To solve the problem that the traditional hyperspectral image classification method cannot effectively distinguish the boundary of objects with a single scale feature, which leads to low classification accuracy, this paper introduces the idea of guided filtering into hyperspectral image classification, and then proposes a multi-scale guided feature extraction and classification (MGFEC) algorithm for hyperspectral images. Firstly, the principal component analysis theory is used to reduce the dimension of hyperspectral image data. Then, guided filtering algorithm is used to achieve multi-scale spatial structure extraction of hyperspectral image by setting different sizes of filtering windows, so as to retain more edge details. Finally, the extracted multi-scale features are input into the support vector machine classifier for classification. Several practical hyperspectral image datasets were used to verify the experiment, and compared with other spectral feature extraction algorithms. The experimental results show that the multi-scale features extracted by the MGFEC algorithm proposed in this paper are more accurate than those extracted by only using spectral information, which leads to the improvement of the final classification accuracy. This fully shows that the proposed method is not only effective, but also suitable for processing different hyperspectral image data. However, hyperspectral images also have many bands with strong correlation and high data redundancy, and feature extraction is one of the main methods to effectively solve these problems.With the rapid development of aerospace technology, the spectral resolution of remote sensing imaging technology has been greatly improved. Hyperspectral remote sensing is a kind of continuous imaging technology for ground target information using imaging spectrometer. Compared with other remote sensing images, hyperspectral images can more accurately reflect the state of the target in the image space and the characteristics in the spectral space. Moreover, the spectral data can be analyzed and processed more reasonably and effectively. Therefore, hyperspectral imaging technology has more advantages in ground object recognition and classification, and it has been widely used in many fields9. These dimensionality reduction methods all extract spectral features of hyperspectral images through linear transformation. Among them, PCA is the most commonly used linear feature extraction method. In this method, the input data is transformed by using the transformation matrix, and the feature with the largest contribution of the corresponding variance in the data is retained, and then the data dimension is reduced. On this basis, a series of PCA improved algorithms were proposed15. For example, PCA algorithm was applied to hyperspectral image, and the method of selective principal component analysis was proposed10. This method has very good prediction results for pixels with small number of features. According to the high correlation of hyperspectral images, a more advanced segment using PCA dimension reduction method was further proposed11. The morphological principal component analysis was proposed and applied to hyperspectral image feature extraction14. Similarly, ICA theory is considered as an extension of PCA, which is based on the assumption that data are independent of each other, and is also used to extract features and make classification for hyperspectral images19. For example, the19 used PCA and ICA to obtain PCA features and edge features of hyperspectral remote sensing images, and then used deep convolution neural network to realize the fusion and classification of spatial-spectral features.The purpose of feature extraction is to use different methods to select bands or features that contain large amount of information to reduce the redundancy of data. The common spectral feature extraction algorithms include principal component analysis (PCA), independent component analysis (ICA) and linear discriminant analysis (LDA)23. The commonly used spatial feature extraction methods include discrete wavelet transform, Gabor filtering, sparse representation and local binary pattern. The local binary pattern (LBP) is a texture feature extraction method, and it is also widely used in spatial-spectral feature extraction of hyperspectral images24. A hyperspectral image classification method based on the combination of dual channel CNN and LBP was proposed, and good classification results were obtained24. The hyperspectral image classification algorithm based on super-pixel and multi-core sparse representation was proposed, and achieved the expected classification effect25.The above method only considers the spectral information of the image and ignores the existence of spatial features when extracting features. Image spatial features mainly include regional shape features, texture features and morphological filtering features. Since hyperspectral images have the characteristics of space-spectrum integration, if the spatial information and spectral information of hyperspectral images are used to extract their features at the same time, the effect will be better. At present, the method of feature extraction combining spatial and spectral information of hyperspectral image is a hot field in hyperspectral remote sensing29. The core idea of multi-scale feature extraction is to realize the abstraction of image information at different scales. Among them, the control of scale parameters has the following situations, such as the designed filter banks, different sizes of structural elements and different sizes of processing windows. For example, morphological filtering, which used circular structural elements of different sizes to perform open and close operations on images, could better smooth noise30. However, the traditional morphological filtering cannot effectively keep the information of the edge structure of the ground object while realizing the smooth of the ground object. The guided filtering algorithm not only has better filtering ability, but also has better edge preserving ability31, so it has been widely used in image fusion, image enhancement and image feature extraction37. For example, a very high-resolution remote sensing image classification method based on guided filtering multi-scale super-pixel features was proposed in literature37, which could obtain multi-scale feature information by setting different filter window sizes, so as to improve the classification accuracy and calculation efficiency of detail information. On this basis, this paper introduced the idea of guided filtering into hyperspectral image feature extraction, and proposed a multi-scale guided feature extraction and classification (MGFEC) algorithm for hyperspectral images. Several hyperspectral remote sensing image datasets were used to verify the experiment, and good classification results were obtained.The spatial features extracted in the above research are generally single scale features. Because it is very difficult for using single scale feature to accurately express the differences between object categories, and cannot well distinguish the boundary of objects, the idea of multi-scale feature extraction has been widely used in the field of hyperspectral image processingThe current paper makes several contributions to the field. The first is the conceptual design for multi-scale guided filtering to get features; the second is that random blocks are extracted as convolution kernel, and features are further extracted for the deep level feature.38. Guided filtering algorithm guides the input image to complete the filtering process. In the process of realization, it is considered that there is a linear processing relationship between the pixel points of the guided image and the output image, and there is a spatial filtering relationship between the input image and the output image. Therefore, the final output image is similar to the input image in structure, and similar to the guided image in texture details.Guided filter (GF) is a very efficient edge-preserving filter with better performance than bilateral filter. Through local linear model, the information of guiding image is skillfully added to the input image, so that the output image can obtain some enhanced features, such as edge preservation and enhancement. Its core technology is a linear shifting filtering equationUsually, it can be considered that an image is composed of background layer and foreground layer (target or detail). Therefore, the filtering and enhancement of the image are almost to suppress the background layer information and to enhance the detail layer information. In most cases, it is a compromise between them. Similarly, the guided filtering process also follows this thinking. Assume that mentclass2pt{minimIt can be seen in Eq.  that as In the filtering window entclass1pt{minimaThe coefficients mentclass2pt{minim in Eqs. .3\documeDuring processing, as the window d by Eq.  are alsomentclass2pt{minimd by Eq. .5\documeIn the process of guided filtering, the size of window Hyperspectral remote sensing image data cube is characterized by many bands and high correlation between bands, so it usually need dimension reduction. In the MGFEC algorithm proposed in this paper, firstly, PCA is used to process hyperspectral image data to reduce its dimension and facilitate subsequent feature extraction and processing. Then, the first three principal component images are acquired and used as input images at the same time, while the first principal component image is used as the guided image. By setting different filter window sizes to complete the guided filtering processing, the multi-scale feature map of each principal component is obtained. In each feature map, mentclass2pt{minimInput hyperspectral image data. The original hyperspectral data is denoted with Let the original hyperspectral image data be represented by matrix The hyperspectral image data is standardized to get matrix The covariance matrix The eigenvalues of covariance matrix The eigenvectors corresponding to the first three eigenvalues constitute matrix Output the data after dimensionality reduction, Due to the strong correlation between adjacent bands in hyperspectral image data, it is necessary to whiten each principal component separately, so that the variance between different bands is similar, so as to reduce the redundancy between different bands, which is conducive to the subsequent feature extraction. The whitening process here refers to the whitening of PCA, namely, to normalize the variance of each one-dimensional feature of the data after PCA dimensionality reduction. In fact, it is to divide the data on each feature axis by the corresponding eigenvalue. Because the eigenvalue is equal to the variance of the dimension corresponding to the data in the rotated coordinates, the normalization amplitude on each feature axis is achieved. The specific calculation formula is as follows.Reduce the dimension of hyperspectral remote sensing image data. The principal component analysis (PCA) theory is used to process the hyperspectral image data, and each principal component feature map is obtained. Because the first three principal components contain more than 95% information, they are extracted for subsequent processing. The PCA algorithm is to transform a set of possible correlation variables into a group of linear unrelated variables through orthogonal transformation. The converted variables are called the main components. The specific calculation steps of PCA algorithm are as follows.Obtain multi-scale guided filtering feature maps. The first three principal components are respectively guided and filtered. Firstly, it needs to build a guided image. The first principal component Acquire random patches. After multi-scale guided filter feature extraction, 12 multi-scale feature maps were obtained from the first three principal component images. Then, The convolution operation is performed and the feature map is obtained. The Data sets are divided into different subsets. From the multi-scale feature set, 75% of the feature maps are randomly selected as the training sample subset, and the remaining features are selected as the test sample subset.The training data set is used to train the model, then the trained model is used to predict and classify the whole test data, and finally the classification results are obtained.Set up classifiers and carry out classification processing. Support vector machine (SVM) is a very good classifier, especially for small sample data sets. In terms of classification accuracy, SVM is better than most classifiers, so it has been widely used. Therefore, in MGFEC algorithm, the efficient SVM classifier is chosen to classify the acquired feature sets. The classification process is as follows.To verify the effectiveness of the MGFEC algorithm proposed in this paper, three groups of hyperspectral data and different feature extraction methods are compared. The basic information of hyperspectral image data used in the experiment is shown in Table The data of Pavia university area was obtained by ROSIS sensor, with spatial resolution of 1.3 m and image size of 610×340. The sensor has a total of 115 bands. After processing, Pavia university data has 103 effective bands. The image contains nine different types of ground objects. The data is shown in Fig. Salinas Valley data was also captured by AVIRIS sensors in Salinas Valley, California. The spatial resolution of the data is 3.7 m, and the size of the image is 512×217. There are 224 bands in the original data. After removing the bands with serious water vapor absorption, there are 204 effective bands left. The data imaging area contains 16 crop categories, and the specific classification is shown in Table 39 and random patch network (RPNet)40 in the comparative experiment. In the experiment, PCA theory is used to reduce the dimension of hyperspectral remote sensing image data. Because the first three principal components contain more than 95% information of the original data, the first three principal components are used as the basic image for extracting spectral features, which are used for spatial feature extraction in the subsequent methods. The above methods and experimental data were used to carry out comparative experiments, and the obtained experimental results were shown in Figs. In addition to the MGFEC algorithm proposed in this paper, there are principal component analysis (PCA), extended morphological profile (EMP)–4, i.e. MGFEC algorithm. For the guided filter, there are two key parameters to be set, namely, the size EMP algorithm. The EMP algorithm uses different sizes of structural elements to complete the opening and closing operations of the original image, so as to achieve multi-scale structural feature extraction. In order to facilitate comparative analysis, the size of structural elements is set to 2, 4, 6 and 8, and each structural element is used for morphological filtering of each principal component image.RPNet algorithm. The RPNet method based on deep learning directly extracts random blocks from the image as convolution kernel. In this process, no training is needed, and multi-scale convolution features can be obtained by convolution between the original image and different scale convolution kernel, which is the advantage of multi-scale of RPNet algorithm. In this paper, the number 1 feature map and Fig. 2 feature map. MGFEC algorithm and EMP algorithm are used to extract multi-scale features from the second principal component feature map PC2. The first principal component image PC1 is only used as a guided image when MGFEC algorithm extracts features. The multi-scale features obtained by MGFEC algorithm and EMP algorithm are shown in Fig. Figure It can be seen in Fig. The images shown in Fig. It can be seen in Fig. In Fig. For the Salinas Valley hyperspectral image data shown in Fig. In order to compare the experimental results more clearly, some experimental results are enlarged and displayed, such as the area marked by the red box in Fig. By comparing and analyzing the above experimental results, the following conclusions can be drawn. From the overall classification effect, MGFEC and RPNet methods have better classification effect, which is close to the distribution of ground truth, while PCA and EMP algorithms have poor classification effect. For the homogeneous block area object with large region, the classification effect of MGFEC method is better than RPNet method, and the quality of classification map is higher. Therefore, MGFEC method has better classification effect for hyperspectral images with obvious boundary ground objects.The analysis of the above experimental results and the comparison of algorithm performance are based on the visual effect of the image. The performance of the selected experimental algorithm is further analyzed and evaluated with quantitative indicators. The quantitative evaluation indexes include the overall accuracy (OA), the per-class accuracy (PA) and kappa coefficient.The definition and calculation of these three evaluation parameters are based on a confusion matrix, which is also a square matrix. Assuming that the hyperspectral remote sensing image contains Overall accuracy. The OA parameter refers to the sum of correctly classified pixels in hyperspectral images divided by the total number of samples. The calculation formula is as follows.Per-class accuracy. The PA parameter of ground objects refers to the elements in each diagonal line of confusion matrix divided by the total number of elements in this line. Its calculation formula is as follows.Kappa coefficient. Kappa coefficient is proposed by researchers to make up for the deficiency of OA as an evaluation of classification accuracy. It utilizes the information of the whole confusion matrix. The overall accuracy of classification only considers the number of pixels correctly classified in the diagonal direction, while kappa coefficient considers all kinds of missing and wrong pixels outside the diagonal, and its value range is , but it is usually greater than zero. It is a consistency test index, which is used to measure the accuracy of classification. The so-called consistency refers to whether the classified image is consistent with the reference image (the true classified image on the ground). The larger the value is, the higher the consistency is and the better the classification accuracy is. The calculation formula is as follows.The results shown in Fig. Table Aiming at the problem that single scale feature cannot effectively express the differences between different objects and distinguish the boundaries of objects in hyperspectral remote sensing image classification, this paper utilizes the PCA theory to achieve the dimension reduction, abstract multi-scale features with guided filtering principle, and get the deep features through the convolution operation of random blocks. Finally, the SVM classifier is used to realize the classification of hyperspectral image. After the above processing steps, MGFEC algorithm not only obtains the structure information and deep-seated features of different scales of images, which is very conducive to the classification of hyperspectral images, but also improves the classification accuracy and quality of hyperspectral remote sensing images. A series of comparative experiments are carried out with actual hyperspectral remote sensing image data, and good experimental results are obtained. These show that the MGFEC algorithm proposed in this paper can not only effectively extract multi-scale features, but also better combine spatial and spectral information, enhance the discrimination ability between classes, and greatly improve the image classification accuracy. Therefore, it is a feasible method, and has some practical value.


Oral repair membrane guided oral bone regeneration, particularly in dental implants, is a guided regeneration technology for bone tissue. The principle is based on the characteristics of rapid migration of epithelial cells and fibroblasts and slower migration of osteoblasts. Materials are placed in the bone defect, creating a relatively closed environment which is conducive to the growth of bone tissue. In this paper, we have evaluated clinical effects of Hai'ao oral repair membrane as a barrier membrane to guide bone regeneration in implants. For this purpose, certain treatment data are collected through data mining and patient's names with bone defects in the implantation area are selected. According to the randomness principles, these patients are divided into experimental and control groups and preoperative examinations along with basic periodontal treatments are performed on the selected cases. Furthermore, we have analyzed different effects by comparing treatment conditions. Experimental results, as a technical shielding film, verify that Hai'ao oral repair membrane meets requirements of safety and no immune rejection. It plays a role in promoting bone formation around the implant. Mid-to-long-term follow-up is satisfactory with no related complications. At the same time, it has the advantages of simple operation, reduced patient suffering, convenient transportation and storage, and longer validity period. Compared with the control group in terms of safety evaluation of postoperative vital signs, laboratory examinations, and incision healing, Hai'ao oral repair membrane has no significant difference. Postoperative osteogenesis effect is equivalent to that of the control group and meets requirements of superiority. Hai'ao oral repair membrane is used as a shielding membrane material in implant surgery technology to guide bone regeneration. Guided bone regeneration is a guided regeneration technology for bone tissue. Its principle is based on the characteristics of faster migration of epithelial cells and fibroblasts along with slower migration of osteoblasts. Placing the membrane material on the bone defect creates a relatively closed environment which is conducive to the growth of bone tissue. The oral barrier membrane establishes a mechanical biological barrier between soft tissue and bone tissue, which prevents fast-migrating heel connective tissue cells and epithelial cells from entering the bone defect area and allows osteoblasts with potential growth ability and slower migration speed priority access to the bone defect area, while protecting the blood clot, reducing the pressure of the covering tissue, forming a space for bone tissue growth under the shielding membrane and on the surface of the implant, and achieving the repair and regeneration of the bone tissue in the defect area. The barrier membrane plays a very important role in the process of guiding bone regeneration. Therefore, this technology is also called membrane guided technology or membrane guided bone regeneration technology . In 1976To evaluate the effectiveness of the Hai'ao oral repair membrane, which is usually used as barrier membrane to guide bone regeneration in implants, in oral bone regeneration and dental implantation process carried out in hospitals.Selected patients are divided into control and experiment groups using a well-known procedure called randomness principles.To perform extensive and thorough investigation on the selective cases, which are chosen based on required conditions. Furthermore, how the treatment process is effective on the recovery process of these groups and follow-up duration, specifically for the treatment of patients, for both groups is advised, which is based on experimental data values.Finally, this article proposes using Hai'ao oral repair membrane as a shielding membrane material in implant surgery technology to guide bone regeneration.To address this challenging issue, clinical effects of Hai'ao oral repair membrane as a barrier membrane to guide bone regeneration in implants are evaluated in this paper. For this purpose, certain treatment data are collected through data mining and patient's names with bone defects in the implantation area are selected. According to the randomness principles, these patients are divided into experimental and control groups and preoperative examinations along with basic periodontal treatments are performed on the selected cases. The major scientific contributions of this paper are as follows:The remainder of the article is planned according to the given guidelines.In the subsequent section, that is, Reasonable incision design, to ensure that there are enough mucoperiosteal flaps to cover the wound. If necessary, the bone plate on the lips and tongue of the tooth extraction wound should be pressed against central reduction, to reduce soft tissue tension.Using a sealing nut to fix the biofilm on the implant or using Shunkang medical glue to fix the biofilm on the alveolar bone to prevent membrane displacement , 15. In Domestic immediate implant research mainly focuses on the histological study of the effect of implant surface microstructure on osseointegration and other basic research and animal experiments on guided tissue regeneration technology and guided bone regeneration technology. There are fewer clinical trials, small sample size, and shorter research time, but the success rate is about the same as that of traditional implants. For example, Hoexter  chose CaWith 30 subjects enrolled, aged between 18 and 70 years, 16 cases in the test group were treated with oral repair membranes, 10 males and 6 females, with an average age of 37 years. In the control group, 14 cases were treated with medical collagen membrane, 12 males and 2 females, with an average age of 36.1 years.Subjects are 18–70 years old, and gender is not limited.Patients with dental implant indications and insufficient bone mass and need to apply technology for bone augmentation.Exclude systemic and local contraindications.Cases that do not meet the inclusion criteria.Unstable vital signs.People with severe heart disease, unstable angina pectoris, cardiac insufficiency, and myocardial infarction.Patients with chronic kidney disease or renal insufficiency.Patients with clear liver disease or liver insufficiency.Diabetic patients.Breast-feeding or pregnant women.People with a history of allergies to multiple drugs or patients with recent allergies, having used drugs known to cause damage to major organs within 4 weeks.Patients with mental illness.Specific allergies, especially those who are sensitive to collagen.Carry out preoperative examination and basic periodontal treatment for selected cases.Carry out implant surgery, implant implants, and measure the “labial alveolar bone thickness of the implant before bone grafting” and the “alveolar bone thickness before bone grafting” using a ten-degree Vernier caliper.Implant bone material as needed. The bone graft material used in the operation is “Tianbo Tegu” bone powder. The bone powder is soaked in normal saline by the surgical assistant according to the instructions and placed on the labial bone defect area of the implant to increase the amount of alveolar bone.Use a tenth-degree Vernier caliper to measure the thickness of the alveolar bone on the labial side of the implant after bone grafting.Choose a suitable oral repair film. According to the shape and size of the wound, trim the “Hai'ao Oral Repair Film” and “Bot Medical Collagen Film” to the appropriate size and place them in the bone graft area of the test group and the control group. Cover 2-3 mm at the edge. After fully relaxing the soft tissue flap, 4–0 silk suture was interrupted to close the wound.After 5 days of postoperative anti-inflammatory treatment and 10 days later, follow-up and suture removal were done.A follow-up visit was one month after the operation, and clinical examination and line film were to observe the condition of the alveolar bone and the osseointegration of the implant.Two-stage implant surgery was performed 3 months after surgery, the mucosa was reopened, the osseointegration of the implant was observed and the amount of new bone tissue was measured and evaluated, and the thickness of the alveolar bone on the labial side of the implant was measured, according to clinical standards; evaluate osteogenesis, and calculate osteogenesis thickness.∗100%.Calculate the bone growth effect and the average growth effect according to the bone growth effect = osteogenesis thickness/bone graft thicknessComplete the restoration of implant denture, as well as clinical inspection of the restoration effect.Follow up every 6 months and take X-rays to observe bone resorption.In order to reduce system errors, all data are measured by the same staff under the same conditions.Thirty patients with bone defects in the implantation area were selected. According to the principle of randomness, they were divided into 16 cases in the experimental group and 14 cases in the control group.In this section, we have described how the experimental setup was created and how many patients were selected, what their selection criteria are and how the results are obtained, and, finally, how the proposed model is realized in actual environmental setting of the traditional hospital.∗100%, calculate the bone growth effect and the average growth effect.Connect frozen physiological saline to the planting machine, turn on the foot switch, connect the planting mobile phone to the planting machine, and finally turn on the power. The test patient rinsed with hydrogen peroxide for a minute before the operation, initially disinfected the oral cavity, and then rinsed with normal saline. The iodophor solution used by the assistant physician disinfects the patient's implantation area and oral cavity face. The subject's chair position is suitable. When the upper collar teeth are planted, the upper collar is approximately 45 degrees Celsius with the ground; when the lower collar teeth are planted, the lower collar is approximately 30 degrees Celsius with the ground, and the mouthpiece is placed, and sterile towels and surgical drapes are placed. Adopt azure articaine epinephrine injection to perform local infiltration anesthesia in the planned implantation area. For some patients who are sensitive to pain, use painless injection instrument for local infiltration anesthesia. The cutting design is fan-shaped, and both sides reach the membrane brewing joint. A round blade is used to cut the mucosa and mucoperiosteal to the bone surface in turn, and use a periosteal dissector to open the mucoperiosteal flap to completely reveal the alveolar bone. According to 31 implantation operation requirements, prepare holes and implant implants in the planned implantation area step by step. Artificial bone material is implanted in the labial bone defect area as needed. Soak the bone meal with normal saline and place it on the bone defect area to increase the amount of alveolar bone. During the operation, the bone graft materials used in the experimental group and the control group were “Tianbo Tegu” bone powder. Use ten-degree Vernier caliper to measure the thickness of the bone graft. Choose a suitable oral repair film, according to the shape and size of the wound, trim the “Zhenghai Oral Repair Film” and the “Bot Medical Collagen Film” to the appropriate size, and place them in the bone graft area of the test group and the control group. We repaired the edge of the film to cover the defect area to 2.0-3.0 mm, completely relaxed the teeth and filled the soft tissue flap, and then sutured the wound with 0–4 mm silk thread. Put sterile cotton rolls in the intraoral surgery area to stop bleeding, take out the sterile cotton rolls after 30 minutes, and observe the condition of the serum after the incision. After the operation, oral scalp and metronidazole anti-inflammatory treatment was given for 5 days, while gargling with medical mouthwash for 1 week. After 10 days, follow up and remove the stitches, conduct a clinical evaluation of the incision healing, perform a physical examination on the patient, and record the examination results. After 1, 3, and 6 months and every 6 months' follow-up, clinical examination and radiographs were performed to observe the condition of alveolar bone and implant osseointegration. Follow up 3 months after surgery, perform physical examination and laboratory examination, and record the results of the examination. Perform implant secondary surgery, reopen the membrane, observe the osseointegration of the implant and measure and evaluate the amount of new bone tissue, measure the thickness of the labial alveolar bone of the implant, evaluate the osteogenesis according to clinical standards, and calculate the result of bone thickness. According to the bone growth effect = osteogenesis thickness/bone graft thicknessA = D3 − D2; and during the second stage operation: the thickness of the alveolar bone is D4. The thickness of osteogenesis=D4 − D1.This test mainly qualitatively studies the effect of different shielding membrane materials in guiding bone regeneration. The indicators to be observed and recorded are as follows: During the first-stage operation: before bone graft, alveolar bone thickness D1; before bone graft, labial alveolar bone thickness D2; after bone graft, the thickness of the labial alveolar bone is D3; the thickness of the bone graft is ∗ 100%.Bone growth effect (%) = (osteogenesis thickness (B)/bone graft thickness (A)) Superior effect: bone growth effect ≥95%.Marked effect: 80% ≤ bone growth effect <95%.Invalid: bone growth effect <80%.Superior effect and marked effect are counted as effective.Main efficacy indicators: bone growth effect (%)Incision Healing.Analyze the relationship between laboratory abnormalities, abnormal signs, and adverse events and applied materials.The researcher must ensure that the data is true, complete, and accurate.All items in the research record must be filled out. “No blanks or missing items can only be crossed when making any corrections.” Note the revised data. The initials of the name of the researcher must be signed and dated. The original records must not be erased or overwritten.Laboratory inspection items are complete. After the trial cases are observed, the research records and other materials will be submitted to the person in charge of clinical trials for review and preservation for final statistics. Data monitoring: the clinical trial supervisors review a record sheet for each original study to confirm that the clinical trial data records are timely, accurate, standardized, and complete.t-test or the paired t-test is used to test the two sets of indicators or the indicators before and after the operation. Data conformed to the normal distribution, using nonparametric test to test the two groups of indicators or indicators before and after the operation. When analyzing qualitative data, use the Pearson chi-square test or the exact probability method to test. In the effect analysis, noninferiority test is used, and other statistical tests are two-sided tests. p less than 0.05 is considered to be statistically significant. Covariance analysis was used to compare the data between groups to eliminate the possible effects of baseline data.Record the required data, input all into the computer, and use the software to analyze the data. Use frequency, median, mean, composition ratio, and so forth to describe quantitative and qualitative data. Before the formal analysis of the quantitative data, the normality test of the data distribution is first performed; for the data that meets the normal distribution, the two-independent-sample In this experiment, GBR technology was used to repair the bone defect around the implant, and the effect of different membrane materials to guide bone regeneration was analyzed qualitatively and quantitatively.Incision healing.Evaluation of implant osseointegration.Evaluation of the effect of occlusal reconstruction.Safety evaluation.Basic information of the patient.Changes in physical examination and laboratory examination data before and after implantation.Comparison of intraoperative bone graft thickness.Comparison of bone formation thickness during the second-stage operation.Comparison of bone growth effects.A total of 30 subjects were enrolled in this trial: 16 in the experimental group and 14 in the control group. The incision healing rates of the experimental group and the control group were 100%, both of which were Grade A healing. Implant osseointegration is good, all repair is completed, and the patient's occlusal function is restored. In terms of safety, one patient in the control group had an adverse event of swelling in the operation area 3 days after the operation. No adverse event was found in the test group during the entire trial.The age composition, gender composition, various vital signs, and laboratory test results of the preoperative test group and the control group are similar. The thickness of the unilateral bone graft in the two groups during the operation is the same. The vital signs of the postoperative test group and the control group are the same. The index and the laboratory examination index are the same; there is no significant difference in the effect of the two components of the bone. Adverse events and side effects found in clinical trials and their treatment are as follows: one patient in the control group developed facial swelling. After symptomatic treatment, the swelling disappeared.Under random sampling conditions, using relevant statistical analysis, the results show the basic information of the test group and the control group , preoperative vital signs, laboratory biochemical examinations, and intraoperative unilateral bone grafting; there is no significant difference in thickness. There was no statistically significant change in the vital signs at the time of postoperative suture removal and when the teeth were shaped. There was no statistically significant change in the laboratory indexes during the postoperative heel shaping compared with the preoperative indexes. The basic situation comparison of the selected cases in the experimental group and the control group is shown in p > 0.05). The difference in age has no effect on the final result, which is shown in A normality test was performed on the age data of the test group and the control group, and the data was found to conform to a normal distribution. Therefore, an independent-sample test was used. The results showed that the difference in age between the test group and the control group was not statistically significant (p > 0.05). The difference in gender composition between the two groups has no effect on the final results, which is shown in Chi-square test was used to compare the sex composition of the two groups. The results showed that there was no statistically significant difference in sex composition between the test group and the control group (Three days after surgery, there was a case of facial swelling in the control group. The swelling disappeared after anti-inflammatory and symptomatic treatment. There were no adverse events such as infection in other cases in the test group and the control group, indicating that the safety of Zhenghai biofilm is equivalent to that of the control group material, and it does not increase the risk of patient infection and other adverse events, which is shown in It can be seen from the figure that the incision healing of all patients in the two groups was Grade A, and the incision healing rate was 100%, which is shown in Since the effective rate of bone growth in the two groups is 100%, noninferiority methods cannot be used for testing. The specific advantages and composition of the two groups are shown in The comparison found that the experimental group and the control group had basic demographic information, case treatment history, unilateral bone graft thickness, preoperative and postoperative vital signs and laboratory examinations, and occurrence of adverse events, incision healing, bone growth effect, and so forth. The difference was not statistically significant. As a shielding film for planting technology, Zhenghai biofilm in the experimental group has no significant difference in safety and functional effects compared with the control material. The safety and effects of the two are equivalent.In summary, in oral implants, GBR technology is used to increase the formation of new bone around the implant and promote the repair of bone defects. Therefore, the postoperative osteogenesis rate is used as the main observation index for the effectiveness evaluation of this clinical trial. Through the 6-month follow-up observation of subjects, the effective rate of bone growth in both the control group and the test group is 100%. In conclusion, in the process of guiding bone regeneration as a dental implant barrier, the use of Zhenghai biofilm can meet the requirements of promoting osteogenesis around the implant.As a technical shielding film, Hai'ao oral repair membrane meets the requirements of safety and no immune rejection. It can promote the formation of bone around the implant. It is satisfactory in the medium- and long-term follow-up. It has no related complications and is easy to operate and reduces patient suffering, with convenient transportation and storage, longer validity period, and other advantages. Compared with the control group in terms of safety evaluation of postoperative vital signs, laboratory examinations, and incision healing, Hai'ao oral repair membrane has no significant difference. The postoperative osteogenesis effect is equivalent to that of the control group. It meets the requirements of superiority and obvious effect. Hai'ao oral repair membrane can be used as a shielding membrane material in implant surgery technology to guide bone regeneration.In the future, we will examine how effective the proposed model would be in various patients if selection criteria were changed and how the proposed system can be converted into a doctors-assisting tool.


This article focuses on the derivation of simplified predictive models for the identification of the overall compressive stiffness and strength of corrugated cardboards. As a representative example an unsymmetrical 5-ply sample (with E and B flute) was used in this study. In order to exclude unreliable displacement measurement in the standard edge crush test, virtual strain gauges were used. Video extensometry was employed to collect measurements from the outer surfaces of the sample on both sides. Additional data allowed real force-displacement curves to be obtained, which were used in the validation procedure. To emulate the experimental results, besides a simple analytical model, a 3D numerical model fully reflecting the geometry of the corrugated board, based on the finite elements method was also built. In both cases good agreement between the experimental results and the analytical and numerical calculations was observed. This proved that the proposed analytical model can be successfully used to determine the overall stiffness and compressive strength of corrugated board, provided that the geometry and properties of all the layers of the board are known. The simple model presented in this work enables quick and reliable design and prototyping of new assemblies without the need to manufacture them. Prediction of material strength is an important issue for designing and manufacturing of products made from corrugated paperboard. In the literature, authors have applied many different approaches for strength investigations of corrugated sandwich structures, including paperboard, i.e., analytical ,2, numerTo perform numerical simulations, detailed knowledge of the material properties of the constituents is required. This is however a challenging task, due to the inherent anisotropy of paper-based materials. As a result, physical testing of corrugated paperboard is much more popular within the industry.A number of typical tests to characterize mechanical properties of corrugated paperboard have been developed to standardize the process. The compressive strength is investigated by performing the ECT, in which the loading is applied perpendicularly to the axis of the flutes. In the bending test (BNT), four-point bending test is performed, in which there are two supports at the bottom of the paperboard and two equal forces acting on the sample from the opposite side. The shear stiffness test (SST) is carried out by applying a pair of forces on the opposite corners (two others are supported), causing the cross-section of the paperboard to be twisted. The torsional stiffness test (TST) is conducted by twisting a sample in both directions. Other tests of the paperboard are namely bursting and humidity tests. In order to investigate the strength of the whole container made from the corrugated paperboard, the box compressive test (BCT) is carried out .Analytical and numerical predictive models of the strength obtained in the ECT are considered in this paper. The ECT is standardized; there are four different methods of the ECT described in the standards. One of the main features that distinguish these tests is the shape of the specimens. These methods are as follows: edge-clamping method , neck-doAs a verification method for the results obtained from analytical and numerical approaches, presented in this study, a video extensometry technique was used, where pairs of points are tracked across images taken at various levels of loading and their relative distance is measured. This is a similar, but simpler approach to digital image correlation (DIC), which is an advanced full-field non-contact optical method of measurement that is recently becoming popular in the area of experimental mechanics, due to its very high accuracy. However, application of those techniques for investigation of the paperboard strength is rather limited in the literature. Hägglund et al. investigated thickness changes during the ECT in the corrugated paperboard using the DIC . The autHere, analytical and numerical models are proposed to identify paperboard stiffness and to predict the compressive strength of the corrugated paperboard. Optical extensometry is employed to validate the obtained results. Both the analytical and numerical approaches achieved accurate results.2. Both corrugated layers (E and B flutes) and the flat layer in between are made from lightweight recycled fluting WB with a grammage of 100 g/m2. As an internal layer again the white test liner with a grammage of 120 g/m2 was used. The arrangement of individual layers and the geometry of the cardboard cross-section are shown in In the research, 5-layer corrugated cardboard named 5EB650C3, produced in Aquila Września—the Polish branch of the VPK Group—was used. This grade consists of an external coated layer of a white recycled base liner board with a grammage of 140 g/mThe geometrical features of both corrugated layers (flutes) are presented in Since corrugated cardboard consists of several layers of paperboard, made of cellulose fibers, its mechanical properties depend on the fiber orientation of its components. In paperboard, two main, mutually perpendicular directions can be determined. First, along the fiber orientation, which is called Machine Direction (MD). Material is both stiffer and stronger in this direction. The second is perpendicular to the MD and is called Cross Direction (CD). The paper-forming fibers make the corrugated board also an orthotropic material, in which the MD is along the waves see . The corSpecified by the producer, the compressive strength, ECT of the combined corrugated board (5EB650C3) in CD is 7.6 kN/m , in The ECT is used explicitly to determine the compressive strength of the corrugated board in CD. Although most testing machines allow the recording of curves from the entire test, it is not possible to use these curves for a reliable determination of e.g., compression stiffness. The measured displacements do not represent the elastic deformation of the specimen as they are significantly affected by the clearance and susceptibility on the crosshead, local pressure on sample unevenness (edge effects), etc. Therefore, non-contact optical techniques are required to reliably measure displacements (deformations or strains). Additionally a measure without direct contact does not influence the measure. In measurements with contact , noise is introduced into the measurement and thus the actual measured values are distorted.In this study, the specimen was also tested using optical extensometry. Two cameras were used to track the deformation of both faces to account for the out-of-plane bending produced by the non-symmetrical section. The front face is the higher flute, while the back face is the lower flute of the paperboard, see Two 5 MPix cameras  were used to record grey scale images during the test, see Two different models were used to estimate the compressive strength of the corrugated board in the CD: (a) a simplified analytical model and (b) a fully detailed 3D numerical model. The former model is based on an iterative procedure, while the latter model is based on the FEM.i-th layer, namely: i-th board thickness; The simplified estimation procedure proposed here consists of a simple analytical model and uses the basic constitutive parameters of the individual  formula ,9,10:(2)lute see ; ti is ti-th layer the relation takes the form:The deformation corresponding to the maximum load can be calculated from Hooke’s law considering the stiffness in the CD direction, sample height, irst see . So for i-th layer reaches zero, we obtain a bilinear curve describing the constitutive behavior of a single panel. It was assumed on the basis of experimental observations, that the ultimate deformation equals:If it is assumed that the failure once initiated successively progresses over time and, for example, for the value of Now, the ECT value can be obtained by simple summation over all layers including the take-up ratio. The displacement-dependent formula for ECT is therefore:® [The second model was built in the Abaqus Unified FEA®  software®  were usegiven in  were traThe in-plane shear stiffness can be computed from the empirical formula :(8)G12i=The Poisson ratio in the 1–2 plane can be assumed from  as:(9)ν1Both transversal stiffnesses were computed using the approximation from :(10)G13ii-th layer.The compressive strength can be determined by dividing the SCT value in the CD by the appropriate thickness of a single All the computed values of the constitutive parameters for each layer are summarized in Here, first the results of the edge crush tests are presented. The dispersion of the obtained results is due to the heterogeneity of the corrugated cardboard samples, including local imperfections, lack of parallelism of the sample edges, local detachment of the corrugated layers, etc. Although the specimen is held by steel blocks during the test to prevent global out-of-plane buckling, local buckling on the outer surfaces of the specimen still could be observed. A slight bend, which is the result of the nonsymmetric cross-section of the sample, also could be observed.It is worth noting that the elastic stiffness, which could be determined from the linear part of the experimental curves, is not the real stiffness because it includes all the effects of the crossbar compliance and the sample imperfections, especially visible in the initial part of the curves see .In the predictive analytical model, constitutive curves are first constructed based on the specific material parameters of the individual layers see , based oi-th plate, calculated as simply supported plate loaded along the In order to correctly calculate the compressive strength using the FEM in the simulation of the ECT, two steps of the numerical procedure had to be used, namely: (1) perturbation analysis, where the eigenmode and eigenvector were calculated, and (2) geometric and material nonlinear iterative analysis, in which geometric imperfections are introduced based on the calculated eigenvalues and eigenvectors from the first analysis.® software. This imperfect geometry was used in nonlinear analysis where the standard Newton–Raphson algorithm was used to find convergence in the subsequent iterations.The first perturbation analysis was only to find the initial shape imperfection in the numerical model of the ECT sample, which was later entered as the first scaled eigenvector of the model see  in the AThe equivalent plastic strains on both sides of the ECT sample model in the last iteration are shown in The displacements on both sides of the ECT sample in the last iteration are shown in Experimental results based on non-contact full-field displacement measurements and crosshead displacement see a are preThe first and main observation (conclusion) to be drawn from this study is that the overall stiffness of a corrugated board sample cannot be determined from a typical ECT. This is clearly seen in The results summarized in The second conclusion is that the proposed analytical formula for estimating stiffness and compressive strength appears to be very promising. It has the same accuracy as a full detailed 3D FE model see  while beIf one would like to optimize the design of the corrugated board by appropriate selection of solid boards for individual layers, it is enough to raise the basis weight of the weakest layer (in this case the TLW120 layer). By drawing the stress–strain curves of individual layers using Equations (3)–(6), it is easy to determine, which layer is the weakest. This paper presents predictive models for the evaluation of compressive strength and stiffness of corrugated board in CD. The models proposed here and the obtained analytical and numerical results were compared with the experimental results. Good agreement in the obtained results was observed. The accuracy achieved with the full 3D FE model was within 95%, while the accuracy of the simplified analytical model was around 94%. Similar results were obtained by Perks et al. , who mod


The data in this manuscript are comprised of loggerhead sea turtle nesting records and coyote depredation events on South Island Beach in the Tom Yawkey Preserve, South Carolina, from 2015 to 2019 comprise the data in this manuscript. We compared the nesting and depredation rates with abiotic factors that may have influenced them. We analysed our data using G-tests to determine whether any abiotic factors were associated with coyote depredations or the timing of loggerhead nesting. Data were collected in conjunction with an experiment testing a possible conservation tool to deter coyote depredation of loggerhead sea turtle nests (Wauson and Rogers 2021).An Excel® file contains the entire data set in the supplemental material. Tables and their corresponding statistics test whether behavioural traits of loggerhead sea turtles and coyotes change based on certain abiotic factors, as well as how coyote depredation rates behaviour change in the presence of wolf urine. Specifications Table•The data show the number of loggerhead nests, as well as the number of those nests depredated by coyotes from 2015 to 2019 on South Island beach in the Tom Yawkey Wildlife Preserve, South Carolina. We evaluated the data in relation to the following abiotic factors: mean daily and nightly temperatures (C), tide type, atmospheric conditions, wind direction, lunar phase and nocturnal precipitation.•Conservation groups can use the data to maximize the efficacy of night patrols, or other conservation efforts to reduce coyote depredation of loggerhead sea turtle nests.•These data can be used when evaluating changes in behaviour regarding loggerhead nesting habits and possibly coyote depredation events. The data may also prove useful when recreating this experiment or in comparisons with the results of other conservation techniques.1All the data below are from an Excel® spreadsheet that is part of the supplementary documents and is prepared so that it can be easily used in SPSS statistics software.The data in this section are the total number of loggerhead nests made each year on South Island beach in relation to given abiotic factor. The totals vary as a result of incomplete historical data regarding that factor.Data in this section are the number of coyote depredation events on loggerhead nests the night they were laid as a function of the abiotic factors listed above. This section's nest totals are lower because they comprise only counts the nights when at least one loggerhead nests were made. The totals vary slightly as a result of incomplete historical data for the abiotic factor being evaluated.2South Carolina Department of Natural Resources Sea Turtle Technician teams collected nesting data every year from 17 May to 08 August from 2015 to 2019 on South Island beach at the Tom Yawkey Wildlife Center, S.C. Teams patrolled the beach each day at dawn; when they located a new nest, they recorded the turtle species based on the width of the carapace drag marks and the elbow joint impression in the sand. They also recorded the GPS coordinates and whether the nest was depredated. For depredated nests, the technicians identified the predator by the tracks it left in the sand.During the 2018 data collection period, we placed urine dispensers four meters apart along 4100 meters along the first dune line parallel to the ocean on South Island Beach. This area consisted of three types of zones. The zones alternated from Treatment to Partial Control to Control back to Partial control and repeated that pattern for a total of 27 zones starting with a Treatment region and ending with a Control (Supplementary figure). Each treatment and control zone was 200 meters, while the partial control zones were only 100 meters. We combined the partial-control and control regions into a single entity, ``Total Control'', because we later established the two were statistically indistinguishable At the end of each treatment and control zone, the dispensers formed a perpendicular transect from the first dune line to 4 meters above the king tide line.Every zone had urine dispensers, but only the treatment zone had wolf urine in the dispensers. Following the manufacturer's guidelines (Maine Outdoor Solutions), we placed the dispensers four meters apart down the beach's length. Each dispenser consisted of a 25.4 cm wooden post with a small hole on one end, a cap, a vial with holes near the top across from each other holding 44.36 mL of urine, and a plastic tie to secure all of the components.We collected environmental data to determine whether there were any correlations with when a nest was more likely to be made or depredated. That data included high and low tide levels (m), mean daily temperatures (C), mean nightly temperatures (C), moon phase groups (lunar visibility groupings), nocturnal atmospheric conditions , whether there was nocturnal rain, and nocturnal wind direction . Wind direction, mean daily temperature, mean nightly temperature, atmospheric conditions, and precipitation data came from Weather Underground 2.1To evaluate the effect of wolf urine on coyote depredation of loggerhead sea turtle nests, the data were broken into two groups, one based on the time it took the urine to evaporate, 9-days, and the other for the length of time the manufacturer stated the wolf urine would be active, 30-days. We initially analyzed the partial-control areas as a separate date zone from the treatment (contained urine) and control (no urine). However, the partial-control was statistically indistinguishable from the control areas in all aspects, and so was combined with the control to form the “total-control” group. This total-control group occupied 2700 meters of the 4100-meter experimental area. We used the G-test with the Williams correction to compare the frequencies of depredated and non-depredated nests in the treatment vs. total-control areas.We used G-tests to examine the effects of atmospheric conditions, nightly precipitation, wind direction, and moon phase groups on the number of loggerhead nests made and the number of loggerhead nests depredated by coyotes each night. If we found an effect, we used Z-scores to determine the influential cells. We used Spearman's rho to test whether a correlation existed between mean daily or nightly temperatures and the number of nests laid or depredated per night.Besides part of the data collection for 2016 when one of the authors was a member of the South Carolina Department of Natural Resource Turtle Technician Team, neither of the authors saw or touched a sea turtle, egg, or coyote throughout the experiment. We only handled the urine dispensers on the beach. All nests and eggs were handled only by the trained SCDNR Sea Turtle Technician Team. As a result of this design, the experiment was approved by the Institutional Animal Care and Use Committee at Winthrop University . No DNR permit was required or requested for the completion of this research.Michael Wauson: Conceptualization, Methodology, writing- Original Draft preparation, Visualization, Investigation, Data Curation; William Rogers: Writing-Reviewing and Editing, Supervision, Formal Analysis, Validation, Methodology, Funding Acquisition, ResourcesThe authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article.


To meet the high demand for lightweight energy-efficient and safe structures for transport applications, a current state-of-the-art light rail vehicle structure is under development that adopts a multi-material design strategy. This strategy creates the need for advanced multi-material joining technologies. The compatibility of the adhesive with a wide range of material types and the possibility of joining multi-material structures is also a key advantage to its success. In this paper, the feasibility of using either epoxy or polyurethane adhesive joining techniques applied to the multi-material vehicle structure is investigated. Importantly, consideration is given to the effect of variation in bond thickness for both families of structural adhesives. Multi-material adhesively bonded single lap joints with different adhesives of controlled bond thicknesses were manufactured and tested in order to experimentally assess the shear strength and stiffness. The torsional stiffness and natural frequency of the vehicle were modelled using a global two-dimensional finite element model (FEM) with different adhesive properties, and the obtained vehicle performances were further explained by the coupon-level experimental tests. The results showed that the vehicle using polyurethane adhesive with a target bond thickness of 1.0 mm allowed for optimal modal frequency and weight reduction. When compared with the aerospace, marine and automotive industries, the rail industry is generally perceived to have been slow in its adoption of new materials; e.g., composites, in load-bearing structural components. This is generally recognised to be due to the lack of suitable certification procedures, with conventional high-strength steel being most commonly used to construct a vehicle body ; howeverThe traditional steel structures within rail vehicles are primarily joined using welding techniques, such as spot/arc welding, which are fast, robust, and low-cost . NeverthThere are still factors, however, that limit both the optimum design and confidence in adhesives, such as joint geometry, adhesive types, and thermal mismatch stresses. To date, a comprehensive number of works and review papers ,6,7,8 haLightweight design of railway vehicles is applied to conserve energy; however, using light materials and adopting a lightweight structural design may also result in deterioration of rigidity of the vehicle body ,24. ThisThe aim of the present work was to evaluate adhesive types and bond line thicknesses on the torsional stiffness and modal performance of the light rail vehicle structure currently under development. Two different kinds of adhesives (SikaFlex 265 polyurethane adhesive and DP490 epoxy adhesive) were considered in this study. Composite-to-aluminium and aluminium-to-aluminium bonded joint coupons with different polyurethane and epoxy adhesive thicknesses were manufactured and tested in the laboratory to evaluate the thickness effect of the joints shear strength and joint stiffness. A two-dimensional global finite element model (FEM) of the entire vehicle was then developed to examine the structural torsional stiffness and free-vibration natural frequency of the global structure by modelling different adhesive scenarios. The mechanical performances of vehicle were explained by the observations from the coupon tests of epoxy and polyurethane adhesives, respectively. Finally, a recommendation was given on the selection of adhesive in improving the structural integrity of the vehicle.This study considered the use of two different kinds of adhesives for investigation: SikaFlex 265 adhesive (1-C polyurethane) and DP490 adhesive (two-component epoxy adhesive with base to accelerator mixed at 2:1 by volume). The key reasons to choose these two adhesives were because they are welcomed and easily accessible adhesives in the laboratory selected for the project. The single lap joint configuration test coupons were manufactured, as the configuration represented the simplest geometry shape.SikaFlex 265 adhesive, provided by Sika Ltd. , is a high-performance, one-component gap-filling polyurethane (PU) adhesive designed for structural assemblies. Hybrid aluminium-composite bonded joints with different adhesive thicknesses  were manufactured in this work. The aluminium alloy 6110A-T6 used in this study was introduced in the automotive industry recently. The composites used for the bonding were cut from a high-strength woven carbon fibre reinforced plastic (CFRP) sheet, provided by EasyComposites Ltd.  Geometry and characteristic dimensions of the SLJ specimens are shown in DP490 epoxy adhesive is a two-component structural adhesive designed for metal and composite assembly supplied by 3M Ltd.  Aluminium-to-aluminium substrates of 2 mm thickness were bonded using the same geometry and dimensions as shown in All the lap-shear tests were conducted using a 30 kN universal testing machine with a cross-head speed of 1 mm/min, according to the BS EN 1465:2009  standardThe extension measurements recorded by the cross-head sensor included the machine’s compliance; this was due to the small tolerances that were injected into the machine with multiple moving parts . In order to mitigate this, a correction test was conducted in conjunction with an extensometer, situated on an epoxy single lap joint, across the span of the adhesive.−4 mm/N. The stiffness of joints were calculated as the slope of the load-displacement line in the range from 5% to 10% of the maximum extension plotted in The video extensometer was used to measure the relative displacement between the two dots at a very close distance to the adhesive overlap, as shown in In general, the epoxy adhesives failed at a lower extension but at higher loads, and the metal substrates began to plastically deform. This led to a relatively small energy absorption capability. In contrast, the polyurethane adhesive could hold the joint together under higher deflection, and thus enabled larger energy absorption. Therefore, it is apparent that using various types of adhesives at different bond gaps is likely to have different impacts on the global performance of the vehicle structure. Whilst actual performance of the structure to failure is of interest to the vehicle designer, the actual joint stiffness will also play a key role in the vibrational performance of the vehicle.The stiffness of the joints bonded with both adhesives at different bond gaps are shown in It should be noted that the stiffness values shown in Generally, the above studies were based on experimental studies for coupon-sized samples; it would also be interesting to discover how these behaviours affect the global performance of a rail structure with adhesive bonding as majority connections. A global vehicle model will be discussed in the next section to investigate the stiffness and modal behavior when using the different bonding scenarios, assuming that the bonding scenarios throughout the vehicles is simply single lap adhesive bonding.The FEM of the vehicle  was creaTherefore, the entire model has the following main characteristics:Number of shell elements: 1,588,638Number of structural grids: 1,620,384The material properties of the main components are summarised in T is the torsional load in N, utorsion is the maximum deflection under torsional load in mm, and W is the distance between the centre of the two bogies, which is 7600 mm.A torsional loading case was studied because it has been seen to result in the largest stresses in the vehicle body. The displacements and rotations were restricted at one end of the bogie mount connection point by rigid connections (RBE2 elements) to the surrounding nodes, while a total torsional load of 22,230 kN mm was applied to the other end as defined by the EN 12663 standard, which defines the structural requirements for railway vehicles . TorsionFurther to torsional analysis, mode shapes and natural frequency have become an important evaluation parameter in vehicle applications to evaluate the necessity of design change, as they are dependent on the stiffness of the structures, and the mass participates within the structure. A modal analysis includes the frequency modes and natural frequency of a given system. In this paper, the normal modes analysis was carried out in the Hypermesh Optistruct package based on the Lanczos method , and theAs the adhesive thickness of the bonded assembly is difficult to control during manufacture, it is important to investigate the effect of the adhesive thickness on the vehicle’s global behaviour. FEM analysis was conducted at different adhesive thicknesses; A significant variation was witnessed for the natural frequencies. Overall, the vehicle had a higher modal frequency value when using polyurethane adhesive compared to using epoxy adhesive. This was not unexpected, as the polyurethane adhesive was more flexible in large deformation and energy absorption, which can be witnessed in For the polyurethane adhesive, the natural frequency of the vehicle varied more significantly with alterations in the thickness. The first natural frequency of the vehicle started from approximately 15 Hz in bending and torsional mode for 0.3 mm and 0.5 mm adhesive, respectively; nevertheless, as the thickness exceeded 1 mm, the mode peaked at approximately 17 Hz, and then it switched to a lateral mode. This implied that when using a thin polyurethane adhesive layer (less than 1 mm), the middle part of the vehicle was weaker, but it became able to perform a larger deformation and absorb more energy as the adhesive thickness increased, leading to an increased frequency and modal transformation. As the adhesive thickness increased beyond 2 mm, the first modal frequency further dropped due to the effect of a combination of the decreased joint stiffness and increased mass, which was successfully demonstrated in the coupon tests. The changes of the modal shapes could also be observed for the second natural frequency, from lateral mode  to torsional (adhesive thickness of 1 mm) and to bending (adhesive thickness larger than 1 mm). This also occurred in the response of the third natural frequency. This indicated that the thickness of a low-shear-modulus adhesive played a significant role in altering the observed dynamic performance of the vehicle structure, and the peak natural frequency was achieved at a 1 mm adhesive thickness.For joints with polyurethane adhesive, the shear strength decreased significantly in terms of adhesive thickness. The joints with thick polyurethane adhesive experienced a relatively large shear deformation, leading to fracture and final failure. The shear strength of the joints with epoxy adhesive presented the highest value for a 1 mm bond, although the mechanical performance was far less sensitive to the bond gap compared to the polyurethane adhesive. At a high load, the aluminium substrates bonded with epoxy experience a large bending moment, leading to a concentrated peel stress at the interface, resulting in final fracture failure.The vehicle using epoxy adhesive behaved on average 10% stiffer than that using the polyurethane adhesive in sustaining torsional load; however, the torsional stiffness of the vehicle was largely not sensitive to the adhesive thickness; The vehicle with polyurethane adhesive had higher modal frequencies compared with that with epoxy adhesive, and the modal shapes also switched with increasing adhesive thickness.The polyurethane adhesive was more flexible and could bear large deformation, resulting in more energy absorption and a higher modal frequency. Therefore, the results indicated that an optimum thickness of approximately 1 mm of polyurethane adhesive is suggested for the current vehicle to optimise the structural performance. During the design phase of a vehicle, it is recommended that consideration be given to the effect of variation in fit-up during manufacturing, as this is likely to influence the vehicle’s vibrational response.In this paper, experimental tests were reported from laboratory single lap joints with different types of structural adhesives (epoxy and polyurethane adhesives) and various adhesive thicknesses, with the aim of understanding the performance (shear strength and stiffness) of bonded joints at a coupon level. These different adhesive bonding strategies were then applied at a vehicle level to a state-of-the-art light rail structure with a multi-material design strategy to investigate the torsional stiffness and modal performance. Thus, it was shown that experimental data at a coupon level, combined with finite element analysis at a vehicle level, could be employed to assist engineering decision making for the viability and reliability of adhesive bonding in the rail industry. Based on the current study, the following conclusions could be drawn:


The design and construction of large synthetic genes can be a slow, difficult, and confusing process, especially in the key step of oligodeoxynucleotide design. Herein we present an integrated algorithm to design oligonucleotide sets for gene synthesis by both ligase chain reaction and polymerase chain reaction. It offers much flexibility with no constraints on the gene to be synthesized. Firstly, it divides the long-input DNA sequence by a greedy algorithm based on the length of the oligodeoxynucleotide overlap region. Secondly, it tunes the length of the overlap region iteratively in an attempt to minimize the melting temperature variance of overlap. Thirdly, dynamic programming algorithm is used to achieve the uniform melting temperature of the oligodeoxynucleotide overlaps. Finally, the oligodeoxynucleotides with homologous melting temperature necessary for ligase chain reaction-based or two-step assembly PCR-based synthesis of the desired gene are outputted. This modern gene synthesis technique can synthesize a whole eukaryotic genome, and current gene synthesis methods rely on the use of overlapped oligonucleotides to construct large genes by ligase chain reaction (LCR)   and polyon (LCR) . Algoriton (LCR) . Other pon (LCR) . In the on (LCR) , but in on (LCR) . In ordeIn the key step of oligodeoxynucleotide design in gene synthesis for gapless PCR or LCR assembly, all oligonucleotides are designed to be exactly adjacent, with no gap between two consecutive oligonucleotides. The given sequence can be seen as the serial connection of all overlapping regions of oligonucleotides. With this simple observation, the problem of designing oligonucleotides with a uniform melting temperature in overlaps will be equivalent to dividing the given sequence into segments with a homologous melting temperature, with each segment representing an overlapping region  Figure .In gapped PCR assembly, oligonucleotides are adjacent, with few base deletions (gaps) between two consecutive oligonucleotides. Compared to gapless assembly, gapped assembly can be more flexible and economical, with an insignificant rise in assembly errors , though Based on above-mentioned simple observation, the presented integrated algorithm firstly starts from a greedy algorithm. The greedy algorithm always selects the apparent best result in every step, without considering other options. Thus, it cannot always obtain the best solution to the problem although it is a fast algorithm . SecondlThe initial greedy algorithm processes the input DNA sequence by dividing the sequence into segments with a similar melting temperature. It cuts down the first segment whose length is from 20 to 30 bp and then cuts down the second consecutive segment whose length is also from 20 to 30 bp. The melting temperature of these segments was computed, and the segment combination with the least deviation in melting temperature was selected. In this way, the first two segments are determined. In The greedy algorithm divides the input sequence into serial connections of segments with an approximately equal melting temperature. In theory, the algorithm cannot guarantee the best result. In an attempt to reduce the melting temperature variance of these serial connections of segments, iteration algorithm is carried out .via PCR  if the first two segments contain n options, respectively. The complexity of iteration algorithm is difficult to determine, but one can change the threshold of the difference between the least deviation and the newly computed deviation for a rapid convergence. According to our experience, the threshold that is set to 0.001 will make a rapid convergence. If the length of a serial connection of segments is L and the number of potential segments in a column is N, the algorithm complexity of dynamic programming algorithm will be O(LN3). On the contrary, the algorithm complexity of exhaustive algorithm is O(NL). The time complexity of dynamic programming algorithm can be more than O(LN3), but for the reason of feasibility, we just consider the combination of three columns in the algorithm, which caused the time complexity to be O(LN3)  of the segment to be cut in the beginning of the greedy algorithm  . It is qproblems . Its uniproblems . This in FastPCR . In this


Thermodynamic phases are the most prominent manifestation of emergent behavior. Among them, crystals and liquids traditionally epitomize the antagonistic concepts of order and disorder . According to common wisdom, the competition for stability between solid and liquid reflects the struggle between the opposite tendencies of energy minimization and entropy maximization, which is regulated by temperature. It goes without saying that the previous statement is in fact too simplistic, since in equilibrium the guiding principle is rather the minimization of the thermodynamic potential (maximization of the Massieu function) appropriate to the given control parameters. For instance, crystallization sometimes occurs with the purpose to maximize entropy (as for hard spheres under pressure). A further example is provided by superfluid, which can be one of the phases of minimum energy/enthalpy at zero temperature.The relevance of the solid/liquid dichotomy for statistical physics cannot simply be overstated. It was under the pressure of accurately locating the solid–liquid transition in simple-fluid models that “exact” free-energy methods were initially developed ,2. SinceTurning to theory, while the exact determination of the partition function for a non-trivial system with solid, liquid, and vapor phases will probably never be accomplished, there are nevertheless variational treatments of the solid–liquid transition  that have by now reached a high degree of sophistication . HoweIt is usually thought that  liquid is a unique phase, while crystals are a multitude. In fact, this is only partially true, since liquids composed of hydrogen-bonded molecules exhibit a number of so-called water-like “anomalies” that make them different in many respects from conventional (rare-gas) fluids . The reCondensed-matter physics also features a wide variety of phases with mixed solid and liquid characteristics. Hexatic fluids , liquid-This Special Issue collects articles published between April 2020 and May 2021, highlighting novel results in the application of statistical thermodynamics to liquids and crystals.In the first of these articles , the focModerately dense particles driven far from equilibrium are much harder to attack theoretically. In this case, numerical simulation is the only method to assess the accuracy of fluid dynamic equations. The second article in this Special Issue  is an atA well-established approach to the entropy of a simple fluid is the so-called multiparticle correlation expansion (MPCE), expressing the statistical entropy as a sum of contributions from increasingly large numbers of particles. Upon truncating the MPCE after the two-body term riterion  that hasriterion  inquiresReference  investigHard-core bosons are the quantum counterpart of lattice-gas particles. Lattice systems of bosonic particles provide models where the competition between itinerant and localized quantum states can be examined in full detail. The prototype of all such models is the celebrated Bose-Hubbard model, describing the behavior of ultracold bosonic atoms trapped in an optical lattice. Upon increasing the intensity of laser light, the confining potential gets deeper until a transition occurs from superfluid to Mott insulator  ,24. The Spatial correlations between triplets of particles in a fluid or solid are notoriously difficult to investigate in full extent, owing to heavy CPU-time and memory requirements. Yet, triplet correlations convey crucial information for any statistical analysis aiming to go beyond the traditional pairwise approximation. Reference  investigThe emergency caused by the COVID-19 pandemic has boosted a large amount of research activities in the last year with the purpose of clarifying the many open questions that arise in connection with the transmission of the infection. Our Special Issue too contains an article on the problem , about dThe last article in this Special Issue  deals wiIt is our hope that this Special Issue leaves the reader with the impression that the field of liquids and crystals is a vivid research area, full of problems still waiting for solution, and open to surprises.


Hippocampal injury is common in traumatic brain injury (TBI) patients, but the underlying pathogenesis remains elusive. In this study, we hypothesize that the presence of the adjacent fluid-containing temporal horn exacerbates the biomechanical vulnerability of the hippocampus. Two finite element models of the human head were used to investigate this hypothesis, one with and one without the temporal horn, and both including a detailed hippocampal subfield delineation. A fluid-structure interaction coupling approach was used to simulate the brain-ventricle interface, in which the intraventricular cerebrospinal fluid was represented by an arbitrary Lagrangian-Eulerian multi-material formation to account for its fluid behavior. By comparing the response of these two models under identical loadings, the model that included the temporal horn predicted increased magnitudes of strain and strain rate in the hippocampus with respect to its counterpart without the temporal horn. This specifically affected cornu ammonis (CA) 1 (CA1), CA2/3, hippocampal tail, subiculum, and the adjacent amygdala and ventral diencephalon. These computational results suggest that the presence of the temporal horn exacerbate the vulnerability of the hippocampus, highlighting the mechanobiological dependency of the hippocampus on the temporal horn. Traumatic brain injury (TBI) is a critical public health and socio-economic problem. In the United States, approximately 5.3 million people are living with a TBI-related disability . At a glin vivo human imaging analyses demonstrate that repetitive concussive impacts or even sub-concussive impacts  are associated with abnormal hippocampal atrophy longitudinally  models have been instrumental in exploring the association of regional vulnerabilities with potential predisposing factors during trauma from the biomechanical perspective . ExtendiThe aim of the current study was to discern whether the presence of the temporal horn exacerbates the biomechanical vulnerability of the hippocampus. To test this hypothesis, two models with and without a detailed anatomic description of the temporal horn profiles are established. By comparing the strain-related responses to identical loadings between the two models, the biomechanical mechanism for the temporal horn’s role in the vulnerability of the hippocampus was uncovered.In this study, we employed computational modeling to discern the biomechanical dependency of the hippocampus on the temporal horn. To achieve that, we utilized a novel, multi-million element 3D head model  that didThe FE head model without the temporal horn  used in this study was previously developed at KTH Royal Institute of Technology in Stockholm, Sweden . The modTo investigate the potential effect of the presence of the CSF-filled temporal horn on the hippocampus, we extended the NTH-model by adding the fluid-filled temporal horn to the cerebral ventricle  1, CA2/3, CA4/dentate gyrus (DG), hippocampus tail (HP Tail), subiculum, and presubiculum) (To facilitate the derivation of deformation-related metrics in regions of interest (ROIs) from completed simulations, the brain segmentation was registered to the coordinate system of the FE head model and then the brain elements were grouped into different sub-regions according to the spatial correspondence with the brain segmentation biculum) , and nonbiculum) .To emulate the fluid properties of the intraventricular CSF and potential CSF flow secondary to exterior loading, the cerebral ventricles in the TH-Model  and NTH-To couple the mechanical responses of the ALE-represented intraventricular CSF with the Lagrangian-represented brain, a penalty-based FSI coupling scheme  was implOwing to the requirement of implementing the penalty-based coupling scheme, any locations to which the fluid may potentially flow during the simulations are required to be meshed. Considering that the intraventricular CSF might flow to regions that were originally occupied by deep brain structures , additional meshes were generated in these regions, referred to as the “void mesh” in Estimation of hippocampal response was obtained from the TH-Model and NTH-Model by simulating six representative football head impacts . At Stanin vitro TBI models on organotypic hippocampal slice cultures from rat. At each timestep, the element-wise strain and strain rate values were calculated as the first principal value of the Green-Lagrange strain tensor and the first principal value of rate of deformation tensor , using an uncorrected significance threshold of −1   on all six hippocampal subfields and the whole hippocampus at significant levels ( < 0.05) . For theAmong the non-hippocampal regions, both strain and strain rate were elevated in the TH-Model in the amygdala, which is along the anterosuperior border of the temporal horn, and to a lesser extent in the nearby ventral DC . For theWe then went on to explain the biomechanical reason for the hippocampal vulnerability. To ascertain the alteration of stress transmission associated The current study attempted to elucidate why the hippocampus is so commonly affected by brain trauma. We used two FE models: one with and the other without the temporal horn, and incorporated an anatomically accurate description of temporal horn, a mechanically realistic representation of intraventricular CSF as fluid elements, and a fluid-structure interaction coupling approach for the brain-ventricle interface. The presence of the temporal horn not only extended the distribution of high strains and strain rates in the surrounding area, but also increased their magnitude in the hippocampus, particularly in the subfields of CA1, CA2/3, HP Tail, subiculum, and presubiculum. Other adjacent regions including the amygdala and ventral DC showed similarly increased strain and strain rate with the presence of the temporal horn, but distant regions  did not. These computational findings suggest that the presence of the temporal horn likely exacerbates the biomechanical vulnerability of the hippocampus following head impacts.This biomechanical finding correlates well with the prevalence of hippocampal trauma in humans data and animal biomechanical models. Several postmortem neuropathological studies  have detOur computational results predicted an altered stress transmission associated with the temporal horn, providing an explanation for the elevations in strain and strain rate in the TH-Model. As illustrated in Two previous computational studies simulated football head impacts, consistently reporting an increased susceptibility of the hippocampus to injury . Howeverin vitro conditions via mathematical equations, which were valid within the strain rate regime of 0.1–50 s−1. Similarly, another in vitro study reported tolerance criteria for hippocampal function impairment in the form of mathematical formulations between input mechanical stimuli  and output electrophysiological alterations  with their magnitudes spanning over the regimes measured from the realistic impacts is planned for future work to identify the critical scenarios that the temporal horn exhibited a more pronounced effect on the hippocampus. Moreover, caution should be exercised when extrapolating the current findings obtained from concussive and sub-concussive impacts to extra injury scenarios .Another aspect of limitation was that multiple comparison correction were not conducted. In the statistical analysis, 26 comparisons were respectively performed based on the strain and strain rate results from 13 ROIs, each of which results from six impact simulations predicted by the TH-Model and NTH-Model were paired and analyzed using a significance threshold of 0.05. Given that we did not perform multiple comparison correction, there might be a chance up to 74% that at least one of the comparisons indicating a statistically significance may not be the case in fact.Thirdly, due to the computational challenges, the brain-skull interfaces in both models in the current study were simulated by approximating the subarachnoid CSF as a Lagrangian-represented structure. Given that the ROIs in the current study are all located at central brain regions, the influence exerted by the brain-skull influence on the deep brain structures was expected to be limited . Per the3 to take advantage of the isotropic high-resolution atlas and incorporate this detailed isotropic segmentation into the FE model. Such a software choice was for the consistency purpose, since the brain profile used for the development of FE model was obtained from Freesurfer. However, it should be highlighted there are many different segmentation methods for hippocampal subfields, presenting certain variances in specific subfield delineation (3 MRI (Fourthly, to incorporate explicit representations of the hippocampal subfields in the FE models, Freesurfer was used to segment the MRI with a resolution of 1 × 1 × 1 mmineation . Thus, cineation . In factn (3 MRI , as is tn (3 MRI , the curLastly, due to the lack of neuroimaging data of these players with their head impacts being simulated, it is hardly possible to ascertain whether hippocampal injury indeed occurred in these six simulated impacts. At Stanford, ongoing effort is dedicated to deploy instrumented mouthguards to football players, obtaining real-time measurements of the impacts sustained by these players . This inThis study investigated the biomechanical mechanism of hippocampal injury associated with the presence of the temporal horn by leveraging two models, with and without the inclusion of the temporal horn. The results showed that the temporal horn has a significant biomechanical effect in the surrounding area and induces increased magnitudes of the strain and strain rate in the hippocampus throughout its subfields, identifying the temporal horn as a contributing factor to the hippocampal vulnerability. This study suggests that proper modeling of the temporal horn be considered when developing mechanical tolerance and designing protective strategies specifically for the hippocampus.