between two feature points in the entire image frame. By evaluating the feature points based on the above criteria, the best 20 feature points are selected among the successfully tracked optical flow points.The score in the equation is used to quantify the quality of the feature points, and the equation consists of three parts, In the process of selecting high-quality feature points, we did not set specific quantitative indicators for the three evaluation indicators but calculated the percentage between all feature points and the best performing feature points based on the best performing feature points of each indicator. In this way, the disadvantage of poor adaptability of evaluation indicators caused by setting fixed quantitative standards was avoided. For example, in the same dataset, the Hamming distance between the feature points of the first two image frames is small, but the Hamming distance between the last two frames is large. If the indicator is set according to the first two frames, too many feature points in the last two frames will be deleted, which will affect the accuracy of subsequent pose estimation, and if the indicator is set according to the first two frames, too many feature points in the first two frames will be regarded as high-quality feature points, which will affect the calculation speed of subsequent pose estimation. Using relative indicators instead of absolute indicators can solve the problem of differences in indicators before and after in the same dataset. The quantitative indicator of the feature point with the best quality in an image frame was set to 1 , and the indicators of the other feature points were the percentages obtained by dividing them with the best indicators. The use of the relative percentages to indicate the indicator differences does not have the problem of non-uniformity of the units of the different indexes; so, there is no need to add a weight to the three indicators. The three indicators do not have the order of priority before and after, but according to the actual data obtained in the actual scene to judge the difference in importance between the indicators, without thinking that the priority of the indicators is set. Thus, the relative evaluation indicators have a good adaptability to different scenarios. When we select high-quality feature points, we only pay attention to the comprehensive indicator ranking of each feature point and do not pay attention to the specific performance of a feature point in an indicator.Local feature point pose estimation was performed with the selected 20 high-quality feature points, and pose estimation was obtained by minimizing the reprojection error, which is based on the correspondence between the two-dimensional pixels of the image and the three-dimensional spatial coordinate points of the selected high-quality feature points. Since there are only twenty feature points for pose estimation, the calculation speed of the process is fast. Although the estimated number of feature points is small, these feature points are screened out after many comparisons; so, we believe that the pose correspondence between these local feature points can represent the pose correspondence between two image frames.If the number of feature points successfully matched by optical flow tracking is too small, the global feature point odometry module is then established. In this module, uniform distribution feature point quadtree selection is first performed to achieve a more uniform distribution of feature points in the image frame. Subsequently, feature point matching is conducted, and finally, the optimized pose is obtained by applying geometric constraint relations to complete the global feature point pose estimate.The distribution of key points in an image is often random, with key points being typically concentrated in specific areas. Computing key point descriptors for all key points in a small, dense area can be time-consuming and inefficient. In order to enhance the computational efficiency of global feature point descriptors, a method is proposed in our odometry that utilizes an improved quadtree with uniform distribution properties to select representative feature points in an image.The process of improved quadtree feature point selection with uniform distribution properties is depicted in As the global feature point odometry method requires computing descriptors for all feature points in the image frame, it consumes significant computational resources. In our odometry method, the quadtree feature point selection method is improved to enhance the uniform distribution property of the quadtree feature point selection method, which can greatly reduce the number of key points selected as feature points, reduce the amount of descriptor computation required, and make the distribution of feature points have better uniformity.The feature point matching in the global feature point odometry method involves computing descriptors for the feature points selected through quadtree selection and establishing the matching relationship between feature points in adjacent image frames based on the Hamming distance between their descriptors. In our odometry, the nearest neighbor matching method  of the iIn the feature point nearest-neighbor matching algorithm, feature points only need to match feature points in the same location area and adjacent area in the next image frame, but do not need to match all feature points in the next image frame. Using our method for region division, feature points in the image frame can be divided as evenly as possible, thereby improving the efficiency of feature point matching. Too many or too few feature points in a certain area can be avoided, too many feature points in one area lead to too much matching calculation, and too few feature points in one area lead to an insufficient matching accuracy.The global feature point pose estimation follows a similar principle to local feature point pose estimation, which involves minimizing the reprojection error. However, there are differences between the two methods. In the global feature point pose estimation, the number of feature points considered is much larger than in the local feature point method, resulting in a longer computation time for the pose estimation process. It is important to note that the majority of the pose estimation work in this odometry system is accomplished through the local feature point method. The global feature point method is utilized only when optical flow tracking is not effective enough. Consequently, the global feature point method has a limited impact on the velocity of the system. However, it plays a crucial role in enhancing the robustness of the odometry system, despite its potential drawback of slower computation speed.The proposed robust and integrated visual odometry framework exploiting the optical flow and feature point method was implemented in C++ under Linux. Performance testing experiments were conducted on our proposed contribution using the MH05  sequence from the publicly available EUROC dataset. This dataset provides ground-truth location data obtained from the Leicra Total Station. The EuRoC dataset consists of video sequences captured using an AscTec FireFly UAV and flown repeatedly in an industrial environment, using a forward-looking camera .The performance evaluation of our method involved conducting experiments to generate trajectories and assess the error between these trajectories and the ground truth values. TUM’s data format was applied, and trajectory evaluation was performed using EVO, a tool commonly used in the SLAM field for error evaluation. In order to achieve a fair and rational comparison, the speed, accuracy, and robustness of our robust visual odometry method, a comparison was made between our method and the front-end odometry part of the ORB_SLAM3 open-source framework. By conducting this comparison, the effectiveness of our method can be assessed, while ensuring fairness in the evaluation process, considering that our system primarily focuses on odometry.The relative pose error refers to each root-mean-square error that takes into account 1 to 2 moments, as shown in Equation (3). It also indicates the pose error method between the actual measurement trajectory and the real trajectory.From the table data, it can be observed that our proposed method achieves superior performance in multiple metrics for APE, except for the minimum value, where our method (0.0055) is slightly inferior to ORB_SLAM3. In terms of mean, median, min, root-mean-square error (RMSE), and standard deviation (STD), our method outperforms ORB_SLAM3. Even the sum of squared errors (SSE) and ORB_SLAM3 improved by 73.97%. This is because our method utilizes optical flow for initial filtering and uses multiple indicators to comprehensively screen feature points in the selection of high-quality feature points, resulting in higher quality local feature points compared to global feature points, thereby yielding better statistical performance in terms of matching error. Regarding RPE, our proposed method generally performs worse than ORB_SLAM3. STD and max are slightly below ORB_SLAM3; error scales are 100% worse than ORB_SLAM3 for min and median; and mean, RMSE, and SSE are approximately 50% worse. This is because RPE compares the performance differences between different instances of odometry. Our method uses a smaller number of feature points for matching, which introduces greater fluctuations in matching accuracy compared to global feature point matching, leading to a decreased stability in odometry.z-axis direction fluctuating at a few moments, the trajectory is more consistent in the x-axis and y-axis directions.In order to provide a more visual and intuitive comparison, the authors processed the data using the EVO tool, which allows for a better visualization of the numerical results, as shown in y-axis value and it be can seen that the error of the ORB_SLAM3 approximately ranges from 0 to 0.25, while the error of our proposed odometry approximately ranges from 0 to 0.14. Subplot (b) more intuitively expresses the different errors at each moment through the colors in the motion trajectory. The closer the color of the trajectory to zero, the greater the error, and the closer the color of the trajectory, the smaller the error. It should be noted here that the color is determined according to the proportion of its own error, and the same color in different figures does not mean that the error is the same but needs to be analyzed according to the color column on the right side of the figure. Some visualizations are used in y-axis and it can be seen that the error of the ORB_SLAM3 approximately ranges from 0 to 3.2, while the error of our proposed odometry approximately ranges from 0 to 4.0. We considered this performance difference to be acceptable and within the same order of magnitude. Subplot (b) is more intuitive to express the different errors at each moment through the color in the motion trajectory. Some visualizations are used in From the above table and figure, it can be concluded that the robust and integrated visual odometry framework using the optical flow and feature point method is more than twice as fast than the front-end visual odometry of ORB_SLAM3 in terms of speediness. ORB_SLAM3’s front-end visual odometry has a median time of 0.0573, a mean time of 0.0667, a max time of 0.2011, and a min time of 0.0166; our odometry has a median time of 0.0225, a mean time of 0.0265, a max time of 0.0721, and a min time of 0.0055. In terms of accuracy, the absolute pose error of our method outperforms the front-end visual odometry of ORB_SLAM3, while the relative pose error is slightly inferior to that of ORB_SLAM3. This is because most of the image frames are estimated by local feature point odometry for posing estimation, and the process only needs to compute some local high-quality feature points instead of all of them; so, the position optimization of our odometry is significantly faster than that of the front-end visual odometry of ORB_SLAM3. The main role of the global feature point method odometry module is to ensure the robustness of the whole odometry.We also conducted robustness experiments on our odometry, through which we found that, if we do not include the global feature point method odometry module in the odometry, the robustness of this odometry becomes poorer in optical flow tracking. The robustness of the odometry is significantly improved by adding the global feature point odometry module to the odometry, and tracking failure almost never occurs. The reason for this is that, in some special scenes, such as the low-light scene in In our study, the acceleration of the feature point matching process in visual odometry was achieved by optical flow tracking, which improves the calculation speed of robust and integrated visual odometry framework exploiting optical flow and feature point methods and retains the traditional global odometry to ensure the robustness of our odometry. We experimentally evaluated the rapidity, accuracy, and robustness of the front-end visual odometry of ORB_SLAM3 and the robust and integrated visual odometry framework exploiting optical flow and feature point method. Our odometry achieved a balance between speed, accuracy, and robustness for the following four reasons: (i) Since the core of the scheme is still the feature point-based method, the system can guarantee the accuracy. (ii) The scheme uses global feature point method of visual odometry when the number of optical flow tracking is not sufficient, and therefore improves the robustness of the whole system. (iii) The scheme performs the optical flow tracking of feature points in the image before the pose estimate by the local feature point method of visual odometry. It is equivalent to the initial pose estimation before the odometry pose estimate, which provides a good initial value for the subsequent local feature point method pose estimate and improves the computational efficiency of the pose estimate. (iv) This scheme uses optical flow tracking to accelerate the odometry process and does not require the computation of descriptors for all the feature points in the image frame as in the traditional feature point method of visual odometry, resulting in a significant increase in computational speed.


The lymphatic system plays a crucial role in maintaining fluid and solute balance in biological tissue, which makes the subcutaneous injection a common approach for delivering therapeutic agents through the lymphatic pathway. The transport of drug solutes is regulated by the interstitial fluid pressure. In our study, a time-efficient poroelastic model is developed to mimic pressure relaxation and build-up in the soft skin tissue. This method can accurately and time-efficiently capture the evolution of pressure, which is validated against both the analytical solution and numerical solution of the previous poroelastic model. The increasing porosity and permeability due to deformation alleviate the high pressure caused by the injection. Furthermore, an improved solute transport model is developed to better address the microscopic properties of the lymphatic vessel membrane. The effects of a varying Stokes radius of drug solute and vessel network structures are investigated on lymphatic uptake. Our comprehensive computation model provides a time-efficient and reliable research tool for studying solute transport into the lymphatic system, which can be utilized to support decision-making regarding lymphatic disturbed diseases.Convective transport of drug solutes in biological tissues is regulated by the interstitial fluid pressure, which plays a crucial role in drug absorption into the lymphatic system through the subcutaneous (SC) injection. In this paper, an approximate continuum poroelasticity model is developed to simulate the pressure evolution in the soft porous tissue during an SC injection. This poroelastic model mimics the deformation of the tissue by introducing the time variation of the interstitial fluid pressure. The advantage of this method lies in its computational time efficiency and simplicity, and it can accurately model the relaxation of pressure. The interstitial fluid pressure obtained using the proposed model is validated against both the analytical and the numerical solution of the poroelastic tissue model. The decreasing elasticity elongates the relaxation time of pressure, and the sensitivity of pressure relaxation to elasticity decreases with the hydraulic permeability, while the increasing porosity and permeability due to deformation alleviate the high pressure. An improved Kedem–Katchalsky model is developed to study solute transport across the lymphatic vessel network, including convection and diffusion in the multi-layered poroelastic tissue with a hybrid discrete-continuum vessel network embedded inside. At last, the effect of different structures of the lymphatic vessel network, such as fractal trees and Voronoi structure, on the lymphatic uptake is investigated. In this paper, we provide a novel and time-efficient computational model for solute transport across the lymphatic vasculature connecting the microscopic properties of the lymphatic vessel membrane to the macroscopic drug absorption. The lymphatic vascular system returns the interstitial fluid to the systemic circulation in the human body, which plays an essential role in homeostasis and immunization in the human body ,2,3,4. Cntration . After bO (10 O (1 cm). A time-efficient and accurate biomechanical model for pressure evolution can be valuable to such a complex computational system to study the transport and absorption of large drug molecules in biological tissues.In our previous works ,12,13, tThe pressure build-up in subcutaneous tissue during the subcutaneous injection has been investigated through experiments in several previous studies ,16. The As one of the two vascular circulatory systems in the human body, the lymphatic vessel network consists of initial lymphatics with only one single layer of lymphatic endothelial cells (LECs), unlike blood vessels which have a complete basement membrane ,3,4. ForIn this paper, an approximate continuum poroelastic model is developed to simulate the pressure evolution in the soft subcutaneous tissue. The pressure evolution of the proposed model is quantitatively validated against the analytical solution and the numerical solution of the poroelasticity model. The effect of elasticity, variable porosity and permeability on pressure build-up is numerically investigated. The convective and diffusive transport of drug solute into the lymphatic vessels is investigated through an improved Kedem–Katchalsky model by implementing the hybrid vessel network in the continuum poroelastic model. At last, heterogeneous vessel networks with various structures are used to investigate the effect of the morphology of the lymphatics on solute transport into the lymphatic system in the multi-layer soft skin tissue.To simulate the microcirculation in the subcutaneous tissue, the physics-based computational model established in this study is combined with physiological models, i.e., transvascular transport models. A continuum approximate poroelasticity model is developed to simulate the interstitial fluid flow in soft subcutaneous tissue. In this section, the governing equations for fluid flow and drug solute transport in the interstitial space and across the vessel network are summarized.p represents the pore pressure applied to the fluid, and An approximate poroelasticity model is developed to include the elasticity and compressibility of the skin tissue. This method is both simple and efficient, making it easy to implement. The computational cost is reduced since the constitutive equation to obtain the displacement of a solid skeleton is not solved. At the same time, it can well capture the relaxation of pressure as accurately as the full poroelasticity model . Here, wM is the bulk modulus of the fluid-solid mixture.Based on Biot’s poroelasticity theory ,34, one E is Young’s modulus of the elasticity. For a 3-D simulation, the volumetric dilation is For the approximate poroelasticity model, we assume that solid displacement is small, the Darcy velocity of the interstitial fluid is small, and the non-zero component of the solid displacement =∂ıi/∂xi , where ıFinally, the pressure equation for the poroelastic tissue becomes:The mass balance in the interstitial space with solid displacement reads as ,6)∇·ı˙quations  and (6),∇·ı˙quatiThe details of the injection model and the qualification of the proposed continuum poroelasticity model can be found in O (1 cm) and the discrete vessels over the length of O ,,(9)∇·uD+itten as ,41. AfteThe net fluid fluxes through the blood and lymphatic vessels are determined by Starling’s law, which read ,38,39,40Jv=LpvSV formulation, is used for solute transport across the blood vessels. The fluid and solute fluxes are expressed as:membrane .w is the opening width, f is the fraction of slit length open to valve width w. w with the pressure difference between the interstitial pressure and the lymphatics w is 10∼40 The lymphatic vessel consists of one single layer of endothelial cells lacking the basement membrane and thus, an improved Kedem–Katchalsky (iK-K) model is developed to describe the transport of macromolecules across the lymphatic vessel wall. The fluid flux Δp=pi−pl ,44.(20)tions in ,27,44,45The tissue and solute properties (such as hydraulic permeability and solute size) and injection process parameters (such as injection duration and volume) are inputs needed for the computational model, as shown in The computational domain for the continuum model without explicit vessel network is e layers . The 80×R is the characteristic length, equal to the radius of the injection bolus, and time step The mesh is refined near the injection point and the smallest mesh size used is k, elasticity modulus of the soft tissue E, the diffusivity of the drug solute in the ECM D and across the vessel membrane The governing equations and computational models are implemented and solved based on the open-source Computational Fluid Dynamics platform, OpenFOAM. The boundary conditions and numerical schemes for the numerical simulation can be found in Interstitial fluid pressure (IFP) plays a crucial role in regulating fluid flow and mass transport in the SC tissue. The pressure build-up process in the soft subcutaneous tissue is important to understand the mechanism of drug transport and absorption. The fluid flow and solute transport across the lymphatic vessels and blood vessels are governed by the pressure in the interstitial tissue and vessels, as shown in Equations  and 12)12). HereE varies a lot for different layers of the tissue, as shown in E of subcutaneous tissue, which is also known as the hypodermis, is around 2 kPa according to [E affects the pressure build-up in soft and deformable tissue compared to rigid porous tissue modeled by Darcy’s law over a wide range of E, assuming the isotropic elasticity in the subcutaneous tissue.First, we study the effect of elasticity on the development of pressure in subcutaneous tissue. The young’s modulus of skin is affected by many factors, such as age, gender, hydration, and skin thickness . The yourding to . Here, wE, the relaxation time is larger, which is proportional to From njection .K and the hydraulic permeability k , and thus, it is called convective diffusion here.OL can be evaluated by the solute permeability OThe convective and diffusive transport across the lymphatic vessel is analyzed here through the local Péclet number, The evolution of normalized drug volume, i.e., drug percentage, in the tissue for various molecules with different Stokes radii on rates , which aCompared to the Kedem–Katchalsky model with only solute permeability In the soft interstitial tissue, the pressure gradually decreases after the end of injection (t = 5 s), as shown in The morphology of the lymphatics varies from fractal trees to interconnected irregular shapes depending on the location of biological tissues and species, as reported in ,28,30,66First, the fractal tree structure is generated using the space-fill fractal algorithm  to repreFirst, we quantitatively compare the evolution of drug volume in tissue for these two structures. As shown in To further illustrate the high absorption efficiency of the Voronoi structure of the lymphatic network, we investigate the drug transport across the explicit vessel wall. The drug distribution is compared in multi-layered tissue with fractal tree-structured and Voronoi-structured lymphatic vessel networks at t = 100 s. As shown in Interstitial fluid pressure plays a crucial role in governing the convective transport and absorption of drug molecules in the tissue. An approximate continuum poroelasticity model is developed to mimic the pressure build-up and relaxation by introducing the time variation of pressure. The effects of elasticity, porosity, and permeability on pressure evolution are investigated after validating the proposed poroelastic model with the analytical solution and previous computational model. The advantage of this model lies in the simplicity of implementation while maintaining good accuracy in capturing the evolution of the interstitial pressure. The small elasticity of the tissue slows down the relaxation of pressure and lowers the magnitude of pressure, while the magnitude of pressure increases dramatically with decreasing hydraulic permeability. Pressure evolution for low hydraulic permeability tends to be more sensitive to small elasticity, i.e., the relaxation time is longer for low hydraulic permeability at the same elasticity. The effect of variable porosity-dependent permeability is found to alleviate the high pressure due to increased porosity and permeability near the injection site.To better address the microscopic structure of the lymphatic vessel membrane, an improved Kedem–Katchalsky model is developed for solute transport across the explicit vessel wall. The transport and lymphatic uptake of drug solutes in a multi-layered medium are investigated to elucidate the effect of heterogeneity of multi-layered structure and the heterogeneous vessel network. We investigate the convective diffusion of various macromolecules through the hybrid lymphatic vessel network by varying the Stokes radius and solute diffusivity across the opening lymphatic valves. With a smaller Stokes radius and larger solute diffusivity, the lymphatic uptake is significantly fast. Finally, the effect of the lymphatic vessel network structure is investigated through the fractal trees and Voronoi structures. We find that the Voronoi structure in the subcutaneous layer is more efficient in drug absorption because there are more interactions between the drug molecules and the vessel surfaces in the dermis and hypodermis layers.For future work, the computational model can be further coupled with the mathematical model for the lymph flow inside the lymphatic vessels. In this way, the effect of the lymph flow rate can be incorporated into the model. The computational models developed here can also be coupled with the pharmacokinetic (PK) models to better predict drug bioavailability.


Atrial fibrillation (AF) is the most common atrial tachyarrhythmia. One of the methods of AF treatment is direct current cardioversion (DCCV), but in the long-term follow-up we observe quite a high percentage of AF recurrences after this procedure. In order to assess the prognosis of DCCV effectiveness, we use clinical, biochemical and echocardiographic parameters. The objective of this review is to systematise the current knowledge on echocardiographic measurements in patients with persistent AF used to assess the progress of remodelling of the atrial wall, which affects the likelihood of maintaining sinus rhythm after DCCV. In this article, echocardiographic parameters for the evaluation of remodelling of the atrial wall are divided into groups referring to structural, mechanical, and electrical remodelling, as well as parameters for the evaluation of left ventricular filling pressure. The article aims to draw attention to the clinical value of echocardiographic measurements, which is the selection of patients who will maintain sinus rhythm after DCCV in the long-term follow-up, which will allow to avoid unnecessary risks associated with the procedure and enable the selection of the appropriate treatment strategy. Atrial fibrillation (AF) is the most common atrial tachyarrhythmia. It is estimated that up to 33.5 million patients may be affected worldwide, which does not include those with an asymptomatic form of the disease. The prevalence expected in 2030 is more than 15 million Europeans and 12.1 million Americans. It is estimated that 886,000 European citizens are first diagnosed with AF each year and that one in four adults over the age of 40 will experience AF in their lifetime . In. In46]. Assessment of the myocardial velocity in the atria appears to be a promising method for evaluating the likelihood of SR maintenance after DCCV in persistent atrial fibrillation, and hence it is important to further evaluate these parameters in a larger patient population.Another parameter in the direct evaluation of the mechanical function of the left atrial appendage is left atrial appendage wall motion velocity (LAAWMV) . Left atp < 0.001) [A recently discovered phenomenon is LAAWMV reduction in the lateral LAA wall compared to the LAA medial wall in patients with AF, while in patients without diagnosed AF, there is an opposite relation. The chances of identifying a patient with paroxysmal AF using this phenomenon was 22.14  .A parameter called left atrial strain (LAS) is used to assess the degree of mechanical deformation of the myocardium. Progressive remodelling leading to atrial wall rigidity leads to a decrease in strain and strain rate during AF. Using the above-mentioned STE and TDI techniques, it is possible to assess the strain and strain rate during echocardiography . An examDecreased strain and strain rate were shown to be predictive of AF occurrence, especially in patients who have suffered a cryptogenic stroke ,51. StraStrain measurements before cardioversion can predict the maintenance of sinus rhythm after DCCV. Morenzo-Ruiz et al. showed that the reservoir phase strain measured during AF before DCCV was predictive of SR maintenance over a 6-month follow-up, both in patients with persistent AF and long-standing persistent AF . Shaikh Loss of coordinated atrial myocardial contraction is the result of mechanical remodelling and is described as dispersion. Strain evaluation also allows for the evaluation of LA wall contractility disorders such as dispersion and dyskinesia. Dispersion is defined as the standard deviation of the time to achieve maximum deformation in different atrial segments and indicates asynchronous contraction of individual LA walls ,58,59. ULeft atrial wall dyskinesia is a new indicator that can be used to assess the likelihood of maintaining SR after DCCV in persistent AF. Left atrial wall dyskinesia means that certain segments of the atrial wall expand rather than contract during the contraction phase. In measurements of the LA strain rate in the contraction phase, dyskinetic segments demonstrate positive strain rate values, which indicates that they are stretching relative to adjacent segments that have negative strain rate values, which indicates that they are contracting . This phThe type of remodelling that occurs first, and is also the least visible and difficult to visualise with echocardiography, is electrical remodelling. Electrical remodelling of the atrial walls is an expression of disorders in intercellular connections, change in ion channels, progressive LA wall fibrosis, and abnormalities in the conduction of electrical stimuli between the cells of the atrial wall, which results in slowing down of electrical stimuli conduction and the development of micro-re-entry substrate ,62. A laTACT was demonstrated to be predictive of AF recurrence after DCCV. Müller et al. showed that patients with prolonged TACT have a higher risk of early AF recurrence after DCCV. Using a multivariate regression model, they demonstrated the superiority of this parameter over a parameter for evaluation of structural remodelling, but they did not assess echocardiographic parameters representing the progression of mechanical remodelling . The proEvaluation of electrical remodelling seems to be a very promising method for evaluating the prognosis of AF recurrence after DCCV in patients with persistent AF, but echocardiography does not currently seem to be a good tool for evaluating these types of changes in the left or right atrial wall.A separate parameter that does not concern the assessment of atrial remodelling but is an important predictor of AF recurrence after electrical cardioversion in persistent atrial fibrillation is left ventricular filling pressure (LVFP), which assesses left ventricular diastolic dysfunction. Left ventricular filling pressure is estimated during sinus rhythm by measuring mitral valve inflow waves E and A, the e’ wave of the diastolic motion of the mitral valve ring, and the mitral inflow E wave deceleration time (DT).Assessment of the filling pressure is also possible during AF, but estimation of the LV filling pressure is difficult because the lack of the A wave in the mitral inflow and the high variability of E and e’ waves in successive heart cycles negatively affects the accuracy of the estimate and the variability of subsequent measurements.In studies evaluating left ventricular filling pressure based on the E/e’ ratio, it was demonstrated that the LV filling pressure assessed during persistent AF and after successful DCCV is a predictor of SR maintenance after DCCV ,73,74. IAlthough ablation is becoming an increasingly common and effective method of treating patients with both paroxysmal and persistent AF, cardioversion remains one of the most frequently performed procedures in cardiology departments ,77. As t


The purpose of this paper is to compare the fracture behavior of interfaces obtained using fourth-generation and universal dental adhesives. The study relies on optic and SEM to evaluate the dentin–adhesive–restoration material interface of the samples and also on FEA simulation of fracture behavior. Specimen fabrication relied on 20 extracted teeth, in which class I cavities were created according to a protocol established based on the rules of minimally invasive therapy. For the direct adhesive technique, the adhesives used were: three-step All Bond, three-batch A and one-step Clearfil Universal Bond Quick-batch B. The restoration was performed with the same composite for both adhesives: Gradia direct posterior. The simulation used a 3D reconstructed molar on which geometric operations were performed to obtain an assembly that replicated a physical specimen. Material properties were applied to each component based on the information found in the literature. A simplified model for crack propagation was constructed, and using the fracture mechanics tool in Ansys 2019, the stress intensity factors that act at the crack tip of the adhesive interface were obtained. Mechanical simulation and microscopic investigation showed us how the interface of the dentine–adhesive–filling material performed in cases of both dental adhesives and for a certain loading condition. Important differences were identified among the adhesives, the fourth generation being superior to the fourth generation especially due to the separate steps in which the tooth surface was prepared for adhesion. Dental adhesives allow clinicians to achieve a more conservative cavity design, preserving healthy tissue since mechanical retention is no longer necessary. The most likely causes of secondary caries are micro-infiltrations, which represent a major problem. Using adhesives, restorations are not only more aesthetically pleasing but also create an intimate seal between healthy tissue and the restoration, thus reducing or eliminating the risk of secondary caries.Dr. Michael Buonocore first introduced dental adhesives in 1955 and discovered the benefits of acid etching to improve enamel adhesion. Advanced technologies and dental adhesives have evolved from no-etch to full-etch (fourth and fifth generations) and to self-etching . The latest innovations in adhesives include universal adhesives that can be used as self-etching [The fourth generation of adhesives was created between the 1980s and 1990s and provided the first adhesives that achieved complete removal of the stain layer. In this generation, three primary components were used: acid, primer and adhesive. According to the protocol, each component was individually packaged and applied sequentially. Compared to previous generations, the hybrid layer consisted of the resin-infiltrated surface layer on the dentin and enamel, providing high bond strengths and a dentin seal with significantly reduced marginal leakage .The marginal integrity of dental adhesion was investigated using different adhesive strategies. The self-etch and selective enamel etch strategies were used in two application periods on primary molars. The authors found that different adhesive strategies provided no statistical difference, while the application time within the same adhesive was significantly different. Therefore, the universal adhesives used to restore class-II cavities, applied either in selective mode or in self-etch mode, resulted in comparable marginal integrities .Some authors have investigated the impact of artificial aging of four nanocomposite resins on color stability and hardness properties. All investigated parameters demonstrated statistically significant results between samples before and after aging .An extensive study of six universal adhesives was conducted on sound and caries-affected dentin after 18 months to reveal the antimicrobial activity, cytotoxicity, resin–dentine microtensile bond strength and nanoleakage. Poor performance in terms of microtensile bond strength and nanoleakage was observed when the adhesives were applied in caries-affected dentin, mainly after 18 months. Additionally, not all universal adhesives behaved the same in terms of antimicrobial activity and cytotoxicity .The bonding properties of three two-step etch-and-rinse adhesives (2-ERAs) to those of three universal adhesives (UAs) applied with an etch-and-rinse strategy (ER) to dentin were determined directly and after one year of water storage. The results revealed that microtensile bond strength and nanoleakage properties are better for universal adhesives after one year of water storage. The authors concluded that the use of UAs applied with the ER strategy seemed to be a more effective technique for maintaining adhesion to dentin substrate than 2-ERAS .The microtensile bond strength and nano-leakage properties immediately before and after 6 months of aging were determined using three types of adhesive systems in relation to the dentin. One adhesive was used in two steps (primer and bonding), while the other two adhesives were used also in two steps  but also in one step by applying the universal adhesive directly. The tissue base consisted of dentin surfaces from human third molars. The results showed superior adhesion in the case of acid etching used as an independent step .AllBond3 (Bisco), the fourth generation adhesive used in this study, offers micro-mechanical adhesion to all types of substrates, being compatible with all materials: photopolymerizable, self-solidifying or dual. The most important feature of this adhesive is its hydrophobicity, which allows it to have a very long intra-oral lifetime compared to other adhesive systems. It is a built bicomponent and is radio-opaque, thus being visible on dental X-rays .Clearfil is a universal one-component photopolymerizable adhesive used in direct and indirect restorations in combination with total and selective surface conditioning techniques. These techniques are directly proportional to the degree of conditioning of the surfaces, as well as to the mode of diffusion of the cement components in the biological structure .The restoration materials widely used in dentistry are resin-based composites (RBCs) and glass ionomer cements (GICs) . The RBCGradia composite resin is based on a co-monomer matrix and a pre-polymer resin reinforcing agent. It is light-curable and offers very good quality in reconstruction aesthetics, degree of polish, wear resistance and good tear toughness. Elastic bending energy determines the flexible behavior of the resin compared to other, more rigid components .From the clinical use point of view, both adhesives have the same type of indications. Despite performance, dentists require a simplified system; thus, universal adhesives were developed. This new generation of self-etching agents has acidic hydrophilic monomers and can be easily used on etched enamel after saliva or moisture contamination.Mechanical testing of dental materials has been performed by many authors. Some approaches have used numerical simulations, in which the stress and strain states of a reconstructed dental model are subjected to physiological loading to determine the mechanical behavior of the structure ,15,16,17IC și KIIC) were determined [Modes I and II of failure of composite resins was investigated by Hidehiko Watanabe et al. using the Brazilian disc method. Twenty-five specimens of each material were subjected to compression on a Zwick machine at a reduced loading rate to allow for crack propagation. The stress intensity factor was then calculated, and based on the results, the averages and standard deviations of the fracture toughness in modes I and II  of the simulated interface fractures were conducted.The investigation protocol  consisteRestoration was performed on 20 extracted teeth. After extraction, they were cleaned and kept in distilled water until their use. The extracted teeth were integrated into artificial arches and divided into two groups of 10 teeth each. Class I cavities were created in each tooth that affected the enamel and dentin, according to a protocol established according to the rules of minimally invasive therapy.For the direct adhesive technique, the adhesives used were: three-step All Bond 3 —batch A and direct Clearfil Universal Bond Quick —batch B. The restoration was performed with the same composite for both adhesives, Gradia Direct Posterior (GC). We selected Gradia Direct Posterior due to the unique UDMA technology, which offers minimal shrinkage during photopolymerization.The composite resin Gradia Direct Posterior is a light-cured, radio-opaque micro-filled hybrid composite resin, selected because of its low polymerization shrinkage stress (1.5 MPa), which reduces latent energy on bonding interfaces. The reduced interface stress has clinical benefits, such as protection of marginal integrity, thereby avoiding microleakage/secondary decay, and decreased postoperative sensitivity. It contains urethane dimethacrylate (UDMA) 20–25%, bismethacrylate 1–5%, dimethacrylate 1–5%, silica and pre-polymerized fillers. Additionally, according to the producer, it shows good fracture toughness to resist occlusal stress, a low modulus of elasticity, resistance to bending and occlusal forces, as well as the spread of cracks. From a clinical point of view, it shows good manufacturability, high wear resistance and low wear on opposing dentition .The cold incorporation of the samples was performed in epoxy resin that was poured into a cylindrical mold and mixed with the hardener to induce the exothermic hardening reaction. An important property of the resin is polymerization shrinkage, which can be high for non-epoxy resins. The disadvantage of resin shrinkage is the cleavage that can occur at the resin–sample interface. To prevent the extraction of the hardened resin from the mold, an agent to prevent adhesion was sprayed on the walls prior to casting. Small clips were used to support the samples and prevent floating inside the mold during the incorporation stage .The sample preparation ended with several steps that were accomplished after incorporation and that are mandatory for microscopic investigation: sample sectioning for obtaining a flat surface, sample grinding and finally, polishing.Batches A and B were subjected to optic and electronic microscopy to inspect the interface bone–adhesive–restoration material. Optical microscopy was performed using the Olympus SZX7. Higher magnifications of the interfaces were achieved by scanning electron microscopy (SEM), using an ESEM Philips XL 30 TMP. The chemical composition of the samples and the presence of the etching acid at the interface were evidenced by energy dispersive spectroscopy (EDAX).The structure, composition and macroscopic properties of adhesives change continuously from the cohesive to the adhesion zone, with the only region where the adhesive respects its intrinsic properties being the cohesive zone. As such, the local properties of the resulting composite will be different and directly dependent on the distance from the substrate to the adhesive. One of the frequent causes of interface separation is the diffusion of some of the adhesive components into the pores of the substrate, thus changing the local composition of the adhesive and therefore its properties.To perform the fracture mechanics simulations, FEA of the assembly was conducted beforehand. A reconstructed molar was used as basic geometry, and using geometric operations, the adhesive layer and the restoration volume were designed, followed by component assembly. The elastic properties of the materials assigned to the individual components were extracted from the literature, namely the properties of dentin and enamel ,30,31, tY-axis); mesial 45° loading on the XoY plane, having the point of application in the central pit; and pure shear loading in the distal–mesial direction (X-axis). The fixed surface was as the bottom end of the model  for the type B loading. The point of application of the forces was the central groove surface in the case of the assembly model and the right–top surface of the simplified interface model. The values used represented average mastication forces ,36,37 anThe second numerical simulation was conducted using the Fracture-PreMeshed Crack module of Ansys 2019 to obtain the crack propagation and fracture behaviors. The loading directions and values were used to match the tooth simulation and were applied at the adhesion interface ,39. ThisThe images obtained by optical microscopy for the two analyzed groups are presented in the following paragraphs. First, the adhesion of All Bond 3, Bisco (ER 3-step) and Gradia Direct posterior to enamel and dentin is presented in Very good adhesion was observed between the adhesive system and the restoration material. The enamel prisms had slightly demineralized ends. The adhesive layer c-II had The adhesive layer a,b-II adThe adhesion of Clearfil Universal Bond Quick, Kuraray Noritake Dental Inc. (1-step) and Gradia Direct posterior to enamel and dentin is presented in The adhesion achieved by the universal adhesive between the adhesive system and the restoration material was similar to the three-step adhesive. The enamel prisms no longer had demineralized ends, and no local demineralization could be observed. Although the adhesive layer had a uniform thickness, it failed to interpenetrate the dental tissue a,b-II.In The investigation of adhesion to enamel and dentin was also evidenced by SEM microscopy at 100 and 500 X magnifications. The adhesion of All Bond 3, Bisco (ER 3-step) and Gradia Direct posterior, GC to enamel and dentin can be observed in At the dentin level, the ends of the canaliculi were infiltrated with bonding from the adhesive three steps, and the adhesive layer had a uniform thickness and homogeneous structure without air gaps a,b.The adhesion of Clearfil Universal Bond Quick, Kuraray Noritake Dental Inc. (SE 1-step) and Gradia Direct posterior to enamel and dentin is illustrated in At the enamel interface, there were microcracks that weakened the adhesion over time and represent weak points from which the fillings could detach, and infiltrations could occur. They appeared in all the tests performed with the Clearfill adhesive due to the higher shrinkage coefficient of the adhesive in one step. The adhesive layer was thinner at the enamel level and thicker at the dentin level, and the entire adhesive–dental tissue junction appeared as a crack, without fusing in any way with the enamel or dentin in any of the images obtained, indicating weaker adhesion a,b.The adhesive was well delimited between the dentin and the obturation material and did not interfere with the dental tissue a,b. The The composition evaluation  of the interface is presented in The simulation of the reconstructed molar model was carried out in a structural static module based on the geometric model and boundary conditions described in The full spectrum of in-plane shear stresses can be seen in Two sequences  of crack growth are presented in the In the second fracture simulation (type B loading), the sequences showed a different direction of the crack propagation line B. It camWhen horizontal loading was applied (type C loading), the stress could act on the longitudinal plane of the molar. By its nature, this load directly exposed the crack to opening and materialized a push stress on the restorative structure. Propagation occurs symmetrically, with a perfectly vertical crack collinear with the initial one (K) is a parameter that determines the fracture toughness property of the material and is used to predict the stress state near the crack tip, when a remote load is applied to the structure. The magnitude of K depends on the specimen geometry, size and crack location, as well as by the loading value. The computation of K is obtained using Equation (1), where σ is the applied stress, a is the crack length, W is the specimen width, and f(a/W) is a geometry-dependent function [The stress intensity factor K=K, while red represents high values of K. The stress intensity factor has the highest value in the middle region of the crack front for mode I failure and the lowest value for mode III, when the vertical loading direction (A) is applied.The values of stress intensity factors (SIFs) were determined for all loading directions and for the three possible failure modes . The crack propagation front for the three modes is shown in K values denoted as high correspond to the fourth-generation adhesive Allbond3, which has a considered mode I fracture toughness of 1.52 MPa·m1/2, while low denotes the SIFs of the universal adhesive Clearfil, with a considered mode I fracture toughness of 0.62 MPa·m1/2 [By analyzing the SIFs values extracted from the crack fonts of all simulation scenarios and comparing them with the fracture toughness of the adhesives, the probability of interface failure for a certain combination can be determined in the material–geometry–loading direction. The stress intensity factors are depicted in MPa·m1/2 ,24,25,28The negative values of SIFs in the In the present study, the stresses acting at the dentin–adhesive–restoration material interface of an onlay restored molar were determined by numerical simulation. The stress values obtained in this simulation correspond to elastic displacement of the interface. The results of this first simulation were used to establish the loading conditions of the simplified crack propagation model. Independent of loading direction, higher stresses were determined in the adhesive layer, confirming that the fracture propagation simulation should be addressed to this thin layer.The fracture mechanics simulation of an adhesive provides new information about the behavior of interfaces when cracks occur in the superficial, marginal layer. The results obtained were then compared with the fracture toughness of the materials to determine fracture susceptibility.The SIFs at the crack front had the highest values for mixed mode loading (B), when the load was placed 45° obliquely to the initial crack. In pure mode I loading (A), the SIF values were also very high for both adhesives  and only 30% higher stress intensity factors recorded for the considered conditions. Therefore, in the given conditions, the three-step adhesive will better perform.The SEM investigation confirmed firm adhesion of dental adhesives to the dental tissue and the presence of a well-represented hybridized layer. The interpenetration of the adhesive layer with the dental one can be observed in Adhesion is firm to dentin and enamel with the All bond 3 adhesive in the presence of acid gavage in a separate step. Micro-retentiveness can be observed, as well as a slightly crenellated surface area after the acid etching, so the mechanical adhesion is increased compared to the case in which there is no acid etching .When using the universal adhesive, we observed slightly poor adhesion of the adhesive, with well-defined interface areas and without fusing . The higThe thickness of the universal adhesive layer is relatively smaller and shows reduced hybridization, while the layer thickness of the fourth-generation adhesive is greater and can repair some imperfections of the cavity preparation stage. It also stands out with greater hybridization. The thickness of the layer is approximately constant for the fourth-generation adhesive, while important thickness variations can be observed for the universal adhesive.The study conducted by Jang JH et al. comparing the Clearfil and Allbond adhesives revealed that the universal adhesive, despite the simplified use technique, presented good adhesion to the dental hard tissues, with slight deficiencies in the case of atypical cavities . ChowdhuFor the three-step adhesive, the EDAX analysis showed an appropriate balance between the calcium ions remaining in the structure, which provide strength to the dental tissue, and the free ones. It is also possible to see the inversion spike between Ca and Si in the demineralized superficial layer, which plays an important role in reducing post-obturation sensitivity. Similar changes in acid demineralization were also found by other authors who considered changing the Ca/Si ratio to be beneficial because the new bonds formed after the dissolution of the superficial layer of dentin can block the dentinal canaliculi and decrease any post-obturation sensitivity . AnotherThis paper presented a comparative study of the interface fracture behavior between fourth-generation three-step adhesive and a universal adhesive. The study relied on optic and SEM investigation of the dentin–adhesive–restoration material interface and also on FEA simulation of fractures. The microscopic qualitative analysis revealed inferior adhesion of the universal adhesive to the surrounding substrates. The layer thickness of the universal adhesive is small and variable, while the layer thickness of the fourth-generation adhesive is greater and approximately constant. Hybridization is clearly superior in the case of the fourth-generation adhesive, and the interface between the dental tissue and the adhesive is slightly crenulated, increasing the contact surface. The large contact surface and the lack of air contact gaps allowed the three-step adhesive to attain much better marginal adaptation compared to the universal adhesive. The simulations showed in comparison that the fourth-generation adhesive has a considerable higher mode I fracture toughness than universal adhesive (about 40% higher) and only 30% higher SIFs recorded. Therefore, in the given conditions, the fourth-generation adhesive will better perform in terms of preventing crack formation and propagation.


The KDR (karst development rate) of rocks and their PCR(porosity of carbonate rocks) are common research topics in Jinfo Mountain. The use of traditional carbonate research methods (TCRMs) for karst studies has been shown to be costly and time-consuming. Therefore, this study attempted to find a new, reliable, low-cost, and time-saving method for karst research. The Jinfo Mountain area is a typical carbonate rock area that is suitable for karst research. In this study, many images of rock samples from the Jinfo Mountain were obtained using rock-polarizing microscopes, which provided a good basis for the karst study of Jinfo Mountain. Furthermore, in this study, image analysis technology was used to find the karst development rate of rocks and their porosity. To ensure the accuracy of these research results, we compared the research results obtained using the image analysis techniques with those obtained using TCRM. The comparison showed that the image analysis technology is a feasible research techniques for studying karst in the Jinfo Mountain area. Furthermore, it has good reference significance for other karst study outside the Jinfo Mountain area. Advances in computer technology have provided new methods for conducting karst research in carbonate rock areas. Many of these karst studies have been conducted using theT C (carbonate) R (research) M(method) (TCRM). The popularization of computer technology, however, should not be ignored in continued karst research in carbonate rock areas. As an important feature of computer technology, imaging technology has shown wide application prospects in karst research in carbonate rock areas.TCRM is a common karst research method ,2, whichScholars have used multi-technical methods for carbonate rock research ,22. Sometechniques can be used to study the karst development rate in carbonate areas should be verified by repeated studies. The research method of image analysis conducted in the Jinping area also should be verified in other carbonate rock areas. Currently, few precedents have been set to the karst study using the image techniques. Therefore, it is impossible to confirm whether the research used in the Jinping area can be applied directly to the Jinfo Mountain. The Jinfo Mountain is an ideal carbonate research area. Therefore, in this study, we attempted to verify the research methods used in the Jinping area by applying those same methods to the Jinfo Mountain area.Whether the image analysis 22.1The Jinfo Mountain area (2.2Karst research has been conducted in the Jinfo Mountain area for a long time. A significant amount of karst research data has been accumulated for the Jinfo Mountain area. Among the karst research data, the KDR and PCR are the most common types of data. In the Jinfo Mountain area, PCR has been obtained using the gasoline methods. Because the use of gasoline in the laboratory must be authorized by the government, water is generally used instead of gasoline in the laboratory. Most of the carbonate porosity data for the Jinfo Mountain area was obtained through water immersion. Our research process was similar to that conducted using gasoline. The carbonate specimens soaked in water were dried, however, instead of burned. The KDR of the rocks in the Jinfo Mountain was determined primarily by putting the carbonate rock specimen shown in 2.3In a karst study in the Jinping area, we found that the PCR in different periods obtained through image analysis had a linear multiplication relationship. This linear multiplication relationship also existed for the KDR of the rocks. The Jinfo Mountain area is similar to the Jinping area. Both the Jinfo Mountain area and the Jinping area have extensive distributions of carbonate rocks (2.4Porosity research on carbonate rock using image analysis has been conducted primarily to obtain the new value of each image pixel point of a rock using an image analysis algorithm. The new value of each image pixel constituted a new matrix. After finding a suitable threshold, the image of the rock was divided into black-and-white binary images using the threshold. This threshold was obtained using statistical methods. From the experience in Jinping, taking the PCR obtained using the TCRM as the target value, we compared the PCR obtained using this threshold value. This is a good research method and gradually should be able to approach the TCRM porosity using the porosity obtained with the threshold value through a target approximation.33.1In this study, we used the image of a slide of Jinfo Mountain rock (If the image analysis method is applicable to karst research of Jinfo Mountain, then the linear multiplication value of the PCR obtained using the image analysis techniques and the TCRM should not be drastically different (3.2Samples 5-1 and others are false in 3.3We conducted carbonate research using image analysis mainly to obtain the PCR. We obtained the karst development rate by analyzing the linear multiplication relationship of the PCR at the same location. Therefore, the karst development rate could be obtained only for the samples tested in 44.1The application of image analysis technology to karst research has been limited by its unreliable results. Therefore, it is necessary to find a correct method to verify the reliability of its results . As can 4.2The results obtained from previous karst image analysis studies were not necessarily accurate. In karst image analysis studies, it is necessary to improve the algorithm step by step to improve the accuracy of the research results (5The advantages of using image analysis methods for carbonate karst studies are obvious. Compared with the TCRM, the time-saving advantage of using the image analysis method for carbonate karst research was obvious. Studies that take days to complete using the TCRM can be conducted in just minutes using image analysis. Compared with the TCRM, the cost advantage of using the image analysis method for carbonate karst research was obvious. Because carbonate slides have to be used for rock property analysis in the TCRM, there is no additional cost to use carbonate slides for karst research. Because the processing cost of carbonate rock slides is low, carbonate rock slides can be processed as long as fragments of carbonate rock samples are available. Some karst studies of carbonate rocks conducted using the TCRM are labor-intensive, whereas image analysis methods are significantly less labor-intensive. Compared with karst studies of carbonate rocks performed using the TCRM, the image analysis method allows researchers to quickly repeat a given study using open-source code. Because of the characteristics of open-source code, the image analysis method allows other researchers to participate in the research or assist more quickly than when using the TCRM. Compared with the TCRM, the image analysis techniques can take advantage of open-source code, which other scholars can also access. Compared with the TCRM, the image analysis techniques can benefit from the latest achievements in disciplines, such as artificial intelligence and big data. The image analysis techniques enables more scholars from other disciplines to enter the field of karst research.The following points should be noted when extending the study to other Karst areas. The purity of the rocks should be relatively high. It is not difficult to collect samples of carbonate rocks. The processing cost of local carbonate slides and specimens is low. Local karst observation research has been conducted for a long time, and therefore enough historical research data can be used for verification. The local karst water and hydrochemical indicators are similar. Carbonate rock areas that meet the noted conditions should also be suitable for use of the image analysis techniques. To extend the study to other karst areas, we must also pay attention to the karst study of the image analysis techniques. To ensure that the application of this research method is meaningful, the accuracy rate of the Karst study of the image analysis techniques should be higher than the accuracy rate of local researchers who make judgments with the naked eye.Honghai Kuang: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Jinghao Li: Contributed reagents, materials, analysis tools or data.Xiyao Wang: Contributed reagents, materials, analysis tools or data.https://doi.org/10.5281/zenodo.6596935.Data associated with this study has been deposited at The authors declare that they have no known competing financialinterestsor personal relationships that could have appeared to influence the work reported in this paper.