SiC is an attractive semiconductor with many excellent properties, that can bring huge potential benefits thorough advances in submicron semiconductor processing technology. Recently, there has been an interest in SiC due to its potential wide application in power electronics, in particular in automotive, renewable energy and rail transport.The inductively coupled plasma reactive ion etching (ICP-RIE) is a selective dry etching method used in fabrication technology of various semiconductor devices. The etching is used to form non-planar microstructures—trenches or mesa structures, and tilted sidewalls with a controlled angle. The ICP-RIE method combining a high finishing accuracy and reproducibility is excellent for etching hard materials, such as SiC, GaN or diamond. The paper presents a review of silicon carbide etching—principles of the ICP-RIE method, the results of SiC etching and undesired phenomena of the ICP-RIE process are presented. The article includes SEM photos and experimental results obtained from different ICP-RIE processes. The influence of O Silicon carbide (SiC) is a prosperous material for different electronic and photonic applications. The physico-chemical properties of SiC, such as high thermal conductivity, resistance to high temperatures, high breakdown voltage, high hardness and high chemical resistance, make it an attractive material for high-power, high-frequency or high temperature semiconductor devices. These features, in combination with SiC polytypism, make SiC an attractive material for optoelectronic applications due to the refractive index specific to a given SiC polytype. The complex microsystems, including MEMS  or MOEMS  based on SiC can be used, among others, in pressure sensors, bolometers, microresonators, micromotors, fuel atomizers, gas turbine rotors, UV radiation sensors, high-temperature sensors, and sensors for chemically aggressive environments ( and RefsAlthough the inductively coupled plasma reactive ion etching (ICP-RIE) method has been known for many years, it is still under development and refinement in terms of etching of various bulk materials, layers or complex multilayer structures. The method is used, for example, for the production of electronic and photonic devices. It is a dry etching method carried out in the plasma of chemically active gases and/or noble gases and enables a selective etching of: metals, semiconductors, polymers, dielectrics, oxide and nitride materials, as well as photosensitive organic materials (photoresists) ,5,6,7. T16–1017 m−3) becomes over an order of magnitude higher than that of the CCP-RIE. In turn, the area of operating pressure vs. plasma density for ICP and ECR-RIE is common to some extent, i.e., in the plasma density range of 1016–1018 m−3, but the ICP can generate a high-density plasma under higher pressure (up to 10 Pa) than the ECR-RIE.The ICP-RIE etching is used in processes of a SiC surface preparation (cleaning and “smoothing”), in the production of Schottky diodes ,9, as weObtaining the appropriate roughness as a result of the microstructure produced is to increase the luminous efficiency of such LEDs . The eff2, SiC/Si and SiC/glass, by direct adhesive bonding and annealing at T ≤ 200 °C, for potential electronic, optical, mechanical and biomedical applications [The plasma etching can be one of the stages in the process of heterostructures fabrication, such as SiC/SiOications . ICP-RIEications ,15, MOS ications ,17, MOSFications ,18, SiC ications ,19,20,21ications .This article is a review on etching of SiC using inductively coupled plasma. The ICP-RIE is still the only technique that enables reproducible etching processes with high anisotropy, selectivity and etching rate. This article presents the principles of the ICP-RIE method and representative results of SiC etching. Moreover, undesired phenomena of the ICP-RIE process were indicated, which should be avoided in technology of semiconductor devices. The article is enriched with our SEM photos and experimental results obtained from various ICP-RIE processes.2 wafer. The etching characteristics were studied depending on RIE source power (25–300 W), ICP source power (1750–2500 W) and total gas flow, with chamber pressure fixed at 7 mTorr or 10 mTorr. Most of the experiments were performed using Cr (~0.1–0.3 µm) etch masks, which were deposited by RF magnetron sputtering. The effect of O2 addition to the SF6 plasma was also investigated to improve the etching rate and/or selectivity.The samples used in this study were commercial 4H-SiC wafers with polished Si-face. An ICP reactor “PlasmaPro100” (Oxford Instruments Ltd.) equipped with two 13.56 MHz RF power supplies was used. The experiments were carried out on a lower (sample) 6-inch electrode covered with a Si/SiOScanning electron microscopy (ZISS AURIGA 60) and contact stylus profilometry  were used to measure the etching profiles, surface morphology and selectivity.The ICP-RIE method offers etching conditions to obtain deep anisotropic, isotropic or directional etching of patterns. A compromise between the etching rate and anisotropy can be achieved by modifying of the etching conditions, for example by changing the composition of the working gas, appropriate selection of process parameters or the mask material selection . EtchingThe ICP-RIE etching technology of structures entails the need to produce patterns in the form of vertical, smooth walls forming: holes, deep and narrow trenches or islands ,24—the sOne of the highest aspect ratio (18.5:1) trench has been presented by Dowling et al. , where tThe ICP-RIE method uses the phenomena occurring in the plasma and the interaction of ions with the etched material. Reactive plasma is created as a result of a glow discharge in the etching gas , as a cThe main mechanisms of interaction with the removed material in the ICP-RIE are chemical and physical ones . The cheVSiC:VNi = 100:12  can be obtained [2 mask, after the etching of trench structures in the SF6/O2 plasma, a SiC surface with carbon clusters and non-volatile CFx compounds was observed [2 mask was poorly suited for ICP-RIE etching of SiC structures.As for the metal mask, the examples in the literature show that the most promising mask in terms of high selectivity and anisotropy of etched profiles is a mask made of copper. It has been observed that the etching rate of a copper mask in SFr nickel . In turn2 plasma . The selobtained ,30. In tobserved . This leA, expressed by the Formula (1):sv—is the etching rate in the direction tangent to the etched surface, and pv—is the etching rate in the perpendicular direction. In the case of an isotropic etching, the etching rates in both directions are comparable. A dry plasma etching is isotropic, unless a polymer that blocks the chemical removal of material  is deposited on the walls of the pattern, e.g., in the plasma process commonly known as the Bosch process, consisting of repeated sequences of etching and passivation steps [The second important parameter of the ICP-RIE process is an etching directivity , definedon steps . In caseSBU). The DC self-bias voltage together with plasma potential (PPU) are in turn the DC bias voltage (DCU) [The cathode powered by the RF generator (13.56 MHz) in the ICP-RIE reactor in  has a smge (UDC) , as it iThe large electric field generated near the electrode surface causes a significant acceleration of the ions in the area of its interaction. The ions can obtain energies above 50 eV.PICP), and the RF power to accelerate the plasma ions, and generate the polarization voltage of the etched material is called RF bias power (PRIE). The power delivered from both sources influences the etching rate [The ICP-RIE reactor enables the RF power control of both RF generators. The RF power affecting the plasma flux is called the inductive power , the ICP-RIE dry etching method is basically the only one that allows obtaining patterns in this material . In the The plasma etching processes presented in the literature were aimed at optimizing the conditions allowing for increasing the etching rate and obtaining, from the point of view of the subsequent production of the device or the microsystem structure, the “favorable” surface of the etched SiC. In the case of the surface, it was important to avoid damage after etching and also to keep the chemical composition unchanged . In general, smooth surfaces increase the performance of microelectronic devices, therefore, after etching, the surface roughness should be kept at a minimum . A low-pExamples in the literature ,42,43 shChoi et al.  showed t2 top layer and the increased surface roughness resulting from the modification of samples’ surfaces with the laser beam could contribute to a higher etching rate.In the literature, the influence of the etching process parameters, for example: plasma composition (change in gas mixture composition), gas flow, pressure in the reactor chamber, distance between electrodes, the applied RF power or temperature on the results of the etching process has been investigated. Among others, the influence of these parameters on the etching rate of SiC and on the SiC surface morphology has been studied see . It has The surface quality is an important factor in technology of electronic devices ,8,36, an2F6 + O2 plasma indicate a close correlation between the surface morphology of the etched SiC and (DCU) [DCU are increased [The experimental results of the SiC etching process in Cnd (UDC) . It was ncreased . The surncreased .In the ICP-RIE processes based on fluorinated gas and oxygen the following chemical reactions (3)–(6) are involved: or both .6 + O2 gas mixture allows to obtain smoother etched surfaces, as well as increases the etching rate (up to ~500 nm/min), in relation to SiC etched without argon. Moreover, at fixed plasma conditions, the etching rate was also increased with increasing RF power and pressure in the reactor chamber. As the gas flow was increased, the etching rate was initially increased and then decreased. This was explained by the chemical interaction mechanism, in which the different flow rate of the gas used in the process affects the residence time of radicals on the surface of the etched SiC, and it also affects the speed of removal of the resulting volatile reaction products. Faster gas flow helps to remove volatile SiFx and CFx compounds faster, however, until a certain optimal flow rate is exceeded , i.e., when the duration of stay of radicals on the surface is shorter than the time needed for a chemical reaction in etching process. It was also noted that the addition of Ar to the SF6 + O2 plasma had no significant effect on the etching profiles of SiC structures. It is worth mentioning that the SiC etching in a pure Ar atmosphere allows for very low etching rates, e.g., 19 nm/min—for RF power of 200 W and pressure in the chamber of 30 mTorr [Jiang et al.  and Osip30 mTorr .6 + O2 plasma is optimal for the highest etching rates [x (1 < x < 4) compounds are formed, and an addition of the oxygen to plasma increases the etching rate of the carbon-rich SiC surface layer as a result of the formation of volatile compounds in the form of CO, CO2 [2 [3 + O2, CHF3 + O2), including the SF6 + O2 plasma [DCU. In the papers [DCU, while in the second one , the chemical mechanism dominates, and the etching rate depends mainly on the concentration of fluorine and oxygen, and on their reaction with silicon and carbon.It was experimentally established that the dependance of the etching rate of silicon carbide on the percentage of oxygen in the total gas mixture is non-linear . It was ng rates ,42,47. I, CO2 [2 . The pre2 plasma ,41. The 2 plasma ,41. Thise papers ,41 a com3 + O2 plasma [2, and after that a sharp decrease almost to zero for 80% O2. The initial increase in the etching rate was due to the increase in oxygen addition, which also led to the increase in the number of broken C-F bonds. Consequently, the proportion of reactive F atoms responsible for etching process was increased. The subsequent decrease in the etching rate (with the amount of oxygen above 20%) could be related to the progressive (as the oxygen content increases) effect of “diluting” the concentration of F atoms in oxygen, as well as the possible formation of the SiOx type oxide layer on the etched surface, which acts as inhibitor [RMSR) where with increasing O2 fraction from 0% to 80%, there was an increase in RMSR from 1.31 to 2.34 nm. An increase in the SiC surface roughness along with an increase in oxygen content was also observed after etching processes in the Cl2 + O2 plasma [The same conclusion regarding the optimal amount of oxygen—20%—allowing for the highest SiC etching rate was made after the SiC etching process in CHF2 plasma . Xia et 2 plasma  observednhibitor ,49. More2 plasma . The inc2 plasma ,49. Sung2 plasma  also not3 + CH4 plasma. They determined the influence of the source power (acceleration of reactive ions), the so-called RF bias power, on DCU and the angle of side walls inclination  of the etched profiles. The obtained experimental results allowed to conclude on the linear relationship between RF bias power and DCU, and that increasing the ion bombardment energy leads to decrease in the angle of side walls inclination. Additionally, reducing the angle is associated with an increase in the faceting of the mask, i.e., the increase of its flat surface area.Due to the appropriate selection of the process parameters, including energy of the bombarding ion beam, it is possible to control the slope of side walls of the etched profiles ,55. Kim 6 + O2 plasma proved to be the most promising for etching almost vertical profiles and to achieve the highest etching rate of 3050 Å/min. The use of BCl3 plasma with addition of nitrogen (BCl3 + N2), for appropriately selected proportions of these gases, made it possible to obtain structures with an inclination angle ranging from 40° to 80°. When the Cl2 + O2 plasma was used (with different oxygen fraction), a smaller angle of inclination of the walls was obtained ranging from 7° to 17°.Obtaining the profiles with different wall inclination angles is also possible by the selection of the appropriate composition of the plasma used in the etching process ,29. Sung6 plasma may contribute to the formation of unevenness in the form of trenches at the bottom of the SiC etched profiles. This unevenness arises as a result of secondary etching, which takes place with the participation of ions reflected from the side walls surfaces  compared to SiC. The increased concentration of ions (attracted by the charged SiFxOy layer) on the surface between side walls of the etched profile, as well as an increased proportion of ions reflected from surfaces of these walls leads to an increase of the rate of chemical reactions affecting the formation of a trench structure. The structure of the trench itself may additionally contain heterogeneity  mask used in the process, leading to the micro-mask effect, i.e., the formation of undesirable “grass-like” structures at the bottom of the etched profiles. Ni mask erosion was also noted by Li et al. [6 + O2 plasma with the use of various masks including SiO2, Ni, Ni/SiO2 and Ni/Al2O3. Microscopic (AFM) and spectroscopic (XPS) investigations of these structures’ surfaces after etching proved that the Ni/Al2O3 mask turned out to be the best one. For this mask, a trench structure was obtained with almost rectangular profile of the sidewall and without traces of metallic contamination. It is worth mentioning that the MOS structure made on a SiC sample etched with a Ni/Al2O3 mask [2, Ni, Ni/SiO2).Tanaka et al.  observedi et al.  who inve2O3 mask  showed t2 + Ar + BCl3 plasma. The authors noticed that as the pressure in chamber increased, the etching rate decreased. The surface roughness of the etched samples also increased. The increase in pressure decreased the mean free path of interaction of plasma “particles” and increased the probability of their collisions, which in turn led to a decrease in plasma density. Taking into account the lower plasma density and the high SiC bonding energy, the etching rate tended to decrease. As the pressure increased from 8 mTorr to 30 mTorr, the etching rate decreased monotonically, which could be related to a decrease in the flux of reactive ions reaching the surface of the etched samples and/or a decrease in the amount of reactive Cl- ions in the plasma. The observed increase in surface roughness of etched samples, with increasing the pressure could, however, result from a decrease in the amount of reactive ions  reaching the sample surface and slowing down the desorption process at the surface. Ekinci et al. [3 to the Cl2 + Ar plasma improves the SiC post-etching surface morphology by reducing the surface roughness.The micromasking effect may also occur when the etching process takes place under elevated pressure in the reactor chamber. Ekinci et al.  investigi et al.  also not2 + O2 plasma it was observed that the addition of oxygen in the plasma may contribute to a change of the sidewall slant angle of etched structures and to the micromasking effect [2-coated silicon substrates which were used in the etching process as load wafers for transferring SiC samples to the process chamber. The use of these load wafers for a Cl2 + O2 plasma etching, in particular silicon wafer, promoted the micromasking effect. When using a silicon load wafer, the formation of SiC micropillars and SiOx micromasks was noted on the etched SiC samples. When using the SiO2-coated load wafer, the SiOx micromasking effect on SiC samples was still observed, but it was significantly reduced.For SiC samples etched by ICP-RIE method with use of the Clg effect . Tseng eg effect  that the6 + O2 etching and for the Ni mask opening width x in the range: 100 µm < x < 1 mm. The SiC trenches with a small pattern width were less susceptible to micromasking than those with a large pattern width [2 + O2 plasma. In the paper [The influence of the width of the metal mask used in the etching process on the intensity of the micromasking effect, the so-called “mask opening width effect” was also reported in the literature ,5. TanakThe mask opening width used in the etching process also affects the depth of the etched structures, which is known as the so-called “microloading effect”. This effect can be defined as a decrease in the etch depth together with a decrease in the mask opening width. The reason for this may be the narrow mask opening preventing the penetration of ions and diffusion of the reaction products . Figure It is obvious that the micromasking effect, which significantly deteriorates the surface quality of etched structures, is particularly undesirable in the construction of electronic devices. Hence, it is so important to select the appropriate parameters of the etching process to eliminate the formation of residual structures. The risk of their formation can be eliminated, for example, by increasing the pumping rate of the chamber, increasing the temperature of the etched sample, or by gradually changing the etching conditions according to the etching depth obtained ,40.The etching rates and selectivities of SiC/Cr etching were investigated under various etching conditions. The etching rates of SiC and chromium mask, as well as, SiC/Cr selectivities were studied as a function of: (i) oxygen concentration , (ii) ap6 + O2 gas mixture has been reported in many papers  is required to obtain the best SiC etching rate in ICP [6 + O2 plasma increases slightly, reaches its maximum at around 20% of O2, and then decreases (probably due to a dilution effect) [6 + O2 plasma can be increased by more than 10 % with optimal oxygen content of 20% compared to SiC-etching in pure SF6 plasma under similar conditions  on the etching rate of the Cr mask used in processes , and on In The etching of SiC by ICP-RIE method is influenced by many factors, which makes it difficult to achieve the desired results. Obtaining a fully repeatable and stable technology of the SiC etching with ICP-RIE method is associated with determination and optimization of process parameters which have a significant impact, among others, on the etching rate, surface morphology, sidewall inclination angle of MESA or trench structures, and the smoothness of the walls. Moreover, these parameters must not cause erosion of the mask material , and also control a degree of the surface damage. These include the etching gas used , working gas flow, pressure in the reactor chamber, glow discharge excitation parameters (RF power applied), surface area of the etched material and the substrate temperature.It is also important to find a balance between the physical and chemical mechanisms of the etching. This allows, among others, to optimize the anisotropy of etched patterns. The idea is to skillfully “control” the flux and energy of reactive ions to avoid sidewalls unevenness  and undercutting of sidewalls . The selection of appropriate etching conditions will allow for etching specific patterns in SiC with the accuracy required for applications in semiconductor devices technology. It should be noted that the control of the SiC etching process is necessary in extending the range of SiC applications in various electronic and optoelectronic devices.The original results of dry etching with the use of chrome mask presented in


In the field of surface defect detection, the scale difference of product surface defects is often huge. The existing defect detection methods based on Convolutional Neural Networks (CNNs) are more inclined to express macro and abstract features, and the ability to express local and small defects is insufficient, resulting in an imbalance of feature expression capabilities. In this paper, a Multi-Scale Feature Learning Network (MSF-Net) based on Dual Module Feature (DMF) extractor is proposed. DMF extractor is mainly composed of optimized Concatenated Rectified Linear Units (CReLUs) and optimized Inception feature extraction modules, which increases the diversity of feature receptive fields while reducing the amount of calculation; the feature maps of the middle layer with different sizes of receptive fields are merged to increase the richness of the receptive fields of the last layer of feature maps; the residual shortcut connections, batch normalization layer and average pooling layer are used to replace the fully connected layer to improve training efficiency, and make the multi-scale feature learning ability more balanced at the same time. Two representative multi-scale defect data sets are used for experiments, and the experimental results verify the advancement and effectiveness of the proposed MSF-Net in the detection of surface defects with multi-scale features. With the rapid development of the manufacturing industry, people are paying more and more attention to the surface quality of various industrial products. The surface quality of the product will not only affect the appearance and visual effect of the product, but also affect the internal quality and performance of the product. In order to reduce production costs, improve production efficiency and product quality, it is very necessary to effectively detect surface defects in the product manufacturing process. Artificial visual inspection, which has the disadvantages of low detection efficiency, high false detection rate and high missed detection rate, high labor intensity, and low speed.The non-contact detection method based on machine vision ,3 usuallAt present, the commonly used surface defect detection methods are as follows :ArtificiIn recent years, with the successful application of deep learning models represented by convolutional neural networks (CNNs)  in compuCNNs are currently used by domestic and foreign researchers and engineers as the preferred architecture for product surface defect classification. However, difficulties and challenges still exist. For different types of products, the surface defects often have the characteristics of different sizes, uncertain positions, and different shapes; even for the same type of product, the color, texture, shape, and size between different types of defects is also very different. The general CNN often contains a few specific scales of receptive fields and is more inclined to express macroscopic and abstract features. It is not strong in expressing local and small defects, which leads to an imbalance in feature expression capabilities. Therefore, how to design a deep CNN that can simultaneously take into account multi-scale feature extraction has become the focus of research.Since the receptive fields of the convolution kernels in the CNN are closely related to the sizes of the target features, the CNN’s ability to express features at different scales directly determines its ability to detect defects of different sizes . This paThe structure of this paper is as follows. The receptive field refers to the input area that neurons can “see” in the CNN , as showThe receptive field In recent years, some researchers have tried to improve the classification performance of CNNs on multi-scale target data sets by optimizing the CNNs’ structure, such as the works of Tang  and Kim As we all know, there is no branch structure in the two CNNs of AlexNet  and VGGNIt can be seen from Unlike AlexNet and VGGNet, GoogLeNet  and ResNIn addition, although in the distribution map of the fourth module in In summary, the existing CNNs have similar characteristics in design, that is, in the early stage of feature extraction of CNNs, they focus on learning the local and concrete information of images, and in the middle and later stages of feature extraction of CNNs, the macro, and abstract feature information are more likely to be learned. Of course, the above law is logical and effective in most classification and detection tasks, but for the surface defect data sets with multi-scale features studied in this paper, the above design ideas of CNNs are obviously not the optimal choice.(1) The magnetic tile defect data set  was coll(2) Roller surface defect data set , which iIn the roller defect data set, the average defect area of defect samples of CI type is close to 17,000 pixels, while the average defect area of defect sample types such as CC, CSc, CSt, EFC, EFSc, EFSt do not exceed 1000. From the above analysis, it can be seen that these two defect data sets have obvious multi-scale defects problems.(1)Increase the diversity of the receptive field of a single convolution module in CNNsFrom the analysis of the receptive field of CNN in Therefore, this paper chooses the convolution module with branch structure as the basic unit of MSF-Net. Among the many representative modules with branch structures, the Inception module has favored domestic and foreign researchers in the field of target classification and detection, because of its lightweight design ideas and excellent characteristic expression ability and classification accuracy. In this paper, the Inception v3  structur(2)Increase the diversity of the receptive field of the feature map output by the last convolutional layer of CNNsIn order to improve the classification accuracy of multi-scale defect samples, it is necessary to ensure that the feature map output by the last convolutional layer before the fully connected layer has sufficient receptive field scales, especially the number of small-scale receptive fields. Therefore, inspired by HyperNet , the fea(3)Improve training efficiencyThe improvement of feature expression ability inevitably means the deepening of the number of layers of CNNs. Therefore, it is essential to improve training efficiency. MSF-Net improves training efficiency and avoids over-fitting by using residual shortcuts and batch normalization (BN) layers.In order to solve the problem that the existing CNNs are not strong in learning small-scale defect features, and to improve the classification and recognize the ability of multi-scale surface defects, this paper proposes Multi-Scale Feature Learning Network (MSF-Net) based on Dual Module Feature Extractor (DMF), where DMF is built by the Concatenated Rectified Linear Units (CReLUs) and the Inception modules. The main design ideas of MSF-Net are as follows:The Dual Module Feature Extractor (DMF) contains two different convolutional modules: the CReLU modules are mainly used in the early stage of feature extraction of MSF-Net, which aims to reduce the calculation cost and speed up the calculation of forward propagation; in the later stage of feature extraction of MSF-Net, the Inception modules are used to increase the depth and width of MSF-Net and improve feature learning ability.The research of Shang et al.  revealedThe Inception V3 module uses convolution kernels of different sizes such as 1 × 1, 3 × 3, and 5 × 5 to obtain different scales of receptive fields, which improves the diversity of features. In addition, the design of the branch structure saves computational costs while enhancing the width and depth of CNN.Based on the native Inception V3 module, this paper proposed an optimized version of the Inception feature module, as shown in In order to increase the diversity of the receptive field of the last convolutional layer of CNNs and improve the ability to learn and express features of different scales, especially small and local features, MSF-Net aligns and integrates the feature outputs of several intermediate layers, and then the fully connected layer is used for classification. (1)At the input of the convolution modules conv3_1, conv4_1, conv5_1, and conv6_1, the convolution kernel with a size of 1 × 1 and a stride of 2 is used instead of the pooling layer to achieve the proportional reduction of the feature map size, so that the feature maps output by conv3, conv4, conv5, conv6 is 1/4, 1/8, 1/16, and 1/32 of the input image, respectively.In order to realize the learning and expression of multi-scale features, the specific design details of MSF-Net are as follows:(2)The output feature maps of the convolution modules conv3_3, conv4_3, and conv5_4 are down-sampled, and the average pooling layers with kernel sizes of 8 × 8, 4 × 4, and 2 × 2 are used, so that their respective output feature maps are consistent with the feature map output by conv6_2(3)The above four feature maps are combined and connected to form the final output feature map, the formula is as follows:(1)Avoiding expression bottlenecks in the early stage of feature extraction. That is, the information flow should avoid highly compressed convolution layers in the forward propagation process, and the width and height of the feature map should be gradually reduced in an orderly manner, especially for surface defect datasets with subtle defect features, it is not wise to compress the feature map too early. Therefore, the convolutional layer conv1_1  and pooling layer pool1_1  are concatenated to slow down the reduction speed of the feature map.(2)In the middle and late stages of feature extraction in CNNs, the width and depth of CNNs should be balanced as much as possible. That is, as the CNN deepens, the feature map gradually shrinks, and the output matrix dimension of each convolutional layer should gradually increase. Therefore, the number of modules, the feature map sizes, and the number of channels of the three module chains of conv 4, conv 5, and conv 6 are designed with full reference to Inception V1  and Ince(3)Average pooling layer is used to replace the fully connected layer, which can greatly reduce the number of parameters and save calculation costs. Specifically, MSF-Net uses an average pooling layer with a kernel size of 7 × 7 and a strider of 1 to replace the fully connected layer. It can be calculated from (4)The residual shortcut connections are used to effectively accelerate training and promote CNN’s convergence. Specially, almost every convolution module in MSF-Net, except conv1_1, uses a shortcut connection, which effectively avoids the problem of gradient disappearance and speeds up training.It is worth mentioning that the architecture design of MSF-Net follows many experiences and guidelines for the design of CNNs, as follows:The performance indicators and parameters of the experimental platform are shown in (1)Inception v3 and ResNet-50 are closer to MSF-Net in terms of the number of convolutional layers and parameters, as shown in (2)MSF-Net is deeply influenced by GoogLeNet v3 in terms of the design of feature extraction modules, the number of modules, and the width and depth of CNN. Therefore, comparing MSF-Net with Inception v3 can more accurately assess the impact of CNN’s structure on classification performance.(3)Except conv1_1, all feature extraction modules of MSF-Net use residual shortcut connections.Inception v3 and ResNet-50 are used as the comparison CNNs for the following reasons:Therefore, it is also essential to conduct comparative experiments with ResNet-50, which is similar in size.(1)Compared with ResNet-50 and Inception v3, MSF-Net’s parameters is reduced by more than 54%, which greatly reduces the computational cost, the most intuitive manifestation is that the number of epochs required for MSF-Net to achieve convergence is greatly reduced;(2)Compared with the Inception modules fully used by Inception v3, MSF-Net uses the optimized CReLU module in the early stage of feature extraction, the optimized CReLU module has outstanding performance in reducing computational costs, which effectively shortens the time consumption of forward back propagation and improves the training performance of MSF-Net.Aiming at the problem that the commonly used CNNs are not ideal for detecting small and local defects on the products’ surface, the multi-scale feature extraction mechanism involved in several mainstream CNNs in the current deep learning field is analyzed, and a multi-scale feature learning network based on dual module feature extractor is proposed, named MSF-Net, the design of the dual module feature extractor, the specific architecture and parameters of MSF-Net, and the optimization to improve training efficiency are introduced in detail. The proposed MSF-Net was trained and tested on two multi-scale surface defect data sets, which verified the advancement and effectiveness in multi-scale defect detection. Future work will focus on exploring the generalization of MSF-Net in more research fields, such as radar image classification and remote sensing image classification.


The use of extracellular matrix (ECM) biomaterials for soft tissue repair has proved extremely successful in animal models and in some clinical settings. The aim of the study was to investigate the effect of the commercially obtained CorMatrix bioscaffold on the viability, proliferation and migration of rat pheochromocytoma cell line PC12. PC12 cells were plated directly onto a CorMatrix flake or the well surface of a 12-well plate and cultured in RPMI-1640 medium and a medium supplemented with the nerve growth factor (NGF). The surface of the culture plates was modified with collagen type I (Col I). The number of PC12 cells was counted at four time points and then analysed for apoptosis using a staining kit containing annexin V conjugate with fluorescein and propidium iodide (PI). The effect of CorMatrix bioscaffold on the proliferation and migration of PC12 cells was tested by staining the cells with Hoechst 33258 solution for analysis using fluorescence microscopy. The research showed that the percentage of apoptotic and necrotic cells was low (less than 7%). CorMatrix stimulates the proliferation and possibly migration of PC12 cells that populate all levels of the three-dimensional architecture of the biomaterial. Further research on the mechanical and biochemical capabilities of CorMatrix offers prospects for the use of this material in neuro-regenerative applications. It contained purified ECM components derived from cows and sharks [®, made of dead donor cells, became the first acellular dermal substitute used in the treatment of burns and breast reconstruction [The fundamental part of tissue stroma is the extracellular matrix (ECM). ECM is the release product of cells settling on a given tissue; that is why both the composition and distribution of ECM particles depend on the tissue that ECM is derived from ,2. ECM mcollagen . In the d sharks . By conttruction . ECM protruction . Recentltruction ,9,10. Thtruction . The soutruction ,13,14, vtruction ,16, humatruction ,18 and ltruction ,20. Biostruction .Collagen, being a biomaterial of animal origin, is widely used as a basic component of the extracellular matrix and it has impact on the structural composition of cell nuclei as well as cell and tissue phenotype . It is oBiological scaffold materials are obtained from various tissues, including heart valves ,31, bloo®, Durasis®, Stratasis®; Cook Biotech, Lafayette, IN, USA; Restore®; DuPuy, West Chester, PA, USA) [®, Autotissue, Berlin, Germany, and CardioCel®, Admedus, Perth, Australia) [SIS was used for the first time in 1989 as an autograft of a large vessel in a dog; however, earlier in the 60 s, it was used also in experiments on vessel grafts. SIS-ECM was used in clinical applications in over 1 m patients. After the capsulation of gingival mesenchymal stem cells, SIS-ECM induced the regeneration of tongue mucosa and the mucosa barrier in ulcerative colitis ,49. TherPA, USA) . One of stralia) . CorMatrstralia) ,52. CorMstralia) ,54, peristralia) ,56,57,58stralia) ,57,59, estralia) .There is research indicating that ECM has impact on the proliferation, migration and differentiation of rat-derived neural and precursor stem cells, the proliferation of mouse-derived neuroepithelial cells , and theLine PC12 is derived from pheochromocytama; geoplastic rat cells were obtained from neural crest tissues [The subject of the present study was the investigation of the behavior of line PC12 cells in in vitro cultures using various substrates: collagen, non-ECM polymer  and commercial biomaterial CorMatrix, as well as the assessment of the possible use of CorMatrix as a biological scaffold in neuro-regenerative medicine.CorMatrix  is a commercially available material created by the decellularization of the small intestinal submucosa of pigs. It has a three-dimensional structure that allows ingrowth of host cells, initiates and maintains control over cell proliferation and differentiation and has low immunogenicity. CorMatrix is certified by the Food and Drug Administration as a medical product and has been used in cardiovascular clinical surgery since 2010, including surgical treatment of congenital heart and vascular diseases, pericardium reconstruction, valve reconstruction in adult and child endocarditis, and repair of damage after myocardial infarction.®ECM® material properties was carried out on the PC12 cells obtained from Sigma-Aldrich . This line is derived from the rat adrenal pheochromocytoma tumor. In the ATCC collection are distinguished two types of this cell line—suspension and adherent. The adherent line was derived from the suspension line as a result of multiple passages. A suspension line was chosen for further investigation. The study was carried out using cells between 15 and 25 passages because at higher passages, there is too much morphological variability. After treatment with nerve growth factor (NGF), these cells become similar to sympathetic neurons, and it has been recognized that they can be a model in neurobiological research. The cells were passaged twice a week.Assessment of sterile CorMatrix2, 37 °C and 95% humidity.Cells were cultured and seeded on multi-well plates for experiments in RPMI-1640 medium supplemented with 10% DHS, 5% FBS, 2 mM L-glutamine and 25 μg/mL gentamicin . After 24 h of cell regeneration, the medium was replaced with RPMI-1640 supplemented analogously with antibiotics, but with serum content reduced to 1% DHS, and with the addition of 100 μg/mL NGF. All of the studies were carried out in 5% COw/v) overnight at 4–8 °C. Before use, the plates were exposed to UV light for 30 min for sterilization. CorMatrix material was cut into 10 mm × 10 mm fragments and then used in experiments in two ways. PC12 cells were seeded at a density of 50,000 cells/well directly onto the material, or the material without cells was put into a well with previously seeded cells. For the adherence of cells to the CorMatrix material, 50,000 cells were seeded directly onto it in a volume of 100 μL medium. After 30 min of incubation, the solution in the well was diluted to a final volume of 1 mL as illustrated in Two types of 12-well plates were used in experiments—plates provided from the producer  and plates with the modified surface of the culture wells with collagen. The modification was carried out using collagen type I solution at a concentration of 0.01% , containing annexin V conjugated with fluorescein and propidium iodide. The PC12 cells were transferred to test tubes and incubated with annexin V and PI solution in the dark at RT for 20 min. The viability of the culture was assessed by the image-based cytometer Arthur .In order to count cells adhered to the CorMatrix material and to the surface of the wells, the PC12 cells were stained with Hoechst 33258 solution for 1 h in the dark at 37 °C. Then, using fluorescence microscopy, 5 photos were taken in various areas of the surfaces of the well and CorMatrix material (on the three different levels). The mean number of cells in the field of view was analyzed. To determine the number of cells in the image, our own software using the OpenCV library was used. Cell counting software implemented, among others, the watershed segmentation algorithm; this proved to be the most effective in the separation of cells. All photos were analyzed with the same application settings, which allowed us to reduce random errors of interpretation.The results were analyzed according to generally accepted principles. Initially, the normal distribution was checked using the Shapiro–Wilk test and equality of variance using Levene’s test. Kruskal–Wallis test and appropriate post-hoc analysis were performed.The performed cell apoptosis test showed that the number of apoptotic and necrotic cells did not exceed 7% in total for all tested cases (data not shown).The number of cells in the medium and CorMatrix material and the surface of the wells modified with type I collagen and without modification were calculated. Assessments were made at four time points: on the 1st, 3rd, 6th and 9th day of cultivation.The number of cells in the collected complete medium and medium containing NGF is shown in Incubation with nerve growth factor (NGF) inhibited the proliferation of PC12 cells in the following days of culture . In the presence of NGF, the proliferation phase is stopped. Earlier surface modification with type I collagen allowed the cells to adhere to the surface, causing PC12 cells to differentiate faster than cells normally grown in suspension .p < 0.05) when the cells were plated directly onto the material  was significantly lower  was greater than in unmodified plates . The greHoechst 33258-stained PC12 cells were counted using the ImageJ open-access platform. PC12 cells were grown in suspension on unmodified collagen plates. The mean number of cells in the field of view was in the range of 14–69 cells. In contrast, in cultures modified with type I collagen, cells adhered and differentiated in NGF’s presence. As for the cells on the surface of the CorMatrix, it could be seen that the cells adhered to the surface of the material .Greater migration of PC12 cells to the surface of CorMatrix was observed than to the surfaces of both unmodified and collagen-modified wells. In the case of sowing cells into collagen-modified wells and putting them onto CorMatrix material cells, a much larger number of cells was counted on the surface of CorMatrix than on the well surface: on day 9, 1900–2900 cells were counted on the CorMatrix-2200, while on the collagen-modified surface only 300–500 cells were counted. However, the mean number of PC12 cells on the collagen-modified surface but without CorMatrix was 800–1500 in the field of view. This study suggests possible greater cell migration to CorMatrix than to the surface modified with type I collagen.p < 0.05) increase in the number of cells on the CorMatrix material was observed between all consecutive days of the study  greater when the well surfaces were not modified. In the case of the second method of CorMatrix application, the increase in the number of cells was higher on the day of culture also on unmodified plates.The effect of covering the surface of the wells on the number of cells was also assessed. The number of cells plated directly onto the CorMatrix  was statistically significantly (p < 0.05) higher when the cells were plated directly onto the material on unmodified plates. However, already by the 6th and 9th days, no differences were observed between the number of cells and the method of material application. This probably indicates the migration of cells to the surface of the tested material. At the same time, the lower number of cells on the 9th day on plates placed directly onto the material compared to placing the material on the surface of the medium with cells may suggest cell differentiation. On plates modified with type I collagen, no statistically significant differences in the number of PC12 cells were observed depending on the method of CorMatrix application.The application of the material was also analyzed. On day 3 cultivation, the number of cells was statistically significantly . This confirms the high survivability of PC12 cell cultures in the presence of various matrices, such as type-I collagen and CorMatrix. The behavior analysis of bone marrow-derived mesenchymal stem cells isolated from adult male L2G85 transgenic FVB mice indicates long-term survivability of Matrigel-supported stem cells, even up to five months . The in In the conducted tests, it was observed that the number of cells in the collected medium on surfaces not modified with collagen increased and the number of cells in the medium containing NGF, regardless of the type of flask surface, decreased (PC12 cell culture without CorMatrix) . The decIn the case of PC12 cell cultures with CorMatrix, it was observed that the number of cells in the collected medium with NGF on days 3, 6 and 9 grew significantly on the collagen-coated surface in comparison with the unmodified one . On daysIn the conducted research, a significant increase in the number of PC12 cells was observed at three different levels of the structure of used biomaterial on culture surfaces not modified with collagen  . CorMatrThe obtained results indicate also the possible migration of cells to the CorMatrix material; PC12 cells tended to select the surface of the tested biomaterial rather than the collagen modified surface. ECM molecules exerted a significant influence on cell migration, which was shown in the research on the expression of cell adhesion molecules and extracellular matrix molecules in human brain development, tests on the influence of ECM components on rat oligodendrocyte progenitor cells (OPCs), and the investigations on the role of ECM in the regulation of rodent and human neural stem cell migration ,80,81. ILimitations of the research carried out could include the subjective choice of the observation areas, as well as the lack of assessment of the level of cell culture differentiation towards neuronal cells.Regenerative medicine encompassing a wide spectrum of diseases, ranging from cardio-vascular ones to neurodegenerative ones, constitutes an extensive area for ECM-derived biomaterial applications. Acellular biomaterials making biodegradable, three-dimensional scaffolds can be an alternative for cell transplants, whose most significant disadvantage is the low survivability of graft cells. Tissue-derived ECM scaffolds, owing to regulator growth factors and chemokines they contain, can shape factors of cell behavior, such as survivability, proliferation, migration and differentiation .Cormatrix can constitute an important alternative to other biomaterials due to its innate abilities (1) to extend; (2) to integrate with host tissue; and (3) to create a micro-environment fostering the growth, possible migration and differentiation of cells. The application of CorMatrix material as a biological scaffold seems to be an interesting method in the repair of nerve tissue damage.


Classical SIR dynamic model and its derivative improved model may not accurately describe the epidemic situation similar to COVID-19 with characteristics of relative long incubation period and a large number of asymptomatic infections. Based on the existing epidemic compartment model, a novel compartment dynamic model considering actual transmission path of the symptomatic and asymptomatic infected is presented. Theoretical analysis and numerical simulation are employed to conduct prediction of development of the epidemic. According to different epidemic response measures, i.e., mitigation measures, suppression measures, medical treatment, evolutionary trend of epidemic situation under the initial population distribution structure are discussed. Results show that the control effects of different response measures on the number of deaths depend on the timing of the implementation of the measures. For mitigation response measures, the timing of the implementation of the measures has no obvious effect on the final epidemic, while for suppression response measures, the effect of suppression response measures in the early stage of the epidemic is significantly better than that in the middle and late stage of the epidemic development. Furthermore, no matter which stage the epidemic is in, the improvement of medical treatment level will play an important role in effectively reducing mortality. This study provides useful enlightenment and decision-making reference for policy makers to choose appropriate epidemic prevention and response measures in practice. I.Recently, the new coronavirus (SARS-CoV-2) has spread all over the world. How to prevent and control the epidemic effectively has become a global hotspot. Existing theoretical research on infectious disease modeling can be generally divided into three categories. The method used in the first category is mainly based on the method of random process, which usually uses the branching process of random process to explore the spread and evolution of infection population With the emergence of a large number of asymptomatic infected cases, there have been some studies on asymptomatic infected population Based on the characteristics of COVID-19 epidemic situation, this article improves the existing asymptomatic infector models according to these three problems. Firstly, in this model, the more realistic transmission path is considered, i.e., the asymptomatic infector population is generated from the susceptible population rather than from the exposed population. Secondly, the recovery group and the dead group are treated separately, and the dead group is regarded as an important indicator to reflect the effect of different response measures. Thirdly, compare the effectiveness of different prevention and response measures under different implementation time conditions, which may provide useful insights for selecting appropriate epidemic prevention and response measures in reality.1I2RD epidemic model and it’s improvement bSEI1I2RD model are given. The main results of these models including preliminary theoretical analysis and numerical simulations are presented in This article is organized as follows. In II.In what follows, infectors are divided into symptomatic infectors and asymptomatic infectors. The basic model without considering external disturbance factors and the improved model with considering external disturbance factors are presented. First, the final state of some population numbers is analyzed theoretically, and then the evolution of population numbers with different parameters is simulated numerically. We assume the initial total population number equals N, the maximal average contact number of one person in unit time is 10.A.Definitions of related parameters and notations are listed in B.(1)All subpopulations number functions s (t), E (t), I1(t), I2(t), R (t), D (t) are continuously differentiable functions on interval (2)the number of exposed persons (infected but not ill) that a patient can infect in unit time is directly proportional to the total number of susceptible individuals S(t) in this environment. Proportion coefficient is the product of the number of people contacted in a unit time, the infection rate (3)At time t, the number of people removed from symptomatic infectious in unit time is directly proportional to the number of symptomatic, and the proportion coefficient is (4)At time t, the number of people removed from asymptomatic infectious per unit time is directly proportional to the number of asymptomatic, and the proportion coefficient is (5)At time t, the number of deaths per unit time from symptomatic patients is directly proportional to the number of symptomatic, and the proportion coefficient is (6)Basic reproductive number of infectious diseases is C.1I2RD model can be described as Considering the transmission path of SARS-CoV-2, first of all, from the susceptible subpopulation, after contact with the virus, it becomes a symptomatic infected individual during a period of incubation time, or become an asymptomatic infected person. The symptomatic infected person finally recovers after treatment, or the condition worsens to death, and the asymptomatic infected person finally recovers. Schematic diagram of the SEI1I2RD model can be formulated as follows:1I2RD model, the total number of all six subpopulation is an constant, i.e., The basic SEI1I2RD model. If we add some external disturb factors into the into the basic SEI1I2RD model, we then get improved SEI1I2RD model as follows:Here the external disturb factors are not taken account into the basic SEIIII.1I2RD and bSEI1I2RD, the properties of the solution are analyzed firstly, and then the solution under different parameters is discussed by numerical simulation.It’s extremely difficult to obtain analytical solution of aSEIA.(1)1I2RD,It can be seen from the first formula of aSEI(2)1I2RD model, we can get Add the 1st, 2nd and 4th equations of aSEI(3)1I2RD model, we can get Add the first four formulas of aSEI(4)1I2RD model, when Because B.In the part of numerical simulation, we consider three different response measures: m1. mitigation measures, m2. suppression measures, and m3. medical promotion. Mitigation measures and suppression measures are reflected by the average number of contacts For example, the population distribution of group 1 is terature .(1)The values of the first group of parameters are as follows: (2)The values of the second group of parameters are as follows: (3)The values of the third group of parameters are as follows: (4)The values of the fourth group of parameters are as follows: (5)Considering the comparison of different response measures, the values of the fifth group of parameters are as follows: assuming that strict control measures are taken to socially isolate residents, the average number of contacts reduces from (6)Considering the comparison of different response measures, the values of the sixth group of parameters are as follows: assuming that strict control measures are taken to socially isolate residents, the average number of contacts reduces from (7)Considering the comparison of different response measures, the values of the 7th group of parameters are as follows: assuming that strict control measures are taken to socially isolate residents, the average number of contacts reduces from (8)Considering the comparison of different response measures, the values of the 8th group of parameters are as follows: assuming that strict control measures are taken to socially isolate residents, the average number of contacts reduces from (9)Assuming the level of recovery rate (10)Assuming the level of recovery rate (11)Assuming the level of recovery rate (12)Assuming the level of recovery rate The parameter settings of the numerical simulation and the corresponding results are shown in C.1I2RD model, related results 1I2RD model. Here we directly carry out numerical simulation analysis. Only the parameters of the first group to the fourth group of population initial distribution structure corresponding to the mitigation measures (m1) are selected for simulation.(1)The values of the first group of parameters are as follows: (2)The values of the second group of parameters are as follows: (3)The values of the third group of parameters are as follows: (4)The values of the fourth group of parameters are as follows: Similar to aSEIIV.In this article, a dynamic model is presented to describe the characteristics of relative long incubation period and a large number of asymptomatic infections such as COVID-19 epidemic. we investigate the effect at three dimensions, i.e., different initial population distribution structure, response measures, and external disturbance on the evolutionary output of the subpopulations. The initial population distribution structures are divided as group1, group2, group 3 and group 4 listed in A.From B.The average number of contacts in It is worth noting that the final death toll in C.The level of medical treatment is reflected by recovery rate This shows that even if the proportion of initial exposure or initial infection is relatively high, the death caused by the final epidemic can be well controlled under the effective medical level, which is consistent with our experience and cognition. At the same time, it also shows that efforts to improve the level of medical treatment, including the development of more effective drugs and vaccines, will play a crucial role in effectively reducing mortality.D.1I2RD, the total population is no longer kept at a fixed value due to the consideration of external disturbance factors of each type of subpopulation, and the final number of recovery and death is significantly reduced compared with According to V.COVID-19 epidemic has a relative long incubation period and a large number of asymptomatic infections, which are significant features of SARS-CoV-2. In view of the lack of consideration on these distinctive characteristics of COVID-19 caused by SARS-CoV-2 in the existing literature, the epidemic of COVID-19 is modeled based on the classical compartmental model. The evolution trend of the number of subpopulations are analyzed theoretically, and the potential effects of three different epidemic response measures, namely mitigation measures, suppression measures and medical treatment promotion are discussed.The results of numerical simulation show that the effect of mitigation measures and suppression measures depends on the timing of the implementation of measures. The effect of adopting suppression measures in initial stages on effectively reducing the number of deaths is significantly better than that in the middle and later stages, while the effect of the implementation timing of mitigation measures is not so obvious. At the same time, the improvement of medical treatment has a positive effect on reducing the number of deaths, no matter when it is used.The results suggest that the use of mitigation measures and suppression measures should be combined with the specific situation of local epidemic development. In the future, we can further investigate the existence and stability of the model solution in theory, calibrate the parameters of the model and expand the validity of the model by using the actual data. In addition, we can consider the feedback incentive effect of different measures and the delay effect of immune measures.


The microstructure of alkali-reactive aggregates, especially the spatial distribution of the pore and reactive silica phase, plays a significant role in the process of the alkali silica reaction (ASR) in concrete, as it determines not only the reaction front of ASR but also the localization of the produced expansive product from where the cracking begins. However, the microstructure of the aggregate was either simplified or neglected in the current ASR simulation models. Due to the various particle sizes and heterogeneous distribution of the reactive silica in the aggregate, it is difficult to obtain a representative microstructure at a desired voxel size by using non-destructive computed tomography (CT) or focused ion beam milling combined with scanning electron microscopy (FIB-SEM). In order to fill this gap, this paper proposed a model that simulates the microstructures of the alkali-reactive aggregate based on 2D images. Five representative 3D microstructures with different pore and quartz fractions were simulated from SEM images. The simulated fraction, scattering density, as well as the autocorrelation function (ACF) of pore and quartz agreed well with the original ones. A  The cracking caused by internal chemical reactions, such as delayed etrringite formation and alkali silica reaction (ASR), is one of the major durability problems in concrete structures ,2,3. ASRDeveloping predictive models is important since it is able to give suggestions about ASR prevention for the to-be-built structure or maintenance measures for the ASR-damaged structures. In the present predictive mechanical models, several models have considered the spatial reaction and initial expansion sites. The models of ,12 simulOur project is to build such a realistic model. The first problem is how to obtain the 3D microstructure of the aggregate. The most regular computed tomography (CT) or focused ion beam milling combined with scanning electron microscopy (FIB-SEM) is a direct method to obtain the 3D microstructure of a porous material . Howeverale (mm) . Such a  be done . Yet, suAlternatively, the 3D microstructure can be derived by simulating it based on the information from 2D images. This approach could deliver 3D information in a fast and inexpensive manner. A number of simulation approaches have been developed in the past , and amoBased on their experiences ,22, the A better understanding of the influence of the aggregate microstructure on ASR is essential to develop the materials’ science-based design procedure for the newly-built concrete to prevent ASR. This study is an effort in that direction, where a 3D aggregate microstructure is simulated from 2D images providing an advantage to overcome the resolution limitation of a CT scan or FIB-SEM. The proposed method can be used by the ASR modelers to consider the realistic microstructure of the aggregate instead of assuming the aggregate to be a homogeneous sphere so that the fundamental mechanism can be simulated more realistically. Moreover, the method can be extended to other porous materials rather than aggregates. For example, the microstructure of cement paste can be reconstructed directly based on one representative 2D image.An alkali-reactive siliceous limestone from Belgium, which has been reported to cause damage to several concrete structures, such as bridges and dams , is usedA 3D microstructure simulation is mainly based on the utilization of 2D quantitative information which can only be calculated from 2D microstructure images with a clear outline between particles. Based on this principle, optical petrography micrographs were firstly taken from thin sections of the limestone to identify its mineral compositions and their particle size. According to the thin section results, most of the minerals have a cryptocrystalline size  were prepared from the bulk sample according to BS EN 932-2  and thenMIP measurements were undertaken to investigate the pore size distribution of the aggregate. In order to contain the diversity of the pore distribution, six aggregates with a size of around 4 mm were taken from the sample kept under vacuum. For each measurement, around 1.3–2.3 g of the sample was used. Intrusion and extrusion were done in a low-pressure and high-pressure PASCAL 140 Series instrument . First, the sample was tested in the low-pressure instrument, where the pressure was gradually increased from 0 to 200 kPa and followed by an extrusion phase, during which the pressure was decreased to 100 kPa. Then, the sample was moved to the high-pressure instrument, where the sample was subjected to mercury pressures ranging from 0.1 to 200 MPa and back to 0.1 MPa. As such, the sample went again through an intrusion and an extrusion phase. The pore size distribution data were computed based on the Washburn equation . The surThe microstructure simulation process can be divided into three parts. Firstly, the petrographic characteristics of the limestone and its 2D microstructure from SEM-BSE images were described in The minerals composing the limestone were identified based on their optical properties, as illustrated in . The limThe rock also contains bioclastic fragments (fossil relics) filled or partly replaced with micro- to crypto-crystalline quartz b. The miTwo-dimensional images with a clear outline between the particles are the prerequisite for 3D microstructure simulation. However, the Leica DMLP petrographic microscope  in the laboratory is limited for this purpose, due to the pixel size limitation  to 255 (white) in a 8-bit image into a binary image with a pixel value of either 0 (black) or 1 (white). The image will be correspondingly separated into two phases by comparing each pixel’s grey value to a grey-scale threshold. Binarization methods can be distinguished by the applied method to determine the threshold. Each method has their own equation and optimal parameters, as compared and concluded by Kim .Due to the low pore fraction of the limestone, a separate pore peak does not occur in the grey-scale histogram of most of images, as seen in As stated in ×1×1 mm3 . If we wWe set the simulation voxel size at a microscale as n method  that theIn order to investigate the influence of resizing on the mineral fraction, we calculated the pore and quartz fraction for each binarized image and the cumulative average fraction for pore and quartz before and after resizing, respectively, using all of the 90 binarized images. x has a normal distribution, then the exponential function of x, Firstly, the normalized histograms of pore fraction and the quartz fraction from all of the 90 binarized images were drawn as shown in X is the pore or quartz fraction, and P is the probability density at fraction X. X itself. X)) and X)), respectively. The fitted  and another five images with a pore fractions of  as indicated by the x-axis values of the red solid circles in The distribution of minerals in the siliceous limestone is very heterogeneous, especially the quartz distribution. Therefore, representative 2D SEM-BSE images with the size of sciences . If x haThe final five simulated 3D microstructures (with microstructure ID from 1 to 5) have pore fractions and quartz fractions as follows: Microstructure 1: ; Microstructure 2: ; Microstructure 3: ; Microstructure 4: ; and Microstructure 5: . Again, the simulated pore fraction is actually the air void fraction pore size over 1 According to Quiblier , a 2D ims is the target phase, which is pore or quartz in this paper. The autocorrelation function of phase s is evaluated using the following Equation .Let Equation  22]:(2):(2)IS1 . The sloThe XRD pattern in ned from .In order to compare the simulated mineral  volume fraction with the real volume fraction from XRD or MIP, a concrete cube of s paper) ,49. The Firstly, the voxel size of the concrete cube was set to be Then, the irregular aggregates from 4 mm to 16 mm were distributed randomly inside the cube using the ANM model of Qian . The volAfter this step, each node in the cube is 0 or 1, where 0 represents mortar and 1 represents the aggregate. By accounting the node number of 1 of the cube, we found that the total nodes occupied by aggregates were 19,136,000 and the simulated aggregate volume fraction in the cube was 29.9%, which is very close to the true volume fraction.The last step was to allocate the five simulated 3D microstructures to these 19,136,000 nodes. Numbers 1 to 5 were used to represent the five simulated 3D microstructures, respectively, as shown in the first column in One should remember that in our model, the simulated pore fraction only considers the aid void .Suitable experimental methods should be chosen to obtain 2D images of the aggregate with a clear outline between particles, considering the particle sizes of the target phase, and suitable binarization methods should be chosen to segment the target phase from the images.(2).The pore and silica fraction distributions on the 2D SEM-BSE images of the limestone can be fitted by a lognormal distribution, which can be used to select representative images for 3D microstructure simulations.(3).The simulated microstructures of the siliceous limestone are able to retain the visual characteristics, such as the particle shape and spatial scattering, as well as the statistical characteristics of the air void and quartz silica, such as the fraction, characteristic particle size, and the specific surface area of the parent limestone. However, the pore information below the simulation voxel size (1 (4).The simulated microstructures can be used to assemble the aggregate at a mesoscale embedded in mortar following the obtained lognormal distribution. The average air void and silica fraction show good consistency with the XRD and MIP results.


Bladder cancer (BC) has remained as one of the most challenging issues in medicine. The aim of this study was to investigate the differential network analysis of stages 2 and 4 of BC to better understand the molecular pathology of these states.  We chose gene expression data of GSE52519 from Gene Expression Omnibus (GEO) database analyzed by the GEO2R online tool. Cytoscape version 3.6.1 and its algorithms are the methods applied for the network construction and investigation of differentially expressed genes (DEG) in these states. Our result revealed that the analysis DEGs provides useful information about a common molecular feature of stages 2 and 4 of BC.  Consequently, the network finding revealed that more investigation about stage 2 is required to achieve an effective therapeutic protocol to block the transition from stage 2 to stage 4. Bladder cancer (BC), with the highest rate of death in underdeveloped countries, is the ninth most common type of cancer worldwide . It is aTo analyze differential and common contributing biomarkers and pathways of BC in stages 2 and 4, the gene expression profiles were examined using bioinformatics. For this purpose, the keyword “Bladder cancer” was searched against the GEO database  , stage 2 of BC, and stage 4 of BC. The dysregulated genes among the cancerous groups were prioritized based on their significant contribution to that specific stage and consequently considered for protein-protein interaction (PPI) network analysis. We used the Cytoscape version 3.6.1. application for the network data visualization and analysis. Network centrality is one of the fundamental concepts for the network . After n network .To elucidate the distribution of expression values of the 3 compared groups for cross-comparison, we used box plotting. Box plot analysis is available through the GEO2R Online Analyzer see . The finAmong 250 top genes in terms of differential expression in stages 2 and 4 of BC, 206 and 178 genes, respectively, showed significant differential changes . The Benjamini-Hochberg procedure  was used as the default option (the data are not shown) for the adjustment test for P-value. Among these genes, the top 10 up-regulated and down-regulated genes in stages 2 and 4 are listed in The related networks of BC stages 2 and 4 were constructed. In these networks, the physical interaction was used for centrality analysis. The obtained network of stage 2, including DEGs and 50 added neighbors, consists of 241 nodes and 1771 links. This query has both a linked component and individual ones. To analyze the network, we conducted subnetwork creation and extracted the first component consisting of 183 nodes and 1771 connections. Another network was made of significantly expressed genes in stage 4 of BC compared with control. This network comprised 278 nodes (DEGs plus 50 neighbors) and 4605 links.To analyze the whole interacting system, a subnetwork of 190 nodes and 4601 links were obtained as a connected component (the data are not shown). The parameters considered for network analysis are degree and betweenness centrality. The detailed interaction analysis for stages 2 and 4 are shown in As the mode of interaction can provide useful information, the network of top DEGs of both the stages was constructed based on pathway contribution, coexpression, colocalization, physical interaction, genetic interaction, and predicted functional relations via GeneMANIA see  and 6.Recent molecular studies have a major impact on disease clarification and are thus helpful for treatment approaches. Prioritizing gene sets related to a specific type of disease in terms of centrality features provides additional information for that disease state . In thisTOP2A, AURKB, BUB1, and BRIC5 are among the dysregulated genes. In addition, AURKB is up-regulated in both stages 2 and 4. It is not included in the selected top 10 changed expression genes, and it is not among the central genes of stage 2.It is reported that high levels of AURKB are associated with cytogenetic abnormality . FurtherThese findings show that up-regulation is dominant among key genes of stage 4 and can be considered as a differential point between the stages.On the other hand, down-regulation is a common aspect between top DEGs of stages 2 and 4. Analyzing top DEGs of stages 2 and 4 as networks in This finding from the GeneMANIA network is in accordance with our finding from gene expression profiling of BC in different stages and further study by PPI network via the STRING database. As it was described in the Methods section, the PPI networks were constructed by additional 50 relevant genes. The poor interactions between the query genes required additional nodes to constitute the interactive units for stages 2 and 4 of BC. However, the pathway relationships between the 2 stages are inconsiderable in comparison with expression links; stage 2 is not as good as stage 4. Relative similarity between top DEGs and the central nodes is reported in other documents .This finding refers to unmatched roles of the elements of stage 2 network relative to the nodes of stage 4 interactome. The main findings of this research can be summarized as follows:The muscular system is the most involved tissue in stages 2 and 4 of BC.There are large amounts of information about the involved elements of stage 4 relative to stage 2 of BC in the databases.Up-regulation of the critical genes is the key feature of stage 4, whereas down-regulation of the top DEGs is the common process for the 2 studied stages.Coexpression is the dominating relationship between DEGs in both the stages.Therefore, it is possible to introduce a differential method for the 2 stages, which may be useful in early-stage diagnosis of the disease. Although magnetic resonance imaging and pathology are used frequently to analyze the stage of different cancers , the molIt can be concluded that based on DEG, there are significant common molecular maps and patterns between stages 2 and 4 of BC. PPI network analysis revealed the top DEGs as central genes in stage 4, but it showed the difference between DEGs and central genes of stage 2 BC. Therefore, more investigation about the molecular mechanism of stage 2 is required, which could be useful in the prevention of transition from stage 2 to 4 in BC.There is no conflict of interest